Viraj Khandesagar

I support View B — Preserve collaborative problem-solving. While AI can significantly improve speed, accuracy, and efficiency in identifying solutions, organizations should not reduce collaborative problem-solving too heavily because the value of teamwork extends beyond simply finding the fastest answer. Collaborative discussions help organizations: build shared understanding across teams, improve employee ownership and accountability, strengthen communication and alignment, encourage innovation through diverse perspectives, and develop long-term problem-solving capability within the workforce. If organizations rely only on AI-generated solutions, employees may become passive executors rather than active thinkers. Over time, this can weaken creativity, critical thinking, and cross-functional learning. AI is extremely effective at analyzing historical patterns and operational data, but human collaboration is essential for understanding organizational context, customer impact, change management, and strategic trade-offs that may not exist in the data. A strong operational example is Toyota and its Kaizen continuous improvement model. Toyota uses advanced analytics and automation extensively in manufacturing operations, but it still strongly emphasizes collaborative problem-solving through cross-functional workshops and employee improvement programs. Teams regularly participate in root-cause analysis and process improvement discussions because Toyota recognizes that collaboration builds operational knowledge, innovation, and long-term workforce capability — not just immediate solutions. Another strong example is NASA during mission operations and engineering investigations. NASA uses highly advanced AI systems, simulations, and analytical tools to identify technical problems quickly. However, major decisions still involve collaborative discussions among engineers, scientists, operations teams, and leadership. This collaborative approach ensures that different technical perspectives are considered, risks are fully understood, and organizational learning continues to grow after each mission or incident. These examples show that organizations achieve the best outcomes when AI enhances collaboration rather than replaces it. AI should accelerate analysis and provide data-driven recommendations, but teams should still engage in collaborative review and decision-making to strengthen innovation, learning, and long-term organizational resilience. Therefore, I believe organizations should use AI to improve the efficiency of problem-solving while preserving collaborative processes that build human capability and shared ownership.

I support View A — Act proactively using AI predictions, but with strong ethical safeguards and human oversight. Employee attrition is expensive and disruptive for organizations. Losing experienced employees can result in loss of operational knowledge, lower productivity, increased hiring costs, and reduced customer satisfaction. If AI can identify early warning signs of disengagement or burnout, organizations should use those insights to support and retain employees before they decide to leave. However, AI predictions should never be used to unfairly label or penalize employees. The purpose of predictive analytics should be supportive intervention, not surveillance or discrimination. Managers should use AI signals responsibly by focusing on positive actions such as: * career development discussions, * workload balancing, * mentorship opportunities, * flexible work arrangements, * and employee wellbeing support. A strong operational example is IBM. IBM has publicly discussed using AI-driven predictive analytics to identify employees at risk of leaving. The company used HR analytics to help managers proactively engage employees, improve retention strategies, and reduce turnover costs. Instead of treating employees negatively, the system was designed to help leadership understand workforce trends and improve employee satisfaction through targeted support and career development initiatives. Another strong example is Microsoft, where employee analytics and workplace insights are used to monitor workload, burnout risks, collaboration patterns, and engagement levels. Managers can use these insights to intervene early by redistributing work, improving team support, or addressing wellbeing concerns before employees become disengaged. This helps improve both retention and employee experience. These examples show that AI can be highly valuable when used responsibly. Organizations should not ignore predictive insights that could help retain talent and improve employee wellbeing. However, human judgment, transparency, and ethical governance are essential to ensure employees are supported fairly and not treated differently based only on predictions. Therefore, I believe organizations should proactively act on AI attrition signals — but only as a tool for employee support, development, and retention rather than control or profiling.

I support View A — organizations should stop projects early when AI consistently predicts a high probability of failure. AI can analyze operational signals faster and more objectively than humans, especially in large organizations where politics, executive pressure, and sunk-cost thinking often delay difficult decisions. If multiple indicators such as budget overruns, missed milestones, low stakeholder engagement, and unresolved risks continue to worsen, continuing the project usually increases financial loss and operational disruption rather than improving outcomes. This does not mean AI should make the final decision alone. Leaders should validate the findings through human review, but ignoring strong predictive evidence simply because a project is politically important can damage the organization further. Real-Life Operational Example 1: Amazon Fire Phone Amazon launched the Fire Phone in 2014 to compete in the smartphone market. Early operational indicators showed: weak customer demand, poor product differentiation, low developer support, and declining sales performance. Despite heavy investment and strong executive backing, the product failed to gain market traction. Amazon eventually discontinued the Fire Phone and absorbed major financial losses. If predictive AI systems analyzing customer engagement, sales trends, and market adoption had been used aggressively earlier, Amazon could have stopped or scaled down the initiative sooner and redirected resources toward more successful products such as Amazon Alexa and cloud services. Real-Life Operational Example 2: Google Google Glass Consumer Rollout Google introduced Google Glass as a consumer wearable technology product. Operational signals quickly revealed: strong public privacy concerns, low consumer adoption, limited practical use cases, and negative media sentiment. Instead of continuing large-scale consumer expansion, Google stopped the consumer rollout and shifted the product toward enterprise and industrial applications where it performed better. This decision prevented continued investment into a weak consumer business model and allowed the technology to survive in a more operationally successful market segment such as manufacturing and healthcare support. These examples show that stopping or redesigning projects early is often the smarter operational decision. AI-driven predictions can help organizations recognize failure patterns sooner, reduce wasted investment, and redirect resources toward initiatives with stronger long-term value.

I support View B — Distribute opportunities more broadly. While AI can accurately identify top performers for critical tasks, organizations must focus not only on current performance but also on long-term capability development, employee engagement, and operational resilience. If important assignments are repeatedly given to the same small group, the organization may achieve short-term efficiency, but it creates several long-term risks: * other employees do not gain experience or develop advanced skills, * team morale and motivation decline, * dependency on a few individuals increases, * and the organization becomes vulnerable if those employees leave or burn out. A strong organization should build a broad talent pipeline rather than concentrate expertise in only a few people. AI is highly effective at optimizing outcomes based on historical performance data, but managers must also consider human development, succession planning, collaboration, and future workforce capability — factors that are difficult to measure purely through performance metrics. A strong operational example is Amazon. Amazon uses extensive performance metrics and operational data to identify high-performing employees in fulfillment, logistics, and customer operations. However, the company also rotates employees into leadership programs, cross-functional projects, and operational improvement initiatives. This broader distribution of opportunities helps develop future managers and ensures operational knowledge is not concentrated in only a few individuals. If only the highest performers handled all major operational tasks, the organization would struggle to scale leadership capability across its global operations. Another strong example is Toyota and the Toyota Production System. Toyota is known for operational excellence and efficiency, but it also focuses heavily on employee development through continuous improvement practices such as Kaizen. Employees across multiple levels are encouraged to participate in problem-solving, quality improvement, and operational decision-making rather than relying only on top-performing specialists. This creates a more skilled and adaptable workforce while reducing dependency on a limited group of experts. Another example is in the airline industry. Airlines may rely on highly experienced pilots for difficult situations, but they also ensure junior pilots receive supervised exposure and training opportunities. If only the most experienced pilots handled all critical operations, future capability development would suffer, creating long-term operational risk. Therefore, I believe managers should use AI as a decision-support tool rather than an absolute decision-maker. Critical work can still prioritize strong performers when necessary, but opportunities should also be intentionally distributed to develop future talent, maintain morale, and ensure long-term organizational sustainability. These examples show that successful organizations do not focus only on maximizing immediate output. They also invest in developing broader workforce capability to improve long-term resilience, innovation, and sustainability.

I support View B — Trust experienced leadership judgment, while still using AI as an advisory tool. AI is extremely valuable for identifying patterns, forecasting trends, and reducing human bias. However, major business decisions are not driven only by historical data. Markets are influenced by timing, customer emotion, competitive pressure, innovation, and strategic vision — areas where experienced leaders often have stronger judgment than AI models. In fast-moving industries, waiting for perfect data can sometimes result in missed opportunities. AI predictions are based on past and current patterns, but breakthrough products often succeed because leaders act before the data fully supports the decision. A strong example is Apple and the launch of the iPhone in 2007. At that time, existing market data suggested consumers preferred physical keyboards, and companies like BlackBerry dominated the smartphone market. If decisions had been made purely from historical behavior patterns, launching a touchscreen-only smartphone would have appeared risky. However, Apple’s leadership trusted their vision of how customer expectations would evolve. The result was one of the most successful product launches in technology history. Similarly, companies such as Tesla made aggressive investments in electric vehicles long before market data strongly supported mass adoption. Leadership judgment and long-term vision played a major role in shaping the market itself. Therefore, while AI should absolutely be considered in decision-making, I believe experienced leadership should make the final call in situations involving innovation, market timing, and strategic opportunity. Great business leaders do not ignore data, but they also understand that transformative success sometimes requires acting beyond what current data predicts.