Sanmathi_Naik_DgYE

I support - View A - Act proactively using AI predictions. As Predictive attrition models can help companies identify patterns associated with burnout, disengagement, compensation dissatisfaction, lack of growth, toxic management, or workload imbalance. If AI identifies patterns showing that employees in a certain role or team are at higher risk of leaving, management can intervene constructively by improving working conditions, offering development opportunities, or addressing leadership concerns. In this sense, predictive AI acts as an early warning system that allows organizations to solve problems before they become costly.Acting before someone resigns can be positive when it leads to meaningful improvements.For example, an organization might: offer career development opportunities, improve workload distribution, review compensation fairness, address management problems, provide flexible work options, or simply initiate supportive conversations. Those uses can improve both employee well-being and retention. A strong real-time example is IBM. IBM used AI-based predictive analytics to identify employees who were likely to leave the company. The system reportedly predicted attrition with high accuracy, allowing managers to proactively offer career development opportunities, salary adjustments, and internal role changes. This helped IBM reduce turnover costs and improve employee retention. Another example is Amazon, where AI and workforce analytics are used in operations to monitor employee trends such as productivity, absenteeism, and engagement levels in fulfillment centers. The company can identify departments experiencing high stress or burnout and make operational changes such as revising workloads or staffing levels. This proactive use of AI helps maintain workforce stability in a high-pressure environment. In conclusion, organizations should act on AI attrition predictions because proactive support can benefit both employees and the company.

View A — Trust the AI’s predictive analysis. Since AI's predictive analysis is data-driven it reduces bias and captures patterns that instinct often misses. Data driven systems can analyze millions of variables, far beyond human capacity. Decisions based on data are auditable and transparent, unlike instinct which is subjective. Instinct is prone to overconfidence and cognitive bias, while data provides evidence-based guardrails. The final call should be made by data, not instinct. Instinct can serve as a valuable check, but when decisions involve measurable outcomes, data provides the most reliable and accountable foundation. Eg 1: Oakland Athletics (2002 Moneyball Strategy) Instinct approach: Traditionally, baseball scouts relied on gut instinct and subjective judgment—looking at how a player “looked” on the field, their style, or charisma. Data approach: General Manager Billy Beane used sabermetrics (data analytics) to evaluate undervalued players based on measurable statistics like on-base percentage. Outcome: Despite having one of the lowest budgets in the league, the Oakland A’s built a competitive team that won 20 consecutive games—a record at the time. Lesson: Instinct said these players weren’t “star material,” but data revealed their hidden value. The success reshaped how professional sports teams worldwide now use analytics. Eg 2: Amazon’s Recommendation Engine Instinct approach: Retailers traditionally relied on human merchandising instincts—placing “popular” items at the front. Data approach: Amazon built a recommendation system using purchase history, browsing patterns, and customer reviews. Outcome: Personalized recommendations became a major driver of sales, accounting for 35% of Amazon’s revenue. Impact: Instinct would never have scaled personalization to millions of customers; data made it possible. Both Oakland aesthetics and Amazon show that data can outperform instinct by revealing patterns invisible to human judgment.

The approval step should not be removed, even if AI analysis shows it rarely changes outcomes. Its value lies in protecting against rare but catastrophic errors, which can outweigh the efficiency gains of removing it. Efficiency gains are linear and predictable while Catastrophic risks are nonlinear and unpredictable, but their impact is exponentially higher. Thus, removing the step is a false economy—it optimizes for average outcomes while ignoring tail risks. eg 1. Zillow’s AI Home Pricing Collapse (2021) What happened: Zillow relied heavily on its AI “Zestimate” model to buy and resell homes. The company reduced human approval checks, trusting the algorithm’s accuracy. Outcome: The AI overvalued homes in a volatile market, leading Zillow to purchase properties at inflated prices. Impact: Zillow lost over $500 million, shut down its home‑flipping business, and laid off 25% of its workforce. Lesson: Removing human approval allowed rare but catastrophic mispricing errors to scale unchecked Air Canada Chatbot Lawsuit (2023) What happened: Air Canada’s AI chatbot gave incorrect refund information. No approval step existed to verify responses. Outcome: A court ruled Air Canada liable, forcing them to honor the chatbot’s misinformation. Lesson: Removing human oversight exposed the company to legal liability. Removing critical approval steps in AI show that the destruction can be financial, reputational, and legal.