Projected faster time-to-insight
Led an enterprise GenAI product through use-case roadmapping, cross-functional governance, architecture review, MVP planning, and an ROI model tied to $60M in annual opportunities.
AI Governance · Data Strategy · Hands-on Delivery
I work at the intersection of enterprise AI strategy, governance, customer intelligence, and practical GenAI delivery. I help define the operating model, align the people who own the risk, and stay close enough to delivery to prove the system works.
Anonymized examples from documented engagements. The useful pattern is the same across them: connect the business case, operating model, governance decisions, and delivery plan instead of treating each as a separate workstream.
Led an enterprise GenAI product through use-case roadmapping, cross-functional governance, architecture review, MVP planning, and an ROI model tied to $60M in annual opportunities.
Led a governed data-platform rollout spanning catalog integration, lineage, stewardship, training, and the change model required for teams to use it.
Built a technical evaluation and integration framework that connected privacy, security, architecture, and business requirements before vendor scoring began.
For a practical starting point, browse the Data & AI Governance field notes →
The common thread is moving organizations from scattered experiments to working operating models: clear data ownership, safe AI usage, measurable business value, and delivery teams that can execute.
Responsible AI policies, tool classification, PII handling, governance forums, and escalation paths for teams adopting LLMs inside real enterprise constraints.
Data-source audits, MDM recommendations, metadata strategy, lineage, catalog adoption, KPI design, and the operating model needed to make analytics useful.
I keep building with Claude, Gemini, local LLMs, Cloud Run, Firebase, Docker, and agent workflows so the strategy advice stays grounded in what actually works.
Least-privilege access for non-human identities: service identities, scoped tokens, and autonomy tiers mapped to data sensitivity.
Why boring governance artifacts become retrieval infrastructure when organizations start putting LLMs on top of enterprise data.
Schema essentials, sessionization, identity stitching, RFM, LTV, and cohort marts from the raw GA4 export.
How to catch hallucinated numbers and unsupported claims in AI-generated enterprise reports before they reach stakeholders.