×
AI Edge

Gen AI Infused Digital Knowledge Workers

By Naomi Kaduwela, Head of Kavi Labs at Kavi Global

Generative Artificial Intelligence, or Gen AI, is a type of machine learning that can generate text, images, videos, audio, and complex designs. The mass adoption of Gen AI through Large Language Models (LLMs) like Microsoft’s Copilot, Anthropic’s Claude, Google’s Gemini, and Meta’s LLama has ushered in a paradigm shift in user expectations, establishing Conversational AI Interaction as the new norm. These expectations have also extended to employees' tools in their workplace. The Gen AI market in manufacturing is projected to reach $7 billion by 2032, driven by its potential to enhance product design, increase employee productivity, and reduce costs (Morrison & Sehgal, 2023).

The manufacturing industry stands at the precipice of a transformative era driven by the relentless pursuit of innovation and efficiency. As enterprises grapple with challenges such as labor shortages, rising costs, and the need for enhanced product quality, the industry is ripe for Digital Transformation. This trifecta of forces has given rise to the Digital Knowledge Worker™ solutions, enabling the optimal blend of human and machine collaboration in our future digitally AI-enabled workforce.

The State of the Manufacturing Industry: Challenges and Opportunities

The manufacturing sector faces three critical challenges: Improving design & quality, combatting the labor shortage, and reducing costs (Figure 1). Tackling these areas poses several challenges. At the same time, these challenges are forcing enterprises to embrace Digital Transformation and adopt a digital mindset. Enterprises that do not embrace this new digital mindset will be left in the dust, unable to compete against their competition.

Figure 1. Three Key Focus Areas & Challenges in Manufacturing Driving Digital Transformation

ai-edge-figure1-kaduwela

To address these challenges, manufactures should focus on:

  1. Improving Design & Quality: Enhancing product design and ensuring high-quality standards.
  2. Combating Labor Shortage: Addressing the need for more knowledge workers or improving the productivity of existing workers. For example, addressing the need for more knowledge workers like inventory planners, demand forecasters, production planners, and quality managers, or improving the productivity of existing workers.
  3. Reducing Costs: Improving productivity and quality to reduce overall costs.

These pressing challenges in the manufacturing sector are not just obstacles but catalysts for a profound digital transformation. Manufacturers increasingly turn to digital solutions as manufacturers grapple with the need for enhanced design capabilities, struggle with labor shortages, and seek ways to reduce costs. This shift drives the industry towards a more data-driven, analytically sophisticated approach to operations.

The Solution: Digital Knowledge Workers™

The answer to these challenges lies in the Digital Knowledge Worker™ (DKW™) concept: a powerful fusion of human expertise and intelligent applications (Figure 2).

By combining the skills of knowledge workers with data-driven insights, advanced analytics, and intelligent applications, DKWs™ represent a new paradigm in operationalizing analytics within manufacturing processes. This integration allows for real-time decision-making, predictive capabilities, and automated workflows directly addressing the industry's core challenges.

For instance, DKWs™ enhance product design through data-driven insights, mitigate labor shortages by augmenting worker capabilities, and identify cost-saving opportunities through advanced analytics. As such, the Digital Knowledge Worker™ emerges not just as a solution but as the cornerstone of a new, digitally transformed manufacturing landscape.

Figure 2. Digital Knowledge Worker™ (DKW) = Knowledge Worker + Intelligent App (Data, Analytics, Intelligent App)

ai-edge-figure2-kaduwela

DKWs™ are particularly valuable in complex business processes that involve:

  1. Frequent, repetitive, and tedious tasks prone to human error.
  2. Combining data from multiple systems.
  3. Processing large volumes of data.
  4. Operating in highly regulated environments.
  5. Working in contexts where mistakes are costly.

Job roles, such as inventory planners, demand forecasters, production planners, and quality managers, can partially or fully automate and integrate AI into their business processes, enhancing efficiency and effectiveness.

Operationalizing Digital Knowledge Workers™ in Manufacturing:

A top priority for CIOs across the manufacturing industry is realizing the benefits projected from their Digital Transformation. To do so, they must operationalize their analytics, which involves embedding analytics into systems and processes to provide timely insights for optimal decision-making.

The integration of DKWs elevates knowledge workers from doers to strategic overseers through Human-in-the-loop (HITL) and Human-on-the-loop (HOTL) frameworks (Figure 3):

  • HITL: Human operators maintain control over every decision, leveraging data and recommendations from the intelligent application for better, faster, and more accurate decisions.
  • HOTL: Human operators delegate decisions to the intelligent application, focusing on exception management and process metric review to ensure expected outcomes and quality.

Figure 3. Human-in-the-loop (HITL) and Human-on-the-loop (HOTL) Framework. Shows the Human integration into the Intelligent App, forming the overall human-machine collaboration, which makes up the Digital Knowledge Worker™. *IA = Intelligent Application

ai-edge-figure3-kaduwela

The six high-value Digital Knowledge Worker™ solutions powered by predictive analytics, optimization, and Gen AI in Manufacturing (Figure 4) are: Next-Gen Product Design, Optimized Production Planning, Intelligent Quality Control, Advanced Supply Chain Management, Knowledge Management & Customer Support, and Conversational Predictive Maintenance. 

Figure 4. Top High-Value Digital Knowledge Worker™ Applications in Manufacturing

ai-edge-figure4-kaduwela

1. Next-Generation Product Design

Gen AI is revolutionizing product design by enhancing the ideation process. Digital Knowledge Worker™ solutions leveraging Gen AI can generate visual representations of novel product innovations, exploring various design possibilities while optimizing for critical constraints such as material strength, weight, and manufacturing feasibility (Dar, 2021).

Ford has employed Generative Design in the automotive industry to create optimal designs for components like engine brackets and suspension parts. This AI-driven approach resulted in lightweight, structurally efficient components that would have been difficult or impossible to conceive through traditional design methods (New Mind, 2023).

Airbus has utilized Gen AI in aerospace to explore countless design options for aircraft components. Gen AI not only optimized the components for efficiency but also accelerated the design process, leading to the creation of more efficient and comfortable jetliners (Master of Code, 2024).

The fashion industry also benefits from this technology. Cala, a fashion tech company, has integrated Gen AI into their tool to brainstorm and visualize fashion designs. This approach is exponentially faster than traditional hand-drawing methods, allowing designers to iterate and rapidly bring their visions to life (Cala, n.d.). Gen AI is beneficial for tasks requiring creativity and innovation, such as product creation and design.

2. Optimized Production Planning

Digital Knowledge Worker™ solutions transform production planning by leveraging historical data analysis and advanced forecasting techniques. These AI-driven systems can predict demand, assess resource availability, and recommend efficient production schedules. This optimization helps minimize waste and balance workloads across different production lines.

The beauty of this approach is that much of the process can be fully automated, elevating the role of human knowledge workers from data processors to strategic overseers. They can monitor productivity, utilization, and quality metrics, focusing on high-level decision-making rather than routine tasks.

For example, Airbus has leveraged AI to optimize its production schedules by analyzing historical production data and demand forecasts. Their AI-infused solution significantly reduced production time and costs, showcasing the power of AI in streamlining complex manufacturing processes (Master of Code, 2024).

An exciting development in this area is the integration of conversational Large Language Models (LLMs) with production data. Conversational AI enables production planners to interact with the data through natural language queries, enabling quick, informed decisions for last-minute manual overrides. It also facilitates real-time monitoring of critical Key Performance Indicators (KPIs) as humans become more supervisory.

3. Intelligent Quality Control

Digital Knowledge Worker™ solutions are enhancing quality control processes through advanced data analysis. These systems can process vast amounts of sensor data in real time, identifying anomalies and predicting potential defects before they occur.

These AI-driven solutions ensure consistent product quality by flagging deviations from expected norms and suggesting corrective actions. This proactive approach to quality control can significantly reduce waste, improve customer satisfaction, and maintain brand reputation.

For instance, in the semiconductor industry, AI-powered visual inspection systems can detect microscopic chip defects that would be impossible for human inspectors to catch consistently. This level of precision is crucial in an industry where even tiny imperfections can lead to product failure. AMD, a leading semiconductor company, has implemented an AI-based visual inspection system that can detect defects as small as 10 nanometers, significantly improving quality control in their chip manufacturing process (AMD, 2023).

4. Advanced Supply Chain Management

Gen AI revolutionizes supply chain management by providing unprecedented insight and predictive capability. Digital Knowledge Worker™ solutions can analyze complex data sets to predict demand fluctuations, identify potential bottlenecks, and recommend optimal inventory levels.

This capability enables just-in-time inventory management, reducing excess stock while ensuring that necessary materials are always available. By streamlining logistics, these systems can significantly reduce costs and improve overall supply chain efficiency (Harvard Business Revenue, 2023).

For example, AI systems can analyze global economic indicators, weather patterns, and historical sales data to predict future demand for specific products. This predictive insight allows manufacturers to adjust their production and inventory levels proactively rather than reactively.

5. Knowledge Management & Customer Support

Gen AI is transforming knowledge management and customer support in manufacturing. Digital Knowledge Worker™ solutions can understand complex product information and accurately answer customer queries.

This capability extends to internal knowledge management, where AI can interpret and provide insights from documents like Product Management (PM) documents for assets. These systems can also generate tailored content, such as customized reports, technical documentation, and training materials. For instance, they can create detailed assembly instructions, maintenance guides, and quality control protocols, significantly streamlining processes on the factory floor.

6. Conversational Predictive Maintenance

Predictive maintenance is another area where Gen AI is making significant strides. By interpreting data from the Industrial Internet of Things (IIoT), the Internet of Medical Things (IoMT), and various other machines, AI algorithms can predict potential failures before they occur. When a problem arises, Generative AI recommends potential solutions and service plans, empowering maintenance teams to rectify issues proactively. This proactive approach reduces unplanned downtime and maximizes asset utilization.

Siemens, for example, has implemented Gen AI for predictive maintenance, analyzing machine telemetry data to predict potential failures and recommend proactive solutions. This approach has reduced unplanned downtime and increased equipment utilization (Siemens, 2024).

In conclusion, these applications of Gen AI-infused Digital Knowledge Worker™ solutions are not just incremental improvements but transformative changes in how manufacturing processes are designed, executed, and optimized. They represent a shift towards more intelligent, efficient, and responsive manufacturing ecosystems.

Challenges

While Gen AI holds immense promise for manufacturing, enterprises must address several challenges for successful implementation. One significant hurdle is the need for extensive retraining and reskilling of the workforce to use and manage AI-driven systems effectively. Training requires substantial investment in education and training programs.

Additionally, there is a prevalent fear among workers of job displacement due to automation, which can lead to resistance and reduced morale. To mitigate these risks, companies should focus on transparent communication about AI's role in augmenting human capabilities rather than replacing them.

Ethical concerns surrounding AI decision-making, particularly in critical manufacturing processes, also need careful consideration. Establishing clear guidelines and maintaining human oversight can help address these issues.

Moreover, the initial cost of implementing and integrating Gen AI solutions with existing systems can be prohibitive for smaller manufacturers. To overcome this, industry leaders and policymakers should consider creating collaborative platforms and financial incentives to make these technologies more accessible.

By proactively addressing these challenges through comprehensive strategies that combine technological implementation with human-centric approaches, the manufacturing sector can more smoothly transition into an AI-enhanced future of work.

Conclusion

Gen AI has the potential to revolutionize the manufacturing industry by improving productivity, reducing costs, and enhancing overall performance. As organizations embrace this technology, they can unlock new opportunities for growth and innovation. While Gen AI holds immense promise, challenges such as establishing clear guidelines and upskilling workers remain. Gen AI-infused Digital Knowledge Workers empower everyone, from frontline workers to design professionals, fostering innovation and bringing workers closer to customers.

Naomi Kaduwela

About the author

Naomi Kaduwela is the Head of Kavi Labs at Kavi Global, where she leads the development of AI-driven Digital Knowledge Worker™ solutions for manufacturing. With expertise in AI strategy and digital transformation, Naomi is at the forefront of integrating Generative AI into industrial processes.

Her work bridges theoretical AI advancements with practical industry applications, helping enterprises improve efficiency and innovation. Naomi is a thought leader in AI-driven industrial solutions, contributing to industry publications and speaking at technology conferences. She also holds several patents, has published research in several scientific journal publications, and is an AI Ethics author.

Naomi holds a Dual B.S. in Computer Science & Applied Psychology from Ithaca College and an M.S. in Machine Learning & Data Science from Northwestern University.

 

Back to AI Edge: Q3 2024 

By Naomi Kaduwela, Head of Kavi Labs at Kavi Global

Generative Artificial Intelligence, or Gen AI, is a type of machine learning that can generate text, images, videos, audio, and complex designs. The mass adoption of Gen AI through Large Language Models (LLMs) like Microsoft’s Copilot, Anthropic’s Claude, Google’s Gemini, and Meta’s LLama has ushered in a paradigm shift in user expectations, establishing Conversational AI Interaction as the new norm. These expectations have also extended to employees' tools in their workplace. The Gen AI market in manufacturing is projected to reach $7 billion by 2032, driven by its potential to enhance product design, increase employee productivity, and reduce costs (Morrison & Sehgal, 2023).

The manufacturing industry stands at the precipice of a transformative era driven by the relentless pursuit of innovation and efficiency. As enterprises grapple with challenges such as labor shortages, rising costs, and the need for enhanced product quality, the industry is ripe for Digital Transformation. This trifecta of forces has given rise to the Digital Knowledge Worker™ solutions, enabling the optimal blend of human and machine collaboration in our future digitally AI-enabled workforce.

The State of the Manufacturing Industry: Challenges and Opportunities

The manufacturing sector faces three critical challenges: Improving design & quality, combatting the labor shortage, and reducing costs (Figure 1). Tackling these areas poses several challenges. At the same time, these challenges are forcing enterprises to embrace Digital Transformation and adopt a digital mindset. Enterprises that do not embrace this new digital mindset will be left in the dust, unable to compete against their competition.

To address these challenges, manufactures should focus on:

  1. Improving Design & Quality: Enhancing product design and ensuring high-quality standards.
  2. Combating Labor Shortage: Addressing the need for more knowledge workers or improving the productivity of existing workers. For example, addressing the need for more knowledge workers like inventory planners, demand forecasters, production planners, and quality managers, or improving the productivity of existing workers.
  3. Reducing Costs: Improving productivity and quality to reduce overall costs.

These pressing challenges in the manufacturing sector are not just obstacles but catalysts for a profound digital transformation. Manufacturers increasingly turn to digital solutions as manufacturers grapple with the need for enhanced design capabilities, struggle with labor shortages, and seek ways to reduce costs. This shift drives the industry towards a more data-driven, analytically sophisticated approach to operations.

The Solution: Digital Knowledge Workers™

The answer to these challenges lies in the Digital Knowledge Worker™ (DKW™) concept: a powerful fusion of human expertise and intelligent applications (Figure 2).

By combining the skills of knowledge workers with data-driven insights, advanced analytics, and intelligent applications, DKWs™ represent a new paradigm in operationalizing analytics within manufacturing processes. This integration allows for real-time decision-making, predictive capabilities, and automated workflows directly addressing the industry's core challenges.

For instance, DKWs™ enhance product design through data-driven insights, mitigate labor shortages by augmenting worker capabilities, and identify cost-saving opportunities through advanced analytics. As such, the Digital Knowledge Worker™ emerges not just as a solution but as the cornerstone of a new, digitally transformed manufacturing landscape.

DKWs™ are particularly valuable in complex business processes that involve:

  1. Frequent, repetitive, and tedious tasks prone to human error.
  2. Combining data from multiple systems.
  3. Processing large volumes of data.
  4. Operating in highly regulated environments.
  5. Working in contexts where mistakes are costly.

Job roles, such as inventory planners, demand forecasters, production planners, and quality managers, can partially or fully automate and integrate AI into their business processes, enhancing efficiency and effectiveness.

Operationalizing Digital Knowledge Workers™ in Manufacturing:

A top priority for CIOs across the manufacturing industry is realizing the benefits projected from their Digital Transformation. To do so, they must operationalize their analytics, which involves embedding analytics into systems and processes to provide timely insights for optimal decision-making.

The integration of DKWs elevates knowledge workers from doers to strategic overseers through Human-in-the-loop (HITL) and Human-on-the-loop (HOTL) frameworks (Figure 3):

  • HITL: Human operators maintain control over every decision, leveraging data and recommendations from the intelligent application for better, faster, and more accurate decisions.
  • HOTL: Human operators delegate decisions to the intelligent application, focusing on exception management and process metric review to ensure expected outcomes and quality.

The six high-value Digital Knowledge Worker™ solutions powered by predictive analytics, optimization, and Gen AI in Manufacturing (Figure 4) are: Next-Gen Product Design, Optimized Production Planning, Intelligent Quality Control, Advanced Supply Chain Management, Knowledge Management & Customer Support, and Conversational Predictive Maintenance. 

1. Next-Generation Product Design

Gen AI is revolutionizing product design by enhancing the ideation process. Digital Knowledge Worker™ solutions leveraging Gen AI can generate visual representations of novel product innovations, exploring various design possibilities while optimizing for critical constraints such as material strength, weight, and manufacturing feasibility (Dar, 2021).

Ford has employed Generative Design in the automotive industry to create optimal designs for components like engine brackets and suspension parts. This AI-driven approach resulted in lightweight, structurally efficient components that would have been difficult or impossible to conceive through traditional design methods (New Mind, 2023).

Airbus has utilized Gen AI in aerospace to explore countless design options for aircraft components. Gen AI not only optimized the components for efficiency but also accelerated the design process, leading to the creation of more efficient and comfortable jetliners (Master of Code, 2024).

The fashion industry also benefits from this technology. Cala, a fashion tech company, has integrated Gen AI into their tool to brainstorm and visualize fashion designs. This approach is exponentially faster than traditional hand-drawing methods, allowing designers to iterate and rapidly bring their visions to life (Cala, n.d.). Gen AI is beneficial for tasks requiring creativity and innovation, such as product creation and design.

2. Optimized Production Planning

Digital Knowledge Worker™ solutions transform production planning by leveraging historical data analysis and advanced forecasting techniques. These AI-driven systems can predict demand, assess resource availability, and recommend efficient production schedules. This optimization helps minimize waste and balance workloads across different production lines.

The beauty of this approach is that much of the process can be fully automated, elevating the role of human knowledge workers from data processors to strategic overseers. They can monitor productivity, utilization, and quality metrics, focusing on high-level decision-making rather than routine tasks.

For example, Airbus has leveraged AI to optimize its production schedules by analyzing historical production data and demand forecasts. Their AI-infused solution significantly reduced production time and costs, showcasing the power of AI in streamlining complex manufacturing processes (Master of Code, 2024).

An exciting development in this area is the integration of conversational Large Language Models (LLMs) with production data. Conversational AI enables production planners to interact with the data through natural language queries, enabling quick, informed decisions for last-minute manual overrides. It also facilitates real-time monitoring of critical Key Performance Indicators (KPIs) as humans become more supervisory.

3. Intelligent Quality Control

Digital Knowledge Worker™ solutions are enhancing quality control processes through advanced data analysis. These systems can process vast amounts of sensor data in real time, identifying anomalies and predicting potential defects before they occur.

These AI-driven solutions ensure consistent product quality by flagging deviations from expected norms and suggesting corrective actions. This proactive approach to quality control can significantly reduce waste, improve customer satisfaction, and maintain brand reputation.

For instance, in the semiconductor industry, AI-powered visual inspection systems can detect microscopic chip defects that would be impossible for human inspectors to catch consistently. This level of precision is crucial in an industry where even tiny imperfections can lead to product failure. AMD, a leading semiconductor company, has implemented an AI-based visual inspection system that can detect defects as small as 10 nanometers, significantly improving quality control in their chip manufacturing process (AMD, 2023).

4. Advanced Supply Chain Management

Gen AI revolutionizes supply chain management by providing unprecedented insight and predictive capability. Digital Knowledge Worker™ solutions can analyze complex data sets to predict demand fluctuations, identify potential bottlenecks, and recommend optimal inventory levels.

This capability enables just-in-time inventory management, reducing excess stock while ensuring that necessary materials are always available. By streamlining logistics, these systems can significantly reduce costs and improve overall supply chain efficiency (Harvard Business Revenue, 2023).

For example, AI systems can analyze global economic indicators, weather patterns, and historical sales data to predict future demand for specific products. This predictive insight allows manufacturers to adjust their production and inventory levels proactively rather than reactively.

5. Knowledge Management & Customer Support

Gen AI is transforming knowledge management and customer support in manufacturing. Digital Knowledge Worker™ solutions can understand complex product information and accurately answer customer queries.

This capability extends to internal knowledge management, where AI can interpret and provide insights from documents like Product Management (PM) documents for assets. These systems can also generate tailored content, such as customized reports, technical documentation, and training materials. For instance, they can create detailed assembly instructions, maintenance guides, and quality control protocols, significantly streamlining processes on the factory floor.

6. Conversational Predictive Maintenance

Predictive maintenance is another area where Gen AI is making significant strides. By interpreting data from the Industrial Internet of Things (IIoT), the Internet of Medical Things (IoMT), and various other machines, AI algorithms can predict potential failures before they occur. When a problem arises, Generative AI recommends potential solutions and service plans, empowering maintenance teams to rectify issues proactively. This proactive approach reduces unplanned downtime and maximizes asset utilization.

Siemens, for example, has implemented Gen AI for predictive maintenance, analyzing machine telemetry data to predict potential failures and recommend proactive solutions. This approach has reduced unplanned downtime and increased equipment utilization (Siemens, 2024).

In conclusion, these applications of Gen AI-infused Digital Knowledge Worker™ solutions are not just incremental improvements but transformative changes in how manufacturing processes are designed, executed, and optimized. They represent a shift towards more intelligent, efficient, and responsive manufacturing ecosystems.

Challenges

While Gen AI holds immense promise for manufacturing, enterprises must address several challenges for successful implementation. One significant hurdle is the need for extensive retraining and reskilling of the workforce to use and manage AI-driven systems effectively. Training requires substantial investment in education and training programs.

Additionally, there is a prevalent fear among workers of job displacement due to automation, which can lead to resistance and reduced morale. To mitigate these risks, companies should focus on transparent communication about AI's role in augmenting human capabilities rather than replacing them.

Ethical concerns surrounding AI decision-making, particularly in critical manufacturing processes, also need careful consideration. Establishing clear guidelines and maintaining human oversight can help address these issues.

Moreover, the initial cost of implementing and integrating Gen AI solutions with existing systems can be prohibitive for smaller manufacturers. To overcome this, industry leaders and policymakers should consider creating collaborative platforms and financial incentives to make these technologies more accessible.

By proactively addressing these challenges through comprehensive strategies that combine technological implementation with human-centric approaches, the manufacturing sector can more smoothly transition into an AI-enhanced future of work.

Conclusion

Gen AI has the potential to revolutionize the manufacturing industry by improving productivity, reducing costs, and enhancing overall performance. As organizations embrace this technology, they can unlock new opportunities for growth and innovation. While Gen AI holds immense promise, challenges such as establishing clear guidelines and upskilling workers remain. Gen AI-infused Digital Knowledge Workers empower everyone, from frontline workers to design professionals, fostering innovation and bringing workers closer to customers.