AI in Enterprise: From Hype to Impact

Q1. Looking back, how has your career evolved and shaped the way you approach your current role?

I started as a Computer Scientist (Ph.D.), focused on the theoretical limits of data. Over 21 years at ADP and IBM, my focus shifted from 'Code' to 'Commercials.' Today, I operate as aScientist-Executive: I use deep technical rigor to validate whether a product roadmap will actually drive P&L impact, rather than just technical novelty

Q2. Generative AI has shifted quickly from experimentation to board-level priority. Where do you see the biggest gap between executive expectation and enterprise readiness?

Executives expect GenAI to be a 'Plug-and-Play' employee. The reality is that Enterprise Data is messy. The biggest gap is the'Data Basement' -frontier models cannot reason effectively over unstructured, siloed legacy data without a massive investment in vectorization and governance first.

Q3. Many organizations pursue AI pilots that demonstrate capability but not scalability. What signals indicate a product is still a vitamin rather than a painkiller?

A 'Vitamin' AI generates text (e.g., 'Draft this email'). A 'Painkiller' AI executes a workflow (e.g., 'Reconcile this invoice and update SAP'). If the AI requires the human to check its work every time, it’s…