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Let us answer this, with the help of an example. Cricket Ball Manufacturing process We will consider a Cricket Ball manufacturing process as a hypothetical example Background: Now, let us say we have 2 types of Cricket Ball manufacturers XYou Sports Inc and YMe Sports Inc. XYou Sports Inc produces ball in Country 1 where the ball will turn (Spin) more a YMe Sports Inc produces ball in Country 2 where there is more swing. Now the national team of Country 1 plays with the national team of Country 2 in one of its Cities… Therefore Country 2 feels uncomfortable playing to a different brand of Cricket ball (made by X) as it has got more spin. So, to counter that, Country 2’s Cricket board looks for XYou Sports Inc manufacturer to supply that brand to its national team. The steps mentioned above, depict the AS-IS process view AI-Enabled Process where human improvements were made As XYou Sports Inc manufacturer found out that, multiple countries may need multiple type of Cricket Balls, it decided to leverage AI in getting useful insights and ultimately getting things done quickly. As a first step, it ensured that AI process (and hence solution) was setup that addressed that the ball will be more conducive to Spin (turn) when implemented in Country 1. The AI was fed with data, and it was more like a supervised learning for the AI. The humans in the loop, were doing manual verification of key differentiating parameters/factors such as weight of the ball (in Ounces), size of the ball, bounce, seam, stitching, materials used … AI solution evolves with the Process Once XYou Sports Inc received the request from Country 2 as well, the team realized that they can still explore on AI technology and leverage it for bettering this process. It started to identify what kind of key differentiating factors can be included as part of the AI. The AI solution agent now has to look for an additional information(parameters) – how the prevailing weather in Country 2 impacts the cricket ball, how does the pitch/soil (in Country 2) supports this type of Cricket Ball (manufactured by XYou Sports Inc). How did the MBB help here for XYou Sports Inc? The MBB envisaged a plan. As this process is an ongoing journey and may have uncertainties and require constant feedback from the stakeholders (eg, National teams, Cricket Boards of respective countries..), he felt using Scrum as an agile framework might help the team to navigate through the uncertainties that might prop up.. He suggested the team to use this and also requested them to put a Kanban board for radiating information on a routine basis to all stakeholders He had put a high-level plan that stated as Planning Type Objective Remarks Vision Planning Developing a scalable tech excellence support Agent that supports the Cricket ball manufacturing process of XYou Sports Inc Release Planning Release Phase 1: Defining the AS-IS process to AI (Basic Knowledge Integration of the Cricket Ball manufacturing process) Release Phase 2: Re-imagining the defined process in phase 1 by considering all key parameters that is required to make the ball suitable in multiple countries (Country1, Country 2) Sprint Planning Release 1 Sprint 1 – Creating the KB agent and AI agent for defining few basic parameters (such as weight and shape) Sprint 2 - Defining the KB agent and AI agent for remaining parameters such as materials, bounce, seam, stitching Release 2 Sprint 3 – Defining the KB and AI agent for additional parameters such as pitch soil, weather conditions of a country (which can influence the ball behaviour) Duration: 2 weeks Note: Sprint 1 will have a minimum viable product (setting up the AI process and adding very limited functionality) Release 1 had 2 Sprints and Release 2 had 2 Sprints Sprint 2 had the feedback incorporated from the stakeholders that came from Sprint 1. Similarly for Sprint 3, 4 the team got feedback from the previous Sprints and incorporated the feedbacks which were relevant to them Subsequent releases had subsequent Sprints based on the emerging needs for the manufacturer. Integrating AI into improvement Cycles and AI process got adapted with proper feedback As we see from the above table, the team leveraged Scrum as a framework to build an iterative and incremental development of their AI based Cricket ball manufacturing process. What was a cumbersome exercise in getting huge amount of data across multiple parameters and few parameters which were changing based on the Country and its weather conditions (worst if there are multiple weather phases – summer, winter, autumn..in a country) , it became much more simpler when done with AI. Every Sprint produced some incremental values keeping the stakeholders happy. The AI system improved itself over a time period as it gathered more data (in the usage of how the cricket balls behaved in those countries across weather seasons) and had reinforced learning to improve itself With every Sprint, there was a Sprint Review that happened where the stakeholders were presented with the finished work (in the Sprint). Whatever feedback was given was taken into consideration and those were implemented. Strategic Role of MBBs in maintaining alignment The MBB was able to devise a strategy for AI solutioning of this Cricket Ball manufacturing process. He was able to setup a vision, release plan and then help the team to come up with Sprint goals for each of the Sprint making the team adjust to the uncertainties Conclusion: We saw here how AI enabled process with human efforts can navigate through complex scenarios/situations in an incremental manner addressing emerging needs with quick feedback cycles. The most important thing is quick release(deployment) of the value that you want your stakeholders to get and have continuous exploration of the market needs and adapt your system accordingly.

In order to ensure the AI system evolves with the process, I would design the AI system with a built-in feedback mechanism that’s linked to the human lead process improvements. Every time a team completes a PDCA, DMAIC, or A3 cycle, the key insights and process adjustments should be captured and fed back into the AI system. It would be a standard process to document every process improvement initiative in a specific template, generate relevant training data and feed this to the AI. An MBBs role here would be to bridge the gap between the process excellence teams and the technical teams managing the AI. The MBB will also be responsible to create the templates needed to capture the insights and updated process steps (If applicable) which can be feed back to the AI through the established feedback mechanism. The MBB would also need work with the technical team to set up a process to effectively use this template for training data generation. Additionally creating and managing a governance plan should also be the MBBs responsibility.

When we talk about Continuous improvement, as a concept, we refer to a constant WIP mode of innovation, enhancement and incremental progress. Take all possible learnings and loop it back an input to further refine a product or process. VOC, VOB, error types, new data or new pattern or behavior of a certain process or a machine that can be studied, performance monitoring. Getting RCAs. Feeding it back into the system and closing the loop of a continuous self-learning improvement process with the help of Artificial Intelligence. Natural language programing Reinforcement Learning : AI agents can learn by interacting with end users and learning best practices from online forums and receiving feedback (rewards or penalties). Over a period of time, AI models or AI agents can improve their decision-making based on outcomes. Example: AI in customer service optimizing responses based on customer satisfaction scores. Also, in an AI agent environment if a customer rates a low score or not resolved on a survey. AI can ask customer if he or she wants to get redirected to additional support Utilize online libraries: System based or conventional training methods have a focused content and is periodically reviewed once or twice a year. Unlike traditional models trained once on a fixed dataset, online learning allows models to update continuously as new data arrives. Can prove to be extremely useful in dynamic environments like fraud detection system that can improve itself whenever a new fraud pattern emerges or email classification or query categorization. Optimized Human-in-the-Loop (HITL): AI backend can incorporate VOC of output or a human feedback to refine and improve performance. On a continuous basis which a key component of continuous improvement For example, customer service agents correcting AI-generated email drafts helps the model learn better phrasing, grammar, formatting, and tone. Use concepts like A/B Testing and Feedback Loops: A tried and tested AI system can test different strategies (e.g., email templates) and learn which ones perform better. Manual or online VOC and Feedback loops help the system adapt to changing customer behavior or business goals. e.g. In a Banking Email Customer Service Context: AI can learn from: VOC (NPS scores, complaints and RCA) Agent corrections to AI-suggested replies, check of all queries are answered in a multi query email. Frequency and Escalation patterns (e.g., which types of queries lead to dissatisfaction) Compliance checks (to avoid regulatory violations) Though there will be challenges to guarantee a continuous improvement on AI based models, if we study it enough it can be overcome. Challenges like Propagation of systematic Bias. For e.g. an AI model might be more favorable to a certain type of machine or high-performance shift timing, or certain region in terms of Sales etc. Distribution or pattern shift. Or drifting of parameters, Real world the situation changes dynamically so AI will have to be trained to Adapt. Failing which it will follow a fixed pattern and might not necessarily be effective. In manufacturing or Healthcare sector or if we speak from a Six sigma perspective AI can Conduct SPC if we feed it in initial stage. Analyze process deviations If we find some points or processes out of control, we will implement solutions to get the process in control, AI agent can learn from such corrective actions It can also Suggest process changes to reduce defects depending upon previous corrective actions taken by us or information available online. Would be better poised to predict process output or future failures or improvement opportunities

Insightful answers to the question. The best answer is from Swapnil Madhav Chaukar. Well done! Answer from R Rajesh is also a recommended read. My 2 cents - The AI solution needs to be made a process document and like we revise process documents (e.g. - SIPOC, process maps, SOPs etc) after every process change, we need to revise the AI solution after every change!!

Yes, absolutely correct that AI and strategic thinking collide Here goes how AI can be plaited into continuous improvement loops like PDCA or DMAIC framework. AI Learning via continuous Feedbacks AI models, especially those integrated with machine learning, can be designed to monitor process performance, Detect inefficiencies or anomalies in real-time. Analyse outcomes and compare expected vs actual results to identify areas for refinement. Adjust dynamically by Use reinforcement learning or online training to adapt based on feedback loops. MI models traditionally relied on static data, but continuous learning enables them to continuously update and refine their understanding. The process of continuous learning involves initial model training, updating the model with new data, and evaluating its performance. DMAIC Frame & AI Frame Define – (AI – Study customer Data to help and define problems) Measure – (AI - Use logs, digital data to track performance) Analyse – (AI - Predictive model) Improve – ( AI – simulation such as Monte Carlo simulation) Control – (AI – Monitor Metrics & prompt for any deviation against standard) Examples · In manufacturing, AI learns from production data to reduce 8 kind of waste and improve quality — form of real-time Six Sigma. · In Service Sector, AI RPA analyse customer complaint patterns and self-adjust responses or escalate intelligently. · In IT, AI-driven observability platforms continuously learn from incidents to pre-empt outages.

In a bigger context, AI can be trained to improve step by step over the time as how humans improve based on regular practice and feedbacks. There are some better ways of training AI towards continuous improvement: Online Learning: Regular trainings will have a fixed data set and the analysis will always provides same results irrespective of multiple trials. But in the online learning, AI models assist in getting the data set update with new data’s as and when required which improves the results and the solutions and help in adapting to different conditions. It also shows the predictions will vary based on data availability. Trial & Error Learning: This is kind of a Reinforcement learning where the agents will chance their actions against suitable strategies to show the improvement in performance over the time to get rewarded. Refresh the prompts: Regularly re-train the AI systems with new or modified prompts, newer data sets or prompt to react based on the interactions, will make AI system to get multiple iterations and align with different scenario’s to provide better results. For example, the search engine will provide better and relevant results based on how to prompt and what we select. Hence, Continuous improvement is the key or backbone of how AI systems developed. However, the AI models will not improve themselves autonomously, but the developers of the models play a vital role in providing how it should enhance and perform better.

Most of the AI agents need continuous redesign to adapt the new optimized process changes & improvements. however futuristic approach should be that AI learns and adapts the changes themselves and evolves as human drive changes rather than a still component 1. Ensuring AI evolves with process Integrate AI into continuous improvement advise chain- that is treat AI as process agent and assess its performance under new conditions Monitor prediction determine model confidence level 2. Create Model Governance Schedule validation to calibrate and assess whether the AI is performing basis latest process improvements. Change Management: Whenever a process change or new development made, review an impact assessment on performance. 3. Creating “Human-in-the-Loop” (HITL) Feedback mechanism AI should be designed to receive feedback from users correcting AI deliverable . Human can review, compare and validate the AI output delivered and establish whether it was at required benchmark 3. Continuous Learning basis backed up data · The new data can assist AI for the required change. you may use this data for to retrain AI Ensure all AI inputs, and outcomes are logged in structured & defined formats. What Role Can MBBs Play? MBBs play as a bridge for process improvement and AI adoption: 1. CTQ and output Alignment MBB reviews whether delivered AI solution meets customer requirement and critical to quality collaborate with different functions from process owners to development team and assess the performance basis requirement 2. Performance Measurement MBBs can help define AI metrics to assess how well the AI is adapting to process improvements and so can assess its contribution to the overall improvement goals. 3 MBB helps in developing the Data Governance: MBB can ensure the quality of data, its ethical use and privacy as the AI continuously learns from evolving process change

To make sure an AI solution grows with ongoing process enhancements, we need to think of it as a living part of the improvement system—not just a one-time setup. This starts by putting AI into the same feedback loops that power Business Excellence, like PDCA, DMAIC, and A3. The main objective is to create a continuous learning system from which the insight collected from the process modifications is leveraged to refine the AI system. Ways to adopt AI to advance improvement 1) AI integration at the operational level: a) Integrate AI in prevailing improvement cycles like PDCA, DMAIC and A3 for implementation b) Adopt MLOps tools to retrain AI after process modifications c) Align process review periods with AI update implementations 2) Clear Feedback System: a) Record improvement data like KPIs and root causes in organized ways b) Enable checks to spot model drift or performance gaps c) Start model updates when real-world results don't match up 3) MBBs key role: a) MBBs to act as mediators for process owners and data science teams b) Define goals for both process and AI success c) Make sure AI models shows the latest process standards and improvements AI can evolve with continuous improvement if it's designed to learn continuously. This requires automated feedback mechanisms, structured data from improvement initiatives, and strategic leadership from MBBs to ensure alignment and sustainability.