1.Perspective 1
Enterprise AI programmes stall when they treat data platforms and decision outcomes as separate projects. The playbook that works connects approved data, decision ownership and operating evidence in one design.
2.Start with the decision, not the model
Start with the decision, not the model. Name the choice that should improve, the owner of that choice, the indicators that would change a meeting, and the residual risk if the signal is wrong.
3.Shared data products then become purposeful
Shared data products then become purposeful. Access rules, quality limits and refresh cadence belong with the decision they support. Unowned lakes and one-off extracts recreate the same briefing delay the programme was meant to remove.
4.Portfolio balance matters
Portfolio balance matters. Near-term operating improvements prove value; platform investments stop every use case from reinventing access, evaluation and hosting. Either extreme — only pilots, or only platforms — produces stall.
5.Perspective 5
Leaders should judge progress by decisions taken with better evidence, not by the number of models in a catalogue. The playbook is complete only when operating reviews consume the system on a fixed cadence.
