Jayaraj

In the Business Process outsourcing domain, our Continuous Improvement team will play a vital role in driving improved service delivery by improving efficiency & waste reduction. If AI has been introduced, the core activities of analyzing data, automate redundant or routine manual activities & identifying the logics or patterns from the data will be very simple. However the Continuous Improvement function will still play certain roles. Without AI, Continuous improvement team focus on: Process mapping through SIPOC, VSM Identify process gaps through audits or collaboration with Stakeholders Do root cause analysis to understand all possible causes Lead Improvement initiatives through Six Sigma or Lean methodologies Track data and compare pre & post implementation impacts. If AI could be introduced, then AI Roles: Monitor performance metrics & highlights areas on concerns in real time Analyse the feedbacks or responses through LLMs Automation of all reports & dashboards Predict trends / issues by continuously analyzing performance Introduce better models based on emerging needs Responsibilities of Human roles: CI Analysts will focus on evaluating AI-driven observations for strategic fixes. Process Consultants will explore new design collaboration models Trainers will concentrate on enhancing the workforce with emerging technologies. Data analysts will shift focus on creating more predictive / prescriptive models. With AI merger, the Continuous improvement team might go for upgrading new skills such as AI Literacy, Digital Tooling, Strategic thinking, etc to understand how the models work or how to interpret the AI insights. Also learn to work with fine tuning the automated dashboards aligning Ops requirements. To ensure a smooth collaboration between the Humans & AI, certain process redesign might require. AI act as first monitoring layer to watch metrics & suggest improvement areas in real time CI team will review the suggestions, validate & prioritize aligning strategic directions CI team will review the logics or prompt regularly to ensure the model accuracy & avoid false alarms. Collaborate AI suggestions with realtime feedbacks to derive strategic solutions. Define clear roles of AI & CI teams. This way Continuous improvement team will upgrade from traditional problem solvers to proactive change agents.

In the Business Process Outsourcing domain, we can think of an AI driven process used for automated email triage and classification of tickets in customer service. Failure Scenario: The AI reads the incoming emails and categorizes the emails into different buckets like billing, support, etc. It also routes the emails to the available associates based on category / topic & priority. The performance of AI might degrade due to various reasons: Change in Input pattern due to new terminology usage by customers; Changes or addition in services where the model hasn’t been updated with the current inputs Outdated prompts or logic leads to incorrect decisions Changes in the ticket integrations due to updated prioritization logic or updated fields, etc. Early Warning Signs: AI models might not crash immediately, but degrades slowly with visible warnings: Reduced efficiency of ticket resolution due to incorrect routing Increased cases of re-assignment with shifting of tickets between teams Unusual pattern of spikes or drops in categories. Increase in customer follow-ups or escalations due to inappropriate resolution. Monitoring Strategies: Its always important to detect the process degradation early to avoid customer impacts. Use Statistical thresholds to validate the sample auto-classified tickets comparing with the results of manual classification. Validate text embedding across tickets to detect any anomalies Calibrate agents with the test cases using Attribute analysis & validate results Compare the models performance with a initial sandbox version to understand difference. Risks with AI Automation: Spikes in volume might mislead the focus on categorization issues Keep an eye on bypassing the AI flaws without escalation.

Traditional audits will be more suitable for regular processes, however to audit an AI Infused process it would be challenging due to static check points. To audit a process which includes AI components, we would require a modernized & robust mechanism which includes dynamic decision making with evolving logics. The Expanded audit criteria for a process with AI components aligning with Business excellence include: Integrity of Prompt/Flow – Verify whether the prompts, logical decisions are properly version-controlled. Track Decisions – Is it possible to validate the model, logic or prompts that how the decisions are made by the model. Regulatory compliance – Verify that the AI models comply with all the required regulatory norms Bias – Does the models take the datasets & logic removing all implicit biases Strategic Alignment – Does the models are mapped & aligned with all Business metrics & KPIs Fragile or outdated model – Verify that the model still providing required outputs with current data or need to be updated. Escalations – Check the frequency of the errors or any repeated exceptions in the flow of the model Interpretation – Can all Business stakeholders understand the decisions taken by the model or any need for explanation Considerable risk factors: · Accuracy of the model goes down and lost its effectiveness · Logical flows not matching / aligned with change in business · Inappropriate mechanism to report issues / improvement suggestions · Outdated knowledge base feed into AI models To ensure Transparency, Fairness and alignment with Business Goals: · Frequent review of modes along with owners, data teams & the users · Maintain a clear version history of all the changes · Create surveys, dashboards and track all override logs to align with KPIs · Validate the models to understand the value it creates against the mapped metrics through different tools. Implementing the same in real-time scenario requires: · Templates with weighted scores across different criteria’s · Dashboard / scorecard to have a better tracking & alignment with Business goals · Train resources to include additional criterias for autiding

Business excellence frameworks are either manual or uses simple automations where making a process robust, more stable & ensure sustainability of the actions for long term is cumbersome. On the other hand, AI solutions make the process easy with prompts, flows & logics. But due to frequent updates in the AI world with more & more new features on a daily basis makes the solution outdated and not meeting the expectations. In such cases, keeping a focus on signs that spots the AI solution becoming fragile and allows proactively defending it. Warnings Enablers Decreased quality of output Change in the data or inputs, outdated models Decreased usage or adoption Non relevant, increasing usability issues Increased Manual corrections Not supporting systemic decision Mismatch in knowledge base Outdated references, guidelines or documents Impact on performance KPIs No improvements observed in business metrics Increased exceptions or errors Gaps in logic or prompts or flows These warnings can be addressed in initial stage to avoid making them perennial. Business excellence professional should act as a catalyst to ensure AI deployments are sustained for long term. Periodic Health Checks: Formulate a periodic review or audits of the models to ensure its relevance, proper data alignment. Also validate the usage of the models through logs to understand the gaps for improvement Feedback system: Create a user-friendly mechanism for the users to share feedbacks, suggestions or report issues. Look for quick win solutions through poka-yoke or kaizens Map Models with KPIs: Regularly validate the results of the models to understand the impact of metrics. If metrics not improved & stagnates, then verify whether the values are drifted. Knowledge Base: Create the prompts, flows or logic in a simpler way instead of static designs to make it easier to update. Regularly monitor the content updates. Ownership: AI solutions are live processes. Process owners, users & data teams should be vibrant for keeping it upto date. As like Business Excellence models which deviate if the users are not discipline in updating the required data, AI systems also requires frequent resilience through sustainability scorecard or a refresh roadmap etc., to keep it not outdated.

Its absolutely vital in today’s scenario where AI is becoming a support in all aspects reducing the human efforts. With more AI platforms & features, it makes an illusion that it is more important than the strategic values. But its always important that we keep AI initiatives aligned with our Business priorities. Before getting into any AI solution, we need to think on simple strategic point that “What is the specific business decision or a KPI metric or the system capability that the AI solution will improve and how is it going to do?” Business Problem: Analyse the problem with prioritization tools like Pareto or COPQ to identify high impact areas where actions are needed. Create a decision making matrix. Map problem with Metric: Map or align a KPI to each high impact area Verify if the AI solution will have an impact on the KPI to improve, if not relook at the solution Tech vs Value: Brainstorm if the pain areas need a AI solution or a simple RPA solution will also yields results. Try to understand & segregate the areas where the problems can we solved with or without AI intervention This gives us a clear understanding on the impact of AI solution with the strategy. It also makes the clear business case for the solutions for easy buy-in from stakeholders. Always align the technology with the Business goals & KPIs. One Simple approach to make the clarity better: Ask, “Do we still look at solving this problem at a top priority, even if there is no AI solution”? This gives a clear demarcation between the Innovation drives Business goals & tech-based approach always.

As part of Continuous Improvement team and integrating Swiss Cheese Model into Continuous Improvement assists in visualizing how defense layers will work together in preventing failures even if the layers had flaws individually. It is all about creating a resilient system and not isolated quality checks where individual weaknesses cause the issues. The Slices of Cheese and the Holes represent various factors which strengthen the overall process: Slices of Cheese (Defense Layers): · Six Sigma / Lean practices; · SOPs (Standard Operating Procedures) · Gemba Walks · Poka-Yoke · Visual Management · KPIs · Qualitative Feedbacks · Continuous Learning · Training & Upskilling Holes (Potential weaknesses): · Improper application of methodologies or lack of buy-in from all stakeholders · Outdated documents in workplace or no proper accessibility of documents · Very high level observations missing the actual problems · Outdated processes or easy to skip the steps · No proper visibility of actual progress · Metrics are not aligned with strategic goals · Feedback responses are minimal · Lessons learnt are not converted into actions · Skill gaps observed due to more generic sessions Example: Process Standardization & SOP updates Slices of Cheese (Defense Layers) Holes (Potential weaknesses) SOP Review measure Not updated regularly. Updated only if issue occurs Training on SOPs No active participation or improper knowledge transfer Audit mechanism Focusing only on compliance of stated requirements, but missing to check any potential gaps in the system Business Excellence reinforces systemic, data driven and people centric approach. · Leader’s involvement to visibly support the Continuous improvement practices makes all levels more robust. · Integrate processes to align SOP’s & KPIs · Improve feedback response rate & bring the customer feedback into regular reviews. · Promote best-in class solutions through Benchmarking · Promote a cultural change to stop & fix wherever required.

Change management is the quiet strength which makes whether a DMAIC project implemented delivers the required result or actions subside after implementation. If change management is not proper, even the best solution will face resistance, poor adoption or quick relapse. Change management always plays a crucial role in all the five phases of DMAIC journey: Define: Creates urgency and gains stakeholder alignment. Resistance will be lesser when all stakeholders understand the necessity of change. Measure: It assesses the readiness for change and inputs voice of the customer. Analyze: Highlight all possible barriers (behavioral and cultural barriers) that will prevent success. Improve: Include stakeholders who understand the change to create pilot solutions to influence the buy in from other stakeholders. Control: Reinforce the change through training, SOPs, metrics, and recognition to make it work. An MBB plays a major role in driving success & sustainability of the project. They act as a change architect than a technical or subject matter expert. Strategic Alignment: To ensure the alignment of the project with the Business goals & expectations of stakeholders. Coaching & Mentoring: Guide BB’s and GB’s to influence and engage with people. Stakeholder Engagement: Facilitate buy-in from all stakeholders and leaders. Sustainability Planning: Design control mechanisms that includes cultural reinforcement and leadership activities. Summary: Without change management: Solutions will be technically good but not adopted properly. Resistance causes delay in timelines and subsides the impact of solutions. Improvements are not sustained. With effective change management: Stakeholders are the champions to drive the initiatives. Improved adoption of solution to realize expected results Results sustains beyond the project lifecycle.

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.

To create better solutions, MBBs and AI teams need to work together to bring their strengths & positive values, in turn combining process excellence and the science to create transformation. Master Black Belts will have in depth understanding of the processes and map the value to the Business. While AI teams assist in creating automation or prediction of how things will turn up while improving the current scenario. MBBs will look for SIPOC or VOC to visualize the problem where AI teams convert the data and the requirements into a model to define the objectives, but both the teams should have a clear understanding on the problem and define the problem statement appropriately. Both MBBs & AI teams should have a common purpose on the objectives to be met with understanding on either of the languages including understanding of CTQs, Control charts or other concepts of Six Sigma and exploring basic AI competencies. Both the teams to create solutions together with a proper documentation starting with mapping the process, identify values, conduct root cause analysis, and identify the potential cause, while keeping all the required stakeholders aligned with the update & progress. In parallel AI teams will help with the data to bisect & dissect to bring out meaningful insights from the data and create matching simulations for the solutions and validate the results to understand the end objectives are met. AI teams to be working hand in hand to better handle the process to have a happy flow where the variations are less which meets the requirements of the Business. Risk management and mitigation of risks will be a key factor during solution generation where both the teams should be trained and create necessary controls & mitigation in the solutions to ensure sustainability in long term. To summarize, we need to create a synergy between both the teams where we Define the Problem, Measure with the data, do necessary analysis with simulations. Create insightful solution with AI and controls with visualizations.

A Process to be improved when the inputs, outputs & the metrics of the process is clearly defined, documented & completely understood. The errors or issues in the process are increasing rapidly without any specific patterns. Also, check if this process is the core activity of the operations and not having impact on the overall strategy of the Business. On the other hand a Process can be reimagined with AI when the process performance is poor and we can’t derive any meaning insights from the data, where AI can be helpful in deriving patterns from the data. Also AI assist in processes where undetermined predictions to be made with complex decisions. Also, when we expect our outcomes to be a radical enhancements or breakthrough results in our process, AI will assist to have a game-changing impact. When your process is not so flexible for changes or contains more manual interactions or our regular Lean/Sig Sigma improvements are not providing results, we may look for AI solutions.

"The training was very good. It all over was a very Interesting and informative session." Jayaraj J, Assistant Manager Quality, National Stock Exchange