By Dr. Rajesh Gharpure, Chief Delivery Officer at Persistent Systems.
Enterprise AI adoption is widening the performance gap. Disciplined organizations are translating AI investments into trust, operational excellence and measurable business impact. Others are scaling rapidly without the foundations required to sustain value. The result is a growing disconnect between reported progress and actual outcomes, declining customer experience, rising costs and front-line teams compensating for systems that should be enabling them.
The difference is rarely the technology alone. It is the quality of the underlying process. That distinction matters even more as enterprises move from traditional automation to agentic AI. Earlier automation followed predefined rules within narrow workflows. Agentic AI goes further by interpreting goals, making recommendations, triggering workflows and acting across systems. That makes it more powerful, but also less forgiving of weak foundations. When processes are riddled with broken flows, data is incomplete, handoffs are unclear or decision logic is poorly defined, inefficiency scales immediately.
If an input is flawed, the system can replicate the error across outcomes. If a manual work-around has become the operating norm, AI can embed it deeper into the workflow. What employees once corrected quietly through judgment, escalation and institutional knowledge can become a scaled business risk with AI flows. This is why human validation remains critical in the agentic AI era, especially at decision points where flawed logic could become embedded into automated actions.
