AI Readiness Is a Governance Question
Engine Room Article 12: Why the Work Before AI Is the Work That Matters
The Readiness Misconception
“AI readiness” typically conjures images of model selection, prompt engineering, and integration architecture. These matter. But they’re late-stage concerns that assume something more fundamental: that you know what data you have and whether you can trust it.
AI readiness isn’t primarily about AI. It’s about whether you can answer a simple question: What data do we have, and can we trust it?
Every AI conversation eventually becomes a data conversation. And every data conversation eventually becomes a governance conversation. The organizations that understand this sequence are the ones positioning themselves to actually use AI effectively.
The OODA Loop for Data Governance
OBSERVE: What data exists? Before you can govern data, you need to see it.
ORIENT: Can I trust it? Knowing data exists isn’t enough - you need to know its quality.
DECIDE: What should I prioritize? Not all data quality issues matter equally.
ACT: How do I improve it? Action flows from understanding, not guesswork.
The Compounding Effect
Governance work compounds in ways that aren’t immediately visible. A searchable data dictionary reduces the time to answer “where does this data come from?” from hours to seconds.
The organizations that will thrive with AI aren’t necessarily the ones with the most sophisticated models. They’re the ones that have done the unglamorous work of knowing what they have and whether they can trust it.
The Real Readiness
AI readiness isn’t a destination - it’s a capability. And that capability rests on foundations that have nothing to do with models, prompts, or inference endpoints.
The work of governance isn’t glamorous. It doesn’t make for exciting demos or impressive announcements. It’s the engine room work - the machinery that makes everything else possible.
AI readiness is governance readiness. The work of observing your data landscape, assessing quality, and building reliable metadata infrastructure isn’t preparation for AI - it’s the foundation that determines whether AI initiatives succeed.
If someone asked you today to list every table containing customer data with its quality grade and last-updated timestamp, how long would that take? That gap - between asking and knowing - is your governance opportunity.
Related: 07-source—engine-room-series