Your AI Strategy Is Built on a Lie — And Your Data Knows It

Every enterprise has an AI strategy. Almost none of them are starting in the right place. Organizations are racing to deploy large language models, copilots, and AI agents while avoiding the uncomfortable question underneath: is your data estate actually ready to support what you are asking these models to do?

The answer, for most organizations, is no. And the consequences are not hypothetical. Failed AI initiatives are already producing measurable losses — not because the models are inadequate, but because the data foundations they depend on were never assessed, governed, or architected for the workloads being deployed against them.

This keynote introduces the AI Readiness Framework — a three-phase methodology built from over two decades of enterprise deployments across government, Defense Industrial Base, and commercial sectors. The framework provides a structured, repeatable approach for organizations to validate their AI vision against business outcomes, confront the real condition of their data estate, and build governance architectures that scale across multi-model environments.

Miguel Wood — one of fewer than 100 Microsoft Certified Solutions Master charter holders worldwide, a U.S. Army veteran, and the CEO of an enterprise AI consulting firm — delivers this keynote as a field report from real engagements: a composite failure case where a seven-figure AI initiative collapsed because no one audited the data, and a composite success case where the same framework turned a stalled program into a governed, production-grade AI deployment. Both are drawn from direct practitioner experience.

Attendees will leave with a concrete methodology they can apply immediately, architectural patterns for building governance into multi-model environments, and the honest clarity that comes from understanding what has to happen before the first model ever gets deployed.