Imtiaz Shaikh - Pune

version control for AI flows and prompt’s is one of the important task to ensure the new AI developed tool is updated with all the looped changes and the changes for what reasons. 1. This version control will have to manage the changes are done due to the feedback/improvement from various reasons 2. Without version control there will be miss of any improvements/ changes that were previously align and will prompt to not incorporate all the changes to the newer version. Thus, will have to rework and waste the time to alter the version again. 3. Thus following below example for version control will help the AI flows and prompt up to date. Version Author Date Change Summary Reason for Change V1.0.0 Imtiaz 10-6-2025 Initial deployment of escalation prompt Launch auto update tracker for production V2.0.0 Imtiaz 7-7-2025 Refined escalation trigger phrasing Improve user experience V2.1.0 Imtiaz 5-Au-2025 Added fallback logic for ambiguous queries Reduce misclassification errors V2.2.1 Imtiaz 15-Aug-2025 Added auto prompt if value of premium is above $ 10 million Attention for high value amount

A successful handover is the crucial part of any project framework which ensures the success is carried out without any risk or interruption to deliver the desire output of the project. Over a period of time the correct handover of the AI project is important to maintain and sustain the way it was projected the output and the same is delivered throughout its lifecycle of the project. Handover is (Project output) it’s a transfer of ownership, understanding, and accountability. Handover plan to be structured like a transition playbook, covering technical, operational, and strategic dimensions. 1. Documentation: - Easy, clear, accessible documentation is the backbone of a sustainable AI solution. 2. Q&A – Database to be maintain to add Q&A for future use 3. Backup- Plan should be define in case of the failure of the AI project failed to give the output 4. Training – Modules to create and train the operation on all the flow and it’s output 5. Security – Data must me maintain a password protected so that no human can change the models that are build to run the models 6. Retirement Plan: Criteria for decommissioning or replacing the solution.

With change the demand to transition from one state to another as driven by evolving circumstance as AI and adaptability as the capacity to adjust effectively to such an evolving situation. Let’s consider a retail company as an example of change and adaptability. Having traditionally operated of clothes stores, only the retailer’s, management realizes the need to stay relevant in the face of e-commerce and shifting consumer preferences. So, they launch online feedback platform, optimize their website for online shopping and ensure seamless customer experience across channels by making these modifications they successfully navigate the evolving business landscape and remain competitive so, in world where Gen AI and other advancements are reshaping the business landscape change is inevitable How well an organization adapts to industry feedback fluctuations determines its success. Key factors in adaptability 1. Cultivating growth mindset 2. Challenging limiting benefits 3. Establishing a learning culture Channels for feedback – Survey, interview and observation are the ways to have the customer preference Prioritize high-impact signals Apply AHP or Monte Carlo Simulation to weigh feedback types based on risk, frequency, and business impact. Retrain with curated feedback data, especially edge cases. Build a feedback dashboard with KPIs tied to business goals. Feedback mechanisms should evolve with the agent’s maturity.

AI systems may degrade quietly; we have customer support system which uses bot for initial communication for chat. This AI degradation may lead to the following because where you identify if the AI is working upto the 100% expectations. 1. Drop in customer score/ feedback score 2. Increase in call logs when we have simple query that could be managed by AI 3. Change in human behaviour for using new jargon words 4. Escalation raised due to mis leading by AI tool on certain queries Proactive measures help to avoid the degradation of AI application 1. Testing AI - Shadow Mode Testing Run new versions alongside the live one and compare outcomes before full deployment. 2. Incorporate new behaviour - Rolling Retraining & Prompt Refreshes Regularly update prompts and models to incorporate fresh vocabulary and changing user behaviours. 3. Frequent Audit - Feedback Loop Integration Incorporate human-in-the-loop validation in edge cases and retrain models using flagged interactions. 4. Dynamic Thresholding Adjust thresholds based on seasonal trends or campaign pushes to avoid false positives.

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.

The journey of pateints need to be map to understand the touchpoints - before picking the metrics study the whole floe from arrival to dischage Pick Metrics with Context Avoid generic “averages.” Instead: Use median to reduce the impact of outliers. Add percentile metrics (like 90th or 95th) to understand the worst-case experience. All Layer in stage-wise times (e.g., waiting for triage vs. waiting for a doctor)—these pinpoint bottlenecks. Include Volume and Capacity Measures Numbers like patients per hour or staff coverage ratios give to wait times. A spike in wait might not mean a process failure—it could be understaffing.

A key risk indicator (KRI) is a metric for measuring the likelihood that the combined probability of an event and its consequences will exceed the organization's risk appetite. This could have a profoundly negative effect on an organization's ability to be successful. KRIs play an important role in enterprise risk management programs. They can warn organizations of potential risks to their business. They also provide insight into possible weaknesses in an organization's monitoring and control tools, as well as ongoing risk monitoring between risk assessments. Companies often prioritize Key Performance Indicators (KPIs) over Key Risk Indicators (KRIs) because KPIs directly measure success, efficiency, and growth, making them more immediately actionable. Here’s why KPIs take precedence: Reasons Companies Favor KPIs Over KRIs Business Impact – KPIs provide tangible metrics like revenue growth, customer retention, or production efficiency, which directly reflect business success. Ease of Measurement – KPIs are typically straightforward, with clear numerical targets, while KRIs require deeper risk assessment and interpretation. Immediate Decision-Making – Since KPIs focus on performance, they help managers quickly identify areas for improvement without waiting for potential risks to materialize. Operational Priorities – Many companies focus on short-term goals, such as increasing market share or optimizing production, making KPIs more relevant for daily operations. Organizational Culture – Businesses tend to emphasize achievement and success over risk mitigation, as KPIs align with growth-oriented strategies. However, overlooking KRIs can be risky—ignoring early warnings may lead to operational disruptions or compliance failures. Companies that balance both KPIs and KRIs can ensure sustainable success. Benefits of Using KRIs Proactive Risk Management – KRIs provide early warning signals to prevent major disruptions. Example: Banks monitor loan default rates as a KRI to anticipate financial instability and adjust lending strategies accordingly. Improved Decision-Making – Organizations can make informed strategic choices by analyzing risk trends. Example: A manufacturing company tracks supplier reliability as a KRI to prevent production delays caused by supply chain disruptions. Regulatory Compliance – Many industries rely on KRIs to meet legal and regulatory requirements. Example: Healthcare providers monitor patient safety incident rates to ensure compliance with health and safety standards. Enhanced Crisis Preparedness – KRIs help businesses prepare for unforeseen challenges and develop contingency plans. Example: Companies track cybersecurity breach attempts to strengthen their IT security defenses before facing major data leaks. Limitations of Using KRIs Complexity in Measurement – Identifying and quantifying KRIs can be challenging due to varying definitions of risk. Example: Assessing employee turnover risk requires deep analysis of multiple factors, including company culture and market conditions. Delayed Impact Recognition – KRIs often highlight risks that may not have immediate consequences, making them harder to act on quickly. Example: Tracking geopolitical risk in international trade may not show immediate disruption but could affect supply chains over time. Lack of Standardization – Different industries require different KRIs, making universal benchmarks difficult to establish. Example: Financial institutions may use credit risk indicators, while retail businesses focus on inventory shrinkage as a risk metric. Resistance to Adoption – Some organizations prioritize short-term performance over long-term risk management, making KRIs less appealing. Example: A startup focused on rapid growth might prioritize KPIs like revenue growth instead of KRIs related to financial sustainability. Conclusion While KRIs are essential for identifying and mitigating risks, they should be balanced with Key Performance Indicators (KPIs) to ensure sustainable success. Organizations that integrate both KRIs and KPIs can optimize performance while minimizing potential threats.

BPR, or business process reengineering, is a radical means of redesigning processes. It is meant to enact change quickly, upsetting the status quo while hoping to achieve drastic improvements in areas like cost, quality, and efficiency. Approach : BPR focuses on a clean-slate approach, typically in a top-down fashion. When undergoing any sort of BPR project, you’re looking at the default assumptions of a given process and trying to figure out how to best accomplish your stated goals. Starts from scratch, rethinking and restructuring workflows, often leading to major organizational changes. Six Sigma is a data-driven approach that hones in on concepts like quality and efficiency. This is typically an exhaustive, organization-wide shift to the methodology. One of the major cornerstones is building a culture of continuous improvement. This doesn’t just pertain to the process and mechanisms in place at an organization, but a much wider look at the whole. Approach: Six Sigma is very methodical by design. This is seen in how you go about process improvement, with frameworks like DMAIC serving as the backbone of any effort. DMAIC itself is a measured, logical response to the inefficiencies of a process. It also extends to how moves are seldom made in Six Sigma without hard numbers to back them up. Uses data-driven analysis, statistical tools, and process optimization, following the DMAIC (Define, Measure, Analyze, Improve, Control) cycle. When to Use: BPR & Six Sigma BPR · When the Existing processes are no longer effective, requiring a complete new technology or shifting to a different operational model. · Competitive pressure demands a disruptive change in how work is done. Six Sigma · When the goal is incremental process improvement, rather than full-scale redesign · Waste, defects, and inefficiencies need to be reduced using data-driven methodologies. · The organization wants to enhance operational performance while maintaining existing structures. · Customer satisfaction and cost reduction are primary concerns. Can They Work Together? BPR focuses on fundamental transformation, LSS can refine and optimize processes after reengineering. Here’s how they can complement each other: BPR first, LSS later: After redesigning a process with BPR, Lean Six Sigma ensures it runs efficiently. LSS first, then BPR: If incremental improvements fail, BPR can introduce the necessary disruptive change. Hybrid approach: Organizations can apply BPR to certain processes while using LSS for continuous refinement in others. both methodologies play a role at different stages of evolution. The key is strategic alignment with the organization's goals and challenges.They both complement each other.

Capacity Utlization : Efficiency often looks good on paper, but in practice, there can be hidden inefficiencies that keep things stagnant. Capacity utilization might appear optimized, but without an accurate Average Handling Time (AHT) assessment, the true bandwidth required for daily operations remains unclear. A Time and Motion Study is a great approach to pinpoint inefficiencies at a granular level. It helps reveal how much time each task truly requires, preventing inflated FTE requirements and ensuring manpower is aligned with actual business needs. Regular AHT revisions keep these calculations up-to-date, ensuring the workforce is adjusted dynamically rather than staying fixed due to legacy operational habits. If done well, this strategy can lead to significant cost savings while maintaining productivity. There are any major pushbacks from the team in implementing such a shift? Sometimes resistance to change plays a big role in keeping inefficiencies alive.