The Future of Business: 5 Trends for Startups to Watch

Startups have become experimental forces that play a crucial role in driving innovation to transform the world with the needs and desires of people to shape the future of the world. Hence, some trends are emerging that will restructure the way startups operate and succeed in the business world. From the next level of technological advancements to analysing the needs of changing consumer behaviours, the following are five trends that startups should follow in order to stay ahead of any business competition.

In a world where change is the only constant, start-ups stand at the forefront of innovation, poised to revolutionize industries and reshape the future. By adopting these emerging trends, start-ups can not just navigate the evolving business landscape but also spearhead the movement toward a more vibrant and prosperous future.


Remote Work

Amidst today’s swiftly changing global environment, characterized by unprecedented challenges and transformations, the integration of remote work has risen as a central trend revolutionizing corporate practices. After the era of the COVID-19 pandemic, the adoption of remote work among companies has become one of the key trends globally. This trend is likely to continue forever, as it can play a key role in cost-cutting. Nowadays startups are recognizing the benefits of a distributed workforce, which includes access to a pool of global talents, decreased costs of overhead, and improved work-life balance for employees. With the support of digital collaboration tools, startups have started fostering unified communication and collaboration among team members irrespective of where they live. As flexibility in work arrangements has become a key factor, it attracts top talents around the world, and startups that prioritize remote work will be better positioned for success.

Key benefits

  • Cost Savings: Reduced overhead expenses for startups.
  • Enhanced Collaboration: Digital tools foster unified teamwork.
  • Global Talent Access: Tap into diverse skill sets globally.
  • Work-Life Balance: Improved employee well-being.
  • Increased Productivity: Remote workers often report higher productivity levels thanks to reduced distractions and the flexibility to work during peak hours.


Artificial Intelligence and Automation

Artificial Intelligence (AI) and automation have already started transforming the global corporate world. They will be futuristic concepts but integral components of the business world, as they are leading towards developing industry 5.0. Gradually, startups are leveraging AI to enhance functional efficiency, streamline every business function, and deliver personalized experiences to customers. Machine learning algorithms are used for data analysis to enable startups to make decisions based on data-driven methods. As AI technology continues to advance, startups should explore its potential applications in various areas such as customer service, product development, business management, and supply chain management.

Adopting AI and automation will not only boost start-ups’ competitive edge but also place them at the forefront of innovation in the constantly evolving business environment.

Key benefits

  • Data-Driven Decisions: Leverage insights for strategic choices.
  • Efficiency Boost: Streamlined business processes for startups.
  • Industry Transformation: AI shaping the future of various sectors.
  • Personalized Experiences: Tailor services for individual customers.
  • Scalability: Expand operations efficiently, handling growth without needing more resources or manpower.


E-commerce Innovation

In recent years, global entrepreneurs and business owners have started focusing on e-commerce innovation, as they want to start a new business to stay competitive in this space. Hence, emerging technologies such as blockchain technology, AI, augmented reality (AR), and virtual reality (VR) are restricting the online shopping experience, as they allow customers to virtually use products before making a purchase. Moreover, the integration of all these technologies in e-commerce platforms enhances personalized recommendations, making the shopping process more convenient and enjoyable for consumers.

Key benefits

  • Competitive Edge: Innovation is crucial for staying ahead.
  • Enhanced Shopping: AR/VR redefines the online retail experience.
  • Immersive Technologies: Virtual product trials elevate customer satisfaction.
  • Personalized Recommendations: AI-driven suggestions improve user engagement.
  • Expanded Market Reach: Enables start-ups to reach global audiences beyond borders.


Health Tech Revolution

The intersection of technology and healthcare, known as health tech, is experiencing a revolution with startups that can play a pivotal role. From wearable devices that monitor health metrics to telemedicine platforms that facilitate remote consultations, startups are at the forefront of transforming the healthcare industry. The ongoing global health challenges have accelerated the adoption of digital health solutions, which presents significant opportunities for startups in this space. The intersection of technology and healthcare is witnessing a revolution spearheaded by start-ups poised to make significant contributions. Entrepreneurs should explore innovations in health tech to address the evolving needs of consumers and healthcare providers.

Key benefits

  • Addressing Needs: Meeting evolving demands of consumers and providers.
  • Digital Health Solutions: Startups drive industry transformation.
  • Remote Consultations: Facilitate healthcare from a distance.
  • Wearable Monitoring: Track health metrics using wearable devices.
  • Enhanced Patient Outcomes: Improved monitoring, diagnosis, and treatment.


Sustainable Practices

Environmental sustainability has become one of the necessities for the modern world that has a rising number of issues over environmental pollution. Consumers are well aware of being socially responsible and becoming increasingly conscious of the environmental impact of their purchases. Hence, startups that prioritize sustainability will gain a competitive edge. Whether through eco-friendly product design, reduced carbon footprints, or ethical sourcing, incorporating sustainable practices into business models is a trend that is here to stay. Investors and consumers alike are showing a preference for businesses committed to social and environmental responsibility.

Key benefits

  • Competitive Advantage: Sustainability attracts conscious consumers and investors.
  • Eco-Friendly Design: Prioritize environmentally responsible product development.
  • Ethical Sourcing: Sustainably sourced materials for social responsibility.
  • Reduced Carbon Footprints: Minimize environmental impact through sustainable practices.
  • Brand Reputation: Commitment to sustainability builds trust and enhances brand image.



Startups that adapt to these emerging trends will be successful in the long run, as the future of business will be based on the above exciting opportunities. Remote work, artificial intelligence, e-commerce innovation, health tech, and sustainability are some of the key trends that are to transform the entire global business landscape. Hence, startups should navigate the challenges ahead to be successful in the rapidly changing business environment with these more advanced and emerging trends.

Embracing Human-Centered AI in Industry 5.0: A Paradigm Shift towards Collaborative Intelligence

As the corporate world is preparing to enter into the world of Industry 5.0, every technology is going to welcome a new era of industrial transformation. The heart of this economic evolution is Human-Centered AI technology, which is a new business model that places humans at the forefront of technological advancements. Industry 5.0 envisions a world where AI-powered machines work together with human workers by fostering a collaborative ecosystem that maximizes productivity in every business function. In this article, let’s study about it in detail.


The Evolution of Industry

The journey through industrial revolutions – from steam power in Industry 1.0 to the digital age of Industry 4.0 – has been a pursuit of automation-centred production and service. After machines became more autonomous, unemployment concerns arose about job displacement, and machines also created a growing divide between technology efficiency and the contribution of human beings.

Hence, recognizing these challenges, Industry 5.0 sets a pivotal history in an era where the focus shifts from automation to collaboration. Human-Centered AI is the linchpin of this transformation by contributing combined skills with a symbiotic relationship between humans and intelligent machines.


Benefits of Human-Centered AI in Industry 5.0

Embracing human–centred AI in Industry 5.0 will bring unlimited benefits to the welfare of human beings. Just a few of them are stated here below for your reference.


Augmentation, Not Replacement: Human-cantered AI technology contributes things to augment human capabilities rather than replace them. By integrating AI technologies into the workplace, employees can streamline tasks efficiently by getting empowered to focus on creative problem-solving functions, critical thinking, and complex decision-making.

Important benefits

  • Collaboration between humans and AI for a more dynamic and adaptable workforce
  • Efficient task streamlining for creative problem-solving and critical thinking
  • Empowerment of employees to focus on complex decision-making
  • Integration of AI to enhance and augment human capabilities at work
  • Enhances workplace safety by leveraging AI for hazardous tasks, reducing human exposure to risk


User-Centric Design: In Industry 5.0, every design is to focus on the end user in mind. Hence, user interfaces are intuitive, and interactions with AI are seamless by allowing workers to collaborate with machines effortlessly. This method develops positive connections between humans and AI, breaking down barriers to adoption.

Important benefits

  • End-user-focused design principles ensure usability and accessibility.
  • Industry 5.0 designs prioritize user experience and ease of interaction.
  • Intuitive user interfaces for seamless collaboration between humans and machines
  • Positive connections between users and AI, facilitating barrier-free adoption
  • Promotes inclusivity and diversity by considering the needs of a wide range of users


Ethical AI: Great power comes from great responsibility. Human-Centered AI places a strong emphasis on ethical considerations. AI systems are designed to prioritize transparency, fairness, and accountability, which will ensure that the technology aligns with societal values and norms.

Important benefits

  • Alignment of AI technology with societal values and ethical considerations
  • Emphasis on transparency, fairness, and accountability in AI system design
  • Ensuring AI systems uphold ethical standards and societal norms
  • Prioritization of responsible AI development and deployment practices
  • Reduces the likelihood of regulatory violations and legal liabilities.


Continuous Learning: The dynamic nature of Industry 5.0 can become highly successful because of its adaptability. Human-Centered AI systems are equipped with continuous learning capabilities by evolving alongside human knowledge and adapting to changing work environments. This culture will ensure that AI remains a valuable asset in an ever-evolving industrial landscape. In this context, let us know the key benefits of Continuous Learning.

Important benefits

  • Adaptability of Human-Centered AI ensures success in an ever-evolving industrial landscape.
  • AI systems evolve alongside human knowledge in dynamic work environments.
  • The dynamic nature of Industry 5.0 thrives on AI adaptability and continuous improvement.
  • Integration of continuous learning capabilities to keep AI systems updated
  • Facilitates rapid response to emerging trends and technologies, enabling organizations to stay ahead of the curve


Increased Productivity: Collaborative intelligence enhances productivity by automating routine tasks by enabling human workers to focus on tasks that require creativity, emotional intelligence, and complex problem-solving.

Important benefits

  • Automation of routine tasks enhances productivity in the workplace.
  • Collaborative intelligence allows human workers to focus on creative tasks.
  • Emphasis on efficiency through AI-driven automation in Industry 5.0
  • Improved overall productivity by leveraging AI for routine tasks
  • Reduced human error and improved accuracy boost quality control and customer satisfaction


Enhanced Innovation: By working alongside AI, humans can leverage advanced analytical capabilities to drive innovation. The synergy between human creativity and AI’s analytical prowess leads to ground-breaking solutions and novel approaches to problem-solving.

Important benefits

  • Advanced analytical capabilities of AI contribute to ground-breaking solutions
  • AI-assisted innovation in Industry 5.0 fosters novel problem-solving approaches.
  • Innovation thrives as humans leverage AI for creative problem-solving.
  • Synergy between human creativity and AI analytical capabilities drives innovation.
  • Improved collaboration and knowledge sharing facilitated by AI’s support for communication and information exchange across teams and departments.


Improved Decision-Making: AI systems can analyze vast datasets in real time by providing valuable insights to support decision-making. Human-Centered AI assists human decision-makers by presenting relevant information, which will decrease the risk of errors, and enable more informed choices. This synergy leads to smarter, more efficient decisions with reduced risk.

Important benefits

  • AI systems support human decision-makers with relevant information.
  • Decreased risk of errors in decision-making with AI-driven insights
  • Informed choices facilitated by AI contribute to improved decision-making
  • Real-time analysis of vast datasets provides valuable insights for decision-making.
  • Strengthened strategic planning and forecasting capabilities


Optimized Workforce: With AI handling repetitive and mundane tasks, human workers can focus on tasks that require emotional intelligence, empathy, and interpersonal skills – areas where machines currently lack proficiency.

Important benefits

  • AI handles mundane tasks, allowing human workers to focus on emotional intelligence.
  • AI-driven optimization leads to a more efficient and proficient workforce.
  • Emphasis on optimizing human skills in areas where machines lack proficiency
  • Human workers focus on tasks requiring empathy, interpersonal skills, and emotional intelligence.
  • Improved workplace culture and collaboration as human workers collaborate with AI to achieve shared goals


Challenges and Considerations

While the promises of Human-Centered AI in Industry 5.0 may exceed any industrial revolution, it is expected that challenges may arise from various levels. When it comes to ensuring data privacy, addressing biases in AI algorithms, and managing the ethical implications of automation, the challenges may become abundant. A detailed research approach is required for analysing of the challenges and it should be addressed by industry stakeholders, policymakers, and the general public necessary to navigate these challenges successfully. Here below are some of the most important challenges.

  • Addressing privacy concerns in data-driven environments.
  • Balancing automation without job displacement.
  • Enhancing transparency and explainability in AI systems.
  • Ensuring inclusivity and diversity in AI systems.
  • Ethical dilemmas in AI decision-making.
  • Human-AI collaboration and skill integration.
  • Managing biases in AI algorithms and data.
  • Mitigating the risk of AI-induced social inequality.
  • Reskilling the workforce for evolving AI technologies.
  • Safeguarding against AI system malfunctions and errors.
  • Navigating legal and regulatory challenges surrounding AI governance and oversight



As the era of Industry 5.0 will update every product and service in the world, Human-Centered AI stands as the beacon guiding us toward a future where AI-powered machines and humans collaborate impeccably. By prioritizing augmentation over replacement, ethical design, and continuous learning, we set a new trend for a harmonious coexistence between humanity and technology. The era of Collaborative Intelligence beckons by promising a future where innovation, productivity, and human potential reach unprecedented heights, reshaping the very fabric of our existence.

Unveiling the Potential: How 5G Can Revolutionize Data Collection and Supercharge AI Capabilities

In this era being dominated by data-driven decision-making and the consistent pursuit of innovation, the convergence of 5G technology and AI stands composed to redefine the boundaries of what’s possible.  Because of the digital revolution, the fifth-generation cellular network technology, commonly known as 5G, has emerged as a great game-changer across various industries. Beyond its capability of lightning-fast internet speeds and low latency, 5G has the potential to transform data collection and significantly improve the competencies of artificial intelligence (AI) systems. This transformative technology is technologically and commercially capable of unlocking a new era of connectivity and data-driven innovation as it is reshaping the way organizations collect, process, and leverage data for improving AI applications.


Accelerating Data Collection

The most significant impact of 5G is its ability to accelerate data collection processes. With the power of ultra-fast speeds and low latency, 5G networks support the seamless transfer of enormous datasets in real-time.  Its tactical collection, analysis, and application have become pivotal in driving advancements across industries. This capability is instrumental in several data-required industries, including manufacturing, healthcare, transportation, and telecommunications. The rapid and efficient collection of diverse data sets, ranging from IoT sensor data to high-resolution video streams, empowers companies to gain comprehensive insights into complex systems and make data-driven decisions at an extraordinary speed.


Enhanced IoT Integration

5G enables the seamless integration of a multitude of IoT devices by developing a network of interconnected smart sensors and devices that can generate continuous real-time data. This interconnected ecosystem supports the collection of rough and real-time information from various sources by empowering companies to monitor and analyse complex systems with heightened accuracy and efficiency. From smart cities, healthcare, and autonomous vehicles, to industrial automation, 5G’s robust connectivity infrastructure lays the foundation for the proliferation of IoT applications, thereby intensifying the scope of data collection and nurturing the development of sophisticated AI-driven solutions.


Empowering Edge Computing

The success of 5G networks also supports the advancement of edge computing because data processing happens closer and faster to the data source rather than in centralized data centers. This approach to data processing considerably decreases latency, allowing AI algorithms to make split-second decisions and real-time insights. With the capabilities of edge computing facilitated by 5G, companies can enhance the responsiveness of AI systems, particularly in time-critical applications such as remote healthcare monitoring, autonomous vehicles, and industrial automation.

By bringing computational capabilities closer to where data is created, businesses and industries are on the edge of a new frontier in real-time decision-making and awareness. From autonomous vehicles to smart factories, the applications are infinite.


Fuelling AI Innovation

The synergy between 5G and AI has become an innovative approach to introducing novel products and services across diverse sectors. With tremendous speeds and near-split-second responsiveness, 5G provides the fertile ground upon which AI applications can flourish. The enhanced data collection capabilities enabled by 5G networks provide AI systems with access to a wealth of high-quality, real-time data to develop more accurate predictive models, robust pattern recognition algorithms, and important decision-making processes. This combination of 5G and AI nurtures the development of various advanced applications, including AI-driven autonomous vehicles, immersive virtual reality experiences, and predictive maintenance solutions.


Challenges and Considerations

While the integration of 5G and AI creates a myriad of new opportunities, it also introduces some challenges to be addressed. Some of these issues are related to data security, privacy, and the potential for increased cyber threats. Companies must prioritize the implementation of robust security protocols and privacy measures to maintain sensitive data and maintain the trust of consumers. Moreover, the deployment of 5G infrastructure requires substantial investments in network upgrades and the development of compatible technologies. –Ok for me



With the continuous global adoption of 5G technologies and the evolution of AI technologies, the combination of these two technological components is expected to drive unparalleled innovation and economic growth. The seamless integration of 5G networks with AI capabilities will empower companies to utilize the power of data meticulously, speed up digital transformation initiatives, and solve corporate issues to create value and drive competitive advantage in the digital era. By leveraging the unprecedented speed, connectivity, and data processing capabilities of 5G networks, companies can propel the development of advanced AI applications for a future based on data-driven insights.

As we gaze into the future, one thing is certain: the journey of continuous innovation and global adoption of these technologies will be nothing less than exceptional.

Transform Your Business with the Power of ChatGPT

Global companies are constantly striving to find innovative ways to streamline operations, boost productivity, and enhance customer experiences. Most innovations are based on digital technology, as every business operation has gradually connected with the benefits of the digital world. Such technology that is restructuring the way businesses interact with customers and handle operations is ChatGPT- state-of-the-art chatbot power-driven by artificial intelligence (AI.) Now, ChatGPT, a powerful language model developed by OpenAI, has started delivering services to every industry. It has the potential to revolutionize the way companies interact with their clients, automate processes, and provide valuable insights. In this article, let us explore how ChatGPT can transform your business with some benefits.


Enhanced Customer Support

Customer support is one of the most instant benefits of ChatGPT for companies in the world. ChatGPT-enabled chatbots can provide instant responses to every customer inquiry at any time. These chatbots can manage troubleshooting, routine queries, and frequently asked questions without the support of human agents.  This not only improves customer satisfaction but also reduces response times, making your business more efficient.


Personalized Marketing

With the capabilities of ChatGPT, companies can analyze customer data to create highly personalized marketing campaigns. By understanding what customer prefers, companies can modify their marketing messages and product recommendations. This can bring higher conversion rates and increased customer loyalty. ChatGPT can also participate in personalized conversations with customers, which is vital for building brand loyalty. By addressing users by their names and referencing their specific needs, ChatGPT can create a more engaging communication experience.


Data Analysis and Insights

ChatGPT can process and analyze vast amounts of data of any business and subject rapidly and professionally. Like other AI models, ChatGPT processes data analysis through a combination of machine learning algorithms and natural language processing. With this ability, chatGPT has become an invaluable product for companies seeking to gain insights from their data. Whether it’s analyzing customer feedback, capturing market trends, or structuring financial data, ChatGPT can provide valuable insights and predictions. These insights can guide companies to make informed strategic decisions, helping them stay competitive and adapt to evolving market conditions.


Streamlined Operations

Integrating the power of ChatGPT into various business functions can streamline operation management and decrease costs. Chatbots can automate monotonous tasks such as appointment scheduling, data entry, and order processing. This automation not only increases functional efficiency but also reduces the risk of errors. Consequently, employees can focus on more strategic and creative tasks that add better value to the organization. Other key advantages of ChatGPT to streamlined operations are reducing workloads, faster response times, 24/7 availability, optimized workflows, and language support.

Overall, ChatGPT contributes to streamlined operations by offering efficiency, scalability, consistency and cost-effectiveness across various business functions. 


Multilingual Support

In today’s global economy, companies are responsible for serving customers from diverse linguistic backgrounds. ChatGPT is one of the best digital tools to get instant multilingual support, breaking down language barriers and increasing your customer base. It is a valuable tool for businesses operating in diverse linguistic environments giving key advantages such as global reach, language accessibility, improved customer service, enhanced user experience, and global expansion. All these can empower companies to target a broader audience and create a global presence without the requirement for extensive language-specific customer support teams.


Training and Onboarding

When it comes to developing interactive training and onboarding programs, ChatGPT can play a better role. Both for (or Whether you’re) recruiting new employees or acquiring new customers, chatbots can guide individuals through the learning process, answer questions, and provide assistance, advancing the onboarding journey with innovative approaches through round-the-clock availability, consistent information, self-paced learning, on-demand support, task guidance, multilingual support, interactive learning and assessment.

Incorporating ChatGPT into an organization’s training and on-boarding processes can lead to more flexible, efficient, and engaging learning experiences the employees.


Market Research and Surveys

Performing market research and collecting customer feedback is vital for companies looking to improve their products and services. ChatGPT comes forward to assist in conducting surveys, collecting feedback, and analyzing the responses in detail.  So the data collection process will become more efficient and insightful. After performing research, companies can use to refine products, services, and marketing strategies with the support of data gained.



ChatGPT, a recent game-changer for global companies, supports staying competitive to meet the evolving demands of the digital age. Its capabilities from functionalities extend beyond simple chatbots, as it offers various opportunities to enhance customer support, personalize marketing efforts, streamline operations, and gain valuable insights. With the power of ChatGPT, companies can improve efficiency, boost customer satisfaction, and position themselves for long-term success in a rapidly changing business landscape. As this technology evolves continuously, the possibilities for its integration into various aspects of business are virtually immeasurable. Adopting ChatGPT today could be the best business strategy to staying ahead of the curve tomorrow and incorporating it into businesses can transform your business operations and elevate customer experiences making you ahead of the game in today’s vibrant and competitive business landscape.

Leveraging AI to Supercharge Sales: Unleashing the Power of Technology

When it comes to today’s fast-paced and ever-evolving corporate world (or business landscape), staying competitive needs a lot more than introducing a quality product or service. Companies are looking for strategic and innovative tools to boost sales. Now the evolution of the present world depends on welcoming AI technology, one of the most powerful business tools that make magic in business. By utilizing the capabilities of AI, companies are trying to optimize their sales processes to drive revenue growth. In this article, let’s study how we can leverage AI to supercharge sales. Or (In this article, let’s delve into the thrilling world of leveraging AI to supercharge sales and discover how this powerful technology is transmuting the sales process as we know it.)


Sales Automation

As routine and repetitive tasks can consume valuable time for salespersons, AI-powered sales automation can manage routine tasks such as follow-up emails, lead qualification, and appointment scheduling. By doing so, salespersons will have much time to focus on developing important strategies to take other decisions. AI tools can streamline the process to enhance efficiency and reduce slow progress.

Benefits of sales automation

  • Automated order processing, improving efficiency
  • Better sales pipeline visibility
  • Consistent follow-ups, reducing missed opportunities
  • Enhanced sales forecasting accuracy
  • Improved task prioritization
  • Streamlined lead management
  • Time-saving for sales teams
  • Improved Lead Scoring
  • Personalization at Scale
  • Enhanced Sales Prediction


Data-Driven Insights

In an era defined by an unprecedented flow of data, the ability to harness this information for strategic advantage has become the holy grail of modern businesses. AI-powered tools can evaluate vast amounts of customer data so that companies can gain valuable insights about behavior patterns, preferences, and buying habits. These types of data enable companies to develop custom-made and targeted sales strategies as they can lead to higher customer satisfaction and conversion rates.

Benefits of data-driven insights

  • Accurate demand forecasting
  • Competitive analysis for effective positioning
  • Customer segmentation for targeted marketing
  • Enhanced customer profiling
  • Informed sales strategies
  • Quick response to changing customer preferences
  • Real-time market trend identification
  • Reduced manual labor & lowered operational costs
  • Detect fraudulent activities
  • Ensure compliance with industry regulations & standards


Predictive Analytics

The ability to predict future outcomes with precision has been a strategic imperative for businesses nowadays. This is where Artificial Intelligence (AI) steps in as a game-changing force, providing a range of transformative benefits through its predictive analysis competencies.  Some AI algorithms are developed to predict future business trends and ever-changing customer needs. Hence, sales teams will have the ability to proactively address these needs. This protective approach will empower companies to become solution providers that will build trust and loyalty among customers.

Benefits of predictive analytics

  • Customized product recommendations
  • Early identification of sales opportunities
  • Efficient resource allocation
  • Improved sales team performance assessment
  • Optimized inventory management
  • Proactive customer engagement
  • Reduced churn through predictive retention strategies
  • Enhances Customer Insights
  • Can help in monitoring & predicting environmental changes


Dynamic Pricing

Setting the right pricing for products and services can make all the transformation between success and stagnation. Gone are those days of fixed price tags. AI is now in control, steering businesses toward a more responsive approach to pricing. AI algorithms can also evaluate competitor pricing, market trends, and customer behaviors around the world to set dynamic pricing strategies, ensuring that prices remain competitive while increasing profit margins.

Benefits of dynamic pricing

  • Demand-based pricing strategies
  • Effective clearance of excess inventory
  • Improved price perception
  • Increased sales during off-peak periods
  • Maximization of revenue
  • Pricing optimization for different customer segments
  • Real-time price adjustments for competitiveness
  • Adaptation to market changes


Chatbots and Virtual Assistants

In the emerging world of sales, where every moment counts and every interaction holds the potential to seal a deal, AI-powered Chatbots and Virtual Assistants have risen as invaluable allies. They are not just tools but powerful sales accelerators. AI-driven chatbots are used to answer instantly for customer inquiries as it provides 24/7 support for enhancing the buying experience. These tools can also support customers in answering questions, product selections, and guiding them through the sales funnel. By performing this, these chatbots and virtual assistants lead to increased conversions.

Benefits of chatbots and virtual assistants

  • 24/7 customer support availability
  • Data-driven personalized interactions
  • Efficient lead qualification
  • Multilingual support, expanding customer reach
  • Quick response to customer queries
  • Reduced response time, improving customer satisfaction
  • Seamless appointment scheduling
  • Cross-Selling and Upselling
  • Nurtures Leads


Continuous Learning

Nowadays the corporate world focuses on continuous learning to make (or keep) their employees updated and skilled and this is where the concept of continuous learning becomes critical. Continuous Learning is not just an enrichment of conventional education, it showcases a fundamental shift in how organizations adapt and thrive in an era of insistent change. Here, AI models come to get them trained to learn from past successes and failures. When they use this feedback, they can refine their sales strategies over time as it will ensure a constant improvement in their business development methods.

Benefits of continuous learning

  • Accelerated onboarding for new sales members
  • Adaptation to market changes
  • Enhanced product knowledge for better selling
  • Improved sales pitch refinement
  • Quick incorporation of customer feedback
  • Regular skill enhancement
  • Up-to-date sales techniques adoption
  • Personalized Learning
  • Curriculum Customization


Personalized Recommendations

In this modern digital world where information overload is a common encounter, the search for personalization has never been more critical and this is where AI steps onto the stage as a radical force. AI is shaping the future of hyper-personalization. If companies implement recommendation systems with the support of AI tools, they can improve products or services based on a customer’s purchasing history. This type of cross-selling and upselling strategy can pointedly boost the ratio of revenue.

Benefits of personalized recommendations

  • Anticipating customer needs
  • Better user experience through relevant suggestions
  • Customized content delivery for individual preferences
  • Enhanced product discovery for customers
  • Higher conversion rates through tailored offers
  • Improved customer engagement and loyalty
  • Increased cross-selling and upselling opportunities
  • Decreased decision fatigue & easier selections
  • Enhanced visibility in content platforms


CRM Enhancement

When companies integrate AI tools with their customer relationship management (CRM) system, they will gain deeper insights into customer interactions.. Hence, their sales teams can improve customer engagement and loyalty.

By instilling the power of AI into Customer Relationship Management systems, businesses are poised to supercharge their sales efforts, improve their customer engagement and reach an unprecedented growth.

Benefits of CRM enhancement

  • Comprehensive customer view for better interactions
  • Efficient lead tracking and management
  • Enhanced customer communication history
  • Improved customer segmentation and targeting
  • Seamless integration with other sales tools
  • Simplified contact management
  • Streamlined sales performance analysis
  • Enhanced Sales Training



According to the latest statistics, AI tools can significantly play a great role to boost (or in boosting) sales performance and revenue generation. By utilizing the power of predictive analytics, automation, data-driven insights, and personalized interactions, companies can create a new sales ecosystem that could be both efficient and customer-centric. Nevertheless, successful AI integration needs continuous learning, careful preparation, and a commitment to preserving the human element in sales interactions.

Top 10 Small Business Tech Trends

In today’s tech-paced digital era, small businesses in the world are increasingly adopting modern digital technologies to improve productivity, drive growth, gain a competitive edge, and modernize business processes. With advancements in various digital technologies, companies are presented with tremendous opportunities to advance the quality of customer services, streamline business operations, and scale their productivity. In this article, let us explore the top 10 small business tech trends that are revolutionizing the way companies in the world perform.


  1. Cybersecurity or can we use ‘Zero-trust cybersecurity’?

As cyber threats are evolving everywhere in the digital world, it is essential for small businesses to focus on cybersecurity measures. ‘Zero trust’ is a method using the idea of ‘never trust, always verify’. This technology uses a zero-trust approach that involves multifactor authentication, SSO, and SAML plus other advanced identity and access management tools.  

Training employees on data protection, implementing robust security protocols, and using advanced encryption technologies will support and confirm the safety of sensitive information to build trust with customers. To add, cybersecurity also:

  • Ensures privacy and confidentiality of information.
  • Prevents financial losses and reputational damage.
  • Protects sensitive data from unauthorized access.
  • Safeguards against cyber threats and attacks.
  • Supports compliance with regulatory requirements.
  • Minimizes compliance and security costs
  • Gives consumers confidence in business


  1. Social Media Marketing

Gradually, social media platforms have captured a prominent place in marketing, as they play as powerful marketing tools for small businesses. Staying abreast of the latest social media marketing trends such as video marketing, TikTok, AR, social commerce, influencer marketing, etc. can help businesses reach their peak in the digital world by posting various types of content to engage with customers, companies run targeted advertising campaigns. The power of social media platforms:

  • Enables cost-effective marketing with measurable results.
  • Expands brand reach to enhance brand visibility.
  • Facilitates targeted advertising to specific demographics.
  • Fosters customer engagement and builds brand loyalty.
  • Provides real-time feedback for immediate campaign adjustments.
  • Expands local targeting that helps reaching targeted audience


  1. Internet of Things (IoT)

The Internet of Things (IoT) is a revolutionary digital approach and it transforms the way small businesses operate and the functions of digital gadgets and devices. In fact, one cannot understand digitalization without IoT. Whether it is a Multinational company or a start-up, IoT knowledge is required to survive the competition. By connecting digital devices, gadgets, and computers, companies can:

  • Enable remote checking and control of devices and gadgets.
  • Enhance automation and efficiency in various industries.
  • Enhance overall connectivity and communication capabilities.
  • Improve decision-making through real-time data analysis.
  • Optimize resource utilization and reduces energy consumption.


  1. VR and AR

Virtual Reality (VR) and Augmented Reality (AR) technologies play an important role in developing marketing functions of small businesses, providing immersive experiences to customers. They have modernized digital methods by means of interactive environments, immersive digital experiences, engagement, and simulation. With the support of these technologies, companies can run marketing campaigns with product demonstrations, virtual tours, and training programs. VR and AR can:

  • Create immersive and interactive user experiences.
  • Enable virtual product demonstrations and prototyping.
  • Enhance training and education through realistic simulations.
  • Enhance visualization and data analysis in various fields.
  • Support remote collaboration and teleconferencing.
  • Can complement the real-world environment by draping digital objects onto it and upgrading its functionality
  • Provides a more engaging environment


  1. Superapps

Super apps link multiple business services in a single platform to allow seamless communication. These apps provide a set of functions, empowering customers to have access to several other niche apps. Super apps can:

  • Enable easy payment and financial transactions within the app.
  • Enhance convenience and efficiency in daily tasks such as payment, messaging, or service requests
  • Facilitate personalized recommendations and tailored experiences.
  • Provide a seamless and combined user experience.
  • Provide a wide range of services in a single platform.


  1. 5G Technology

The business implications of 5G technology are growing constantly. 5G networks are the modern generation of mobile connectivity providing faster speeds and lower latency than the recent past. With the adoption of 5G technology, companies enjoy significantly faster data transmission speeds. According to studies, 5G promises the next-level advancement in data transfer, which revolutionize the areas such as remote work, autonomous vehicles, and smart cities. With the support of 5G, companies can:

  • Enable autonomous vehicles and remote surgery capabilities.
  • Enable real-time communication and seamless connectivity.
  • Enhance virtual and augmented reality experiences.
  • Gain faster data transfer for enhanced productivity.
  • Support IoT and facilitates smart city development.


  1. Cloud Computing 

Cloud computing is one of the greatest technologies that drive growth. Before cloud computing, companies used to purchase and maintain their own servers containing just enough space to inhibit downtime and outages and maintain peak traffic volume.  As every technology is getting updated, cloud computing supports the need for storing data in business. Smaller tech businesses are gradually opting for cloud computing to improve their business functions by securing their data. As a wave in the business world, cloud computing:

  • Enables remote collaboration and data accessibility.
  • Enhances data backup and improves recovery capabilities.
  • Facilitates rapid deployment and software updates.
  • Provides scalable and flexible computing resources.
  • Reduces infrastructure costs and IT maintenance efforts.
  • Helps businesses to execute more effective and less costly solutions which don’t require expensive hardware


  1. Management Tools 

Managers are always overloaded with various types of work-related responsibilities. As the saying goes, not all tasks are equal and not all teams and managers can use only one methodology. As they have to stay ahead of the competition, the project managers have to deal with a growing list of tasks and responsibilities. Here, work management tools come to help them to:  

  • Enable real-time monitoring and performance tracking.
  • Enhance project planning and task management capabilities.
  • Facilitate effective team communication and collaboration.
  • Improve efficiency to streamline business operations.
  • Provide data-driven insights for informed decision-making.


  1. Unified Technology Platforms

With unified technology platforms, small businesses have been performing several functions on one platform.  This permits a unified integration and data exchange between tools.  Some of them are accounting, administration, sales, marketing, human resources, and business analytics.  Unified software platforms make it easy for small business owners to organize their business functions and marketing content to collaborate with a dispersed team. These platforms:

  • Enhance data sharing and collaboration across departments.
  • Integrate disparate systems for a seamless workflow.
  • Provide a centralized view for holistic business management.
  • Streamline IT infrastructure and reduces complexity.
  • Support cross-platform compatibility and interoperability.
  • Provides an efficient and consistent customer experience


  1. Data Analytics

As businesses are now going online, there is a growing need for online databases and reports because ‘Data’ is one of the most valuable assets for businesses.  Hence, companies are becoming increasingly interested in business analytics to assess the performance of their online marketing channels. The power of data analytics:

  • Enables targeted marketing and personalized customer experiences.
  • Enhances operational efficiency and process optimization.
  • Identifies patterns and trends for predictive analytics.
  • Supports data-driven innovation and competitive advantage.
  • Unlocks actionable insights for informed decision-making.



The fast development in digital technology and advancement in business functions are reshaping the landscape of small companies, providing entrepreneurs with numerous opportunities to grow and succeed. By adopting the above 10 tech trends, small businesses can develop new business strategies to perform well in the market. As digital technology continues to evolve, it is essential for every entrepreneur to stay ahead and leverage these digital innovations to unlock their full potential.

Pros and Cons of Integrating IoT into Your Business Strategy

The Internet of Things (IoT) has created several major transformations and has set new trends in digital life. It is a trending business technology that connects all digital gadgets, devices, and technologies in one powerful approach. Different types of digital devices are interconnected in the IoT. Unexpectedly, we never realized that these devices virtually surround us in major interactions. It has become a common thing that most companies to use IoT in their business life. In this article, let us go through and learn the pros and cons of the Internet of Things (IoT).

The major pros of IoT

The Internet of Things enables companies with several advantages and has a number of advantages to offer practically in any area we can think of. Let’s learn some of its advantages here below:


Easy Communication
The key advantage of IoT is that it encourages communication between devices and allows companies to control and automate taxes daily or how about- IoT ecosystem delivers the protocols and infrastructure required to transport data from devices to real-world applications. Machine-to-machine connections support IoT applications that will help companies to obtain:

  • Automated communication for streamlined processes and reduced errors.
  • Efficient communication between supply chain partners for smoother operations.
  • Enhanced collaboration and coordination across teams and departments.
  • Improved customer communication and engagement through personalized interactions.
  • Real-time data exchange for instant decision-making.
  • Remote monitoring and control of devices and operations.
  • Seamless device-to-device connectivity for efficient operations.


Business Benefits 
With the support of IoT, companies can explore new business opportunities apart from direct revenue. IoT plays a major role in collecting data from the network and applying advanced analytics to discover business insights and opportunities. IoT devices predict the needs of interests of customers and help companies to gain:

  • Data-driven insights for informed business strategies and decision-making.
  • Enhanced operational efficiency and cost savings.
  • Improved customer satisfaction through custom-made experiences.
  • Increased competitiveness through differentiation and market positioning.
  • New revenue streams through innovative IoT-enabled products and services.
  • Predictive maintenance to reduce downtime and optimize resource usage.
  • Streamlined supply chain management for improved inventory control and logistics.
  • Helps gather big volumes of user-specific data employed for enhancing business strategies, refined pricing, targeted advertising, and other management activities


Increased Productivity
Increasing productivity is the most important step in the profitability of any business. Ordinary tasks can get done instantly and in effect, human resources can be more focused on bigger tasks, which require personal skills. IoT enables companies to increase productivity with just-in-time employee training and labour efficiency, reducing the mismatch of skills. With several benefits of IoT, companies gain:

  • Automated processes to reduce manual effort and human error.
  • Collaborative tools and remote access for flexible work environments.
  • Data-driven insights for continuous process improvement.
  • Efficient resource allocation and task management.
  • Just-in-time training for improved employee skills and performance.
  • Real-time monitoring and analytics for optimized workflows.
  • Seamless integration of systems and applications for streamlined operations.
  • Minimized number of workers, which results in lower business operations costs


Lower operating costs
IoT technology can be cost-effective because it helps companies enhance their workflows and lower operating costs by providing real-time data. IoT devices simplify management within different departments across the whole business structure. For example (or remove ‘for example’) Smart devices can track, monitor and control usages and get the benefits of:

  • Energy management systems for reduced utility expenses.
  • Improved inventory management to avoid overstocking and stockouts.
  • Optimized resource usage to reduce wastage and inefficiencies.
  • Predictive analytics for cost-effective supply chain management.
  • Proactive maintenance to avoid costly breakdowns and repairs.
  • Process automation for reduced labour and operational costs.
  • Remote monitoring and control to minimize onsite visits and travel costs.


The major cons of IoT

The Internet of Things also creates a significant set of disadvantages. Let’s learn some of the IoT disadvantages here below:


The technical advancements and improved user experience have created a lot of drawbacks to IoT. The methodical resources in an organization that are capable to handle IoT’s complexity are limited, and growing those resources might cost a lot. This makes the entire process of integrating IoT into business functions more complicated. Let’s see some of the most complexities here below:

  • Complexity in handling and analysing large volumes of data.
  • Increased complexity of network infrastructure and data management.
  • Integration challenges due to diverse devices and protocols.
  • Potential compatibility issues between different IoT systems.
  • Potential disruptions in operations due to technical issues or failures.
  • Technical expertise required for setup and management.
  • Training needs for employees to adapt to IoT technologies.
  • Complexity in combining IoT data preparation with more organized resources, such as CSV files


Security and data privacy challenges
Although security and data privacy is a high priority, IoT devices are always facing risks. Inadequate security measures are one of the most prevalent drawbacks that disable the development of IoT. Companies should be aware of protecting devices from physical tampering, internet-based software attacks, network-based attacks, and hardware-based attacks. Without the right strategies, companies may face various challenges such as:

  • The complexity of implementing robust security protocols for IoT systems.
  • Specific compliance with data protection regulations and legal requirements.
  • Difficulty in securing IoT devices with limited computing resources.
  • Lack of standardized security measures across IoT devices.
  • Privacy concerns regarding the collection and usage of personal data.
  • Risk of data breaches and unofficial access to sensitive data.
  • Vulnerabilities to cyber threats and hacking attempts.
  • Disastrous, expensive, and tragic consequences such as loss of corporate confidentiality, thefts or product sabotage, etc.


Hidden technology
Although IoT devices perform simple tasks, there’s a lot of complex technology involved in the process of how IoT functions. If the IoT devices that provide essential data to any workflow work inaccurately, they could negatively affect every process connected to it. Hidden technology may bring:

  • Challenges in diagnosing and resolving technical issues.
  • Complexity in integrating IoT devices with existing infrastructure.
  • Dependency on the complex underlying technology for IoT functionality.
  • Difficulties in troubleshooting and identifying faults in interconnected devices.
  • Limited visibility into the inner workings of IoT systems.
  • Need for specialized expertise to manage and maintain hidden technology.
  • Potential disruptions in operations due to hidden technical failures.


Costly and Time-consuming
It is highly expensive to deploy IoT devices and time-consuming to install them. There are devices to purchase and install, manpower to set it up, others to incorporate them into the network, and support calls to the manufacturer. However, companies want to transform their business functions with cost-effective approaches and quick results. So organizations face various issues such as:

  • Financial risks associated with failed or unsuccessful IoT implementations.
  • High upfront costs for purchasing and deploying IoT devices.
  • Investment in infrastructure upgrades to support IoT systems.
  • On-going maintenance and support costs for IoT infrastructure.
  • Potential complexities in scaling up IoT deployments.
  • Time and resources that need for installation and configuration.
  • Training and re-skilling costs for employees to adapt to IoT technologies.


As the Internet of Things has made business functions easier and more advanced, we must analyse the pros and cons of this technology. It’s high time to study how both effective and ineffective this technology is. To mitigate its drawbacks and to make this technology perform best for the advancement of the corporate world, companies should decide how intelligently they use the dominance of IoT-enabled innovative products.

Top 10 Benefits of Chatbots in Business

Customer communication is the most important aspect whether for sales, marketing, or customer support. If your communication is not delivering smooth engagement, customers would not be attracted to your products or services.

AI-powered chatbots come here to make customers feel as if they communicate with a related team. In a nutshell, it is a section of the software, which helps in conversations in normal language via audio or text. Companies use chatbot technology to conserve three things: money, time and labor. According to studies, 35% of consumers want that companies should use chatbots to improve their communication technics and strategies and deliver a better customer experience. Chatbots are quite advanced forms of communication to ensure many advantages for customers.  Here are 10 different benefits of a chatbot that can positively impact customer communication and support increased sales and marketing.


  1. Customer Engagement

Chatbots help companies to implement the next level of customer engagement services. Given the fact that they are automated, they don’t have breaks like the conventional way humans perform. With conversational data, AI chatbots perform customer engagement based on the user data and make it more interactive. Chatbots are smart enough to understand customer responses based on their previous chat history. 

Key benefits

  • Availability 24/7 ensures immediate customer assistance.
  • Chatbots offer personalized recommendations based on user preferences.
  • Chatbots provide consistent and reliable support.
  • Continuous learning improves chatbot responses over time.
  • The conversational approach creates personalized experiences.
  • Instant responses enhance customer interaction.
  • Interactive chatbot experiences increase customer loyalty.
  • Natural language processing improves conversational flow.
  • Proactive engagement boosts customer satisfaction.
  • Convenient for approaching international markets as you need not to hire fluent-speaking customer service agents from abroad
  • Quick resolution of customer queries improves engagement.


  1. Lead Generation   

Bots are highly powerful at engaging customers with personalized messages throughout their chat journey. Every company can use chatbots for lead generation to guide and motivate customers in making quick decisions. Chatbots can use tailor-made questions to influence the visitors for increasing lead generation, making sure higher conversion rates.  It can help you to become available to website visitors 24/7 and 64% user respondents in a survey conducted  are claiming it is their best feature.  We can use chatbots for both better lead qualification and healthier lead nurturing.

Key benefits

  • Automated lead scoring identifies high-value prospects.
  • Capture lead information efficiently and effectively.
  • Capture valuable data for lead profiling and segmentation.
  • Chatbots assist with lead qualification questions and criteria.
  • Chatbots qualify leads through automated conversations.
  • Engage leads with personalized content and recommendations.
  • Offer interactive experiences to engage and convert leads.
  • Promptly follow up with leads to nurture the relationship.
  • Provide targeted offers and incentives to convert leads.
  • Seamlessly integrate with CRM systems for lead management.


  1. Consumer Data

Chatbot is one of the best tools for tracking the patterns of purchasing and analyzing consumer behaviors by observing user data. Consumer data can help businesses market their products or services effectively. Likewise, companies can use bots to collect feedback through simple questions and update products or advance the designs of websites. Chatbots can also be customized to give incentives in a form of discounts or special offers in exchange for participation in the surveys of customers and is surely an effective way to encourage  feedback.

Key benefits

  • Examine customer data to classify the latest trends and patterns.
  • Chatbots gather valuable customer insights and preferences.
  • Data collected improve personalization and targeting.
  • Enable a better understanding of customer behaviors and needs.
  • Find out areas of customer-related issues for further improvement.
  • Improve customer segmentation and create targeted campaigns.
  • Optimize marketing strategies with data-driven insights.
  • Personalized recommendations increase cross-selling opportunities.
  • Provide data-driven recommendations and product suggestions.
  • Segment customers based on preferences and demographics.


  1. Customer Expectations  

Chatbots fulfil the customer expectations of fast responses to make complaints or queries. Since chatbots are gradually getting updated with digital technologies and data science, companies can use them to automate some of the repetitive conversations and fulfil customer expectations. By nature, customers prefer quick replies from support teams when they approach them with basic questions. But since many support teams are handling a huge volume of queries, it’s not always possible to answer in an instant, regardless of how simple the inquiries are. Chatbot is the answer to this problem as it can handle a large number of queries at once with quicker response.  Some of the tips to use chatbots to meet customer expectations are proactive, personalized experience, and effective targeting.

Key benefits

  • Adapt to customer communication preferences and channels.
  • Expect customer wants and offer support.
  • Chatbots provide 24/7 instant support, meeting expectations.
  • Enhance customer trust and satisfaction through reliable support.
  • Improve response times and reduce customer wait periods.
  • Multichannel support ensures a seamless customer experience.
  • Personalize interactions to meet individual customer needs.
  • Provide accurate and consistent information across channels.
  • Real-time assistance ensures timely issue resolution.
  • Seamlessly transfer customers to human agents if needed.


  1. Increased Sales

With the support of chatbots, it is possible for every company to increase sales. Bots can turn every website visitor into a new customer by disclosing your new products and related discounts to attract potential clients. Moreover, bots also proactively send notifications to every website visitor to motivate them to purchase your products or services. Chatbots can also guide clients to find what they are in search for by the use of a ‘retail chatbot’ to highlight personalised product recommendations and assist in placing an order. In a prediction by ‘Juniper Research’, it was said that transactions made through chatbots would reach $112 billion by this year in ecommerce sales only.

Key benefits

  • Evaluate customer data to recognize cross-selling opportunities.
  • Chatbots drive conversions by guiding customers through purchases.
  • Collect feedback to improve products and sales processes.
  • Offer discounts and promotions to encourage immediate sales.
  • Offer personalized incentives based on customer preferences.
  • Personalized product recommendations increase upselling opportunities.
  • Provide detailed product information and answer customer queries.
  • Provide seamless integration with e-commerce platforms.
  • Remind customers of abandoned carts and encourage completion.
  • Simplify the checkout process for a frictionless experience.


  1. Cost-effective Approach

A cost-effective approach is another essential benefit of using chatbots. It can automate everyday tasks that can include everything from answering questions to making suggestions, scheduling appointments and answering FAQs. With this capability, your customer support team can focus on more complex queries. And also, implementing a chatbot is much cheaper than recruiting customer service employees to deal with repetitive tasks.

Key benefits

  • Automate repeated tasks, letting agents concentrate on complex issues.
  • Chatbots reduce customer service costs by automating tasks.
  • Cut down on training costs by automating onboarding processes.
  • Eliminate the need for physical call centers or large support teams.
  • Handle multiple inquiries simultaneously, saving resources.
  • Minimize the need for extensive human customer support.
  • Provide cost savings compared to traditional customer service methods.
  • Reduce average handling time and improve operational efficiency.
  • Reduce staffing requirements for basic support queries.
  • Scalable solutions that handle increasing customer volumes without significant cost increases.


  1. Scalability

If companies have the scalability of support, they can handle any traffic surge successfully. Scalability is an important factor in the success of any business and with Chatbot, business can conveniently scale up or down by allowing businesses to adapt to changes in customers’ demands and add other features as required.

Chatbots can help companies manage conversations during peak hours without adding more data analysts since AI-powered bots can handle thousands of conversations and answer each question immediately.

Key benefits

  • Adapt to fluctuating customer demand and traffic patterns.
  • Add new functionalities and expand capabilities as needed.
  • Chatbots handle high volumes of inquiries effortlessly.
  • Easily accommodate growing customer demands without delays.
  • Effortlessly serve customers across various channels and platforms.
  • Ensure consistent service quality regardless of customer volume.
  • Manage multiple conversations concurrently without losing productivity.
  • Provide instant responses even during peak hours.
  • Scale support operations without significant resource investment.
  • Seamlessly integrate with existing systems to handle increased demand.


  1. Fewer Bounce Rates

Any website’s bounce rate depends on how captivated the users are in browsing the content of your website. It is the percentage of visitors that stop browsing your website after opening the first page. If you have a website with high bounce rates, it will show that potential customers cannot find out what they were looking for and leave it to your competitors. Here, a chatbot can help you by popping up when a visitor is about to leave. Bots can help lessen the bounce rates by engaging to your audiences and at the same time, helping them navigate. This engagement keeps people on your website in a longer time which can also help in the SEO improvement and customer care.

Key benefits

  • Capture visitor attention and encourage longer sessions.
  • Engaging in chatbot interactions reduce website bounce rates.
  • Guide visitors through the website and highlight key offerings.
  • Identify and address common reasons for bounce rates through chatbot interactions.
  • Offer interactive experiences to captivate and retain users.
  • Offer personalized recommendations to keep visitors engaged.
  • Optimize user experience based on chatbot analytics and feedback.
  • Proactively assist visitors in finding what they are looking for.
  • Provide relevant information to reduce user frustration and bounce rates.
  • Utilize persuasive techniques to encourage exploration and engagement.


  1. Customer On-boarding Process  

Every customer likes to be guided and pampered. It doesn’t matter how powerful and informative your guidance is. They will still feel reluctant to find the information on their own. So chatbot can help customers understand what a customer has or hasn’t found in order and we can use this information to smartly push customers along with the conversions.  Chatbots are multifunctional for a variety of sales and marketing tasks, generating leads, answering queries in a personalized manner, brand customization and these traits make chatbots useful in the customer onboarding process.

Key benefits

  • Address user concerns and alleviate anxieties during onboarding.
  • Chatbots streamline onboarding with automated guidance.
  • Collect necessary information and preferences efficiently.
  • Educate users on product features and functionalities.
  • Offer interactive tutorials and guided tours for a smooth onboarding process.
  • Offer self-service onboarding options for user convenience.
  • Personalize onboarding experiences based on user profiles.
  • Provide step-by-step instructions and answer common queries.
  • Reduce onboarding time and ensure quicker product adoption.
  • Seamlessly transition users from onboarding to regular usage.


  1. Multilingual support

The most important aspect of implementing bots is that bots are available in many languages and there a number of reasons on why businesses should consider using a multilingual AI chatbot, most importantly if you have an international footprint.  So, no matter what kind of language your customer is most comfortable with, they can get proper support. You can program the bots into many languages according to your needs. This supports clients to describe their issues accurately and get useful support. By accommodating customers’ needs in their preferred languages, it will surely make their transaction experience better.

Key benefits

  • Chatbots offer language versatility for global customer reach.
  • Connect with customers in their mother tongues and favoured languages.
  • Enhance brand reputation as a customer-centric and inclusive organization.
  • Expand market reach by catering to different language-speaking demographics.
  • Improve customer satisfaction by offering support in native languages.
  • Overcome language barriers and improve accessibility.
  • Provide localized content and recommendations to diverse audiences.
  • Seamlessly switch between languages during conversations.
  • Serve customers in multiple languages effortlessly.
  • Support multilingual customer communities and forums.



Chatbots are gradually transforming customer communication in the corporate world. They are presenting new benefits to different aspects of companies be it sales, marketing, customer service, and customer engagement. Consequently, every company should have a plan to get the most out of bots to advance customer insights, lead generation, onboarding, and customer support scalability.  Prepare your company for the future with the support of Chatbots to start adding value to customer experiences. 

Top 10 Tech Trends That Will Positively Influence Your Marketing Strategies

With ever-changing technology, companies are on the urge to discover new tech trends to market their brands. Today’s consumers see the importance of transparency, authenticity, and privacy compared to as before. Among a pool of technologies, they (or companies) must stay abreast of new tech trends that could positively influence their marketing strategies. It is essential for every company must rely on the latest tech trends to challenge their competitors. In this article, let’s explore the top ten tech trends that will positively influence the marketing strategies of any company. Find out how you can successfully apply these top tech trends to amplify your reach and engage with your customers in various ways. 


  1. Big Data and Analytics

With the technical support of big data and analytics, companies can handle real-time analysis of marketing activities across all channels more efficiently. Moreover, advanced analytics advances documentation, and customer retention, content planning, pricing decisions, boost performance, greatly benefitting businesses transitioning to digital processes.

Key Benefits

    • Customer insights for personalized marketing.
    • Identify trends and predict future behaviour.
    • Optimize ad targeting and campaign performance.
    • Brand awareness
    • Cost and time savings by augmenting marketing performance


  1. Social Media Management Tools

Social media management tools will support you to build an interactive online relationship with your customers. The process here involves social Media Managers applying their experiences and combine them with tools and services to produce a content, work with users, and assess performances. By publishing various types of content such as videos and blogs, you empower your customers with useful information about your products or services. If the videos you publish go viral positively, your company reputation will go higher with positive comments. 

Key Benefits

    • Analyze social media data for insights.
    • Engage with customers and build relationships.
    • Monitor brand reputation and customer sentiment.
    • Save time by outsourcing time-consuming work such as making content and scheduling posts
    • Get professional advice on specific areas like social media advertising
    • Advances social media growth by gaining more followers and engagement


  1. Search Engine Optimisation

If you have come across a topic about digital marketing techniques, definitely you have heard the term SEO. Search Engine Optimization (SEO) is a key function of digital marketing because people conduct trillions of searches every day. In simple words, an excellent SEO raises your online presence and has a big effect on quantity and quality because it is not only for attracting new customers. It also enables you to have a deeper business relationship with those customers because of the trust you have built with them. When you use different social media platforms, you must know secretes of publishing content with key terms. Use key terms of your business functions and website features in the content you publish so that people can find you when they do search. If your website is good on back and front ends, you will surely get a better traffic.

Key Benefits

    • Establish trust between you and your customers
    • Improve user experience and website performance.
    • Increase website visibility and traffic.
    • Target specific keywords and optimize content.


  1. Streaming Service

In today’s digital era, video marketing strategy is not anymore optional but a fundamental part in businesses being said that 87% of businesses are using video as their primary marketing tool and there’s a nearly 25% increase in utilization for the past two years. More and more people are turning their eyes toward streaming services for quenching their movie-watching thirsts.  Hence, companies need to reimagine their advertising strategy if they want to align with the customers that use streaming services, companies need to find innovative approaches to market their brands on these platforms. 

Key Benefits

    • Companies can present original content to new audiences.
    • This technology leverages data to personalize recommendations.
    • With this technology, we may partner with influencers to reach followers.
    • Expands SEO as companies that have embedded media results to higher search results and are 45x more likely to rank on 1st page of Google


  1. Artificial Intelligence

The past year was mostly about updated and innovative trends in marketing, specifically in the tech space. Instant communication is in-demand and online consumer behavior is evolving. One trend that is having a big impact is AI in marketing. AI automates key marketing activities such as behavioral analysis, personalization, lead generation, customer relationship management (CRM), and other tasks that need automation. So marketing teams will have more time and become highly productive to create innovative strategies and analyze complex marketing models to increase their return on investment (ROI).

Key Benefits

    • AI automates repetitive tasks and improves efficiency.
    • We can personalize customer experiences and recommendations.
    • It is possible to predict customer behaviour and trends.
    • Maintains a more refined and strategic content curation process


  1. Extended Reality

Companies in the world are increasingly utilizing extended reality to create an immersive experience for beloved customers. These tech-based marketing solutions play a key role in measurable results for location-specific targeting with virtual events and storefronts. Businesses can also create global virtual events to reach their worldwide audience and sell their products and services around the world. Extended Reality has given companies a better way of emotionally connecting with their audiences in a post-pandemic scenario, where face-to-face communications are less common.

Key Benefits

    • It is possible to provide immersive experiences for customers.
    • With virtual product demos, we can increase sales.
    • It provides unique entertainment and brand experiences.


  1. Web3 Marketing

Web3 marketing is a revolutionary marketing approach that decentralizes marketing activities and provides customers with a gamified and interactive user experience. Marketers can employ blockchain technology-based tools to enable targeted audiences, ad fraud prevention, decentralized web hosting, and peer-to-peer interactions. Web3 marketing also empowers marketers to navigate brand relationships from the physical world to the virtual world while providing a better customer experience.

Key Benefits

    • We can create engaging, interactive experiences.
    • Use this platform to leverage decentralized platforms for marketing.
    • We can offer rewards and incentives for customer engagement.
    • Enhancement of real-world advertising


  1. Blockchain Technology

Recently blockchain technology has captured the tech world as a helpful tool for advertising and marketing. While a lot of us relates digital marketing with Analytics and AI, blockchain can be the most disruptive technology that is soon to impact marketers in all types of industry. Most of the uses for blockchain revolves around crypto-currencies and finance, but the fundamental technology might be vast for marketing. Since it is a decentralized ledger technology, companies can perform marketing and advertising with better data, gain deeper insights into audience interactions with ad campaigns and cultivate meaningful customer relationships.

Key Benefits

    • This technology enables us to create loyalty programs and reward customers.
    • It provides higher reliability to ensure transparency and authenticity of data.
    • We can improve supply chain management.
    • Creates verified chain from the ad dollar to the end user which results big savings for companies


  1. Voice Marketing

Voice marketing is a kind of digital strategy and tactic that companies use to promote their brands through voice-enabled devices. According to researchers, currently, above 20% of all searches are voice-led. Some common platforms for voice marketing are Google Assistant, Amazon Alexa, Spotify, Soundcloud, or Vocads. Marketers are focusing on this growth by advancing voice commerce, programmatic audio advertising, and remarketing. Companies are developing solutions such as quality equipment, editing and hosting platforms, and marketplaces to connect podcasters with brands.

Key Benefits

    • We can develop content suitable for voice search.
    • It supports us to create voice-activated ads and promotions.
    • You may use voice assistants to engage customers.


  1. Omnichannel Marketing

Omnichannel marketing is a one-stop marketing approach that gives customers a cohesive, integrated shopping experience across all types of digital locations, events, mobile devices, and online stores. This is highly useful for eCommerce store owners because it is vital for them to know where their customers come from. Omnichannel enables campaign management across all types of content such as SMS, phone calls, email, and chatbots through a single platform, both offline and online. With data and analytics, it gives consistent data whenever customers buy products and services.

Key Benefits

    • It is possible to personalize messaging for each customer.
    • We may use data to track customer journeys.
    • We offer consistent experiences across all channels.
    • Engage customers in real time with customized experiences.
    • Aligns all messaging across marketing and sales channels



Every technology continues to evolve and new innovations are being introduced all over the world.  Technologies have become an integral part of our everyday life. We use technologies in our everyday lives and our career lives. By understanding the latest tech trends that can influence your marketing strategies, make sure that your marketing paths are effective and productive to deliver successful results.

Use ChatGPT in Your Start-ups and Stay Ahead of the Game

Artifical intelligence has been dominating the game competently and ChatGPT which stands for Generative Pre-Training Transformer, a cutting-edge AI language model developed by OpenAI, has become one of the key business development tools recently.  With the support of several machine learning algorithms, it analyses vast amounts of data, learns patterns, and generates human-like responses to textual inputs. Moreover, it has the ability to provide you with context-specific data. With a diverse range of trained texts, including books, news articles, and social media posts, ChatGPT can understand natural language and respond immediately and logically. Start-ups can leverage ChatGPT to gain a competitive advantage to stay ahead of the game.


Customer Support
Today’s era of fast-paced customer expectations and digital world, almost all businesses are consistently improving their customer support together with e-commerce experience. The success of every start-up depends on customer satisfaction. Customers come from all walks of life and expect quick and trustworthy support whenever they face any issue related to the product or service. ChatGPT revolutionized industries by providing content for all types of corporate purposes and has been earning credits as a potential as a game-changer at an early stage. ChatGPT can learn from customer interactions and recognize common issues. So it can suggest solutions to resolve customer issues quickly, thus decreasing response times and improving customer satisfaction. You can use ChatGPT to obtain content for the following types of customer support.

  • Chat support
  • Email communication
  • Knowledge base support
  • Multilingual content
  • Q/A section
  • Self-service support
  • Social media post
  • Telephonic conversation
  • Text message
  • Video support


Business Operations
As start-ups have limited resources, they need to enhance their business functions to maximize productivity. With the support of ChatGPT, start-ups can streamline their operations in several ways to create clear and concise standard operating procedures, develop performance metrics for analysis and improvement, and focus on cost control and budgeting. ChatGPT has been programmed to perform human-like reactions to a selection of prompts. It has the capability to absorb conversation in a variety of languages and to come up with comprehensive writing. Here are 10 types of business functions that can be enhanced with the techniques acquired from ChatGPT.

  1. Accounting and bookkeeping
  2. Customer relationship management
  3. Financial reporting and analysis
  4. Human resources management
  5. Inventory management
  6. Marketing automation
  7. Project management
  8. Sales forecasting and analysis
  9. Supply chain management
  10. Workflow automation


Product Development
Product development is a major function of every business. ChatGPT can help companies by providing techniques to build a minimum viable product, conduct market research for product validation, innovate with new features and technology, and prototype and iterate for continuous improvement. ChatGPT is a useful tool to Product Managers in terms of Predictive Analysis, Outreach emails, drafting survey questions, expanding product lines, monitoring competitors, and product recommendations.


Types of product development

  • Agile development methodology
  • Concept testing and validation
  • Continuous improvement and iteration
  • Idea generation and ideation
  • Market research and analysis
  • Product design and prototyping
  • Quality assurance and testing
  • Release and deployment management
  • User adoption and engagement
  • User experience testing and optimization


Skill Development
Greater efficiency can boost the overall growth of a company, making more profits and decreasing excessive expenses. To attend training for new knowledge acquisition, observe experts for analysis and learning, practice to improve proficiency and consistency, and set specific, measurable goals for development, employee skill development is essential. Let’s see the top 10 skills that are needed for business development and use ChatGPT to obtain techniques for improving related skills.


Types of skills

  • Communication
  • Conflict resolution
  • Customer service
  • Diversity and inclusion
  • Leadership
  • Project management
  • Sales and marketing
  • Team building
  • Technical skills
  • Time management

AI-supported management has been relevant for companies that aim to achieve 360-degree sustainability and ChatGPT is a tool that can seed up the transition by saving time and lowering the cost of sustainability management.


Cost Savings
With some cost-saving techniques, start-ups can reduce their labour costs and improve their bottom line. By using GPT, companies can generate content for obtaining cost-saving techniques to conduct a cost analysis for budget optimization, implement telecommuting for office cost savings, minimize waste for cost and environmental benefits, and upgrade equipment for energy efficiency and longevity.


Cost-saving techniques

  • Asset management
  • Automation and robotics
  • Cloud computing and virtualization
  • Energy efficiency improvements
  • Outsourcing and offshoring
  • Process optimization
  • Procurement optimization
  • Supply chain optimization
  • Vendor contract renegotiation
  • Waste reduction and recycling

ChatGPT may not be capable of providing service as humans do but it is cost-effective considering that it can work 24/7 in the customer service department which is highly advantageous for companies especially to the ones with worldwide customer base. 


Market Research
If you don’t know who your customers are, your sales and marketing efforts will become worthless. You need to make a clear statistics-based picture of your customers with the support of the market research team and ChatGPT is a prevailing tool that can provide useful insights and can help companies to make good decisions. You may use ChatGPT to get the following types of techniques for performing better market research.


Types of market research

  • A/B testing – Comparing two versions for performance
  • Case studies – In-depth analysis for insights
  • Competitor analysis – Researching competitors for insights
  • Customer feedback – Gathering feedback about products/services
  • Industry reports – Reports on specific industries or markets
  • Interviews – One-on-one conversations for in-depth insights
  • Observation – Collecting data by watching consumers
  • Online analytics – Analyzing data from online platforms
  • Surveys – Questions to gather information from people
  • User testing – Testing product with users for insights


Content Marketing
Content marketing is one of the key marketing approaches focused on creating and distributing content in the digital world although it has been proven that it can be time-consuming and the process is a bit redundant.  Content must be valuable, relevant, and consistent to attract and retain the target audience and to drive profitable customer action and the good news is that you can get some tasks programmed making the work more convenient and easier using ChatGPT. With this tool, you can systematize content optimization, generate leads and do keyword research.


Types of content that uses ChatGPT

  • Blog posts – Written articles on a topic
  • Case studies – Customer success stories
  • FAQs – Answers to commonly asked questions
  • How-to guides – Step-by-step instructional content
  • Infographics – Visual data representation
  • Interactive content – Engaging audience participation content
  • Podcasts – Audio education or entertainment
  • Social media posts – Short updates on platforms
  • Videos – Engaging educational content
  • Whitepapers – Industry insights and solutions


ChatGPT is a highly useful AI tool that will allow start-ups to go progressive with the speed of content production. However, if you don’t know the exact direction of obtaining the required data, it’ll generate useless and uninteresting data. So you still need to be able to think before you asking data from GPT. The more creative and productive questions you ask the more highly beneficial data you can get from ChatGPT.

Defining And Getting Ready For The Future Of Connectivity

While 5G has just barely tip-toed its way into the world with its numerous promising opportunities, businesses have already started preparing for the 6G in full swing. It is often heard that connectivity has transformed business modes in the areas of safety and efficiency. This is because the revolutionary era of 6G technology is approaching us with a near-instant and unrestricted complete wireless connectivity fostering a promising future of an incredibly connected world.

6G is the acronym for the sixth generation of cellular technology. It is the next generation of mobile internet which means that it is the successor of 5G with enhanced capacity anticipated to deliver exceptional wireless connectivity and it is expected to become highly functional in the 2030s, building on social, consumer, and industry use-case mobile connectivity revolutions enabled by the promising features of 5G. 

Because there is immensely reliable low latency with 6G which means the network is highly optimized to respond with negligible delay, it offers the possibility of endless opportunities. These opportunities include three-dimensional holographic communications, the Internet of Drones (IoD), the Internet of Everything (IoE), digital twins / massively extended reality (XR) /virtual reality (VR), border surveillance, remote patient monitoring systems or telesurgery, Augmented Reality, Autonomous Vehicles (AVs) and many more, in a short way beyond the 5G can offer.

Although 6G technology is not going to be here till around the end of this decade, the business world comprehends that it is just the right time to prepare for the roadmap of forthcoming innovative technology.

So how should businesses move towards this leading-edge technology? What is the essential preparation needed for this radical era?

Let’s have look at the obligatory preparations the 6G technology calls for.


Build The Suitable Infrastructure

Businesses have to modify their setup to suit the new innovative technology they are adopting. The 6G technology market is anticipated to expedite massive improvements in the areas of imaging, presence technology, and location alertness. In collaboration with Artificial Intelligence, the 6G infrastructure will be capable of identifying the best place for computing to take place as well as data storage, processing, and sharing.  It is not any different for 6G too. For the implementation of this next-generation cellular technology, a business has to face the challenge of adapting effectively as well. For this, the business has to incur the cost of infrastructure that the 6G network necessitates.

Businesses must understand that for a 6g network, as it is expected to have a speed of 1Tbps, a relatively enormous infrastructure would be required. It may escalate the cost of the infrastructure, proportionately.

Another challenge that needs to be addressed is 6G networks may entail a considerable amount of spectrum to reach the projected speed. This can be very tricky as 6G networks can have just a limited amount of spectrum accessible.


Develop A Strong Cybersecurity Strategy

6G has the potential to connect practically 10 million IoT devices in a specific area. It will improve our dealings with Cyberattacks as researchers are saying that it will minimize the risk of digital dangers in the future. This aggravates the vulnerability of the connected devices to cybersecurity risk. With so many interconnected IoT devices, the risk is not just limited to computers and phones, in addition, it can pose threat to the network infrastructure too.

So, the outdated cybersecurity strategy has to be eliminated.  All businesses incorporating 6G technology must instigate the latest security, testing, as well as training standards and put them into practice. Robust cybersecurity must be designed and incorporated into the SDLC (Software Development Life Cycle) with integral securities that detect vulnerabilities and support the network’s recovery promptly in case of any attacks.

To conclude the vision of 6G, researchers are constructing a number of approaches to areas such as antennas, spectrum regulation, artificial intelligence, and machine learning- all of which will be needing full-bodied cybersecurity features to qualify their extensive adoption by industry and consumers in the future. 


Revise Seamless Networks

This sixth-generation (6G) wireless communication network is predicted to incorporate aerial, terrestrial, and maritime communications into a vast network to which they would be faster, more reliable, and able to support multiple numbers of devices with very minimal latency requirements.  6G entails omnipresent connectivity and thus necessitates pioneering radio technologies that support seamless integration of all wired and wireless networks as well as non-terrestrial networks which were tough to achieve with the previous generations of cellular technology.

Also, Artificial intelligence can be incorporated as the in-built element of the 6G network model. This can aid in boosting the performance of the intricate sixth-generation networks while making the same more efficient and flexible on top. Not just that, 6G technology also calls for further developments in IoT, and additional improvement in mobile broadband, apart from ultra-reliable communications beyond 5G.


Employ Policy To Facilitate Innovation, Availability, And Security

This is the most appropriate stage to develop the regulatory base and form global policies for 6G networking technology. Policymakers must come together with industry leaders across the world to decide on further spectrum allocations for mobile services, IMT identifications as well as harmonization.

Countries across the world must recognize their requirements, partake in the international regulatory process and outline their roadmaps, to enable their residents and economic sectors to get the greatest value from this upcoming technology while protecting themselves and their data.

The task of businesses here is to keep eyes open for these policies and create future business ideas plans and strategies accordingly if it involves 6G technology.


Build Consistent Global Industry Standards

Now as businesses have their hands on 5G while weighing up and modifying the established standards they can get a clear picture of the requirements of 6G. So, this is an apt time to initiate setting the basis and standards for the next generation of connectivity. A single, international standard that is applicable to all types of industries and topographies can do a lot in ensuring consistency and economic scale during the 6G rollout. 

A solid and consistent set of standards that is valid for each and every industry, all over the world is a way to guarantee standardization. All must contribute to assessing 5G standards through various demonstrations to prepare for the needs of 6G.  It is also a way to benefit companies by making their processes efficient and will minimize geopolitical issues that might lead to standard competition. However, at this stage, only general standards and not very specific ones can be framed.


Develop The Right People

People like scientists, physicists, researchers, engineers, and academicians are the ones who innovate and create new technologies and make them accessible to the world. So, when the newest technology is incorporated into a business, the business must fortify itself by fostering technology experts as well.

These experts may help the business by coming up with new ideas for the 6G application which can be commercialized by the business profitably. Therefore, businesses must put the effort into building a team of proficient individuals, and provide them with resources so that they can educate and train them to their best ability and close their skill gaps.

Although 6G is merely in the research stage currently, it is crucial to prepare and plan for the coming times. Businesses must be aware of the fact that transitioning from 5G to 6G technology can throw in huge challenges and it is never too early to set off, planning for 6G. This is actually the most appropriate period to take action to keep up the momentum of 5G while opening the way for its successor – 6G technology.

Add Value to Your Business Transformation via Cloud Solutions

When you are looking for world-shattering approaches to transform your business, you face roadblocks because of limited resources and a lesser amount of development strategies. If you dive deep into cloud computing, you may add value with unlimited sources and strategies to your business transformations that will help you accelerate digital transformation. According to the statement of Fortune Business Insights, the market size of global cloud computing is predicted to reach USD 1,712.44 billion in 2029, at a CAGR of 19.9% during the period of year 2022-2029.

Everything in the world is moving fast in the age of digitization. We could have come across a sudden rise and fall in several industries. If companies need to meet modern-day demands, they can go for cloud computing to transform their businesses and stay ahead of the curve. Cloud computing offers you the fastest transformations to help grow your business with advanced resources such as servers, storage, networking, databases, software, analytics, and intelligence. Organizations could leverage the power of the cloud to move quicker, respond faster, and improve new revenue streams, generating exponential, lasting value. In this article, let’s analyse in detail how cloud computing could contribute more to business transformations.


The business model of every company needs to be constantly upgraded with innovative elements, the latest trends, or the requirements of new generations. Cloud computing delivers the essential infrastructure with continuous optimization across the organization with great agility which is not possible earlier. When companies are ready to be based on utilizing cloud computing elements, firms get ready to stay agile and are willing for the transition.

  • Ability to quickly adapt to market changes
  • Easy to collaborate with others
  • Easy to customize solutions
  • Easy to scale up/down
  • Easy to test and experiment
  • Faster time to market
  • Rapid deployment of new features
  • Reduced development time and costs
  • Reduced time to deploy
  • Reduced time to troubleshoot

Computing competencies are simply limitless.

Cost Effectiveness

Generally, companies are not interested to invest more and more to create a new infrastructure, as it needs a lot of cost and labour effectiveness. They want to advance the existing infrastructure with cost-effective approaches. Most companies in the world play this cost-effective business game for a long time. Nowadays, cloud computing allows companies to enhance their business functions with the minimum resources they use. When it comes to quick overall development, the cloud is, undeniably, more suitable as compared to other solutions.

  • Increased cost transparency
  • Lower energy costs
  • No maintenance fees
  • No need for physical storage
  • No need for upfront investment
  • Pay-as-you-go pricing model
  • Reduced need for hardware
  • Reduced software licensing costs
  • Reduced staffing requirements
  • Scalable usage and costs


For more than one decade, several companies have started developing continuously to promote their brands in the form of apps, as apps are delivering effective customer experiences with many advantages such as improved ROI, reduced complexity, and less manual operations.

Gradually, apps of all big enterprises and several SMEs are being more widely used by customers from all walks of life, which increase the demand for management of application integration in the form of cloud computing. Apps connected to cloud computing give users the ability to use them effectively, as cloud-based applications can also be accessed from any location using any internet-connected devices such as PCs, smartphones, and tablets.

  • Accessibility on-the-go
  • Anytime, anywhere access
  • Easy accessibility features
  • Global accessibility
  • Improved customer experience
  • Improved user experience
  • Increased collaboration
  • Increased engagement
  • Multi-device compatibility
  • Reduced barrier to entry


Infrastructural issues and data loss are common in the digital world. However, when it comes to moving data to cloud computing, it authentically decreases the dangers of data loss, illegal access, and any other problems related to infrastructure. We can also protect data from potential dangers, as backups are generated automatically. Companies prefer to cloud computing because of more secure industry-wide advancements developed by cloud service providers and numerous cloud providers offer a wide set of technologies, policies, and controls that reinforce the security posture of any business by helping protect your data, application, and infrastructure from possible threats. Here below are some security techniques of cloud computing that companies might use in their business functions.

  • Advanced encryption technologies
  • Automatic software updates
  • Compliance with industry regulations
  • Dedicated firewalls and intrusion detection
  • Dedicated security teams
  • Reduced risk of data breaches
  • Regular security audits and testing
  • Regular security updates
  • Two-factor authentication options
  • User access control and monitoring


Cloud scalability in cloud computing can increase or decrease IT resources as required to meet the changing demands of every situation and it is at its peak of becoming the new normal. Cloud solutions provide resolutions to anticipate problems like cybersecurity, managing big data, and quality control. To add up, evolving technologies such as AI are becoming accessible through cloud solutions. Companies will have an opportunity to increase data storage capacity, processing power, and networking by joining cloud computing infrastructure. This is one of the most technical, beneficial, and cost-effective features of cloud computing, as companies can grow up or down to meet the demands depending on the season, development, projects, etc. By distributing workloads among more servers as demand rises, cloud computing empowers businesses to maintain business continuity while attaining maximum benefits.

  • Access to advanced technologies
  • Easy to scale up/down
  • No need for hardware upgrades
  • Pay-as-you-go pricing model
  • Reduced energy costs
  • Reduced need for in-house IT staff
  • Reduced need for physical storage
  • Reduced software licensing costs
  • Reduced staffing requirements
  • Scalable usage and costs


Apart from the benefits given above, there can be several other reasons to adopt cloud computing. Hence, companies should assess their needs and solutions to create a future-based cloud migration plan. According to the study by Gartner, 95% of data workloads will be hosted in the form of the cloud by 2025, up from 30% in 2021. They have predicted that over 95% of businesses would go for cloud computing by 2025. For more efficient business operations and cost savings, every company should adopt cloud computing as it can support data backups and redundancy, disaster recovery solutions, and reduced risk of data loss.

Must-have Digital Technologies for Optimizing CX in 2023

A digital-first approach is a key marketing aspect in 2023, as it empowers companies to reach and engage with customers through various digital channels. The majority of customers expect quick and convenient access to information and services. Hence, digital channels can help companies advance customer satisfaction, stay ahead of competitors, and drive growth. In this article, let’s study the top ten must-have digital technologies for optimizing CX in 2023. By embracing these ten tools and technologies, companies can increase the quality of customer service without increasing costs.

  1. Contact Center as a Service (CCaaS)

CCaaS is a cloud-based solution that allows companies to use the software provided by a CCS provider. It is another option for an on-premises call center and bundles an entire communication solution fixated on scalable customer experience. By lowering the amount of technology, companies can reduce the requirement for internal IT support. Due to the ability to pay for the essential technology in a consumption model, they can provide better service to their clients with minimum investment. It will also empower companies to provide an omnichannel communication strategy while delivering an excellent customer experience.

  1. Marketing automation software

This type of software helps companies send messages to their target customers based on their purchase history, demographics, and interests. It is user-friendly and can create campaigns with just one click. It has a streamlined user interface and drag-and-drop components which is very much the same as the other apps being used nowadays. With the support of this software, all types of companies can develop and execute marketing campaigns quickly and effortlessly.  So companies can reduce their time consumption for customer service so that they can focus on solving critical issues.

Benefits of Marketing Automation Software

  • Better customer experience
  • Cross-channel marketing campaigns
  • Full control of customer interactions
  • Value-added accountability in the workplace
  • Increased lead-to-sale conversion rates
  • Marketing and sales alignment
  • More space for creativity
  • Smooth customer service
  • Customized marketing strategies
  • Precise reporting
  • Reduces staffing expenses
  1. Chatbots

Chatbots work with the support of AI to have conversations with human customers via the internet. Companies have developed various chatbots for customer support activities and large number of brands have invested to Chatbots to improve their customer experience. Several firms, due to the ability of chatbots to respond to client inquiries, have installed on their websites to meet modern consumer needs.

Benefits of Chatbots

  • More generated leads and increased sales
  • Better customer insights with 24/7 availability
  • Better user experience because of multilingual support
  • Enhances operational efficiency
  • Cuts expenses to businesses while giving convenience to customers
  1. Customer relationship management (CRM) systems

Managing customer relationships is one of the key technologies that provide a great experience to companies. It assists customer-centric companies by placing customers at the centre of the business with proper strategy and plans.  Technology nowadays can improve human potential by having the proper CRM technology and tools that are capable of doing the processes and helping people to focus on their priorities requiring human touch.   A CRM system empowers companies to keep track of all types of data related to customer interactions. It also helps companies handle their pipeline, discover opportunities, and quantify the success of digital marketing campaigns.

Benefits of CRM

  • Automated sales reports with more accurate sales forecasting
  • Better customer service with increased sales
  • Centralized database of information
  • Higher productivity and efficiency with detailed analytics
  • Streamlined internal communications
  • Helps manage campaigns across sales and marketing
  • Consistently generate quality leads and opportunities
  • Customized customer experience
  1. Email marketing software

Email will always be an essential digital marketing component despite the fact that there are new online platforms being launched. It’s a recognized method of boosting customer acquisition and engagement. Email marketing software empowers companies to design and send customized email messages to their customers. With various types of messages such as product announcements, coupons, or information about upcoming events, it supports companies to track the success of their marketing campaigns. It also helps companies to identify the right customers that are likely to respond to particular types of offers.

Benefits of Email Marketing Software

  • Collecting feedback and surveys
  • Communicating with your audience
  • Creating personalized content
  • Generating traffic to your site
  • Having a setting for self-promotion
  • Producing cost-effective and timely campaigns
  • Providing more value to your audience


  • Reaching the right people at the right time
  • Heightens Brand Awareness
  • Minimize expenses for the promotion campaigns and overall business
  1. Mixed realities

Mixed realities, the combined forms of Virtual reality (VR) and augmented reality (AR), transform how customers and sales reps communicate and interact. Basic examples for this are virtual makeup applications and Snapchat or Instagram filters. In order to come up with a mixed reality experience, you don’t need to stress about physical hindrances, but you will be needing artificial intelligence and cloud computing. This technology will reimagine how sales reps or agents can interact with customers more effectively by adding more visual value to their communications. Companies can suggest better solutions with the support of an immersive visual user experience.

Sectors that need the support of Mixed Realities

  • Construction
  • E-learning
  • Entertainment
  • Healthcare
  • Manufacturing
  • Retail
  • Sports
  • Tourism
  • Construction and Engineering
  • Training
  • Marketing
  1. Self-Service

Customer self-service portals have become an excellent section, as they enable customers to help themselves. Self-service technology puts a control power in our hands. It permits is to perform various tasks without needing the help of another human being and almost all of us come across self-service technology daily from digital touchpads that can take orders in restaurants to gas stations with credit card readers. Without any fair knowledge of technology, customers can easily browse through the knowledge base, use self-service tools, and contact customer support team if they want to know more details about products and services.

Benefits of Self-service

  • It boosts website traffic.
  • It advances agent productivity.
  • It leverages personalised information.
  • It decreases customer service costs.
  • It imparts new skills to customers.
  • It provides bigger customer retention
  • 24/7 availability
  • It heightens customer satisfaction


  1. Speech analytics

We can define speech analytics as a contact hub intelligence instrument that uses technologies such as audio analysis, data visualization, natural language processing and automatic speech recognition. It is widely used for automated surveys, letting customers respond to survey questions via call and extracting insights. Speech analytics can reveal keywords or themes that normally provoke certain feelings. With the support of speech analytics, companies can identify emotional signals, customers’ sentiments, and positive interactions.

Benefits of Speech Analytics

  • Identifying customer needs and interests
  • Offering personalized services
  • Understanding customers better
  • Supported training data and performance improvement
  • Provides feedback to fasten up sales
  1. Customer experience management (CXM) systems

For the past years, companies have been prioritizing the relevance of managing cuthe stomer experience and software firms have been working on creating a CRM software to help businesses handle their customers. A CXM system goes beyond managing CRM. With the ability to collect feedback and data from all customer touchpoints, it can provide a holistic view of the customer experience. CXM focuses on the listening part and hotels, airlines and F&B industry are realizing its value to customer experience. CXM is driving growth for companies such as retail, CPG, media, technology, healthcare, and financial services.  It can advance marketing campaigns, customer interactions, and website design.

Benefits of CXM

  • Better customer engagement
  • Higher customer retention
  • Improved crisis management
  • Increased brand equity
  • Reduced costs of service and marketing
  1. Customer data platform (CDP)

A customer Data Platform (CDP) is a set of applications that works together to develop a unified permanent customer database. It helps a business to get to know their customers more. CDP’s main function is to construct an integrated client database that can at the same time address multiple downstream problems. This database will have an access to other applications. Data gathered from various sources is cleansed and merged to provide a comprehensive consumer profile. This data is subsequently accessible to other marketing platforms. With the support of the insights from the CDP, companies can predict customer behaviour and perform a host of tasks based on data.

Benefits of CDP

  • Eliminating data silos
  • Ensuring data protection and privacy
  • Increasing operational efficiency
  • Customized communication
  • Systematize processes
  • Come up with time-suitable marketing messages
  • Heightens customer engagement


The above digital technologies can play a key role in the process of customer experience optimization. By leveraging these ten technologies, companies can improve their interactions with customers and enhance their customer experience. Nevertheless, companies should be aware of the appropriate technologies that will be able to reach and engage their target audience for their every promotion, since every type of promotion needs unique channels. And also companies should ensure that they have properly implemented these technologies for getting better results.


Top Artificial Intelligence Trends to Capture the Global Tech Market

Artificial intelligence is the hottest technology in the global tech market. It has transformed the corporate world with innovative processes and gadgets and made everyone’s life more convenient. AI models provide the world with autonomous systems, cybersecurity, automation, RPA, etc. With artificial intelligence trends to boost productivity and efficiency, tech companies are changing the way we live. This article will help you to understand the power of artificial intelligence by explaining the current trends and the following are the top artificial intelligence trends in 2023 in the tech market:

Predictive analytics
Predictive analytics is the most important trend of artificial intelligence since it is highly helpful for better business and market research. It has gained a number of attention in recent years in the areas of machine learning and big data. With the support of data, statistical algorithms, and machine learning techniques, companies can make decisions for future outcomes. The key theme of this technique is to utilize the trends of the past to provide the best in the future. This trend has captured the attention of business analysts and market experts.

Embedded Application (EA)
The essential attributes of Embedded Application are fault tolerance, real-time, reliability, portability, and flexibility. This AI software application perpetually resides in a consumer or industrial device. This AI-based software and is programmed to have a special function in a device with a specific purpose that must meet size, time, energy, and memory constraints. There are different types of embedded systems in various gadgets and devices such as smartphones, digital cameras, digital wristwatches, embedded medical devices, and sensors. Embedded systems are transforming our lifestyles by creating newer opportunities and challenges. This is one of the best AI trends to follow if entrepreneurs want to play well in the tech market.

The Metaverse
Metaverse is an immersive virtual world in which everyone can work, play, live, transact, and socialise, enabled by the practice of mixed reality (virtual reality and augmented reality). Here, users are linked to their avatars or other digital illustration and the information gathered about their activities is personal data, which is accountable to data protection and privacy laws. It is the next evolution of the digital world, facilitated by multiple technologies including blockchain, artificial intelligence, smart objects, and edge computing. In a recent market study, the Metaverse Technology market had a valuation of $32 billion in 2021 and is possible to become $224 billion by 2030.

In the next decade onwards, the Metaverse is likely to provide the most incredible business opportunities to make the world evolve with innovative business concepts. Furthermore, a number of remarkable technologies are introduced for taking place within the Metaverse that could provide innovative business opportunities. Companies can enter into the Metaverse trends that are going to make our life prosperous in the future.

Security and Surveillance
A new level of security and surveillance has also become one of the best trends in artificial intelligence technologies. Surveillance technology is a software useful for monitoring activities, behaviour, and handling information. With video surveillance that combines biometric authentication using face and voice recognition with automated image analysis, we could more accurately identify objects. With the support of video capture and analysis software, we can help secure large public and private spaces by spotting potential threats. Companies may focus on this trend to protect every organization with the security and surveillance techniques of AI. It is said that this year, the global surveillance technology market had a valuation of over 130 billion U.S. dollars.

Manufacturing has changed and entered a dynamic phase. It has promoted innovation and productivity in today’s economy and a global transformation is in progress to authorize manufacturing with AI.  AI plays various roles in manufacturing sectors. For example, automated production control is used to monitor equipment and check for quality control. AI-powered inspection is used to control the suitability of components for assembling cars and to sense product defects on the conveyor. In manufacturing, AI also plays a major role in using technology to automate multifaceted tasks and unearthing formerly unidentified patterns in manufacturing processes.

It is a must for any financial industry to ensure its traditional priorities such as the speed and accuracy of transactions, the prevention of errors and abuses, the preservation of data privacy, and the responsibility for the confidentiality of transactionsThe fast growth of Fintech in several sectors has created many benefits that include:

  • The key benefits of vendors are faster processes in accessibility and loan approvals. On account of a quick and hassle-free process, users become more adaptable to this new fast-paced technology.
  • In a one-stop platform, users can enjoy a very easy payment method and feel a better experience whenever they process different types of payments from various devices such as smartphones and tablets.
  • Several latest systems depend on chatbots and robot advisors to help users understand their finances. As Fintech comes with a very low-cost option, customers get more useful functions.
  • Fintech is powerful software that is very helpful for companies to collect payments accurately. It also helps everyone to know their updated account status.

It is predicted that the Global Financial Technology market will grow progressively and is expected to reach approximately $324 billion market value by 2026.

There is an improved uptake of AI technologies in the healthcare sector and the efficiency, accuracy, and convenience of AI in the healthcare sector played a big part in the driving factor for its growth in the global technology market. Artificial intelligence has already proven that it is a great boon to healthcare providers since it could facilitate care more efficiently and allow patients greater access to safe medical care. It will transform many aspects of patient care together with the related administrative processes. The potential benefits of artificial intelligence in the healthcare market are enormous as it:

  • Develops healthcare sectors with more trustworthy methods.
  • Provides patients with medical records of all communications and prescriptions.
  • Offers a great deal of transparent communication related to patient billing.
  • Allows healthcare professionals to access the patient’s data easily.
  • Provides data which that cannot be altered by anyone.

AI and IoT
Artificial Intelligence and the Internet of Things have provided magnificent changes in today’s business environment. When we connect any internet-connected devices with other gadgets wirelessly, the gadgets in this system can transmit and receive data from each other. With the support of IoT, workplaces and modern management have become smart with various hands-on facilities. It also supports companies to reduce operational costs and enhance overall efficiency and productivity. As IoT comes along with blockchain technology, companies can advance IoT industry processes to protect communications, modernize the software, and monitor usage and functions on the whole.

The AI in IoT market size is said to grow to 34 Billion USD in 2027 and the growing need of refining the human-machine and machine-to-machine interaction across households, healthcare and transportation activities will fasten the growth of AI in the IoT market for the coming years.

Artificial intelligence is capable to transform any type of organization. It has the key to unlocking a digital world where we can make more informed decisions based on data. The domination of AI affects every sector, from manufacturing to finance, bringing about never before seen increases in efficiency and productivity. Since every sector starts experimenting with this technology, the trends of AI keep evolving. Embrace AI, the benefits of AI to business are immense if used wisely.

Why Machine Learning and the ‘New AI’ won’t be Replacing your Friendly Post – Keynesian Macroeconomist Anytime Soon


The paper provides a brief history of recent developments in machine learning and the “New AI”.  This sets the scene for a review of debates over machine learning and scientific practice, which brings to the forefront the hubris of those appealing to a naïve form of materialism in this specific domain at the intersection between philosophy and sociology of science. The paper then explores the “unreasonable effectiveness” of machine learning to shine a spot-light on the limitations of contemporary techniques. The resulting insights are subsequently applied to the particular question of whether current machine learning platforms could capture key elements responsible for the complexity of real-world macroeconomic phenomena as these have been understood by Post Keynesian economists. After concluding in the negative, the paper goes on to examine whether efforts to extend deep learning through differential programming could overcome some of the previously discussed limitations and stumbling blocks.

Keywords: machine learning, the “New AI”, macroeconomic modelling, fixed-point theorems, backpropagation, the capital debates, uncertainty, financial instability, differential programming


An avalanche of recent publications (Zuboff, 2019; Gershenfeld, Gershenfeld & Gershenfeld, 2017; Carr, 2010; Lovelock, 2019; and Tegmark, 2017) reflect the emotional range of our current obsessions about the Digital Economy, which are concerned, respectively, with: its inherent capacity for surveillance, domination, and control; its opportunities for extending the powers of digital fabrication systems to all members of the community; its retarding effects on deep concept formation and long-term memory; the prospect of being watched over by “machines of loving grace” that control our energy grids, transport and weapon systems; and, the limitless prospects for the evolution of AI, through procedures of “recursive self-improvement”. In my own contribution to the analysis of the digital economy (Juniper, 2018), I discuss machine learning and AI from a philosophical perspective that is informed by Marx, Schelling, Peirce and Steigler, arguing for the development of new semantic technologies based on diagrammatic reasoning, that could provide users with more insight and control over applications.[1]

AI and Machine Learning practitioners have also embraced the new technology of Deep Learning Convolution Neural Networks (DLCNNs), Recursive Neural Networks, and Reservoir Neural Networks with a mixture of both hubris and concern[2]. In an influential 2008 article in Wired magazine, Chris Anderson claimed that these new techniques no longer required a resort to scientific theories, hypotheses, or processes of causal inference because the data effectively “speak for themselves”. In his response to Anderson’s claims, Mazzochi (2015) has observed that although the new approaches to machine learning have certainly increased our capacity to find patterns (which are often non-linear in nature), correlations are not all there is to know. Mazzochi insists that they cannot tell us precisely why something is happening, although they may alert us to the fact that something may be happening. Likewise, Kitchin (2014) complains that the data never “speak for themselves”, as they shaped by the platform, data ontology, chosen algorithms and so forth. Moreover, not only do scientists have to explain the “what”, they also have to explain the “why”. For Lin (2015) the whole debate reflects a confusion between the specific goal of (i) better science; and that of, (ii) better engineering (understood in computational terms). While the first goal may be helpful, it is certainly not necessary for the second, which he argues has certainly been furthered by the emerging deep-learning techniques[3].

In what follows, I want to briefly evaluate these new approaches to machine learning, from the perspective of a Post Keynesian economist, in terms of how they could specifically contribute to a deeper understanding of macroeconomic analysis. To this end, I shall investigate thoughtful explanations for the “unreasonable effectiveness” of deep-learning techniques, which will therefore focus on the modelling, estimation, and (decentralised) control of system (-of systems) rather than image classification or natural language processing.

The “Unreasonable effectiveness” of the New AI

Machine learning is but one aspect of Artificial Intelligence. In the 1980s, DARPA temporarily withdrew funding for US research in this field because it wasn’t delivering on what it had promised. Rodney Brooks has explained that this stumbling block was overcome by the development of the New AI, which coincided with the development of Deep Learning techniques characterised by very large neural networks featuring multiple hidden layers and weight sharing. In Brooks’ case, the reasoning behind his own contributions to the New AI were based on the straightforward idea that previous efforts had foundered on the attempt to combine perception, action, and logical inference “subsystems” into one integrated system. Accordingly, logical “inference engines” were removed from the whole process so that system developers and software engineers could just focus on more straightforward modules for perception and action. Intelligence would then arise spontaneously at the intersection between perception and action in a decentralized, but effective manner.

One example of this would be the ability of social media to classify and label images. Donald Trump could then, perhaps, be informed about those images having the greatest influence over his constituency, without worrying about the truth-content that may be possessed by any of the individual images (see Bengio et al., 2014, for a technical overview of this machine learning capability). Another example of relevance to the research of Brooks, would be an autonomous rover navigating its way along a Martian dust plain, that is confronted by a large rock in its path. Actuators and motors could then move the rover away from the obstacle so that it could once again advance unimpeded along its chosen trajectory—this would be a clear instance of decentralized intelligence!

In their efforts to explain the effectiveness of machine learning in a natural science context, Lin, Tegmark, and Rodnick (2017), consider the capacity of deep learning techniques in reproducing Truncated Taylor series for Hamiltonians.  As Poggio et al., (2017) demonstrate, this can be accomplished because a multi-layered neural network can be formally interpreted as a machine representing a function of functions of functions… :


At the end of the chain we arrive at simple, localized functions, with more general and global functions situated at higher levels in the hierarchy. Lin, Tegmark, and Rodnick (2017) observe that this formalism would suffice for the representation of a range of simple polynomials that are to be found in the mathematical physics literature (of degree 2-4 for the Navier-Stokes equations or Maxwell’s equations). They explain why such simple polynomials characterise a range of empirically observable phenomena in the physical sciences, in terms of three dominant features, namely: sparseness, symmetry, and low-order[4]. Poggio et al., (2017) examine this polynomial approximating ability of DLCNNs, also noting that sparse polynomials are easier to learn than generic ones owing to the parsimonious number of terms, trainable parameters, and the associated VC dimension of the equations (which are all exponential in the number of variables). The same thing applies to highly variable Boolean functions (in the sense of having high frequencies in their Fourier spectrum). Lin, Tegmark, and Rodnick (2017) go on to consider noise from a cosmological perspective, noting that background radiation, operating as a potential source of perturbations to an observed system, can be described as a relatively well-behaved Markov process.

In both of these cases, we can discern nothing that is strictly comparable with the dynamics Post Keynesian theory, once we have abandoned the Ramsey-Keynes (i.e. neoclassical) growth model as the driver of long -run behaviour in a macroeconomy. From a Post Keynesian perspective, the macroeconomy can only ever be provisionally described by a system of differential equations characterised by well-behaved asymptotic properties of convergence to a unique and stable equilibrium.

The Macroeconomy from a Post Keynesian Perspective:

In The General Theory, Keynes (1936) argued that short-run equilibrium could be described by the “Point of Effective Demand”, which occurs in remuneration-employment space, at the point of intersection between aggregate expenditure ( in the form of expected proceeds associated with a certain level of employment) and aggregate supply (in the form of actual proceeds elicited by certain level of employment). At this point of intersection, the expectation of proceeds formed by firms in aggregate is fulfilled, so that there is no incentive for firms to change their existing offers of employment. However, this can occur at a variety of different levels of employment (and thus unemployment).

For Keynes, short-run equilibrium is conceived in terms of a simple metaphor of a glass rolling on a table rather than that of a ball rolling along in a smooth bowl with a clearly defined minimum. When it comes to the determination of adjustments to some long-run full-employment equilibrium, Keynes was no less skeptical. Against the “Treasury-line” of Arthur Pigou, Keynes argued that there were no “automatic stabilizers” that could come into operation. Pigou claimed that with rising unemployment wages would begin to fall, and prices along with them. This would make consumers and firms wealthier in real terms, occasioning a rise in aggregate levels of spending. Instead, Keynes insisted that two other negative influences would come into play, detracting from growth. First, he introduced Irving Fisher’s notion of debt-deflation. According to Fisher’s theory, falling prices would transfer income from high-spending borrowers to low-spending lenders, because each agent was locked in to nominal rather than real or indexed contracts. Second, the increasing uncertainty occasioned by falling aggregate demand and employment, would increase the preference for liquid assets across the liquidity spectrum ranging from money or near-money (the most liquid), through short-term fixed interest securities through to long-term fixed interest securities and equities and, ultimately, physical plant and equipment (the least liquid of assets).

In formal terms, the uncertainty responsible for this phenomenon of liquidity preference can be represented by decision-making techniques based on multiple priors, sub-additive distributions, or fuzzy measure theory (Juniper, 2005). Let us take the first of these formalisms, incorporated into contemporary models of risk-sensitive control in systems characterised by a stochastic uncertainty constraint (measuring the gap between free and bound entropy) accounting for some composite of observation error, external perturbations, and model uncertainty. While the stochastic uncertainty constraint can be interpreted in ontological terms as one representing currently unknown but potentially knowable information (i.e. ambiguity), it can also be interpreted in terms of information that could never be known (i.e. fundamental uncertainty). For Keynes, calculations of expected returns were mere “conventions” designed to calm our disquietude, but they could never remove uncertainty by converting it into certainty equivalents.

Another source of both short-run and long-run departure from equilibrium has been described in Hyman Minsky’s (1992) analysis of Financial Instability, which was heavily influenced by both Keynes Michal Kalecki. As the economy began to recover from a period of crisis or instability, Minsky argued that endogenous forces would come into play that would eventually drive the system back into crisis. Stability would gradually be transformed into instability and crisis. On the return to a stable expansion path, after firms and households had repaired their balance-sheet structures, financial fragility would begin to increase as agents steadily came to rely more on external sources of finance, as firms began to defer the break-even times of their investment projects, and as overall levels of diversification in the economy steadily came to be eroded (see Barwell and Burrows, 2011, for an influential Bank of England study of Minskyian financial instability).  Minsky saw securitization (e.g. in the form of collateralized debt obligations etc.) as an additional source of fragility due to its corrosive effects on the underwriting system (effects that could never be entirely tamed through a resort to credit default swaps or more sophisticated hedging procedures). For Minsky, conditions of fragility, established preceding and during a crisis may only be partially overcome in the recovery stage, thus becoming responsible for ever deeper (hysteric) crises in the future[5].

An additional, perhaps more fundamental, reason for long-run instability is revealed by Piero Sraffa’s (1960) insights into the structural nature of shifts in the patterns of accumulation, within a multisectoral economy, as embodied in the notion of an invariant standard of value. Sraffa interprets David Ricardo’s quest for a standard commodity—one whose value would not change when the distribution of income between wages and profits was allowed to vary—as a quest that was ultimately self-defeating. This is because any standard commodity would have to be formally constructed with weights determined by the eigenvalue-structure of the input-output matrix. Nevertheless, changes in income distribution would lead to shifts in the composition of demand that, in turn would induce increasing or decreasing returns to scale. This would feed back onto the eigen-value structure of the input-output matrix, in turn requiring the calculation of another standard commodity (see Andrews, 2015, and Martins, 2019, for interpretations of Sraffa advanced along these lines). If we return to the case of the neoclassical growth model, Sraffa’s contribution to the debates in capital theory has completely undermined any notion of an optimal or “natural rate of interest” (Sraffa, 1960; Burmeister, 2000). From a policy perspective, this justifies an “anchoring” role for government policy interventions which aim to provide for both stability and greater equity in regard to both the minimum-wage (as an anchor for wage relativities) and determination of the overnight or ‘target’ rate of interest (as an anchor for relative rates-of-return).

From a modelling perspective, Martins (2019) insists that Sraffa drew a sharp distinction between a notion of ‘logical’ time (which is of relevance to the determination of “reproduction prices” on the basis of the labour theory of value, on the basis of a “snapshot” characterization of current input-output relations) and it’s counterpart, historical time (which is of relevance to the determination of social norms such as the subsistence wage, or policies of dividend-retention). When constructing stock-flow-consistent macroeconomic model this same distinction carries over to the historical determination of key stock-flow norms, which govern long-run behaviour in the model. Of course, in a long-run macroeconomic setting, fiscal and monetary policy interventions are also crucial inputs into the calculation of benchmark rates of accumulation (a feature which serves to distinguish these Post-Keynesian models from their neoclassical counterparts).[6]

Machine Learning and Fixed-point Theorems

In this paper’s discussion of macroeconomic phenomena, I have chosen to focus heavily on the determinants of movements away from stable, unique equilibria, in both the short-run and the long-run. Notions of equilibrium are central to issues of effectiveness in both econometrics and machine-learning. Of pertinence to the former, is the technique of cointegration and error-correction modelling. While the cointegrating vector represents a long-equilibrium, the error-correction process represents adjustment towards this equilibrium.  In a machine-learning context, presumptions of equilibrium underpin a variety of fixed-point theorems that play a crucial role in: (i) techniques of data reduction; (ii) efforts to eliminate redundancy within the network itself with the ultimate aim of overcoming the infamous “curse of dimensionality”, while preserving “richness of interaction”; and, (iii) the optimal tuning of parameters (and hyper-parameters that govern the overall model architecture). Specific techniques of data compression, such as Randomized Numerical Linear Algebra (Drineas and Mahoney, 2017), rely on mathematical techniques such as Moore-Penrose inverses and Tikhanov regularization theory (Barata and Hussein, 2011). Notions of optimization are a critical element in the application of these techniques. This applies, especially, to the gradient descent algorithms that are deployed for the tuning of parameters (and sometimes hyper-parameters) within the neural network. Techniques of tensor contraction and singular value decomposition are also drawn upon for dimensionality reduction is complex tensor networks (Cichoki et al., 2016, 2017). Wherever and whenever optimization techniques are required, some kind of fixed-point theorem comes into play. The relationship between fixed-point theorems, asymptotic theory, and notions of equilibrium in complex systems is not straightforward. See both Prokopenko et al., 2019 and Yanofsky, 2003, for a wide-ranging discussion of this issue, which opens onto a discussion of many inter-related “paradoxes of self-referentiality”.

For example, a highly-specialized literature on neural tangent kernels focuses on how kernel-based techniques can be applied in a machine learning context, to ensure that local rather than global maxima or minima are avoided during the whole process of gradient descent (see Yang, 2019). Here, the invariant characteristics of the kernel guarantee that tuning would satisfy certain robustness properties. An associated body of research on the tuning of parameters at the “edge of chaos”, highlights the importance of applying optimization algorithms close to the boundary of, but never within the chaotic region of dynamic flow (see Bietti and Mairal 2019, and Bertschinger and Natschläger, 2004). There are subtle formal linkages between the properties of neural tangent kernels and notions of optimization at the edge-of-chaos that I am unable to do justice to in this paper.

From a Post Keynesian perspective and despite this evolution in our understanding of optimization in a machine learning context, it would seem that efforts to apply the existing panoply of deep learning techniques may be thwarted by contrariwise aspects of the behaviour of dynamic macroeconomic system. For macroeconomists working with Real Business Cycle Models and their derivatives, none of this is seen as a problem because unreasonably-behaved dynamics are usually precluded by assumption. Although perturbations are seen to drive the business cycle in these models, agents are assumed to make optimal use of information, in the full knowledge of how the economy operates, so that government interventions simply pull the economy further away from equilibrium by adding more noise to the system. Although more recent dynamic stochastic general equilibrium (DSGE) models allow for various forms of market failure, notions of long-run equilibrium still play a fundamental role[7]. Instead, in a more realistic, Post Keynesian world, optimization algorithms would have to work very hard in their pursuit of what amounts to a “will-o-the-wisp”: namely, a system characterised by processes of shifting and non-stationary (hysteretic) equilibria[8].

Differential Programming

Recent discussions of machine learning and AI, have emphasized the significance of developments in differential programming. Yann LeCun (2018), one of the major contributors to the new Deep learning paradigm has noted that,

An increasingly large number of people are defining the networks procedurally in a data-dependent way (with loops and conditionals), allowing them to change dynamically as a function of the input data fed to them. It’s really very much like a regular program, except it’s parameterized, automatically differentiated, and trainable/optimizable.

One way of understanding this approach is to think of something that is a cross between a dynamic network of nodes and edges and a spread sheet. Each node contains a variety of functional formulas that draw on the inputs from other nodes and provides outputs that in turn, either feed into other nodes or can be observed by scopes. However, techniques of backpropagation and automatic differentiation can be applied to the entire network (using the chain rule while unfurling each of the paths in the network on the basis of Taylors series representations of each formula). This capability promises to overcome the limitations of econometric techniques when it comes to the estimation of large-scale models. For example, techniques of structural vector autoregression, which are multivariate extensions to univariate error-correction modelling techniques can only be applied to highly parsimonious, small-scale systems of equations.

Based on the initial work of Ehrhard and Regnier (2003), a flurry of research papers now deal with extensions to functional programming techniques to account for partial derivatives (Plotkin, 2020), higher-order differentiation and tensor calculus on manifolds (Cruttwell, Gallagher, & MacAdam, 2019), how best to account for computational effects (which are described in Rivas, 2018), and industrial-scale software engineering (The Statebox Team, 2019). Members of the functional programming and applied category theory community have drawn on the notion of a lens, as means for accommodating the bidirectional[9] nature of backpropagation[10] (Clarke et al., 2020; Spivak, 2019; Fong, Spivak and Tuyéras, 2017).


The potential flexibility and power of differential programming, could usher in a new era of policy-driven modelling, by allowing researchers to combine (i) traditionally aggregative macroeconomic models with multi-sectoral models of price and output determination (e.g. stock-flow-consistent Post Keynesian models and Sraffian or Marxian models of inter-sectoral production relationships); discrete-time and continuous-time models (i.e. hybrid systems represented integro-differential equations), and both linear and non-linear dynamics. This would clearly support efforts to develop more realistic models of economic phenomena.

The development of network-based models of dynamic systems has been given impetus by research in three main domains: brain science imaging, quantum tensor networks, and Geographical Information Systems in each case, tensor analysis of multiple-input and multiple-output nodes has played a key role. In each of these cases, the complexity associated with tensor algebra has been ameliorated by the deployment of diagrammatic techniques based on the respective use of Markov-Penrose’ diagrams, the diagrammatic Z-X calculus, and the development of “region-” rather than “point”-based topologies and mereologies. These same diagrammatic techniques have been taken up by the Applied Category Theory community to achieve both a deeper and more user-friendly understanding of lenses and other optics (Boisseau, 2020; Riley, 2018), alongside diagrammatic approaches to simply-typed, differential, and integral, versions of the lambda calculus (Lemay, 2017, Zeilberger and Giorgetti, 2015).

As I have argued, in more general terms, in Juniper (2018), the development of new software platforms based on diagrammatic reasoning could mean that differential programming techniques could potentially be disseminated to a much larger number of users who might have limited programming knowledge or skill (to some extent, today’s spreadsheets provide an example of this)[11]. In the case of AI, this could allow workers to regain control over machines which had previously either operated “behind their backs” or else, on the basis of highly specialized expertise. Improvements of this kind also have the potential to support higher levels of collaboration in innovation at the point-of-production. In the more restricted macroeconomic context, modelling could become less of a “black-box” and more of an “art” than a mystifying “science”. Diagrammatic approaches to modelling could help to make all of this more transparent. Of course, there are a lot of “coulds” in this paragraph. The development and use of technology can and should never be discussed in isolation form its political and organizational context. To a large extent, this political insight, was one of the main drivers and motivating forces for this paper.


[1] One intuitive way of thinking about this is that it would extend principles of “human centred manufacturing” into some of the more computational elements of the digital economy.

[2] See Christopher Olah’s blog entry for a helpful overview of various deep-learning architectures.

[3] For this reason, I will avoid any further discussion of convolution-based techniques and kernel methods, which have contributed, respectively, to rapid progress in image-classification and in applications of support-vector machines. An animated introduction to convolution-based techniques is provided by Cornellis (2018) while kernel-based techniques and the famous “kernel trick” deployed in support vector machines is lucidly described in Wright (2018). Rectified Linear Units or ReLU’s—the activation functions most commonly-used in deep learning neural networks—are examined in Brownlee (2019).

[4] The importance of symmetries in mathematical physics is examined in a recent paper by John Baez (2020), who investigates the source of symmetries in relation to Noether’s theorem.

[5] Some of these components of fragility, such as loss of diversification and deferment of breakeven times, would obviously be hard to capture in a highly aggregative macroeconomic model, but certain proxies could be constructed to this end.

[6] Of course, the rate at which labour—dead and living—is pulled out of production, also determines intra- and inter-sectoral economic performance, growth in trade, and overall rates of accumulation. It is also one of the key drivers of fundamental uncertainty for investors.

[7] See Stiglitz (2018) for a critical review of DSGE models, and Andrle and Solmaz (2017) for an empirical analysis of the business cycle, which raises doubts about the dynamic assumptions implied by a variety of macroeconomic models. The contribution of non-discretionary expenditure to instability in the business cycle has been highlighted by the recent Post Keynesian theoretical literature on the so-called “Sraffa super-multiplier” (Fiebiger, 2017; Fiebiger and Lavoie, 2017).

[8] Important sources of hysteresis, additional to those of a Minskyian nature, include those associated with rising unemployment, with its obvious impacts on physical and mental health, crime rates, and scarring in the eyes of prospective employers. Rates of innovation (and thus, productivity growth) are also adversely affected by declining levels of aggregate demand.

[9] The implementation function takes the vector of parameters and inputs and transforms them into outputs, while the request function takes parameters, inputs and outputs and emits a new set of inputs, whereas the update function takes parameters, inputs and outputs and transforms them into a new set of parameter values. Together, the update and request functions perform gradient descent with the request function passing back the inverted value of the gradient of total error with respect to the input. Each parameter is updated so that it moves a given step-size in the direction that most reduces the specified total error function

[10] For an introduction to some of the mathematical and programming-based techniques required for working with optics see Loregian (2019), Boisseau and Gibbons (2018), Culbertson and Kurtz (2013), and Román (2019).

[11] Software suites such as AlgebraicJulia and Statebox can already recognise the role of different types of string diagrams in representing networks, dynamical systems, and (in the latter case) commercial processes and transactions.


Anderson, C. (2008). The end of theory: The data deluge makes the scientific method obsolete. Wired, 23 June. Available at:  (accessed 18 July, 2019).

Andrews, David (2015) . Natural price and the long run: Alfred Marshall’s misreading of Adam Smith. Cambridge Journal of Economics39: 265–279.

Andrle, Michal, Jan Brůha, Serhat Solmaz (2017). On the sources of business cycles: implications for DSGE models. ECB Working Paper, No 2058, May.

Baez, John (2020). Getting to the Bottom of Noether’s Theorem. arXiv:2006.14741v1 [math-ph] 26 Jun 2020.

Barata, J. C. A. & M. S. Hussein (2011). The Moore-Penrose Pseudoinverse. A Tutorial Review of the Theory. arXiv:1110.6882v1 [math-ph] 31 Oct 2011.

Barwell, R., & Burrows, O. (2011). Growing fragilities? Balance sheets in The Great Moderation. Financial Stability Paper No. 10, Bank of England.

Bengio, Yoshua; Aaron Courville; and Pascal Vincent (2014). Representation Learning: A Review and New Perspectives. arXiv:1206.5538v3 [cs.LG] 23 Apr 2014.

Bertschinger, N. & T. Natschläger (2004). Real-Time Computation at the Edge of Chaos in Recurrent Neural Networks. Neural Computation, July, 16(7): 1413-36.

Bietti, Alberto and Julien Mairal (2019). On the Inductive Bias of Neural Tangent Kernels. HAL Archive. (accessed 18 July, 2019)

Boisseau, Guillaume and Jeremy Gibbons (2018). What you needa know about yoneda: Profunctor optics and the yoneda lemma (functional pearl). Proc. ACM Program. Lang., 2(ICFP):84:1–84:27, July 2018.

Boisseau, Guillaume (2020). String diagrams for optics, arXiv:2002.11480v1 [math.CT] 11 Feb 2020.

Brownlee, J. (2019). A Gentle Introduction to the Rectified Linear Unit (ReLU) for Deep Learning Neural Networks. 9 Jan in Better Deep Learning . 

Burmeister, Edwin (2000) The Capital Theory Controversy. Critical Essays on Piero Sraffa’s Legacy in Economics, edited by Heinz D. Kurz. Cambridge: Cambridge University Press.

Carr, Nicholas (2010). The Shallows: How the Internet Is Changing the Way We Think, Read and Remember. New York: W.W. Norton and Company Inc.

Cichocki, Andrzej; Namgil Lee; Ivan Oseledets; Anh-Huy Phan; Qibin Zhao; and Danilo P. Mandic (2016). Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions. Foundations and Trends in Machine Learning. 9(4-5), 249-429.

Cichocki, Andrzej ; Anh-Huy Phan; Qibin Zhao; Namgil Lee; Ivan Oseledets; Masashi Sugiyama; and Danilo P. Mandic (2017). Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 2 Applications and Future Perspectives. Foundations and Trends in Machine Learning. 9(6), 431-673.

Clarke, B., D. Elkins, J. Gibbons, F. Loregian, B. Milewski, E. Pillore, & M. Roman (2020). Profunctor Optics, a Categorical Update. arXiv:2001.07488v1 [cs.PL] 21 Jan 2020.

Cornelisse, Daphne (2018). “An intuitive guide to Convolutional Neural Networks”, available at FreeCodeCamp, .

Cruttwell, Gallagher, & MacAdam (2019). Towards formalizing and extending differential programming using tangent categories. Extended Abstract, Proc. ACT 2019, available at:,%20Geoff%20Cruttwell%20and%20Ben%20MacAdam.pdf .

Culbertson, J. & K. Sturtz (2013). Bayesian Machine Learning via Category Theory. arXiv:1312.1445v1 [math.CT] 5 Dec2013.

Ehrhard, Thomas and Laurent Regnier (2003). The differential lambda calculus. Theoretical Computer Science, 309 (1-3):1-41.

Drineas, Petros and Michael W. Mahoney (2017). Lectures on Randomized Numerical Linear Algebra. arXiv:1712.08880v1 [cs.DS] 24 Dec 2017.

Fiebiger, B. (2017). Semi-autonomous household expenditures as the causa causans of postwar US business cycles: the stability and instability of Luxemburg-type external markets. Cambridge Journal of Economics, vol. 42, Issue 1, 2018, pp. 155–175.

Fiebiger, B., & Lavoie, M. (2017). Trend and business cycles with external markets: Non-capacity generating semi-autonomous expenditures and effective demand. Metroeconomica.2017;00:1–16.

Fong, Brendan, David Spivak and Rémy Tuyéras’s (2017). Backpropagation as Functor: A compositional perspective on supervised learning.

Gershenfeld, Neil, Alan Gershenfeld, and Joel Cutcher-Gershenfeld (2018). Designing Reality: How to Survive and Thrive in the Third Digital Revolution . New York: Basic Books.

Hedges Jules, Jelle Herold (2019). Foundations of brick diagrams. rXiv:1908.10660v1 [math.CT] 28 Aug 2019.

Juniper, J. (2018). Economic Philosophy of the Internet-of-Things. London: Routledge.

Juniper, J. (2005). A Keynesian Critique of Recent Applications of Risk-Sensitive Control Theory in Macroeconomics, Contemporary Post Keynesian Analysis, proceedings of the 7th International Post Keynesian Workshop, Northhampton: Edward Elgar, UK.  

Keynes, J. M. (1936). The General Theory of Employment, Interest and Money, London, Macmillan, Retrieved from: .

Lin, H. W., M. Tegmark & D. Rodnick (2017). Why does deep and cheap learning work so well? J. of Stat. Physics. arXiv:1608.08225v4 [cond-mat.dis-nn] 3 Aug 2017.

LeCun, Yann (2018). Deep Learning est mort. Vive Differentiable Programming! Facebook blog entry, January 6, 2018: 020-01-07

Lemay Jean-Simon Pacaud (2017). Integral Categories and Calculus Categories. Master of Science Thesis, University of Calgary, Alberta.

Loregian, Fosco (2019). Coend calculus—the book formerly known as ‘This is the co/end’. arXiv:1501.02503v5 [math.CT] 21 Dec 2019.

Lovelock, James (2019). Novacene: The Coming Age of Hyperintelligence. London: Allen Lane.

Martins, Nuno Ornelas (2019). The Sraffian Methodenstreit and the revolution in economic theory. Cambridge Journal of Economics, 43: 507–525.

Minsky, Hyman P. (May 1992). The Financial Instability Hypothesis. The Jerome Levy Economics Institute of Bard College, Working Paper No. 74: 6–8. .

Olah, Christopher (2015). Colah, Blog entry on “Neural Networks, Types, and Functional Programming”. Posted on September 3, .

Plotkin, Gordon (2020). A complete axiomatisation of partial differentiation. The Spring Applied Category Theory Seminar at University of California, Riverside, 7 June, 2020, .

Poggio, T., H. Mhaskar, L. Rosasco, B. Miranda & Q. Liao (2017). Why and When Can Deep—but not Shallow—Networks Avoid the Curse of Dimensionality: A Review. International Journal of Automation and Computing, 14(5), October 2017, 503-519.

Prokopenko, Harre, Lizier, Boschetti, Peppas, Kauffman (2019). Self-referential basis of undecidable dynamics: from the Liar paradox and The Halting Problem to The Edge of Chaos. arXiv:1711.02456v2 [cs.LO] 21 Mar 2019.

Riley, M. (2018). Categories of Optics. arXiv:1809.00738v2 [math.CT] 7 Sep 2018.

Rivas, E. (2018). Relating Idioms, Arrows and Monads from Monoidal Adjunctions. Chapter in R. Atkey and S. Lindley (Eds.): Mathematically Structured Functional Programming (MSFP 2018) EPTCS 275, 2018, pp. 18–33.

Román, Mario (2019). Profunctor optics and traversals. MSc Thesis in Mathematics and Foundations of Computer Science, Trinity, Oxford University. arXiv:2001.08045v1 [cs.PL] 22 Jan 2020.

Spivak, David I. (2019). Generalized Lens Categories via Functors Cop → Cat. arXiv:1908.02202v2 [math.CT] 7 Aug 2019.

Sraffa, Piero (1960) Production of Commodities by means of Commodities: A Prelude to the Critique of Neo-Classical Economics. Cambridge: Cambridge University Press.

Tegmark, Max (2017). Life 3.0: Being Human in the Age of Artificial Intelligence. London: Penguin Books.

The Statebox Team (2019). The Mathematical Specification of the Statebox Language, Version June 27, 2019, .

Stiglitz, J. E., (2018) Where modern macroeconomics went wrong, Oxford Review of Economic Policy, 34(1-2), pp. 70–106.

Wright, A. (?). Appendix A-Brief Introduction to Kernels. Mimeo. University of Lancaster. .

Yang, G. (2019). Scaling Limits of Wide Neural Networks with Weight Sharing: Gaussian process behavior, gradient independence, and neural tangent kernel derivation. arXiv preprint arXiv:1902.04760, 2019.

Yanofsky (2003). A universal approach to self-referential paradoxes, incompleteness and fixed-points. arXiv:math/0305282v1 [math.LO] 19 May 2003.

Zeilberger, Noam and Alain Giorgetti (2015). A correspondence between rooted planar maps and normal planar lambda terms. Logical Methods in Computer Science, Vol. 11, 3(22): 1–39.

Zuboff, Shoshana (2019).  The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. London: Profile Books.

Semantic Technologies for Disaster Management: Network Models and Methods of Diagrammatic Reasoning


The Chapter will provide a brief and informal introduction to diagrammatic reasoning (DR) and network modelling (NM) using string diagrams, which can be shown to possess the same degree of rigor as symbolic algebra, while achieving greater abbreviative power (and  pedagogical insight) than more conventional techniques of diagram-chasing. This review of the research literature will set the context for a detailed examination of two case-studies of semantic technologies which have been applied to the management of emergency services and search-and-rescue operations. The next section of the Chapter will consider the implications of contemporary and closely related developments in software engineering for disaster management. Conclusions will follow.


This Chapter is concerned with developments in applied mathematics and theoretical computing that can provide a formal and technical support for practices of disaster management. To this end it will draw on recent developments in applied category theory , which inform semantic technologies. In the interests of brevity, it will be obliged to eschew formal exposition of these techniques, but to this end, comprehensive references will be provided. The justification for what might at first seem to be an unduly narrow focus, is that applied category theory facilitates translation between different mathematical, computational and scientific domains.

For its part, Semantic Technology (ST) can be loosely conceived as an approach treating the World-Wide-Web as a “giant global graph”, so that valuable and timely information can be extracted from it using rich structured-query languages and extended description logics. These query languages must be congruent with pertinent (organizational, application, and database) ontologies so that the extracted information can be converted into intelligence. Significantly, database instances can extend beyond relational or graph databases, to include Boolean matrices, relational data embedded within the category of linear relations, and that pertaining to systems of differential equations in finite vector space, or even quantum tensor networks within a finite Hilbert space.

More specifically, this chapter will introduce the formalism of string diagrams, which were initially derived from the work of the mathematical physicists, Roger Penrose (1971) and Richard Feynman (1948). However, this diagrammatic approach has since been extended and re-interpreted  by category theorists such as Andre Joyal and Roy Street (1988, 1991). For example, Feynman diagrams can be viewed as morphisms in the category Hilb of Hilbert spaces and bounded linear operators (Westrich, 2006, fn. 3: 8), while Baez and Lauda (2009) interpret them as “a notation for intertwining operators between positive-energy representations of the Poincaré group”. Penrose diagrams can be viewed as a representation of operations within a tensor category.

Joyal and Street have demonstrated that when these string diagrams are manipulated in accordance with certain axioms—the latter taking the form of a set of equivalence relations established between related pairs of diagrams—the movements from one diagram to another can be shown to reproduce the algebraic steps of a non-diagrammatic proof. Furthermore, they can be shown to possess a greater degree of abbreviative power. This renders an approach using string diagrams extremely useful for teaching, experimentation, and exposition.

In addition to these conceptual and pedagogical advantages, however, there are additional implementation advantages associated with string diagrams including: (i) those of compositionality and layering (e.g. in Willems’s 2007  behavioural approach to systems theory, complex systems can be construed as the composites of smaller and simpler building blocks, which are then linked together in accordance with certain coherence conditions); (ii) a capacity for direct translation into functional programming (and thus, into propositions within a linear or resource-using logic); and, (iii) the potential for the subsequent application of software design and verification tools. It should be appreciated that these formal attributes will become increasingly important as the correlative features of what some have described as the digital economy.

This chapter will consider the specific role of string diagrams in the development and deployment of semantic technologies, which in turn have been developed for applications of relevance to disaster management practices. Techniques based on string diagrams have been developed to encompass a wide variety of dynamic systems and application domains, such as Petri nets, the π-calculus, and Bigraphs (Milner, 2009), Bayesian networks (Kissinger & Uijlen, 2017), thermodynamic networks (Baez and Pollard, 2017), and quantum tensor networks (Biamonte & Bergholm, 2017), as well as reaction-diffusion systems (Baez and Biamonte, 2012). Furthermore, they have the capacity to encompass graphical forms of linear algebra (Sobociński, Blog), universal algebras (Baez, 2006), and signal flow graphs (Bonchi, Sobociński and Zanasi (2014, 2015), along with computational logics based on linear logic and graph rewriting (on this see Mellies, 2018; and Fong and Spivak, 2018, for additional references).

1.  Applied Category Theory

Category theory and topos theory have taken over large swathes in the field of formal or theoretical computation, because categories serve to link together the structures found in algebraic topology, and with the logical connectives and inferences to be found in formal logic, as well as with recursive processes and other operations in computation. The following diagram taken from Baez and Stay (2011), highlights this capability.

John Bell (1988: 236) succinctly explains why it is that category theory also possesses enormous ormous powers of generalization:

A category may be said to bear the same relation to abstract algebra as does the latter to elementary algebra. Elementary algebra results from the replacement of constant quantities (i.e. numbers) by variables, keeping the operations on these quantities fixed. Abstract algebra, in its turn, carries this a stage further by allowing the operations to vary while ensuring that the resulting mathematical structures (groups, rings, etc) remain of a prescribed kind. Finally, category theory allows even the kind of structure to vary: it is concerned with structure in general.

Category theory can also be interpreted as a universal approach to the analysis of process, across various domains including: (a) mathematic practice (theorem proving); (b) physical systems (their evolution and measurement); (c) computing (data types and programs); (d) chemistry (chemicals and reactions); (e) finance (currencies and various transactions); (f) engineering (flows of materials and production).

This way of thinking about processes now serves as a unifying interdisciplinary framework that researchers within business and the social sciences have also taken up. Alternative approaches to those predicated on optimizing behaviour on the part of individual economic agents include the work evolutionary economists and those in the business world who are obliged to work with computational systems designed for the operational management of commercial systems. However, these techniques are also grounded in conceptions of process

Another way of thinking about dynamic processes is in terms of circuit diagrams, which can represent displacement, flow, momentum and effort—phenomenon modelled by the Hamiltonians and Lagrangians of Classical Mechanics. It can be appreciated that key features of economic systems are also amenable to diagrammatic representations of this kind, including asset pricing based on notion of arbitrage, a concept initially formalized by Augustin Cournot in 1838. Cournot’s analysis arbitrage conditions is grounded in Kirchoff voltage law (Ellerman, 1984). The analogs of displacement, flow, momentum and effort are depicted below for a wide range of disciplines.

Applied Category Theory: in the US, contemporary developments in applied category theory (ACT) have been spurred along and supported by a raft of EU, DARPA and ONR Grants. A key resource on ACT is Fong and Spivak’s (2018) downloadable text on compositionality. This publication explores the relationship between wiring diagrams or string diagrams and a wide variety of mathematical and categorical constructs, including as a means for representing symmetric monoidal preorders, signal flow graphs, along with functorial translation between signal flow graphs and matrices and other aspects of functorial semantics, graphical linear algebra, hypergraph categories and operads, applied to electric circuits and network compositionality. Topos theory is introduced to characterise the logic of system behaviour on the basis of indexed sets, glueings, and sheaf conditions for every open cover.

2. Diagrammatic Reasoning

Authors such as Sáenz-Ludlow and Kadunz (2015), Shin (1995), Sowa (2000), and Stjernfelt (2007), who have published research on knowledge representation and diagrammatic approaches to reasoning, tend to work within a philosophical trajectory that stretches from F. W. Schelling and C. S. Peirce, through to E. Husserl and A. N. Whitehead, then on to M. Merleau-Ponty and T. Adorno. Where Kant and Hegel privileged symbolic reasoning over the iconic or diagrammatic, Peirce, Whitehead, and Merleau-Ponty followed the lead of Schelling for whom ‘aesthetics trumps epistemology’! It is, in fact, this shared philosophical allegiance that not only links diagrammatic research to the semantic (or embodied) cognition movement (Stjernfeld himself refers to the embodied cognition theorists Eleanor Rosch, George Lakoff, Mark Johnson, Leonard Talmy, Mark Turner, and Gilles Fauconnier), but also to those researchers who have focused on issues of educational equity in the teaching of mathematics and computer science, including Ethnomathematics and critical work on ‘Orientalism’ specialized to emphasize a purported division between the ‘West and the Rest’ in regard to mathematical and computational thought and practice.

As such, insights from this research carry over to questions of ethnic ‘marginalization’ or ‘positioning’ in the mathematical sciences (see the papers reproduced in Forgasz and Rivera, eds., 2012 and Herbel-Eisenmann et al., 2012). In a nutshell, diagrammatic reasoning is sensitive to both context and positioning and, thus, is closely allied to this critical axis of mathematics education.

The following illustration of the elements and flows associated with diagrammatic forms of reasoning comes from Michael Hoffman’s (2011) explication of the concept first outlined by the American philosopher and logician, Charles Sanders Peirce.

The above Figure depicts three stages in the process of diagrammatic reasoning: (i) constructing a diagram as a consistent representation of key relations; (ii) analysing a problem on the basis of this representation; and (iii) experimenting with the diagram and then observing the results. Consistency is ensured in two ways. First, the researcher or research team develop an ontology specifying elements of the problem and the relations holding between these elements, along with pertinent rules of operation. Second, language is specified in terms of both syntactical and semantic properties. Furthermore, in association with this language, a rigorous axiomatic system is specified, which both constrains and enables any pertinent diagrammatic transformations.

3a. Case-Study One:

A 2010 paper by SAP Professors, Paulheim and Probst reviews an application of STs to the management and coordination of emergency services in the Darmstadt region of Germany. The aim of the following diagram, reproduced from their work, is to highlight the fact that, from a computational perspective, the integrative effort of STs can apply to different organizational levels: that of the common user interface, shared business logics and that of data sources.

In their software engineering application, the upper-level ontology DOLCE is deployed to link a core domain ontology together with a user-interface interaction ontology. In turn, each of these ontologies draws on inputs from an ontology on deployment regulations and various application ontologies. Improved search capabilities across this hierarchy of computational ontologies, are achieved through the adoption of the ONTOBROKER and F-Logic systems.

3b. Case-study Two:

An important contribution to the field of network modelling has come from the DARPA-funded CASCADE Project (Complex Adaptive System Composition and Design Environment), which has invested in long-term research into the “system-of-systems” perspective (see John Baez’s extended discussion of this project on his Azimuth blog). This research has been influenced by Willems’s (2007) behavioural approach to systems, which in turn, is based on the notion that large and complex systems can be built up from simple building blocks.

Baez et al. (2020) introduce ‘network models’ to encode different ways of combining networks both through overlaying one model on top of another and by setting each model side by side. In this way, complex networks can be constructed using simple networks as components. Vertices in the network represent fixed or moving agents, while edges represent communication channels.

The components of their networks are constructed using coloured operads, which include vertices representing entities of various types and edges representing the relationships between these entities. Each network model gives rise to a typed operad with an associated canonical algebra, whose operations represent ways of assembling a more complex network from smaller parts. The various different ways to compose these operations characterize a more general notion of an operation, which must be complemented by ways of permuting the arguments of an operation a process yielding a permutation group of inputs and outputs).

In research conducted under the auspices of the CASCADE Project, Baez, Foley, Moeller, and Pollard (2020) have worked out how to combine two formalisms. First, there are Petri nets, commonoly used as an alternative to process algebras as a foralism for business process management. The vertices in a Petri net represent collections of different types of entities (species) with morphisms between them used to describe processes (transitions) that can be carried out by combining various sets of entities (conceived as resources or inputs into a transition node or process of production) together to make new sets of entities (concived as outputs or vertices are positioned after the relevant transition node). The stocks of each type of entity that is available is enumerated as a ‘marking’ specific to each type or colour together with the set of outputs that can be produced by activated the said transition.

Second, there are network models, which describe processes that a given collection of agents (say, cars, boats, people, planes in a search-and-rescue operation) can carry out. However, in this kind of network, while each type of object or vertex can move around within a delineated space, they are not allowed to turn into other types of agent or object.

In these networks, morphisms are functors (generalised functions) which describe everything that can be done with a specific collection of agents. The following Figure depicts this kind of operational network in an informal manner, where icons represent helicopters, boats, victims floating in the sea, and transmission towers with communication thresholds.

By combining Petri nets with an underlying network model resource-using operations can be defined. For example, a helicopter may be able to drop supplies gathered from different depots and packaged into pallets, onto the deck of a sinking ship or to a remote village cut off by an earthquake or flood.

The formal mechanism for combining a network model with a Petri net relies on treating  different type of entities as catalysts, in the sense that the relevant species are neither increased nor decreased in number by any given transition. The derived category is symmetric monoidal and possesses a tensor product (representing processes for each catalyst that occur side-by-side), a coproduct (or disjoint union of amounts of each catalyst present), and within each subcategory of a particular catalyst, an internal tensor product describes how one process can follow another while reusing the pertinent catalysts.

The following diagram taken from Baez et al. (2020), illustrates the overlaying process which enables more complex networks to be constructed from simpler components. The use of the Grothendieck construction in this research ensures that when two or more diagrams are overlayed there will be no ‘double-counting’ of edges and vertices. When components are ‘tensored’ each of the relevant blocks would be juxtaposed “side-by-side”.

Each network model is characterized by a “plug-and-play” feature based on an algebraic component called an operad. The operad serves as the construct for a canonical algebra, whose operations are ways of assembling a network of the given kind from smaller parts. This canonical algebra, in turn, accommodates a set of types, a set of operations, ways to compose these operations to arrive at more general operations, and ways to permute an operation’s arguments (i.e. via a permutation group), along with a set of relevant distance constraints (e.g. pertinent communication thresholds for each type of entity) .

One of Baez’s co-authors, John Foley, works for Metron, Inc., VA, a company which specializes in applying the advanced mathematics of network models to such phenomena as “search-and-rescue” operations, the detection of network incursions, and sports analytics. Their 2017 paper mentions a number of formalisms that have relevance to “search-and-rescue” applications, especially the ability to distinguish between different communication channels (different radio frequencies and capacities) and vertices (e.g. planes, boats, walkers, individuals in need of rescue etc.) and the capacity to impose distance constraints over those agents who may fall outside the reach of communication networks.

In related research paper, Schultz, Spivak, Vasilakopoulou, Wisnesky (2016) argue thay dynamical systems can be gainfully thought of as ‘machines’ with inputs and outputs, carrying some sort of signal that occurs through some notion of time”. Special cases of this general approach include discrete, continuous, and hybrid dynamical systems. The authors deploy lax functors out of monoidal categories, which provide them with a language of compositionality. As with Baez and his co-authors, Schultz et al. (2016) draw on an operadic construct so as to understand systems that result from an “arbitrary interconnection of component subsystems”. They also draw on the mathematics of sheaf theory, to flexibly capture the crucial notion of time. The resulting sheaf-theoretic perspective relates continuous- and discrete-time systems together via functors (a kind of generalized ‘function of functions’, which preserves structure). Their approach can also account for synchronized continuous time, in which each moment is assigned a specific phase within the unit interval.

4. Related Developments in Software Engineering

This section of the Chapter examines contemporary advances in software engineering that have implications for ‘system-of-sytems’ approaches to semantic technology. The work of the Statebox group at the University of Oxford and that of Evan Patterson, from Stanford University, who is also affiliated with researchers from the MIT company, Categorical Informatics, will be discussed to indicate where these new developments are likely to be moving in the near future. This will be supplemented by an informal overview of some recent innovations in functional programming, which have been informed by the notion of a derivative applied to an algorithmic step. These initiatives have the potential to transform software for machine-learning and the optimization of networks

The Statebox team based at Oxford University have developed a language for software engineering that uses diagrammatic representations of generalized Petri nets. In this context, transitions in the net are morphisms between data-flow objects represent terminating functional programming algorithms. In Statebox (integer and semi-integer) Petri nets are constructed with both positive and negative tokens to account for contracting. Negative tokens represent borrowing while positive tokens represent lending and, likewise, the taking of short and long positions in asset markets. This allows for the representation of smart contracts, conceived as separable nets. Nets are also endowed with interfaces that allow for channelled communications through user-defined addresses. Furthermore, guarded and timed nets, with side-effects (which are mapped to standard nets using the Grothendieck construction), offer greater expressive power in regard to the conditional behaviour affecting transitions (The Statebox Team, 2018).

Patterson (2017) begins his paper with a discussion of description logics (e.g. OWL, WC3), which he interprets as calculi for knowledge representation (KR). These logics, which are the actual substrates responsible for the World-Wide-Web (WWW), lie somewhere between propositional logic and first-order predicate logic possessing the capability to express the (∃,∧,T,=) fragment of first-order logic. Patterson highlights the trade-off that must be made between computational tractability and expressivity before introducing a third knowledge representation formalism that interpolates between description logic and ontology logs (see Spivak and Kent, 2012, for an the extensive description of ologs, which express key constructs from category theory, such as products and coproducts, pullbacks and pushforwards, and representations of recursive operations using diagrams labelled with concepts drawn from everyday conversation). Patterson (2017) calls this construct the relational ontology log, or relational olog, because it is based on, Rel, the category of sets and relations and, as such, draws on relational algebra, which is the (∃,∧, , T,⊥,=) fragment of first-order logic. He calls Spivak and Kent’s, 2012, version, a functional olog to avoid any confusion, because these are solely based on Set, the category of sets and functions. Relational ologs achieve their expressivity through categorical limits and colimits (products, pullbacks, pushforwards, and so forth

The advantages of Patterson’s framework are that functors allow instance data to be associated with a computational ontology in a mathematically precise way, by interpreting it as a relational or graph database, Boolean matrix, or category of linear relations. Moreover, relational ologs are, by default, typed, which he suggests can mitigate the maintainability challenges posed by the open world semantics of description logic.

String diagrams (often labelled Markov-Penrose diagramsby those working in the field of brain science imaging) are routinely deployed by data-scientists used to represent the structure of deep-learning convolution neural networks. However, string diagrams can also serve as a tool for representing the computational aspects of machine-learning.

For example, influenced by the program idioms of machine-learning, Ghica and Muroya (2017) have developed what they choose to call a ‘Dynamic Geometry of Interaction Machine’, which can be defined as a state transition system operating whose transitions not only account for ‘token passing’ but also for ‘graph rewriting’ (where the latter can be construed as a graph-based approach to the proving of mathematical hypotheses and theories). Their proposes system is supported by diagrammatic implementation based on the proof structures of the multiplicative and exponential fragment of linear logic (MELL). In Muroya, Cheung and Ghica (2017), this logical approach is complemented by a sound call-by-value lambda calculus inspired, in turn by Peircean notions of abductive inference. The resulting bimodal programming model operates in both: (a) direct mode, with new inputs provided, new outputs obtained; and, (b) learning mode, with special inputs applied for which outputs are known; to achieve optimal tuning of parameters to ensure desired outputs approach actual outputs. The authors contend that their holistic approach is superior to that of the TensorFlow software package developed for machine-learning, which they describe as a ‘shallow embedding’ of a domain specific language (DSL) into PYTHON” rather than a ‘stand-alone’ programming language.

Adopting a somewhat different approach, Cruttwell, Gallagher and MacAdam (2019) extend Plotkin’s differential programming framework, which is itself a generalization of differential neural computers, where arbitrary programs with control structures encode smooth functions also represented as programs. Within this generalized domain, the derivative can be directly applied to programs or to algorithmic steps and, furthermore, can be rendered entirely congruent with categorical approaches to Riemannian and Differential geometry such as Lawvere’s Synthetic Differential Geometry.

Cruttwell and his colleagues go on to observe that, when working in a simple neural network, back-propagation takes the derivative of the error function, then uses the chain rule to push errors backwards. They point out that, for convolution neural networks, the necessary procedure is less straightforward due to the presence of looping constructs.

In this context, the authors further note that attempts to work with the usual ‘if-then-else’ and ‘while’ commands can also be problematic. To overcome these problems associated with recursion, they deploy what have been called ‘join restriction tangent categories’, which express the requisite domain of definition and detect and achieve disjointness of domains, while expressing iteration using the join of disjoint domains (i.e. in technical terms, this is the trace of a coproduct in the idempotent splitting). The final mathematical construct they arrive at, is that of a differential join restriction category along with the associated join restriction functor which, they suggest, admits a coherent interpretation of differential programming.

It should be stressed that each of these category-theoretic initiatives to formalize the differential of an algorithmic step will become important in future efforts to develop improved, yet diagrammatically-based forms of software for machine learning that have greater capability and efficiency than existing software suites. The fact that both differential and integral categories can be provided with a coherent string diagram formalism (Lemay, 2017) provides a link back to the earlier discussion about the role of diagrammatic reasoning in semantic technologies.

It is clear that techniques of this kind could also be applied to a wide variety of network models (e.g. for the centralized and decentralized control of hybrid cyber-physical systems), where optimization routines may be required (including those for effective disaster management).

5. Conclusion

In conclusion, the innovations in software engineering described above, have obvious implications for those attempting to  develop new semantic technologies for the effective management of emergency services and search-and-rescue operations in the aftermath of a major disaster. Hopefully, the material surveyed in this Chapter should serve to highlight the advantages of a category-theoretic approach to the issue at hand, along with the specific benefits of adopting an approach that is grounded in the pedagogical, computational, and formal representational power of string-diagrams, especially within a networked computational  environment charactrised by Big Data, parallel processing, hybridity, and some degree of decentralized control.

While a Chapter of this kind cannot go into too much detail about the formalisms that have been discussed, it is to be hoped that enough pertinent references have been provided for those who would like to find out more about the mathematical detail. Of course, it is not always necessary to be a computer programmer both to understand and to effectively deploy powerful suites of purpose-built software. It is also to be hoped that diagrammatic reasoning may assist the interested reader in acquiring a deeper understanding of the requisite mathematical techniques.

Author: Professor Dr. James Juniper – Conjoint Academic, University of Newcastle; PhD in Economics, University of Adelaide

Chapter References

Baez, John (2006). Course Notes on Universal Algebra and Diagrammatic Resoning. Date accessed 15/11/19. Available at

Baez, John C. & Jacob D. Biamonte (2012). A Course on Quantum Techniques for Stochastic Mechanics. arXiv:1209.3632v1 [quant-ph] 17 Sep 2012.

Baez, John C., Brandon Coya and Franciscus Rebro (2018). Props in Network Theory. Theory and Applications of Categories, 33(25): 727-783.

Baez, J., J. Foley, J. Moeller, and B. Pollard (2020). Network Models. (accessed 1/7/2020)  arXiv:1711.00037v3  [math.CT]  27 Mar 2020.

Baez, John and Brendan Fong (2018). A Compositional Framework for Passive Linear Networks. arXiv:1504.05625v6  [math.CT]  16 Nov 2018

Baez, John C. & Aaron Lauda (2009). A Prehistory of n-Categorical Physics. Date accessed 5/02/2018.

Baez, John C. and Blake Pollard (2017). A compositional framework for reaction networks. Reviews in Mathematical Physics, 29 (2017), 1750028.

Baez, John C. and Michael Stay (2011). Physics, Topology, Logic and Computation: A Rosetta Stone. New Structures for Physics, ed. Bob Coecke, Lecture Notes in Physics vol. 813, Springer, Berlin, 95-174.

Bell J. T. (1998). A Primer of Infinitesimal Analysis, Cambridge, U.K. Cambridge University Press.

Biamonte, J. and V. Bergholm (2017). Quantum Tensor Networks in a Nutshell. Cornell University Archive. Date accessed 15/11/19. arXiv:1708.00006v1 [quant-ph] 31 Jul 2017.

Blinn, James F. (2002). Using Tensor diagrams to Represent and solve Geometric Problems. Microsoft Research, Publications, Jan. 1. Date accessed 15/11/19. .

Bonchi, F., P. Sobociński and F. Zanasi (2015). Full Abstraction for Signal Flow Graphs. In Principles of Programming Languages, POPL’15, 2015.

Bonchi, F., P. Sobociński and F. Zanasi (2014). A Categorical Semantics of Signal Flow Graphs. CONCUR 2014, Ens de Lyon.

Cichocki, Andrzej; Namgil Lee; Ivan Oseledets; Anh-Huy Phan; Qibin Zhao; and Danilo P. Mandic (2016). Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 1 Low-Rank Tensor Decompositions. Foundations and Trends in Machine Learning. 9(4-5), 249-429.

Cichocki, Andrzej ; Anh-Huy Phan; Qibin Zhao; Namgil Lee; Ivan Oseledets; Masashi Sugiyama; and Danilo P. Mandic (2017). Tensor Networks for Dimensionality Reduction and Large-scale Optimization: Part 2 Applications and Future Perspectives. Foundations and Trends in Machine Learning. 9(6), 431-673.

Cruttwell, Gallagher & MacAdam (2019). Towards formulating and extending differential programming using tangent categories. Extended abstract, ACT 2019. Date accessed 15/11/19. Available at:,%20Geoff%20Cruttwell%20and%20Ben%20MacAdam.pdf .

Ehrhard T., and L. Regnier (2003). The differential lambda-calculus. Theoretical Computer Science. 309, 1–41.

Ellerman, David (2000). Towards an Arbitrage Interpretation of Optimization Theory. (accessed 1/7/20), .

Feynman, R. P. (1948). “Space-time approach to nonrelativistic quantum mechanics,” Review of Modern Physics, 20, 367.

Fong, Brendan and David I. Spivak (2018). Seven Sketches in Compositionality:An Invitation to Applied Category Theory. Date accessed 15/11/19. Available at .

Forgasz, Helen and Ferdinand Rivera (eds.) (2012). Towards Equity in Mathematics Education: Gender, Culture, and Diversity. Advances in Mathematics Education Series. Dordrecht, Heidelburg: Springer.

Herbel-Eisenmann, Beth, Jeffrey Choppin, David Wagner, David Pimm (eds.) (2012). Equity in Discourse for Mathematics Education Theories, Practices, and Policies. Mathematics Education Library, Vol. 55. Dordrecht, Heidelburg: Springer.

Hoffman, M. H. G. (2011). Cognitive conditions of diagrammatic reasoning. Semiotica, 186 (1/4), 189–212.

Joyal, A. and R. Street (1988). Planar diagrams and tensor algebra. Unpublished manuscript. Date accessed 15/11/19. Available from Ross Street’s website:

Joyal, A. and R. Street (1991). The geometry of tensor calculus, I. Advances in Mathematics, 88, 55–112.

Kissinger, Aleks and Sander Uijlen (2017). A categorical semantics for causal structure. .

Lemay, Jean-Simon Pacaud (2017). Integral Categories and Calculus Categories. PhD Thesis, University of Calgary, Alberta.

Melliès, Paul-André (2018). Categorical Semantics of Linear Logic. Date accessed 15/11/19. Available at: .

Milner, Robin (2009). The Space and Motion of Communicating Agents. Cambridge University Press.

Moeller, Joe & Christina Vasilakopolou (2019). Monoidal Grothendieck Construction. arXiv:1809.00727v2 [math.CT] 18 Feb 2019.

Muroya, Koko and Dan Ghica (2017). The Dynamic Geometry of Interaction Machine: A Call-by-need Graph Rewriter. arXiv:1703.10027v1 [cs.PL] 29 Mar 2017.

Muroya, Koko; Cheung, Steven and Dan R. Ghica (2017). Abductive functional programming, a semantic approach. arXiv:1710.03984v1 [cs.PL] 11 Oct 2017.

Patterson, Evan (2017). Knowledge Representation in Bicategories of Relations. ArXiv. 1706.00526v1 [cs.AI] 2 Jun 2017.

Paulheim, H. and F. Probst (2010). Application integration on the user interface level: An ontology-based approach. Data and Knowledge Engineering, 69, 1103-1116.

Penrose, Roger (1971). Applications of negative dimensional tensors. Combinatorial mathematics and its applications, 221244.

Penrose, R.; Rindler, W. (1984). Spinors and Space-Time: Vol I, Two-Spinor Calculus and Relativistic Fields. Cambridge University Press. pp. 424-425.

Sáenz-Ludlow, Adalira and Gert Kadunz (2015). Semiotics as a Tool for Learning Mathematics. Berlin: Springer.

Shin, S-J. (1994) The Logical Status of Diagrams, Cambridge: Cambridge University Press.

Sobociński, Pawel. Date accessed 15/11/19. Blog on Graphical Linear Algebra Blog.

Sowa, John F. (2000). Knowledge Representation: Logical, Philosophical, and Computational Foundations. Pacific Grove, CA: Brooks Cole Publishing.

Spivak, David I., Christina Vasilakopoulou,and Patrick Schultz (2019). Dynamical Systems and Sheaves. arXiv:1609.08086v4  [math.CT]  15 Mar 2019.Statebox Team, University of Oxford. Statebox. Date accessed 15/11/19. .

Schultz, P., D. Spivak, C. Vasilakopoulou, & R. Wisnesky (2016). Algebraic Databases. arXiv:1602.03501v2 [math.CT] 15 Nov 2016.

Stjernfelt, Frederick (2007) Diagrammatology: An Investigation on the Borderlines of Phenomenology, Ontology, and Semiotics, Synthese Library, V. 336, Dordrecht, the Netherlands: Springer.

Vagner, D., Spivak, D. I. & E. Lerman (2014). Algebra of Open Systems on the Operad of Wiring Digrams, Date accessed 15/11/19. arXiv:1408.1598v1[math.CT] 7 Aug 2014.

Westrich, Q. (2006). Lie Algebras in Braided Monoidal Categories. Thesis, Karlstads Universitet, Karlstad, Sweden.

Willems, J.C. (2007). The behavioral approach to open and interconnected systems: Modeling by tearing, zooming, and linking. Control Systems Magazine, 27(46): 99.

Artificial Intelligence – An Avalanche of Business Opportunities

“Artificial intelligence is the future and the future is here.”
~ Dave Waters

AI or Artificial intelligence, today’s most innovative technology, is all about creating intelligent machines that do tasks usually done only by human intelligence. In simple words, it is the brainpower validated by machines and computers that are automated through codes to impersonate the natural intelligence demonstrated by human beings.

Not many of us know that Artificial intelligence is impacting our day-to-day life immensely. Yes! Have you ever wondered how your smartphone unlocks with your face ID or how social media feeds are personalized & how google gives recommendations when you search for a term on google search? You guessed it right. It’s AI!

AI is creating an avalanche of business possibilities today. It has different categories such as Mundane to Formal and also Expert tasks! Let’s look at some domains where Artificial Intelligence proves to be highly lucrative.

Travel, Tourism & Hospitality Industry

Personalization guarantees guest satisfaction. The travel & tourism industry and sectors like the hotel industry, airline industry, restaurant industry, and travel agents within it have adopted AI for several assistances, some of them are: 

  • Chatbots and Online Customer Service

AI chatbots offer a relevant response to the customers by understanding their queries & give them related information, just like a human does. But unlike humans, it is very prompt & can function 24/7 without breaks or pays yet it provides a pleasant experience to the guests.

  • Data Processing and Data Analysis

Apart from customer service AI is used in this sector to gather and interpret data about their customers. AI can also sort this data more rapidly and precisely than a human can, and that too without errors. 

  • Personalized Recommendations

AI is applied to offer personalized travel recommendations & options by using the data like the interests, budgets, and past search history of the customers. This helps the customers to effortlessly make their travel choices which in turn improves the profits of the company. 

  • Tracking and booking trips

AI-based booking apps track prices & recommend the customers the best times to book flights as well as make hotel reservations, by accurate prediction of prices, well in advance.

Fitness Industry

Yes, AI is revolutionizing the Fitness Industry in several ways and transforming home workouts into a smarter, better, and less expensive method to keep people’s health on track.

  • AI-Based Personal Trainers

People desire fitness but with their hectic schedules & time shortage, do not go to the gym. Also, hiring a personal trainer is not affordable at all. AI comes as a huge rescue for such people. AI-based fitness app provides the luxury of personalized trainers to guide & monitor the accuracy and the pace of the exercise, at any time and any place. 

  • Smart Wearables and Exercise Equipment

Wearables assist their users in tracking their fitness

activities, counting the calories burnt, detecting irregular heartbeats and signs of diabetes, etc. AI fortified exercise equipment when fed with some personal details, and offers recommendations to their users to exercise competently. 

  • Sales promotion

AI integrated fitness apps help fitness companies to find their prospective customers and collect & sort their data. Companies use this data to boost their sales and improve profits.

Healthcare Industry

AI plays a significant role in the Healthcare Industry by accomplishing tasks that are usually done by humans only, and that too faster, more precisely & cost-effectively than humans. 

  • Medical Diagnosis

Artificial Intelligence (AI) has been synonymous with competence in the medical field. It has grown to become the second pair of eyes that never need to rest. AI-based medical diagnoses are automated & and can detect diseases like cancer even if the symptoms are not explicitly evident. Such diagnoses are mostly accurate.

  • Symptoms examination 

When patients mention their symptoms & health complaints in symptoms examining AI Chatbots, it uses its algorithm to precisely diagnose the disease. It also guides the patients toward appropriate health care.

  • Drug discovery and Development

This use of AI has been amassed in various sectors of humanity, especially in the pharmaceutical industry. AI help in discovering & designing new drugs and enhances R&D while speeding up the time and cutting off the extensive process involved in it. 

Logistics & Supply Chain Management 

AI has positively transformed the logistics and supply chain industry. It contributes a lot to reducing operating costs and is more efficient to use when it comes to responding to clients.

  • Accurate Inventory Management 

AI helps to prevent understocking and overstocking of inventory with its smart algorithms that can predict and determine consumer habits & seasonal demands.

  • Timely Delivery 

AI speeds up the warehouse processes by eliminating manual work, & operational shortcomings in the value chain. Thus, timely delivery goals can be smoothly accomplished. 

  • Warehouse Management 

AI manages warehouse security by tracking individuals who are entering and exiting the warehouse. Not just that it also tracks the goods in the warehouse with their barcodes and thus helps in keeping the inventory data updated. 

Marketing sector

  • Product recommendations 

AI recommends products & services to prospective customers based on their online search. AI understands & speculates people’s choices based on their behavior on the internet and recommends to them the products/services they are likely to purchase. More importantly, AI is effective in the Marketing Sector as speed is necessary. It empowers scalable growth, pushes profit, and customizes customer experience.

  • Dynamic pricing

AI automatically prices a product based on its demand & availability in an online market. This process needs no human intervention at all.

  • Targeting Ads

AI can be used to display ads to potential customers based on their relevant search on a search engine or social media. 

Cybersecurity Industry 

With increasing cyber-attacks & complications associated with them, the cybersecurity industry is applying AI in its operations to keep cyber threats at bay.

  • Threat exposure

AI-empowered security systems reveal the new trends hackers follow, worldwide as well as in a specific sector. This information can be used to make crucial decisions to protect against cyber danger. 

  • Phishing Detection

AI-based cybersecurity systems are capable of recognizing spam emails, determining if a website is genuine or fake and thus preventing phishing threats, breaches as well as data loss caused by malicious emails. 

  • Biometric Authentication

Biometric systems with AI make very precise and fail-proof verification with Face Recognition, Voice Recognition, Fingerprint Recognition, etc. 

Retail Industry

Just like the online marketplace, the retail industry also prefers the usage of AI for boosting sales and enhancing customer experience. AI supports retail systems to work together and enhance customer experiences, managing inventory, forecasting, and more.  

  • Smart Product Searches

Artificial intelligence simplifies product search for customers by allowing them to click a picture of any product online or offline and letting them search for the retailer who sells over the internet.

  • Personalization and Customer Insights 

Consumers can enjoy a personalized shopping experience with AI-based technology. It makes use of face recognition to spot a customer who is revisiting a shop and recommends products based on their preferences. 

  • Better In-Store Experience

AI-built system can cut down the operational cost of any retail store by taking away the need for a salesperson & a cashier, thus eliminating queues too. It also helps to monitor stocks & restock them instantly. 

In conclusion, AI has the power to improve the output and profits of any business. And so, companies are dynamically searching for new ventures to make the best of AI. Companies must create AI usage ideas for any specific sector to generate promising AI business opportunities. 

Can Content Automation Do Better Marketing?

“Technology, through automation and artificial intelligence, is definitely one of the most disruptive sources.” – Alain Dehaze

In the past, converting content into a marketing material was so tricky. It consumed our time and the cost of using various writing utensils and printing services. Once we started surviving in the digital life where content is king, the marketing industry entered the digital world and digital publishing services became a great boon for content marketers in every sector, thanks to the technology that changed the scenario and revolutionized the way every marketing industry followed earlier.

The new lock: where is the key?
Currently, the scenario comes with new challenges. When it comes to content marketing, present online content marketers face new marketing and production challenges because of a plethora of content in the digital world. On the one hand, content marketing is one of the essential parts of any industry, but on the other hand, it has currently become a complicated process since the variety of content goes unlimited and the way how the content is consumed by people has become unimaginable.

A large amount of the content generated today is consumed in its digital form. Digital content publishing has gradually entered into every sector such as IT, entertainment, business, and education. The form of content has evolved as variety of formats in different platforms and has created complex production processes that need special workflows for the variety of content produced. Every step in production of digital content requires customization and careful examination to suit the output required by the marketers.

Content + Automation process = ?
Content marketers need to overcome these challenges with innovative tools and ideas if they want to rule the kingdom of content. Here, automation, an obvious answer for many of the problems currently faced by publishing houses and content marketing industry, comes as an ultimate problem solver which makes content production smooth, uncomplicated, fast and easy. Content automation supports digital marketing strategy which integrates big data, blockchain, artificial intelligence, and natural language in order to accelerate the process of both production and distribution.

For the past few years, the ship of content marketing has definitely steered in the direction of automation. Around 51% of digital companies have started using marketing automation, according to statistics. If information is reliable, relevant, insightful and actionable with a proven and powerful method, customers will be attracted to marketing strategies. Here, technology plays a major role in providing fruitful data to customers.

With one-stop automation services processing in the cloud, all the processes in publishing which can be automated are scripted to work on the infrastructure of cloud computing. Workflows are developed by lining up different types of processes in the expected flow. The platform is exclusively designed to carry out a variety of automated tasks – composition, transformation and enhancement based on tailor-made automated workflows. Irrespective of at what stage your content production lines currently, automation can be applied at any phase of production, either the entire process or adopting it progressively, platforms and features that can be tailored to suit the expected demands.

In fact, recent content market demands have necessitated the inclusion of elements in content like info graphics, images, and GIFs, and interactive elements like videos and games. The content we produce is to be enriched with dynamic indexing, metadata, and semantic empowerment. In the method of content production with automation, the content is empowered with all the strategies that need for effective marketing. With the support of automation in content production, content marketers are able to streamline and accelerate the entire workflow, from the initial draft to the final output that they need.

Benefits of content automation in marketing
Content automation with a set of technologies supports for automating manual processes in content marketing. Its key aim is to automate the process of production and distribution in every stage and to keep the content up-to-date without the support of human intervention. Here let’s study some of the most important benefits of content automation in marketing.

  • Content automation improves the credibility of your product or service with content marketing strategies and can make your brand trendy.
  • Technically, it helps put sales on autopilot, sharing content across multiple digital platforms and optimizing content with SEO techniques. Your brands can catch a good place on Google search engine.
  • It converts content into other formats such as translated versions, audio or graphics, proofreading content to solve spelling and grammar issues and publishing content with reminders and notifications
  • Content automation supports your branding pages to receive more visibility and makes your social media engagement following.
  • With content automation, you can manage the entire process of your content strategy since you can virtually track every possible statistics on your campaign.
  • With the support of content automation, you can have the chances of converting high-quality qualified leads into sales. Content automation drives sales on specific products or services, empowering you control the way you sell.

Content automation tools for marketing

Content automation tools for marketing make a task a little more painless.  Here are some effective tools that you would help you streamline marketing functions.

  • io: It helps you send messages to targeted customers for specific products in a customer-friendly way.
  • Constant Contact: It is an email-marketing automation tool that helps you take your marketing to the next level.
  • Marketo: It is a sort of marketing software that lets you drive revenue with lead management.
  • Dialog Tech: It can be highly useful when you focus on voice-based marketing automation.
  • Oracle Eloqua: It lets marketers plan automated as well as personalized campaigns.
  • Bizible: Bizible is a tool that supports you to close the gap between sales and marketing.
  • Bremy: It lets you configure a customized content marketing package of database publishing, email newsletters and video editing.
  • Genoo: It enhances the success of your marketing plans.

With delivering excellence of designs and formats, automation services have become a better platform for content marketers to motivate the community with new words. The content is not only described words but also an art with alluring designs giving a new shape to the world. With a new perspective of content, we can imagine a better world and make reading fun for aspiring readers. Start-ups are increasingly turning to marketing strategies with content automation. The more marketing functions become automated, the more the marketing teams can focus on marketing strategies and digital marketing campaigns.

Content marketing is essential for a company to executing any long-term marketing strategy, but it is difficult to identify the most effective working content. In this case, automation will provide the data to answer your questions and enhance your content marketing processes. With the support of automation, you may:

  • Identify cost-effective and customer-friendly channels and campaigns.
  • Find out how your content influences buyer behaviour, and helps to increase leads on a particular content marketing campaign.