AI PRODUCT OPERATIONS · CASE 01
Enterprise AI Strategy
A plan for turning scattered AI experiments into a repeatable, business-owned program.
01 / Business problem
The decision behind the work
Access to AI tools was not the real constraint. The organization needed a repeatable way to decide where AI belonged, what context it could use, who owned the workflow, which approvals were mandatory, and how one investment would make the next one easier.
I framed AI as a transformation and operating-model problem, not a software acquisition problem.
02 / My product decisions
How the decision works
- 01No separate AI journey. I integrated AI into existing business roadmaps and initiatives instead of creating a disconnected queue of demonstrations.
- 02Anchor on four business outcomes. Customer and retailer growth, gross-margin improvement, better forecasting and planning, and faster decisions gave every capability a reason to exist.
- 03Reuse the hard-won context. Business definitions, workflows, ownership, and guardrails improve the next use case; one-off prompts do not.
- 04Establish five operating pillars. Governance, context, education, workflow delivery, and portfolio measurement move together.
- 05Require ownership before deployment. Risk, data sensitivity, business value, approval gates, and accountable teams are explicit before scale.
03 / Evidence
What the demonstration proves
The complete 11-slide deck preserves the strategic sequence: business outcomes, operating pillars, shared context, review rules, portfolio decisions, and concrete capability examples.
04 / Outcome
6-agent portfolio
The strategy organized six proposed agents around the same criteria for value, complexity, risk, data sensitivity, and human approval.
05 / What I’d carry forward
The advantage is not access to a model. It is clear business context, decision rules, ownership, and a person reviewing the output before anyone acts on it.