Raja M

Why I Support View A – Tell Customers It's AI1. The Decision Is About Trust, Not TechnologyAt first glance, the numbers seem to support the opposite decision. Acceptance falls from 82% to 68%, around 15% of customers request a human review, and the organization incurs an additional $2 million per year in rework costs. If we only optimize for short-term operational metrics, not disclosing AI appears to be the logical choice. However, the scenario also states that 60% of the organization's revenue depends on customer trust. Once trust becomes a strategic business asset, the question shifts from "How do we maximize acceptance today?" to "How do we sustain customer confidence over the next decade?" 2. Quality Is Already Proven—Transparency Is the Real IssueOne important point in this scenario is that the AI is not producing lower-quality work. In blind testing, where reviewers did not know whether the work was created by AI or a human, AI actually scored 4.3/5, slightly higher than the human score of 4.2/5. This tells us something important: Customers are not rejecting poor-quality outcomes. They are reacting to the perception of AI rather than its actual performance. Therefore, disclosure is not about compensating for poor quality—it is about respecting customers' right to understand how decisions, recommendations, or content affecting them were created. 3. AI Is More Than Just Another Software ToolSome argue that AI should be treated like Microsoft Excel, spell checkers, or CRM software. I don't completely agree. Traditional software helps people perform work. Generative AI increasingly creates the work that customers directly experience—whether that is recommendations, assessments, draft documents, screening decisions, or customer responses. That makes AI fundamentally different because it has a direct influence on customer outcomes. When technology directly affects customer-facing decisions, transparency becomes part of responsible business practice rather than simply a communication preference. 4. History Shows That Transparency Builds Long-Term AdoptionMany breakthrough technologies faced skepticism before becoming widely accepted. Examples include: Online banking, where customers initially distrusted digital transactions. Self-checkout systems, which many consumers resisted before appreciating their convenience. Cloud computing, where businesses hesitated before recognizing its value. The same pattern is visible with AI today. Leading technology companies such as Microsoft (Copilot), Google (Gemini), and Adobe (Firefly) openly communicate where AI is being used. Rather than hiding the technology, they explain its purpose while maintaining accountability for the customer experience. This approach helps normalize AI instead of creating suspicion around it. 5. Research Supports TransparencyIndependent research also supports being open about AI. According to the 2024 Edelman Trust Barometer, trust remains one of the strongest drivers of customer loyalty, particularly when organizations adopt emerging technologies responsibly. Similarly, IBM's Global AI Adoption Index found that consumers are significantly more comfortable using AI-powered services when organizations clearly explain: why AI is being used, where human oversight exists, and how quality is maintained. Transparency alone does not eliminate skepticism, but it creates the conditions for trust to grow over time. 6. The Biggest Risk Is Not Today's Acceptance Rate—It's Tomorrow's Trust LossThe scenario already hints at the greatest business risk. If AI use is discovered later through: regulatory audits, AI detection tools, whistleblowers, media investigations, or future disclosure laws, customers are likely to ask a different question: At that point, the issue is no longer AI. The issue becomes honesty. Rebuilding credibility after perceived deception is significantly harder than overcoming initial hesitation. 7. Real Business ExamplesSeveral well-known companies demonstrate how trust can be damaged when customers believe information was intentionally withheld. Facebook–Cambridge AnalyticaThe controversy was not simply about data collection. Much of the reputational damage resulted from users feeling they had not been fully informed about how their information was being used. Volkswagen Emissions ScandalThe crisis became a global reputational issue because customers and regulators believed the company had intentionally concealed the truth. Although these examples are not about AI, they reinforce an important business principle: Customers often forgive mistakes more readily than they forgive a lack of transparency. 8. Transparency Doesn't Mean Creating FearI don't believe organizations should simply attach a label saying: Without context, that statement can create unnecessary uncertainty. Instead, organizations should communicate responsibly. For example: This approach provides transparency while simultaneously reinforcing accountability and customer choice. 9. How Organizations Can Retain Customers While Being TransparentDisclosure should be accompanied by actions that build confidence. Organizations should: Explain how AI improves consistency, speed, and quality. Clearly communicate where human oversight exists. Offer customers the option of human review for high-impact decisions. Publish quality metrics and customer satisfaction results. Regularly audit AI systems for fairness, accuracy, and bias. Continuously educate customers about responsible AI use. Transparency becomes much more effective when customers see evidence that the organization actively governs AI rather than simply deploying it. 10. Final ThoughtsThe reported 14% reduction in acceptance should be viewed as a short-term adoption challenge rather than a long-term business disadvantage. Customer attitudes toward new technologies consistently evolve with familiarity, education, and positive experience. For an organization where 60% of revenue depends on trust, transparency is more than an ethical responsibility—it is a strategic investment. The organizations that will succeed over the next decade will not be those that hide AI most effectively. They will be the ones that use AI confidently, explain it honestly, remain accountable for every outcome, and continuously demonstrate that technology is serving customers—not replacing their interests.

Supporting View A — Deploy the AI Vision System: Prioritize Customer Safety and Eliminate Escaped DefectsIntroductionFor a Tier-1 automotive supplier manufacturing 200,000 safety-critical brake subassemblies every month, the decision to deploy an AI inspection system should be evaluated using a risk-based quality management approach rather than only short-term production cost. In automotive manufacturing, especially for safety-critical systems such as brakes, airbags, steering, and powertrain components, the most important quality objective is to prevent defective products from reaching the customer. A defect discovered internally results in scrap, rework, and operational cost. However, a defect discovered after reaching the vehicle creates external failure risk involving recalls, warranty claims, regulatory consequences, OEM relationship damage, and potential customer safety impact. Therefore, although the AI vision system creates a higher false reject rate, deployment is justified because it significantly reduces consumer risk. The philosophy followed by world-class automotive manufacturers is: “Internal failures increase cost, but external failures can destroy customer trust and business sustainability.” 1. Comparison of Human Inspection vs AI Inspection PerformanceThe plant currently experiences: Production volume: 200,000 brake subassemblies/month Actual defective units entering inspection: 4,000 units/month Good units: 196,000 units/month Current Human Inspection PerformanceHuman defect detection rate: 94% This means: Escaped defective parts reaching customers: 240 units/month False rejection of good products: 2,940 units/month Human inspection provides better yield performance but allows a higher number of defective products to escape. AI Vision System PerformanceAI detection rate: 99.3% This results in: Escaped defective parts: 28 units/month Additional defects prevented from reaching customer: 212 units/month This represents: ≈88% reduction in customer escapes However, AI creates: False rejects: 10,780 units/month Additional good parts scrapped: 7,840 units/month Additional internal cost: ≈$3.8M/year At first glance, the additional scrap appears expensive, but automotive safety decisions cannot be based only on visible cost. 2. Why Consumer Risk Should Dominate Producer RiskQuality failures are generally classified into two categories: Internal Failure CostExamples: Scrap Rework Additional inspection Production loss These failures happen before shipment. They are: Known Measurable Controllable Correctable through improvement The AI system creates this type of cost. External Failure CostExamples: Customer complaints Warranty failures OEM line stoppage Product recall Legal claims Loss of future business These failures happen after shipment. They are: Unpredictable Difficult to control Highly damaging Human inspection exposes the company to this higher risk category. 3. Risk Calculation PerspectiveThe safety impact becomes clearer when considering field risk. Assumption: 1 out of every 50 escaped defects can potentially trigger a serious safety issue or recall event. With Human InspectionEscapes: 240 defects/month Potential serious events: 240 ÷ 50 ≈ 5 high-risk incidents/month With AI InspectionEscapes: 28 defects/month Potential serious events: 28 ÷ 50 ≈ Less than 1 high-risk incident/month Therefore, AI does not only reduce defect quantity; it significantly reduces the probability of a catastrophic quality event. The company is essentially paying $3.8M/year as a prevention cost to protect against failures that could cost tens or hundreds of millions in direct and indirect losses. 4. Industrial Example — Takata Airbag Recall: The Cost of Escaped Safety DefectsOne of the strongest automotive examples showing the importance of preventing safety defects from reaching customers is the Takata airbag recall. Takata was one of the largest global automotive component suppliers. The company supplied airbag inflators to several major automobile manufacturers. The original product issue affected a small percentage of parts compared with total production volume, but because the component was safety-critical, the impact became enormous. Consequences included: More than 100 million vehicles recalled worldwide Billions of dollars in recall-related costs Loss of trust from OEM customers Severe damage to brand reputation Financial collapse of Takata Corporation The important manufacturing lesson from this case: A safety defect does not need to happen frequently to create a major business crisis. Even rare failures can become unacceptable when the consequence severity is extremely high. For the brake subassembly supplier in this case, allowing 240 defective components per month to escape creates similar risk exposure. The probability may be low, but the severity is extremely high. The AI system acts as a stronger containment barrier by preventing defects from leaving the factory. 5. Why the Additional Scrap Cost Should Not Stop AI DeploymentThe $3.8M annual scrap cost is not a permanent loss. It represents the initial maturity stage of the AI model. Traditional inspection improvement follows the principle: First improve detection → Then optimize efficiency If the company focuses only on reducing false rejects before deployment, it continues accepting customer risk during the development period. A better approach is: Deploy AI immediately for protection and improve false rejects through continuous optimization. 6. Improvement Roadmap After AI DeploymentStep 1: Introduce AI + Human Hybrid InspectionDo not immediately scrap every AI reject. Create three inspection categories: Category 1 — Confirmed Good PartsAI confidence >99% Action: Auto release Category 2 — Confirmed DefectsAI confidence very high Action: Reject immediately Category 3 — Borderline PartsAI uncertainty zone Action: Human inspector verification This maintains safety while recovering incorrectly rejected good parts. Expected benefit: If only 50% of false rejects are recovered: Recovered parts: ≈5,000 units/month Potential savings: ≈$2M–$2.5M/year Step 2: Continuous AI Model TrainingFalse rejects should become improvement data. Every week: Analyze wrongly rejected components Identify AI confusion patterns Add more acceptable variation images Retrain algorithm Examples: AI may incorrectly reject: Acceptable surface marks Normal machining variation Lighting differences Cosmetic variation By teaching the AI acceptable limits, false rejection reduces without sacrificing safety. Step 3: Use AI Data for Root Cause EliminationThe AI should not only inspect quality; it should improve manufacturing. AI defect analytics can identify: Machine trends: Example: 70% defects generated from one assembly station Material trends: Example: Higher failures linked to one supplier batch Time trends: Example: Defects increase after tool running hours exceed limit Operator/process trends: Example: Higher variation during changeovers This allows the company to move from: Detection-based quality to Prevention-based quality Step 4: Reduce the Incoming 2% Defect RateThe biggest opportunity is not reducing inspection accuracy; it is improving the manufacturing process. Apply: Six Sigma DMAIC projects Process capability improvement Poka-yoke systems Preventive maintenance Supplier quality improvement Example improvement: Current defect generation: 2% After improvement: 1% Defective parts reduce: 4,000/month → 2,000/month This lowers both real rejects and inspection cost. Step 5: Periodic Threshold OptimizationAI sensitivity should be continuously optimized. During early launch: High sensitivity → maximum protection After confidence improves: Optimize threshold → reduce false rejects The goal: Maintain: Reduce false reject: 5.5% → below 2% Final RecommendationThe AI vision system should be deployed because safety-critical automotive manufacturing requires prioritizing customer protection over short-term yield loss. The additional $3.8M/year scrap cost is a visible and controllable internal failure cost. However, escaped brake defects create unpredictable external failure risks that can result in recalls, regulatory action, loss of OEM confidence, and long-term business damage. The best strategy is not AI versus cost. The correct strategy is: Deploy AI to immediately reduce customer escapes. Add human verification for borderline rejects. Continuously retrain the model. Use AI data to eliminate process defects. Optimize yield after achieving customer protection. In safety-critical manufacturing, preventing one major failure event can justify years of additional prevention cost. A world-class automotive supplier does not simply ask: “How much does quality cost?” It asks: “What is the cost of poor quality reaching the customer?”

Position: AI should preserve long-term organizational memory.Artificial Intelligence should continuously learn from recent business conditions, but it should never completely forget historical knowledge. In manufacturing, the biggest business losses are rarely caused by everyday operations—they are caused by rare, high-impact events that may occur only once every few years. Recent data makes AI more responsive, while historical data makes AI more resilient. The best AI combines both. Example 1 – Naira Devaluation (Financial Crisis)At Insignia Print Technology, a significant portion of our raw materials—including BOPP films, PET films, inks, adhesives, solvents, and printing consumables—are imported. When the Nigerian Naira experienced a major devaluation, import costs increased dramatically within a short period. Many overseas suppliers revised prices, payment terms changed, and procurement became significantly more expensive. To maintain uninterrupted production, we had to: Activate previously qualified local suppliers. Purchase from alternate regional suppliers. Accept prices that were 30–50% higher than our normal procurement costs. Prioritize production continuity over cost optimization. If AI only analyzed the last 12 months of purchasing history, these emergency suppliers might never appear in its recommendations because they had not been used recently. During the next currency crisis, AI would recommend only the regular suppliers, even though they might no longer be practical. Historical purchasing records become a valuable playbook for crisis response rather than simply old transactional data. Example 2 – Iran War & Global Supply Chain DisruptionThe recent Iran conflict and instability across the Middle East affected international shipping routes, increased freight charges, delayed vessel movements, and created uncertainty in global logistics. Although our regular suppliers remained technically approved, material availability became unpredictable because of: Longer shipping lead times Port congestion Increased freight costs Container shortages Supply uncertainty During this period, procurement decisions shifted from "lowest cost supplier" to "fastest available supplier." Even though alternative suppliers were more expensive, purchasing from them prevented production stoppages and ensured customer deliveries continued. If AI had forgotten similar historical disruptions, it would have little guidance on: Which emergency suppliers had previously delivered successfully What emergency pricing was acceptable Which materials could be substituted How long disruptions typically lasted Instead of learning from experience, AI would have to rediscover everything during the crisis. Visual 1 – Normal Business vs Crisis Decision NORMAL OPERATIONS ------------------------------- Recent demand │ Recent supplier performance │ Lowest total procurement cost │ Regular Approved Suppliers │ Stable Production DURING A CRISIS ------------------------------- Geopolitical conflict Currency devaluation Port congestion Supplier failure │ Historical organizational memory │ Previously qualified emergency suppliers │ Emergency procurement decisions │ Continuous ProductionVisual 2 – AI with and without Historical Memory AI USING ONLY LAST 12 MONTHS Recent Data │ ▼ Regular Supplier A Regular Supplier B Regular Supplier C ❌ Crisis occurs AI has no memory of: • Alternate suppliers • Emergency pricing • Previous disruptions Decision time increases Production risk increases AI WITH LONG-TERM MEMORY Recent Data │ ▼ Normal supplier recommendation + Historical Crisis Database • Naira Devaluation • Shipping disruption • Supplier failures • Emergency suppliers • Alternate materials ✔ Faster decisions ✔ Lower business risk ✔ Production continuity Business ReasoningManufacturing organizations accumulate knowledge over decades through difficult experiences. Events such as geopolitical conflicts, pandemics, currency devaluations, transportation disruptions, and supplier bankruptcies may occur infrequently, but they often have the greatest operational and financial consequences. Removing this historical knowledge from AI effectively removes the organization's institutional memory. As a result, the AI becomes highly efficient during normal conditions but less capable when exceptional events occur. In other words: Both perspectives are essential for effective decision-making. Recommended AI SolutionInstead of deleting older data, AI should adopt a dual-memory architecture: Short-term memory (last 12–24 months): Used for day-to-day inventory planning, forecasting, and supplier selection. Long-term memory (5–10+ years): Activated automatically during disruptions such as currency fluctuations, geopolitical conflicts, pandemics, transportation bottlenecks, or major supplier failures. This allows AI to remain adaptive during normal operations while leveraging historical experience during exceptional situations. ConclusionThe objective of AI is not simply to optimize today's decisions but to strengthen an organization's ability to respond to tomorrow's uncertainties. At Insignia Print Technology, events such as the Naira devaluation and the recent Iran conflict demonstrate that rare disruptions can fundamentally change procurement strategies overnight. During these periods, historical supplier data, emergency purchasing records, and past decision outcomes become invaluable assets. An AI system that forgets this knowledge may optimize routine purchasing but fail when the business needs it most. Therefore, AI should preserve long-term organizational memory and intelligently determine when historical experience should guide present decisions. The best AI does not choose between the past and the present—it learns from both, using recent data for efficiency and historical knowledge for resilience.

Supporting View A: Accept the AI's RecommendationIntroductionContinuous improvement has long been recognized as the cornerstone of operational excellence. Methodologies such as Lean Manufacturing, Six Sigma, and Total Quality Management (TQM) have enabled organizations to improve quality, reduce waste, increase productivity, and strengthen customer satisfaction. However, organizations operate with limited resources. Capital, engineering talent, production capacity, management attention, and time are finite. Every investment in one improvement is also a decision not to invest in another opportunity. Artificial Intelligence introduces a powerful capability—not only identifying opportunities for improvement but also objectively evaluating whether those improvements create sufficient business value. In this scenario, the organization has already achieved outstanding operational performance: 99.4% On-Time Delivery 99.8% First-Pass Yield 18% Reduction in Operating Costs over two years The AI recommends not implementing another improvement because the expected gain (99.8% to 99.9% First-Pass Yield) requires a $12 million investment, causes six weeks of production disruption, and provides only marginal financial returns over the next five years. I support View A because continuous improvement should remain an organizational philosophy, but investment decisions should always be guided by value creation, strategic priorities, and optimal resource allocation rather than by the pursuit of perfection. Why View A Makes Business Sense1. Every Improvement Has an Economic LimitOne of the fundamental principles of economics is the Law of Diminishing Returns. The initial stages of improvement usually produce significant benefits because obvious inefficiencies are eliminated. Quality improves rapidly, defects decrease, and productivity increases with relatively modest investment. As performance approaches world-class levels, however, each additional improvement becomes increasingly difficult and expensive. Achieving the final fraction of improvement often requires disproportionately larger investments in technology, engineering effort, process redesign, and implementation time. In this case: Current First-Pass Yield = 99.8% Expected First-Pass Yield = 99.9% Improvement = 0.1% Investment Required = $12 million Production Shutdown = 6 weeks Financial Return = Marginal over five years The investment required is far greater than the value expected from the improvement, illustrating a classic case of diminishing returns. 2. Opportunity Cost Must Be ConsideredEvery capital investment has an opportunity cost. The same $12 million could potentially generate significantly greater value if invested in initiatives such as: Digital transformation AI-enabled predictive maintenance Supply chain resilience Employee capability development Sustainability initiatives New product development Market expansion Organizations create competitive advantage not by investing in every improvement opportunity, but by investing where returns are highest. AI helps identify where capital can generate the greatest overall organizational benefit. 3. Operational Risk May Outweigh the BenefitsThe proposed improvement requires a six-week production shutdown. Such disruption introduces several operational risks: Delayed customer deliveries Reduced production capacity Revenue loss Increased implementation costs Supply chain instability Potential customer dissatisfaction When the expected improvement is only 0.1%, these operational risks may outweigh the potential gains. 4. AI Enables Objective Decision-MakingExecutives are often driven by a culture of continuous improvement and may feel compelled to pursue every opportunity for optimization. AI, however, evaluates projects objectively using measurable criteria such as: Return on Investment (ROI) Net Present Value (NPV) Payback Period Operational Risk Implementation Cost Long-term Business Value Unlike humans, AI is not influenced by organizational pride, sunk-cost thinking, or the belief that every possible improvement must be pursued. Its recommendation is based on maximizing organizational value rather than maximizing the number of improvement projects. 5. Continuous Improvement Should Focus on Value CreationContinuous improvement should not be confused with continuous spending. The true objective of improvement is to create greater value for customers, shareholders, and the organization. If an improvement delivers only a negligible increase in performance while consuming substantial financial and operational resources, it may not represent the best use of organizational capital. Therefore, stopping a low-value improvement is not abandoning continuous improvement—it is practicing strategic continuous improvement. Organizational Example: Amazon Fire PhoneA compelling example supporting this approach is Amazon's Fire Phone. Amazon launched the Fire Phone in 2014 with the ambition of competing in the smartphone market. The company invested heavily in advanced features such as Dynamic Perspective (a 3D-like display), Firefly object recognition, and deep integration with Amazon's ecosystem. Despite these innovations, the product failed to gain meaningful market acceptance. Customer adoption remained low, reviews were mixed, and the phone struggled to compete against established smartphones. Rather than continuing to invest large amounts of money in incremental improvements to the Fire Phone, Amazon made a strategic decision to discontinue the product. The company recorded a significant financial write-down and redirected its engineering talent, investment, and leadership attention toward initiatives with far greater long-term potential. These redirected investments helped accelerate innovations in areas such as: Amazon Web Services (AWS), which became one of the world's most profitable cloud computing businesses. Alexa and Echo smart devices, creating a leading position in voice-enabled technology. Logistics automation and fulfillment technologies, strengthening Amazon's operational efficiency and customer experience. Amazon recognized that continuing to improve a product with limited market potential would have consumed valuable resources while generating limited returns. Instead of pursuing improvement for its own sake, the company accepted the evidence, discontinued the initiative, and invested where greater strategic value could be created. This reflects exactly the principle behind the AI recommendation in the given scenario. The decision was not to stop innovating or improving, but to stop investing in an initiative where the marginal returns no longer justified the cost and risk. Recommendation to OrganizationsOrganizations should not allow AI to make the final decision independently. Instead, AI should serve as a strategic decision-support system while leadership retains accountability for the final investment decision. A structured decision framework should include the following evaluation criteria: Decision Factor Key Question Financial Return Does the improvement meet the organization's required ROI or NPV threshold? Customer Value Will customers experience a meaningful improvement? Strategic Alignment Does the initiative support long-term business objectives? Operational Risk What is the impact on production, supply chain, and service levels? Opportunity Cost Could these resources generate greater value in another initiative? Regulatory or Safety Requirement Is the improvement necessary for compliance or risk reduction? AI Recommendation What does predictive analysis indicate regarding long-term value? Executive Review Does leadership agree that the investment aligns with organizational priorities? Only when an improvement delivers meaningful value across these dimensions should it proceed. If the benefits are marginal while the costs, risks, and opportunity costs are high, organizations should confidently redirect resources toward initiatives with greater strategic impact. ConclusionI support View A because the objective of continuous improvement is not to implement every possible enhancement but to maximize organizational value. As organizations approach world-class performance, the cost of achieving further incremental gains often increases dramatically while the benefits become progressively smaller. AI provides an objective, data-driven assessment of when an improvement has reached the point of diminishing returns. However, AI should act as an advisor rather than the sole decision-maker. Final decisions should combine AI-driven analytics with executive judgment, strategic priorities, customer expectations, and organizational vision. The strongest organizations are not those that pursue every possible improvement. They are those that know which improvements to pursue, which to defer, and when to redirect resources toward opportunities that create the greatest long-term value. The Amazon Fire Phone case demonstrates that choosing to stop investing in a low-return initiative is not a failure of continuous improvement—it is an example of disciplined strategic decision-making.

Position: View B – AI Should Adjust for Circumstances I support View B because performance evaluation should measure not only results, but also how effectively teams perform under the conditions they face. Evaluating outcomes alone may seem objective, but it can lead to inaccurate conclusions when employees operate under very different circumstances. For example, our lamination department had a productivity target of 1,200 tons. However, due to the Iran conflict, adhesive shipments were delayed in Nigeria, forcing us to use local alternatives. The quality challenges with these adhesives required lower machine speeds and additional controls, reducing productivity. A results-only evaluation would label this as poor performance, despite the team successfully managing a major supply chain disruption. Similarly, we launched a cost-saving initiative to replace imported granules with locally produced Nigerian granules in our blown film section. However, rising crude oil prices caused local granule prices to increase above imported prices, eliminating the expected savings. The project's outcome was affected by external market conditions rather than poor execution. These examples show that results do not always reflect true performance. AI should consider context because the goal of evaluation is not just accountability, but accuracy. By assessing both outcomes and the challenges faced, organizations can make fairer and more informed performance decisions. To address such situations, AI should evaluate performance using three factors: Results, Difficulty of Circumstances, and Response Quality. For example, the AI could assign difficulty scores for factors such as raw material shortages, geopolitical disruptions, machine breakdowns, or market price fluctuations. It should then assess how effectively the team responded to these challenges, including maintaining quality, ensuring customer supply, and implementing corrective actions. These examples show that results do not always reflect true performance. The goal of AI-driven evaluation should not be just accountability, but accuracy. By assessing both outcomes and the challenges faced, organizations can make fairer, more informed decisions and better recognize resilience, problem-solving, and operational excellence under difficult conditions.