Harjeet

Position - I support View B — Preserve flexibility. Why? Real-world service operations contain tail cases and local constraints that standardized AI often under represents. When the environment is heterogeneous (regulations, geography, customer mix, infrastructure, competition), rigid standardization can increase the cost of errors in exceptional situations. The right move is not to reject AI standardization but to place it inside governed guardrails with structured local overrides. This keeps consistency for the 80–90% of routine cases while protecting outcome quality in the high-impact 10–20% where context matters most. Experienced practitioners hold tacit knowledge (micro-climate patterns, regional vendor reliability, local regulatory nuances, community expectations). Constraining them to a one-size-fits-all recommendation risks worse service outcomes, lower customer trust, and compliance exposure. Specific Operational Example: Utility Field Service Dispatch (Outage Restoration) Scenario: A national utility uses AI to schedule crews and prioritize outages across multiple regions. The AI is trained to minimize average restoration time and travel distance while honoring standard safety protocols. What goes right with standardization: Routine outages get restored faster and consistently. New dispatchers ramp quickly using AI playbooks. SLAs become more predictable. Where local flexibility is essential: Regional factors: Terrain accessibility, weather micro-patterns, wildfire risk zones, union crew constraints, regional curfews, and road closures vary widely. Critical customers: Hospitals, wastewater plants, and emergency services require local prioritization beyond generic “critical asset” labels. Community context: Some regions expect proactive communication or temporary generators for vulnerable populations — practices not uniformly codified. A flexible model outperforms in these edge conditions: A dispatcher in a mountain region overrides the AI’s “shortest path” assignment because the recommended route crosses a pass likely to ice after 17:00. They choose a valley route, add chains, and split the crew to cover both ends of a feeder line before temperatures drop. Another region elevates restoration for a hospital feeder despite the AI’s lower estimated kWh impact because the ICU surge is forecasted after a local event, increasing patient risk. In both cases, governed local overrides produce better safety, reliability, and community outcomes than strict adherence to standardized AI recommendations. How to Preserve Flexibility Without Losing Consistency: A Governed, Tiered Decision Design Baseline policy and AI guardrails Define a standardized “default” policy set: safety rules, compliance constraints, SLA targets, core optimization (e.g., travel and restoration time), and minimum communication standards. Require the AI to always enforce hard constraints (safety, regulatory rules) while exposing soft preferences to adaptation. Local policy packs (parameterization, not free-form deviation) Maintain region-specific parameter sets (e.g., weather risk thresholds, terrain travel multipliers, union shift rules, critical asset lists). Give local leaders authority to update parameters within bounded ranges without enterprise approval, enabling rapid context alignment. Structured overrides with reason codes Allow experienced staff to override AI recommendations with mandatory reason codes (e.g., hospital feeder priority, wildfire zone precaution, road closure, weather front timing). Capture supporting evidence (incident ID, timestamps, data sources). Automatically log overrides for audit. Exception playbooks Codify common override patterns into reusable playbooks (e.g., “Wildfire Risk Protocol,” “Hospital Feeder Escalation,” “Mountain Pass After-Dusk Policy”) that the AI can proactively suggest. Promote recurring overrides to policy updates through monthly governance. Risk-tiered autonomy Routine, low-risk cases: auto-approve AI decisions. Medium-risk: require one local reviewer plus reason code if deviating. High-risk/critical: mandate local lead approval; AI proposes options but does not auto-decide. “Override budgets” and transparency Set target override rates by region and process. High override rates trigger a model/policy refresh; ultra-low rates trigger a check for under-reporting or unaddressed local context. Publish override dashboards to both central governance and local teams to keep trust and accountability. Why This Beats Pure Standardization It protects the gains from standardization (speed, training, predictability) while preventing the “average-case bias” from harming high-stakes local contexts. It converts local expertise into structured data (reason codes, playbooks, parameters), continuously improving the AI rather than sidelining it. It maintains compliance and safety through hard guardrails, avoiding the risks of unconstrained flexibility. Summary Preserve flexibility — implemented through governed local overrides, region-specific parameterization, and exception playbooks within standardized AI guardrails. This hybrid design delivers consistent performance for routine work and superior outcomes where local context is decisive.

I support View B — do not adopt the AI system in its current form. In airline operations, tail events dominate outcomes. A 2–3% rate of cascading failures can erase months of average gains in a single day, destroying customer trust and creating systemic brittleness. Improve resilience first, then scale efficiency. Core Argument (why View B wins) Asymmetric downside: Average gains are linear; tail failures are nonlinear. One network-wide disruption triggers mass misconnects, crew legality breaks, aircraft out-of-position, compensation, and brand damage that dwarfs the value of a 15% mean improvement. Safety-critical expectations: Airlines are judged on predictability and recoverability, not just averages. Customers, regulators, and partners have low tolerance for brittle systems. Network contagion: Tight schedules amplify propagation. Hub banks, crew rotations, and scarce gates make small shocks cascade. If your optimizer aggressively removes slack, it will eventually find a scenario it cannot recover from. Ethics and trust: “Better on average” while increasing severe failures is not an acceptable service posture in critical infrastructure. Concrete Example (airline operations) Scenario: A tightly optimized turn plan at a major hub removes buffer across three inbound waves. A microburst forces 25 minutes of ground stop. With no slack: The first wave departs late, misconnecting 200+ passengers. Crews time out on second legs; replacement crews aren’t staged. Aircraft rotations slip, stranding a widebody for a long-haul. Result: A 2–3% event becomes a 12–18 hour rolling disruption, plus rebooking, accommodations, and reputational hits. Resilient alternative: A risk-aware scheduler preserves 20–30 minutes of buffer for hub-critical turns and last-segment legs feeding long-hauls, stages a reserve crew at the hub, and pre-authorizes dynamic resequencing. The same weather shock yields local delays but no system-wide cascade. How to keep the upside without the tail-risk (product + process)? Product: Resilient Optimization Guardrails Objective shift: Replace pure mean delay minimization with a risk-aware objective (e.g., penalize worst-case and near-worst-case outcomes). In practice: prioritize stability on hub banks, long-hauls, and high-connection flights. Hard constraints: Minimum slack per critical turn (e.g., 20–30 minutes on hub-critical rotations, larger on last flights of day). Crew legality guardrails with dynamic buffers for de-icing, ATC flow programs, and known chokepoints. Gate and pushback separation buffers during peak banks. Recovery levers: Pre-allocated spare aircraft and standby crews sized to hub complexity and seasonality. Autonomy throttle with human-in-the-loop for any decision that reduces buffer below a threshold. Instant rollback switch to a conservative schedule template when anomaly detectors fire. Process: Prove resilience before scale Stress-testing: Monte Carlo sims with historical and worst-case weather/ATC scenarios. Pass/fail gates based on tail metrics, not averages. Shadow and canary: Run the AI in shadow for 4–6 weeks, then canary on 10–15% of short-haul ops. Compare against controls. Tail-risk KPIs: Frequency of multi-flight cascades ≥120 minutes. Passenger misconnects per 1,000 pax on hub banks. Crew duty violations and aircraft out-of-position counts. CVaR-like tail delay metrics (e.g., 95th percentile spillover minutes per event). Acceptance criteria: Tail metrics must be no worse than baseline. Retain at least two-thirds of the 15% average improvement. Demonstrate stable recovery times under scripted stress events. Why this beats View A View A asks you to accept brittleness today for efficiency now and “deal with” rare extremes later. In airline networks, that’s backwards. You can capture most of the 15% efficiency gain while materially shrinking the tail with small, targeted buffers and recovery capacity at the right points in the network. This moves you to a better point on the efficiency–resilience frontier instead of optimizing to fragility.

My Take on Moving Fast with AI-Backed Changes I’m not convinced that we should sit on our hands until everyone feels perfectly comfortable before acting on an AI-driven improvement. To me, holding off on a validated enhancement in the name of “prudence” is really just choosing to pay the day-to-day costs of the old way of doing things. When your AI model shows a solid 25% cut in delays, it shifts the burden of proof: we don’t need to keep proving that change will work — we need to prove that continuing to wait is worth the extra cost. A Real-World Example: CCGT Power Plant Operations At our combined-cycle plants (54 GW in total), AI doesn’t just warn us about equipment risks; it also spots process snags—like less-efficient load sequences, scheduling quirks that ramp up wear, and handover lapses that slow our response. When our predictive analytics team suggested a new turbine dispatch sequence, the pushback was immediate: - Shift supervisors insisted they’d been running the same sequence for years - Managers doubted an algorithm could “get” the nuances that seasoned engineers already knew - They worried retraining would gum up shift changes in the short term Sound familiar? We didn’t back down—we rolled it out in stages, not on the back burner. In 90 days, thermal stress events dropped noticeably and those same shift supervisors were teaching the updated process to others. Resistance was alive and well—yet it never justified sticking with an inferior approach. What “Immediate Implementation” Actually Means “Act fast” isn’t “act recklessly.” It simply means we don’t let perfect-readiness become a forever excuse. Checks and Balance is a must-have before go-live - AI recommendation validated with real operational data - A clear owner accountable for rolling out the change - Essential training completed for the frontline roles During rollout: - Keep skeptical managers in the loop with hard data, not sales pitches - Budget for a short productivity dip as part of transition costs - Track old vs. new process metrics side-by-side so the team sees real-time results After implementation: - Address lingering doubts with results, not debates - Feed transition learnings back into the next AI cycle - Cement the new process in our standard playbook The Real Cost of Waiting Every week we wait adds up: Decision to defer by 4 weeks → 4 weeks * 25% extra delays Hesitation to win over skeptics → usually just delays more Avoiding a “dip” in productivity → trading a short-term hit for months of underperformance Waiting under the banner of “building trust” actually erodes it—trust grows from seeing improvements in action, not from endless prep. The Bigger Picture: Type I vs. Type II Errors - Type I (false positive): roll out a change too soon - Type II (false negative): let a proven improvement languish If we obsess over avoiding Type I errors, we guarantee ongoing Type II errors—and that’s a cost we feel every hour, every shift, every quarter. Where I Stand I agree that a botched rollout damages credibility. But the cure isn’t postponing until the stars align; it’s executing better. Readiness isn’t something you wait for—it’s something you build through early wins and real experience. Teams that trust AI the most are the ones who said “Let’s do it” early, saw the gains, and learned through the process—not the teams who planned forever. My Closing Point - Once Detected Change is Must – backed by Human Intelligence. AI spots the opportunities. Leadership needs to move on them—smartly, in stages, but without creating a moving target of “readiness.” To pull it off, we need: - Solid data backing every recommendation - A clear transition plan with an expected bump in learning curve - Live visibility into process metrics so outcomes speak for themselves - A commitment to act on evidence rather than debate hypotheticals Postponing isn’t neutral—it’s an active vote for a slower, costlier process we already know underperforms.

My View on this question and align to: View B — Continue unless failure is certain I don't support rolling back a feature every time AI flags an issue for a minority segment. Reacting to every signal — even when AI monitoring is highly accurate — creates a different problem: we reduce defect risk for the few, but we damage flow, progress, and trust in the system for the majority. My reasoning based on given Scenario Product Operations (Netflix — Feature Rollout Strategy) Netflix runs hundreds of A/B experiments simultaneously across 200M+ subscribers. Their AI observability layer flags anomalies in engagement, buffering, and error rates in real time. When a feature degrades experience for a subset — say, users on older Android builds or slower networks — their response is never automatic rollback. It is a tiered intervention based on signal strength, segment impact, and severity. They famously kept their personalization algorithm live through early edge-case failures affecting specific device cohorts, iterating in place rather than retreating. The result: a core product engine that now drives ~80% of content watched. If they had rolled back on every minority-segment flag, that feature would never have matured. What a Risk-Tiered Response Looks Like High-Risk Signal Errors spreading beyond the flagged segment Data loss, payment failure, or accessibility impact Signal growing, not stable Action: Immediate rollback or feature kill switch. No debate. Medium-Risk Signal Errors stable and contained to a specific cohort Friction, not failure Affected segment identifiable and reachable Action: Keep feature live for unaffected majority Deploy feature flag to serve old experience to affected cohort Set a hard remediation SLA (days, not weeks) Communicate proactively to affected users Low-Risk Signal Edge-case degradation on minority of older devices No data or trust impact Signal weak or inconsistent Action: Monitor and log Queue for next sprint No disruption to majority experience Bringing It Back to the Scenario Let's look at the actual numbers in the 8–10% scenario: If we roll back on every AI flag: Impact Cost Feature removed for 90%+ users Engagement gains lost immediately Re-release cycle 2–6 weeks of delayed progress Team credibility Repeated rollbacks signal instability to all users Minority actually helped Only if rollback was the right fix — often it isn't False rollback cost (device-specific issue example): Rolling back doesn't fix the underlying device compatibility debt The same failure will resurface on the next release You've paid the full cost of regression with none of the long-term fix That's not risk management. That's deferred technical debt dressed up as caution. The Real Lens: Type I vs Type II Error This is fundamentally the same trade-off as any signal-response system: Type I error (false rollback) → removing a working feature because a minority segment struggled Type II error (missed fix) → keeping a feature live while the affected segment gets worse Rolling back on every minority-segment flag tries to eliminate Type II error — but at the cost of consistently high Type I error impact across the product. In product operations, both errors have cost. The goal is not to eliminate one — it is to balance them intelligently based on severity, segment value, and fix feasibility. Where I Differ from the Rollback-First View I agree on one point: a degraded experience for any user segment is not acceptable and must be acted on. But rolling back is not acting on it — it is retreating from it. Rollback removes the problem from view without solving it. Selective fixing with a committed SLA solves it while protecting the value already delivered to the majority. AI gives early warning. Product teams must convert that warning into proportionate action — not reflexive reversal. To Summarize - AI monitoring should trigger attention and triage, not automatic rollback. Rollback should be reserved for: High-confidence signal + high-severity impact + no containment option available Everything else should be handled through: Feature flagging for affected cohorts Targeted compatibility fixes Proactive user communication Hard remediation timelines If not, we solve one problem — minority-segment errors — by creating another: loss of momentum, regression of majority value, and a product culture that mistakes caution for discipline.

Yes, AI Should stop The Core Logic: Why "After" is Already Too Late My reasoning - Think of it this way — if a doctor could see a heart attack coming 10 minutes before it happens, would you want them to wait until it occurs before acting? Of course not. The same logic applies to manufacturing and industrial processes. A defect that has already formed means: - Waste of Material, Energy and Time. Critical – a bad/service/product/out may reach the customer AI doesn't just detect defects. It can Predict them. And prediction without action is pointless. Point 1 — Defects Don't Appear Suddenly. They Drift In. No defect in manufacturing is truly "sudden." Before a weld cracks, before a chip misfires, before a tablet dissolves wrong — there are micro-signals. Tiny deviations in temperature, pressure, vibration, or material composition that, individually, look normal but together scream danger ahead. Humans cannot catch these signals. AI can — and it can catch them milliseconds before the point of no return. Point 2 — The Cost Multiplier Effect Every stage a defective product passes through multiplies the cost of that defect. Stage Defect Caught Relative Cost Before process starts 1x During process (AI stops it) 2-5x After production, during QC 10x After shipping, at customer 100x+ Rule of Ten - in quality engineering. AI stopping the process early keeps you at 2–5x. Ignoring the signal pushes you to 100x. The math is undeniable. Point 3— AI is Built for This. Exactly This. Traditional Statistical Process Control (SPC) sets fixed thresholds — if X crosses a line, alarm triggers. But real-world processes are dynamic. AI uses machine learning models trained on thousands of historical defect events to understand combinations of variables, not just single ones. It doesn't wait for a threshold to be crossed. It recognizes the pattern that leads to the threshold and acts before arrival. Case Study 1- BMW — Predictive Welding Quality (Automotive) BMW uses AI vision and sensor fusion systems on their body-in-white production lines. When welding parameters (current, electrode pressure, material gap) begin trending toward a defect pattern, the AI halts the welding robot and triggers recalibration — before a single bad weld is made. Result: near-zero weld defects reaching final assembly. Case Study 2. Pfizer — Pharmaceutical Batch Control In drug manufacturing, a contaminated or improperly mixed batch cannot be "fixed." It must be destroyed — at a cost of millions. Pfizer deployed AI-based Process Analytical Technology (PAT) that monitors real-time chemical signatures during mixing. The moment deviation is detected, the system stops the batch process and alerts engineers before the product is compromised. This has saved entire production batches that would otherwise have been scrapped. Case Study 3. TSMC — Semiconductor Fabrication In chip manufacturing, a wafer goes through 1,000+ steps. One microscopic deviation in a deposition layer can ruin the entire wafer — worth thousands of dollars. TSMC's AI systems monitor etching depth, gas flow, and plasma uniformity in real time. Any drift beyond learned safe boundaries triggers an automatic process pause for engineer review. This protects downstream layers that build on top of that one critical step. Case Study 4. Siemens — Wind Turbine Blade Manufacturing Siemens uses AI during composite blade layup to detect air pockets and resin distribution issues. The moment the AI model flags an anomaly, the curing process is paused so technicians can inspect and correct — rather than completing a blade that will fail structural testing (or worse, in the field). A failed turbine blade mid-operation can be catastrophic and life-threatening. Self Counter - What About False Positives Stopping Production Unnecessarily? - Fair concern. But this is a model maturity problem, not a concept problem. Modern AI systems: - Use confidence thresholds — only stop when prediction confidence is high - Are continuously retrained on new process data - Work in human-in-loop modes initially, escalating to full autonomy once trusted Over time, false positives reduce while detection accuracy improves. The solution is better AI — not no AI. The Bottom Line Stop the process. Save the product. That's what AI is for.