The Secret to Successful AI-Driven Process Redesign

In the late 1940s an engineer named Taiichi Ohno began developing the Toyota Production System, basing it on the Japanese principle of kaizen, or continuous improvement. As more operations become digitized, kaizen—augmented by generative AI and other advanced technologies—is once again reshaping process management. Now that features like natural-language interfaces have made gen AI accessible to nontechnical employees, it’s driving both large and small process changes. With the help of AI, employees can synthesize data of all kinds, including unstructured data. They can turn once-inscrutable masses of numerical information into insight-driven workflow improvements, continuously increasing performance, reducing waste, and achieving higher levels of quality. Rather than displacing humans, as gen AI is widely presumed to do, kaizen 2.0 is moving them to the center of new machine-assisted processes and achieving a long-held aspiration of much management theory: putting business transformation in the hands of all employees.

But successfully reimagining business processes isn’t as easy as asking ChatGPT to audit workflows. To get up to speed, leaders need to learn which processes are ripe for algorithm-powered redesign and understand how other companies have used gen AI to revamp them.  Some of the lessons learned from  how the best companies are deploying gen AI are:

1. Empowering Employees Throughout the Enterprise.  Across industries from automaking to life sciences to consumer products, and across functions from R&D to manufacturing to supply chain management, gen AI is boosting employee empowerment in new ways.  Using prompts in everyday language, rather than technical database queries, a production employee – for example can ask about assembly-line bottlenecks or hard-to-notice opportunities for streamlining processes and receive data-rich insights from the AI. Such insights amplify, rather than replace, workers’ ability to generate improvements based on their own experience, powers of observation, and creativity.
2. Redesigning Scientific Processes.  In the pharmaceutical industry, gen-AI-powered synthetic data is helping workers create data-rich processes, reduce waste, speed up analysis, and strengthen quality control.  Drug discovery is also being transformed by gen AI.  Creating them via AI instead of through trial and error could reduce the time it takes to get new biologics into the clinic from as much as six years down to 18 months, while increasing their probability of success. Waste, as kaizen has taught us, is not only a matter of materials but also a matter of time and effort.
3. Augmenting Creative Processes.  Several leading consumer-products companies are harnessing cutting-edge AI and digital technologies to catalyze the human creativity that drives growth in the sector.  Product and component design has long been a mix of art and science—combining the experience and sensibilities of a designer with the rigor of prototyping and testing. Across industries, gen AI is accelerating and transforming numerous elements of the process: creating 3D models of new ideas, suggesting modifications to designs, recommending the materials to be used, optimizing costs, rapidly creating digital prototypes, and identifying which ideas are most promising.
4. Animating Physical Operations.  Gen AI is also transforming the ways humans interact with complex physical systems, from robots to the human body to organizations like hospitals.  What’s next? The convergence of gen AI and digital twins, already underway, provides a glimpse of a future in which continuous process improvement becomes even more democratic. Digital twins are used to model complex systems—such as jet engines, wind turbines, factories, and human hearts—and simulate their functioning with an accuracy that allows users to remotely create solutions to any problems that arise (and often before problems arise). Digital twins can be used to make production processes more efficient, improve quality, increase operational performance, and create more-robust and -resilient supply chains.
5. Autonomous Agents.  The new AI agents take kaizen to a new level, not only offering advice but making decisions, taking action, and improving processes on their own. They range from simple chatbots to self-driving cars to robotic systems that can run complex workflows autonomously.
6. Ecosystems of Autonomous Agents.  Completing some tasks requires more than a single agent. In those cases companies may custom-build a system of agents wherein each one is expert in a specific task. Take the mortgage-underwriting process. When a human underwriter provides the instruction “Review this loan application based on our company’s lending policies,” one agent might extract relevant information from the application. Another agent might act as a librarian of bank policies, making them available to agents that compare the application against them. Yet another agent might generate a final report, recommending a course of action to the underwriter considering the loan. A “connector” agent might oversee and orchestrate the activity of all these agents.