Satheesh

To ensure an AI-enabled process stays aligned with continuous improvements driven by human teams, the AI must be redesigned into the organizations existing feedback loops and its like how we treat any other critical process tool. Incorporate AI feedback into the PDCA/DMAIC cycle Performance should be monitored as part of the Control phase in DMAIC or the Check/Act phase in PDCA. Need to clearly define clear metrics for the AI, such as accuracy, false positives/negatives, or ROI impact. Build a routine cadence daily /monthly, quarterly to review these metrics alongside process KPIs. Close the loop with structured human feedback Design touchpoints where front-line process teams and SMEs can flag AI misclassifications, missed opportunities, or changing business rules. Use simple feedback channels — e.g., error tagging dashboards, feedback forms, or auto-logging when exceptions are handled manually. Feed this data to the AI team for periodic retraining or rule adjustments — this makes the human-in-the-loop model part of standard work.

Using the symptom identify the root cause, in the following example sales drop is the symptom due frequent stockout. Stockout happening across all locations, products and check the historical data points. Once i have the answer using fishbone or 5 why analysis identify the root cause why sales drops --> Why products not available --> Why inventory not replaced on time --> Root cause poor forcast and next Fishbone analysis Is the challenge because of People, process, technology. This helps to identify the root cause visually to correlate with the symptom. perform hypotheses test with data. Chasing the symptom instead of root cause leads no permanent solution!

From US Healthcare - Claim submitted are denied for various reasons from insurance companies. these denials are corrected by human, who then resubmit the claim with corrected information. Identify a pattern from combination of Insurance, procedure and diagnosis. using this trend AI can learn and track from particular insurance with combination of particular procedure will always get denied. Design a proactive denial predication and alert the respective department to solve the denial even before it could happen. This way AI can reduces denials and improve quick turnaround time on provider collection.

I am from US healthcare background, In my view i can trust on some of the repetitive tasks or data entry copy information from excel or PDF in to application with not much of complexity. and i dont trust on area where AI is the sole deciding maker on departments like patient payment plans or patient calling where you need a human judgement to take decision