Value-First Platform: AI Data Readiness - Apr 14, 2026

Here's a pattern we keep circling back to on this show: the distance between what organizations believe about their AI readiness and what's actually true.

New research puts numbers on it. 87% of data leaders say their infrastructure is AI-ready. At the same time, 42% of those same leaders identify infrastructure as their biggest obstacle to AI success. That's not a rounding error — it's an organizational blind spot with real consequences.

The same disconnect shows up everywhere you look. 86% say their teams have the skills. 41% say skills are the barrier. 88% say their data is ready. 43% say data readiness is what's blocking them.

What creates this gap? Decades of deferred data quality decisions. When processes were manual, humans could paper over inconsistencies — reading between the lines, making judgment calls, filling gaps with institutional knowledge. AI doesn't do that. AI treats your data as truth. If your data carries twenty years of shortcuts and workarounds, AI amplifies every one of them.

This is what Trisha calls "compounding data quality debt." And it's coming due now, not because the debt got worse, but because AI made it visible.

The governance question is where this gets interesting. The conventional framing treats governance as friction — another layer of bureaucracy slowing down innovation. The data says the opposite. Organizations with governance programs report 71% high trust in their data, compared to 50% without. And the organizations that expanded their existing data governance to include AI governance outperform those who created separate AI governance programs or — worst of all — reduced data governance to focus on AI.

Governance isn't a brake. It's the engine. Andrew's phrase lands here: traction, not friction.

Then there's the missing role nobody's hired for. The top skill gap isn't algorithmic or technical — it's "ability to deploy AI at scale in a business environment." 30% of organizations identify this as their biggest miss. The infrastructure exists. The tools exist. What's missing is the system that makes tools usable — the orchestrator who connects customer interactions in one system, service history in another, and billing in a third into something AI can actually work with.

This connects directly to the Unified Customer View as a measurement prerequisite. You can't measure AI-Assisted Revenue Closed or Revenue Protected when your data doesn't connect across systems. The measurement gap isn't a metrics problem — it's an architecture problem.

Join Trisha Merriam, Erin Wiggers, and Chris Carolan as they unpack what AI data readiness actually requires — not the confidence survey version, but the version that shows up when your AI agents start making decisions based on the data you gave them.