Anshuman Mishra

I support View B: Preserve collaborative problem-solving. While View A offers an enticing shortcut to operational efficiency, it mistakes the output of a decision for the outcome of an organization. Relying solely on AI for problem-solving creates a brittle operational environment. Without human collaboration, you sacrifice the buy-in required for execution, blind the organization to novel edge cases, and starve your workforce of the skill development needed to innovate when the AI inevitably hits a wall. Here is the strategic and operational breakdown of why View B is the superior path for long-term organizational health. 1. The Implementation Paradox: Great Ideas Fail Without Buy-In An elegant, AI-generated solution on paper is worth nothing if the frontline teams refuse to execute it, or execute it poorly due to a lack of understanding. The Psychological Reality: When people spend three hours in a room wrestling with a problem, they emerge with a sense of ownership. They will fight to make their solution work. The AI Friction: When a black-box algorithm hands down a mandate, employees feel like cogs in a machine. Resistance, passive aggression, and malicious compliance increase. Reducing collaboration to prioritize "execution speed" actually slows down execution at the ground level because you must spend twice as much time managing change and forcing adoption. 2. The Feedback Loop: AI Optimizes the Present; Humans Invent the Future AI models are inherently retrospective; they train on historical data to find the "best" statistical path forward within existing parameters. Collaborative problem-solving sessions are rarely just about fixing the immediate glitch. They are breeding grounds for accidental innovation. In a cross-functional argument over a broken billing process, a developer and a customer success representative might suddenly realize an entirely new product feature could eliminate the billing step altogether. AI cannot synthesize that type of lateral, empathetic innovation because it doesn't experience the human frustration that drives it. 3. Real-World Operational Example: The Toyota Production System (TPS) vs. Rigid Automation To see this play out in high-stakes operations, we can look at the philosophy behind the Toyota Production System (TPS), specifically the concepts of Andon and Kaizen. Imagine a modern automotive manufacturing plant utilizing advanced predictive maintenance AI. The AI Approach (View A): The AI detects that a robotic arm on the assembly line is misaligning parts due to a microscopic calibration drift. The AI immediately pushes a software patch to fix the calibration. It is fast, efficient, and requires zero meetings. Issue resolved. The Collaborative Approach (View B): Under TPS, when a defect occurs, a cross-functional team (operators, engineers, maintenance) gathers at the Gemba (the actual place of work) to do a "5 Whys" analysis. Why View B Wins in the Long Run: During the collaborative session, the team discovers that the robotic arm drifted because a new operator was loading parts at a slight angle to avoid a sharp metal edge on the safety guard. If the organization relied strictly on the AI's technical fix, the root human cause would remain hidden. The operator would keep loading parts poorly, the machine would keep drifting, and the safety hazard would persist. Furthermore, by discussing it, the veteran engineer teaches the rookie operator why the alignment matters, building organizational capability. 4. Flipping the Script: Reimagining Bex’s Analysis If Bex champions View A by pointing to metrics like "Time-to-Resolution (TTR)" and "Cost per Incident," Bex is falling into a classic data trap: measuring what is easy to count rather than what counts. To beat Bex’s analysis, we must redefine the role of AI in the workshop. The goal should not be to replace the collaborative session, but to supercharge it. Instead of choosing between a 4-hour human brainstorming session and a 2-minute AI decision, organizations should use a hybrid framework: The AI-Augmented Workshop Pre-work: AI analyzes the data and generates 3 potential root causes and solutions. The Session: The cross-functional team skips the tedious data-crunching phase and spends 1 hour debating the AI's assumptions, assessing human friction points, and planning the rollout. Conclusion By choosing View B, an organization treats collaborative problem-solving not as an expensive expense to be minimized, but as a compounding investment in culture, alignment, and resilience. AI should be the analytical engine that fuels the discussion, never the dictator that replaces it.

I support View A, Act proactively using AI predictions. While the concerns raised in View B regarding trust and bias are completely valid, they are problems of implementation, not flaws in the core strategy. Choosing to ignore predictive data doesn't make the underlying issues (burnout, poor management, stagnation) go away; it just ensures the organization remains blind to them until it's too late. Losing key talent is incredibly disruptive and expensive, often costing up to twice an employee's annual salary to replace them. Acting proactively is the only sustainable business choice, provided the intervention is handled correctly. The Operational Solution: Systemic vs. Individual ActionThe critical mistake organizations make and the root cause of the dilemma in View B is treating an attrition risk score as a personal indictment or a reason to isolate the employee. Instead of treating the AI's output as a prediction of employee intent, smart organizations treat it as a diagnostic tool for systemic failure or management gaps. When the AI flags an employee, the intervention should never be, "We see you're thinking of leaving, here is a bribe to stay." Instead, it should trigger broader, positive operational adjustments. Real-World Example: Voluntary Attrition MitigationConsider a major technology and consulting firm like IBM, which pioneered the use of predictive analytics to curb employee turnover (saving the company nearly $300 million in retention costs). IBM's AI system didn't just point fingers at individuals; it analyzed skill sets, promotion intervals, and market demand. When an employee was flagged as a high turnover risk, the organization didn't penalize them. Instead, they took the following operational steps: Proactive Skill Matching: The AI identified if the employee’s skills were underutilized or if they were stagnant in their current role, automatically suggesting internal mobility options or new project openings. Manager Enablement (Not Bias): Managers weren't told, "This person is a flight risk." They were prompted with action-oriented management coaching, such as, "It has been 18 months since this team member had a formal career progression check-in; we recommend scheduling one this week." Workload Rebalancing: If communication signals and absenteeism trends pointed to burnout, the organization used that data to audit the team's overall workload distribution, fixing a broken environment rather than treating the employee as an anomaly. Why Inaction (View B) is FancifulProponents of View B argue that employees should only be judged on "actual actions." In the context of retention, an employee's definitive "actual action" is handing in a resignation letter. By that point, the psychological contract is already broken. Counter-offers made during a resignation notice have a notoriously low success rate, roughly 70% to 80% of employees who accept a counter-offer still leave within a year because the root causes of their dissatisfaction were never addressed. ConclusionUsing predictive AI is not about policing or profiling employees; it is about keeping organizations accountable to their workforce. When an AI flags an attrition risk, it is usually screaming that an employee is burnt out, underpaid, or ignored. Acting proactively allows a company to fix those operational failures before they lose their most valuable asset.

I support View B: Distribute opportunities more broadly. The reason organizations must override the AI and distribute high-impact work is not about fostering unstructured innovation; it is about mitigating systemic operational risk and preventing the experience trap. The Argument: The Experience Trap and Systemic RiskWhen AI optimizes purely for historical speed and accuracy, it prioritizes short-term execution risk at the expense of long-term systemic risk. This creates a self-fulfilling loop i.e. the Matthew Effect ("the rich get richer"). Because top performers get all the critical reps, their data profiles improve exponentially. Because the rest of the team gets no reps, their data profiles stagnate or degrade. Over time, the AI effectively engineers Single Points of Failure (SPOFs). If those top performers burn out, go on leave, or get poached by competitors, the organization is left with a hollowed-out talent bench incapable of handling complex operations. The business hasn't actually improved its capability; it has simply leveraged a localized asset until it breaks. To build a sustainable organization, AI should not be used merely to find the best executor; it must be engineered to balance execution with capacity scaling. Operational Example: To practically apply View B without sacrificing the quality of critical work, organizations must shift from solo task allocation to a paired operational framework. A premier example of this is the Incident Response model used in mature DevOps and SRE teams. The Context: When a catastrophic software failure occurs (a Sev-1 outage), an AI-driven paging system analyzes the issue and knows that "Engineer A" is the fastest, most accurate person to resolve it based on past metrics. The Flaw of View A: If the AI constantly pages Engineer A, the outage is resolved quickly today. But Engineer A burns out, and Engineers B never learn how to fix the core infrastructure. When Engineer A eventually quits, the next Sev-1 outage lasts for hours instead of minutes. The Solution (View B in Action): Instead of assigning the task to one person, the operational process utilizes a Driver-Navigator (or Shadowing) protocol: The AI modifies its allocation: The AI assigns the urgent ticket to two people. It pairs a high-potential, lesser-experienced employee (The Driver) with the top-tier performer (The Navigator). The Execution: The lesser-experienced employee handles the keyboard, drives the client communication, and runs the escalation. The top performer acts purely as a safety net, advising, correcting course, and ensuring the final outcome meets excellence standards. The Outcome: The critical task is executed successfully, safeguarding the customer outcome. However, the organization has simultaneously minted a new high-performer. ConclusionWe must distribute opportunities broadly because high-impact work is the only piece that builds high-impact talent. Following View A turns AI into a machine that burns out your best people while atrophying the rest. By adopting View B through structured operational pairing, organizations ensure that immediate business performance remains secure while actively engineering the next generation of top-tier talent.

I support View B: Trust experienced leadership judgment 1. The House of Cards Fallacy Bex points to Netflix’s House of Cards as a triumph of predictive AI. However, this comparison fails in the context of a major product launch: Low-risk vs. High-stakes. For Netflix, greenlighting a show based on data was an internal portfolio decision. If it failed, Netflix wouldn't go under. For a product company, delaying a major launch to "refine" it can permanently close a narrow market window. AI is backward-looking; it predicts based on historical patterns. It cannot model competitor moves, shifting macroeconomic sentiment, or the sheer force of a company's marketing push. 2. The Core Argument: The "First-Mover" Feedback Loop In business, shipping is a forcing function. Real-world market feedback is infinitely more valuable than predictive modeling of "early usage signals." When experienced leaders push for a launch despite negative early signals, they are leveraging three critical human insights that AI cannot quantify: a. The Speed-to-Market Premium: Being first or early defines the category. If a competitor launches first, they capture the mindshare, secure the distribution channels, and begin their own learning loop. b. The Post-Launch Pivot: A product is not static. Experienced leaders know that "weak long-term adoption" predicted by AI can be actively corrected after launch through rapid software updates, pricing adjustments, and aggressive sales plays. The "Good Enough" Threshold: AI optimizes for perfection; leaders optimize for survival. A 70% perfect product launched today is often worth more than a 95% perfect product launched six months too late. 3. Industry Case Study: Apple iPhone (2007) vs. The "Data" Perhaps the greatest testament to View B is the launch of the original iPhone in 2007. If Apple had relied on a predictive AI system in late 2006 to analyze early usage signals and market data, the system would have screamed for a launch delay: The device lacked 3G. It lacked basic features like copy-paste, MS Exchange support, and even an App Store. Early internal testing showed frequent dropped calls and system crashes. A predictive model looking at existing Blackberry and Nokia dominance would have concluded that customer retention would plummet due to these glaring technical deficiencies. However, Steve Jobs and Apple's leadership team understood a human truth the data couldn't capture, the window to redefine the smartphone category was open now. Competitors were asleep, and delaying the launch to refine the hardware would have given Google, Microsoft, and Nokia time to react. Apple launched a flawed, 2G-only phone, captured the world's attention, and used the revenue and real-world feedback to rapidly iterate the iPhone 3G and App Store a year later. Conclusion: Data Informs, but Leaders Decide AI is an exceptional advisor for optimization, but a terrible guide for creation and timing. If we always delayed launches because the predictive models flagged "weak early retention," revolutionary but initially flawed products would never see the light of day. Experienced leaders understand that a launch is not the end of a process, it is simply the beginning of the real data-gathering phase. For this reason, when the market window is open, we must trust the intuition, speed, and strategic timing of human leaders over the cautious, backward-looking predictions of AI.

I support View A i.e. remove or reduce the blanket approval step in favour of targeted safeguards due to: 1. The Invisible Cost of Delay From a Six Sigma perspective, a mandatory workflow step that adds 8–10 hours of waiting time but yields a 99% no change rate; which is a profound systemic defect. In a healthcare environment, delaying 99% of standard treatments will severely degrade patient flow, cause prolonged distress and will create backlogs that can impact other operational areas. 2. The Fallacy of the Blanket Safeguard View B assumes that forcing 100% of volume through a manual bottleneck is the only way to catch the critical <1%. If the AI was able to retroactively identify that changes occur in less than 1% of cases, then the data exists to define the parameters, variances, and risk profiles of that specific 1%. Instead of a blanket approval step, the organization can implement Escalation Matrices, a targeted safeguard designed for the majority. The AI can be deployed to evaluate incoming treatment plans against historical data. The 99% (Low Variance): Standard, by the book diagnoses from frontline doctors bypass the senior specialist entirely, accelerating care by 8-10 hours. The <1% (High Variance): Cases with complex issues, rare drug interactions, or historical patterns of high misdiagnosis are automatically flagged and routed to the senior specialist. By doing this, we protect vital customer sentiment and patient outcomes of the 99% through speed and efficiency, while reserving the highly specialized human expertise for the exact moments they are needed most.