Omsharan

AI should absolutely accelerate problem-solving — but organizations should not eliminate collaborative problem-solving sessions. The real competitive advantage is not AI alone, but how intelligently humans and AI work together. My position is clear: Organizations should reduce low-value discussions, not collaborative thinking itself. AI is exceptionally strong at identifying patterns, detecting root causes from large datasets, and recommending optimized corrective actions within minutes. In operational environments where delays directly impact cost, production, or customer service, speed matters. If an AI system can solve a recurring process issue in 10 minutes instead of a 3-day workshop, ignoring that capability would be inefficient. However, organizations often misunderstand the purpose of collaboration. Team discussions are not valuable only because they generate solutions. They also create alignment, accountability, operational understanding, trust, and innovation capability across functions. When employees stop participating in problem-solving: • ownership of execution decreases, • silo thinking increases, • future leaders lose decision-making exposure, and • organizations become operationally dependent on AI recommendations without developing human judgment. The long-term risk is subtle but dangerous: companies may become operationally efficient while becoming intellectually weaker. The best operating model is therefore “AI-first analysis, human-centered decision integration.” A strong organization should allow AI to: • rapidly identify probable root causes, • simulate solution scenarios, • prioritize corrective actions, and • eliminate repetitive analytical work. But collaborative sessions should evolve — not disappear. Instead of spending hours collecting data manually, teams should use meeting time to: • challenge assumptions, • evaluate implementation risks, • assess customer or supplier impact, • identify unintended consequences, and • generate breakthrough improvements beyond historical data patterns. For example, in manufacturing operations, AI may identify that repeated downtime is caused by delayed preventive maintenance intervals and recommend increasing maintenance frequency. The AI solution may statistically improve uptime immediately. But during cross-functional discussion, operators may reveal that the actual issue is poor spare-part availability or an impractical maintenance schedule during production peaks. Engineering may suggest redesigning the process entirely instead of repeatedly treating symptoms. This is where innovation emerges — something historical-data-driven AI may not fully capture. Similarly, companies like Toyota built world-class operational systems not only through problem-solving speed, but through continuous employee participation and learning culture. The strength of lean systems comes from developing people alongside processes. Therefore, organizations should not measure problem-solving success only by “time to solution.” They should also measure: • employee engagement in improvement, • cross-functional learning, • innovation quality, and • long-term organizational capability. AI should remove friction from problem-solving, not remove humans from thinking. The future winners will not be organizations that replace collaboration with AI. They will be organizations that use AI to make collaboration smarter, faster, and more strategic.