How One Engineering Excellence Leader’s ‘Innovation Funnel’ Turns AI Experiments Into High-Impact Outcomes

The power of enterprise AI lies not in experimentation, but in the operational transformation. By integrating specialized agents, companies can anticipate challenges, accelerate initiatives, and provide leaders with actionable insights at scale. No longer a nice-to-have, AI is a lever for measurable impact, enabling faster, more informed decisions. Now, the biggest difference between organizations that succeed and those that fail will be how effectively they embed AI into the core of their operations and navigate change management.

For an expert's take, we spoke with Palanivel Rajan (PVR) Mylsamy, Director of Engineering Program Management at Cisco, whose work focuses on this precise approach. A Forbes Technology Council member with a change management background from Cornell University, Mylsamy leverages over two decades of experience with frameworks like Kotter’s to solve long-standing enterprise challenges.

"My primary entry criterion is the scale of customer impact. The number of end users a change will affect determines whether it’s an R&D pilot or a production-ready solution," Mylsamy said. Once a high-impact idea is identified, it undergoes a disciplined, multi-step validation process within a sandbox to evaluate its potential.

The real breakthrough lies in using AI to anticipate and guide organizational change, Mylsamy explained. "With AI, I can predict schedule impacts well in advance, avoiding the last-minute realization that a timeline will slip by months or go over budget." With enterprise executives often flooded with AI proposals, however, teams frequently struggle to separate genuine value from flashy capabilities.

• The 80% rule: The best thing an enterprise team can do for new AI projects is to play with their effectiveness against specific performance benchmarks in a sandbox environment, Mylsamy said. "We sandbox any new agent to measure its efficacy. Once the outcome is close to 80%, we know it’s ready to be rolled out to customers." This structured approach balances experimentation with rigor, allowing teams to move promising ideas from pilot to production with more confidence.