The situation I was solving
AI access had expanded across paid, free, approved, and emerging tools, but provision did not describe real adoption. Strategy needed a clearer view of who was using what, for which work, at what sensitivity, under which controls, and whether cost matched business value.
Read the underlying principle: An enterprise AI strategy is a portfolio decision, not a tool list. →What becomes better
The portfolio approach creates a defensible basis for tool selection, investment, consolidation, governance, and adoption. It helps match users and tasks to the right capability while making risk, cost tolerance, and expected value explicit.
How I work through it
I combined evidence from licenses, usage, employee demand, use-case backlogs, tool categories, cost, and risk. I mapped platform roles against task sensitivity and consequence, compared current behaviour with the intended state, and designed controls, measures, and recurring reviews so the strategy could change as evidence improved.
Go deeper: AI adoption is an operating model, not a launch campaign. →What I carry forward
Enterprise AI strategy is not a procurement list. It is a living portfolio decision that must continuously reconcile capability, adoption, business value, cost, and acceptable risk.
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