Free executive playbook.
Before you buy or scale another AI model, answer one question: is our data and architecture actually AI-ready — and if not, which layer do we fix first? RAND finds more than eighty percent of AI projects fail, close to twice the rate of non-AI technology projects (RAND, 2024); MIT's NANDA initiative separately finds ninety-five percent of enterprise generative-AI pilots deliver no measurable return (MIT NANDA, 2025). No model repairs data that was never AI-ready — yet the same evidence kills the instinct to fix all the data first: readiness is necessary, and it is not sufficient. This playbook reads your readiness in an afternoon and names the one layer blocking a funded use case — so you remediate that layer, not the whole estate.
What's inside
- The map — AI-Ready Business Architecture from L1 Foundation up to L5 Activation, read bottom-to-top, with security as a seam through all five and a human layer holding the stack up
- The five layers — Foundation, Access, Meaning, Governance, Activation: each with its one Readiness-Read question, what ready looks like, and the failure mode when it is missing
- The Readiness Read scorecard — score every layer from one (fragmented) to four (AI-operational), plus the layer that binds for a copilot, for fraud and AML, for credit underwriting, and for an autonomous agent
- Dual-Track — enterprise minimum standards set once, and the rest engineered as reusable, governed data products that accrete back to the foundation instead of stranding as one-off pipeline debt
- Monday in five steps — score, find the binding layer, remediate that one, run Dual-Track, close with the human layer — with the DBS, Citigroup and Apple Card proof, read both ways
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