Most AI initiatives die between the demo and the balance sheet. We build the bridge: business ownership, an experimentation framework agreed before the build, and an operational plan for what happens after launch — so AI investments show up as revenue, not as innovation theater.

Unifying your data is the easy half. We treat the CDP as a product, not just an IT project — with a value thesis, a roadmap, an adoption strategy, and an owner accountable for the revenue it's supposed to influence. Built on experience delivering a first-of-its-kind cross-tier CDP and operationalizing 800+ ML models.

New technology only produces returns when the organization changes around it — decision rights, budgets, roles, and frontline work. We treat readiness as the core of go-to-market strategy, not a change-management afterthought, so adoption is designed in rather than hoped for. 

Whether standing up a product function from scratch or maturing one at scale, we install the discipline first — business case rigor, stage-gate decisions, voice-of-customer as habit — then build the team around it. Process before headcount; that's how a PM org grows revenue instead of roadmap documents.