Generative and agentic AI applied to real enterprise workflows — adopted deliberately, with a clear owner and a human still in the loop.
Most enterprise AI conversations focus on the technology; the harder part is fitting it into a workflow with real accountability. I advise on generative and agentic AI adoption grounded in the same governance discipline I bring to data platforms — where an AI assistant or agent takes over a step, but a person still owns the outcome.
AI pilots are easy to launch and hard to operationalize — a promising demo often stalls because nobody has defined where the AI's output needs review, what happens when it's wrong, or how it fits the existing process rather than replacing it wholesale.
AI assistants that help teams find and use internal information faster, with sources kept visible.
Agents that carry out multi-step tasks against existing systems, with defined checkpoints for review.
Taking a promising AI pilot and defining what it takes to run it reliably in production.
AI use cases scoped to processes it can genuinely improve, not applied everywhere by default.
A clear owner and review point for every AI-driven step.
Adoption grounded in the same governance discipline used for enterprise data platforms.
I start by identifying which workflows have a genuine efficiency gap AI can close, then design the review and escalation points before anything goes live — the same governance-first approach used across MDM and operations. This keeps the AI accountable to a person, and keeps adoption realistic rather than aspirational.
Let's identify the workflows where it will actually move the needle.
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