Value-First Platform: AI Data Readiness
Value-First Platform: AI Data Readiness - Apr 8, 2026
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Value-First Platform: AI Data Readiness — 5 Themes Recap (May 6, 2026)
📅 May 6, 2026 🎙️ Trisha Merriam , Chris Carolan , Erin Wiggers
Six months of weekly conversation. Fourteen episodes recorded. Seven with full notes. Across them, five themes have surfaced about what AI data readiness actually requires. This recap is for the people who haven't seen any of it — and for the ones who saw a single episode and want a map of where it sits. Trisha Merriam, Chris Carolan, and Erin Wiggers walk the throughline. Pick the theme that hu...
Key Takeaways
1 You can't measure what you can't connect. AI measurement is an architecture problem, not a reporting problem. Until your data flows between systems with context intact, you measure activity (emails sent, tools opened) and call it outcome. The Unified Customer View is the prerequisite, not the destination.
2 Your data is incomplete in ways you haven't named. Shadow data, unstructured data, missing context — three names for the same problem. Most of the real signal in an organization never reaches a system AI can see. It lives in Slack threads, meeting notes, inboxes, recordings that were never connected.
3 Nobody owns the system, and that's the problem. Tools don't fail. Systems without owners fail. Organizations getting real value from AI have someone deciding where data comes from, how AI connects to workflows, and when humans step in — even when the role doesn't have a formal name yet.
4 Foundation before agents. The path to autonomous AI doesn't start with AI. It starts with data quality, documentation, process clarity, and governance. These aren't prep work. They are the work. The technology isn't the hard part; the organizational clarity underneath it is.
5 AI readiness is organizational, not technical. Leadership behavior, psychological safety, structured rollout instead of "go figure out AI." The organizations pulling ahead aren't just building cleaner data. They're building environments where people can experiment, fail, iterate, and actually change how they work.
Show Notes
Key Topics Covered
- ✓ AI data readiness
- ✓ Unified Customer View
- ✓ AI Orchestrator role
- ✓ Metrics Gap
- ✓ Shadow data
- ✓ Unstructured data
- ✓ 12 Complexity Traps
- ✓ Governance architecture
- ✓ Foundation before agents
- ✓ Organizational change
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