Dr. Kishor Sonawane

The AI teams Co-Create and Business Excellence Experts (MBBs) must collaborate from the beginning (not in silos or as hand-off partners) in order to provide better solutions for today's complex organizational issues. MBBs are aware of how tasks are completed. They concentrate on enhancing the functioning of people, systems, and procedures in order to address real-world issues. They pose the appropriate queries: What is the objective? What's causing the delays? What is generating confusion or waste? AI teams, on the other hand, provide the means to address these issues in novel ways by automating repetitive processes, accelerating decision-making, and predicting potential outcomes using data. However, AI is most effective when it is directed towards the correct issue, which leads to the discovery of quicker and more intelligent solutions. They may create solutions that are both inventive and practical, based on actual demands and supported by cutting-edge tools, if they collaborate from the start. How they can work better together? 1. Begin with a common objective: MBBs and AI teams should jointly and concisely characterize the issue. 'Why' it matters is as important as 'what' has to be fixed. 2. Make use of actual facts, not conjecture: MBBs are able to identify process gaps and discrepancies. Data from such domains may then be used by AI teams to create clever solutions. 3. Build gradually rather than all at once: Begin modestly. Put the concept to the test in actual settings. While AI teams modify the model or tool to match what is effective, MBBs assist with feedback and outcome tracking. 4. Pay attention to people rather than simply technology: The best solutions are those that people use. MBBs are adept at leading change, developing teams, and ensuring that novel concepts are retained. 5. Continue learning along the way: Co-creation is a continuous process. It's important for MBBs and AI teams to maintain communication, continuously refining the solution, and facilitate scaling. The true benefit occurs when the astute powers of AI combine with the practical expertise of MBBs to transform business challenges into significant, long-lasting advancements. What Does Co-Creation Look Like? 1.Identifying the true issue should come first. MBBs are able to pinpoint instances in which a process is inefficient, inconsistent, or sluggish. In addition to solving technical problems, they also assist the team address the 'right' problem. 2. Utilize the appropriate data to comprehend the situation: AI teams may examine the data to identify trends, and MBBs assist in interpreting the data's meaning in the context of the actual world. 3. Test and collaborate to improve: Take little actions to build solutions. Pilots or trials are led by MBBs, who also assess the results and make adjustments depending on what is effective. In response, AI teams improve models or tools. 4. Design with humans in mind, not just machines: If no one uses an AI tool, even the most intelligent one will fail. MBBs make ensuring that the solution works with people's actual workflows. 5. Continue to learn and adjust: Over time, both business procedures and AI models require fine-tuning. Co-creation is a continuous collaboration that continues after a project is launched. Real-World Example: Cutting Down on Manufacturing Plant Delays The problem: The production line of a sizable manufacturing business had regular delays. Delivery deadlines were being missed as a result of machines halting suddenly. MBB's Role: During a process analysis, an MBB discovered that equipment failures were occurring more frequently during particular shifts, but the maintenance team lacked a discernible pattern to follow. The MBB also found that planned maintenance did not correspond with the real patterns of wear and tear. Role of AI Team: Using data from machine sensors, the AI team developed a predictive maintenance model. Before a failure happened, it detected early warning indicators and sent out notifications as necessary, minimizing unscheduled downtime. The Result: Together, they revamped the maintenance procedure, - Replacing set timetables with predictive warnings - The signs for the maintenance crew were clearer and earlier - Delivery on time increased by 25% and downtime decreased by 40% This achievement was a result of both the AI model and the cooperation of the MBB, which made sure the solution was workable and in line with operations, and the AI team, which provided the technical understanding. Conclusion: When AI teams' potent tools are paired with MBBs' extensive process understanding, great solutions are produced. It's about collaborating to create something better, quicker, and more beneficial than either could achieve on their own, not about picking one over the other.

Well in my opinion, every organization want to function more efficiently, effectively, and successfully. At times, this involves modifying and improving an existing procedure. In other cases, it entails taking a step back and radically rethinking how the job is done, particularly in light of the latest developments in AI, which provide new approaches to activities that previously required human intervention. When to Improve the Existing Process: Start by determining whether this procedure still accomplishes its goals but simply needs to be more effective. If so, conventional techniques of improvement, such as eliminating pointless procedures, cutting down on wait times, and standardizing activities, might be beneficial. Such modifications are beneficial when: -Although there are delays or inefficiencies, the process is steady. -Employees are following the procedures, yet the results differ. -Though the experience might be more seamless, customers are typically happy. -Though insights are difficult to act upon, data is already being used. In these situations, we enhance what is currently in place by streamlining operations, educating employees, modifying schedules, or streamlining chores. When to rethink the Process Using AI: But sometime, the procedure is outdates, many things are manual, or designed to solve non-existent problems. Simple improvement won't result in significant change in certain situations. We then pose the question: Can AI enable us to radically rethink the way this task is carried out? Consider rethinking with AI when: -People devote too much time to commonplace, repetitive jobs. -Despite producing a large amount of data, the process is not being used efficiently. -Consumers anticipate quicker, more individualized service that is impossible for people to provide alone. -The present procedure wasn't created to meet the demands of the modern world years ago. AI can anticipate human needs, make judgements in real time, automate replies, and recognize patterns that humans would overlook. This has the power to change a process in a way that adds more value as well as making it faster. AI is capable of making judgements in real time, automating replies, anticipating human needs, and seeing patterns that humans would overlook. In addition to making a process faster, this can fundamentally change it in a way that adds value. An Example: A hospital could wish to shorten patient wait times. How? Option-1: Process enhancement might entail quicker check-ins, clearer communication, or improved staff scheduling. Option-2: Conversely, AI-driven redesign may incorporate systems that prioritize patients according to urgency and historical health data, predictive models to identify busy periods, or virtual assistants to respond to enquiries instantly. Though they meet distinct needs, both strategies are beneficial. One last observation: Before taking any action, need apply critical thinking: -Is this procedure still appropriate? -Will it make enough of a difference to be improved? -Or is it time to start over, using AI to help us accomplish things in a more intelligent, contemporary manner? Enhancing and rethinking are methods for different contexts and are not mutually exclusive. Making the correct choice is essential for sustained success.

In a DMAIC project's fourth phase is known as "Improve Phase", the progressive significant improvement depending upon the output of 'Analyze' phase. Where it setting the parameters for quantifiable change as well as it immediately builds upon the verified findings from the 'Analyze' phase, and outputs of this stage will help to make effective 'Control' phase for manufacturing and service sector projects. At this point, deliberate solutions start to take shape not as band-aid remedies, but as focused interventions based on proven core causes. This phase create an enhancements that specifically target the variance and inefficiencies that were previously discovered. Ideas for improvements are not only generated, they are put to the test under pressure. While impact-effort matrices rank the activities that will have the greatest impact with the least amount of disturbance, tools such as FMEA (Failure Modes and Effects Analysis) aid in anticipating such hazards. Each given improvements, modification, and qualitative changes are supported by a hypothesis testing and where it possible, validated through simulations (most of them are Monte Carlo) or pilot runs. Before scaling, these small-scale experiments aid in identifying unforeseen repercussions and improving ideas. In the DMAIC at this stage the co-operation becomes even more important. I work with stakeholders and frontline employees to jointly develop and improve solutions, making sure that adjustments are reasonable, palatable, and long-lasting. Standardizing processes, removing stages that don't add value, restructuring forms, improving system logic, or using Poka-Yoke techniques to prevent errors are some possible solutions. Lean tools also excel at this stage; standard work, visible controls, Kaizen bursts, and 5S help lock in changes. In order to assess the impact, data collecting continues and even escalates. Many different dashboards, graphical charts, and control charts monitor early performance changes, and I check improvement by comparing pilot outcomes to baseline data. During the Improve phase, understanding turns into action (mostly proactive rather than, reactive). It is purposeful, team-based, and data-driven, turning proven core causes into significant, quantifiable change that aligns with business objectives and client expectations.

The 'Analyze Phase' is the third stage of a DMAIC project. The Analysis phase is purely about precision and clarity, taking inputs from the previous 'Measure' phase and generating outputs for the next 'Improve' phase. I resist the urge to dive right into averages or superficial statistics when I begin the Analyze phase. Instead, I look for patterns and the real core causes of process variance using Lean Six Sigma methods. Purposeful analysis is fuelled by a targeted problem statement that is founded on VOC and process mapping. There are many visual aids, such as box plots, histograms, and run charts can be used to highlight odd patterns and changes. To find the areas with the highest concentration of issues, regular process gaps, I stratify data by time, provider, or visit type. Pareto charts rank the most important problems; on the other hand, control charts separate normal fluctuation from unique reasons. To delve deeply and refrain from making snap judgments, I recommend the Fishbone diagrams and the Five Whys. Data is validated through triangulation—Gemba walks, manual logs, and system data comparisons. The Analyze phase is where I turn raw data into insight, targeting variation, validating root causes, and aligning improvement with what matters most to the customer and the business.

Very basic thing in the lean six sigma to count on measure phase which is one of an important component. The goal of business excellence's Measure Phase is to quantify present performance in order to pinpoint areas that require improvement. At the beginning of continuous process improvement creating process maps, generating baseline measures, defining pertinent metrics (KPIs), gathering trustworthy data, statistical analysis, detecting performance and quality gaps, recording results, and involving stakeholders are all important tasks. Organizations may obtain insights that enable targeted changes and overall performance enhancement by measuring processes consistently. Collecting an accurate data sets and focusing only on process performance along with efficiency and quality improvment is a main objective of measures phase. To get a comprehensive insight of process performance and pinpoint areas for improvement, the Measure Phase is essential. Organizations may create a strong basis for further stages of development by carefully carrying out this phase, making sure that any adjustments are data-driven and in line with strategic objectives. More client happiness, higher quality, and more operational efficiency are the ultimate results of this.

There are many organizations globally use measures 'Key Risk Indicators (KRIs)', which helps to provide an early warning signs of potential threats/risks that might impact their goals, objectives, plans, or operations significantly. The KRIs are basically essential aspects of risk management frameworks that allow organizations to address, track, and assess their risk prone exposures over the periods of time. KRIs are often selected based on their unique predictive ability, relevance to specific risks, and relationship to organizational objectives accordingly. Process experts, decision-makers may react proactively to new threats because effective KRIs are measurable, understandable, and actionable. Financial performance, operational effectiveness, compliance, and strategic objectives are just a few of the areas they may address. Through consistent evaluation of KRIs, organizations may strengthen resilience, streamline their risk management procedures, and make well-informed choices to lessen possible risks. Last but not least, properly crafted KRIs help firms better manage uncertainty and foster a culture of risk awareness.

Now a days processes are becoming a back bone of every organization across the world. Processes might be segregated like manual, semi-automatic, and fully automatic, etc. in manufacturing and service sectors. Every process required change, adaption and finetuning over the period of time. Therefore, Lean Six Sigma (LSS) and Business Process Reengineering (BPR) are came into the light. LSS and BPR are both techniques designed to improves organizational performance, although they differ greatly in their own unique goals, expertise, and approaches. BPR emphasizes on drastically rethinking, re-considering and re-evaluating the important business processes to improve efficiency, quality, and services. A comprehensive overhaul of existing processes including workflows, production units, qualitative systems are often required, which forces companies to re-evaluate how they do their duties. On the other hand by comparing LSS blends pure Six Sigma based techniques, which concentrate on identifying process gaps, lowering process variation and enhancing quality, with Lean manufacturing concepts, where it priorities efficiency and waste reduction. As opposed to major redesigns, Lean Six Sigma strives for unique continual improvement throughs with little adjustments, which makes it more flexible to changing operational requirements whenever required over the period of time. Significant improvements may result from BPR, but there may be additional challenge which should not overlooked the risk and employee resistance. In contrast, Lean Six Sigma promotes a culture of ongoing improvement, making process mature, and employee engagement, which makes it more sustainable in overall scenarios. By being aware of the differences between these approaches, organizations may select the one that best suits their unique objectives and situation per requirements.

When we think on the the over-reliance on automation and standardized procedures without considering the diversity of customer's expectations, demands is any one approach in service organizations that may appear efficient but is frequently ineffective/ most likely having good case for the improvement. Many times automations initiated without considering the risk prone areas and end with chaos. Although the aim of automating techniques and processes is to reduce errors, time consuming gaps, and simplify processes, they might result in a lack of adaptability and reactivity. In a way while reducing variability and enhancing quality should be the main aim of an any organization in the view of process experts, placing so much emphasis on standardization may obscure the particular needs of each customer in unnecessary automation. This will increased consumer discontent and an inability to adequately handle particular concerns, might arise. At the end, putting efficiency with quality first through inflexible automation or procedures may degrade service authentication, reliability, quality and impede long-term success and client loyalty.

I believe that the most important things that make an AI agent fun to use. As the world becomes more digital, user satisfaction is more important than ever, and AI agents should focus on things that make the user experience better. A user-friendly interface makes it easy for everyone to use time to time, and the personalization lets the AI change to suits & fit needs of everyone . Responsiveness and understanding of natural language make interactions smooth, which makes conversations feel more like real people 🙂. However an interesting personality can help the end user and the AI get along well per requirements. Being aware of the context in details and having multiple modes of communication make interactions even far better, making them more intuitive. Adding ways for users to give feedback lets you keep getting better, and keeping their data safe and private builds trust. Lastly, adding fun features, user-friendly bots can make the experience more enjoyable.