I work with enterprise leaders to make regulated AI executable at the integration seam, and I build the people and methods so the second one is easier than the first.

I have spent 25 years making enterprise systems talk to each other in financial services, life sciences, and aerospace manufacturing. It is the same seam, and it was there before AI arrived at it. In life sciences, that meant validated environments, where changing an integration means changing a controlled system, and the audit trail is part of the architecture rather than a report you run afterward.

At Capgemini, I work as a senior enterprise AI architect on production AI in regulated industries.

Most AI programs fail in the gap between the agent, CRM, intelligent applications, data platform, ERP, and the operating model. I work in that gap. Some days that means helping leadership decide whether the architecture, the vendor, and the sequence are the right bet. Other days it means getting into the room before it runs in production. When delivery is stuck, I step in on cadence, decision rights, and controls, then I hand the work back. I still sit close to the system when a pattern has to be proven in a live environment.

I also lead and coach the architects, designers, builders, and operations team who have to run this after the architecture meeting ends. Career development is part of the job, not a side activity.