AI Data Readiness
Six months of weekly conversation. Fourteen episodes. Five themes that determine whether AI works in your organization.
Pick the theme that hurts most. Watch that episode. The others will pull you in.
How to find AI help outside the organization — and what data readiness looks like in your partners.
Series at a glance
6
Months
14
Episodes
5
Themes
"Trisha Merriam, Chris Carolan, and Erin Wiggers walk the throughline."
— Recap episode, May 6, 2026
Seven of fourteen episodes have full notes published.
Why Do Most AI Initiatives Stall?
Five patterns surfaced across six months of conversation. Different organizations, different industries, different sizes — the same five gaps every time.
What people expected to see
A clean technology problem. Pick the right model, configure the right integrations, the rest follows.
What actually surfaced
Five organizational and architectural gaps that compound on each other. Foundation before agents. Owners before tools.
The throughline
Each theme stands on its own — but they reinforce each other. Solve one, the others get easier. Skip one, the others compound.
You can't measure what you can't connect.
AI measurement is an architecture problem, not a reporting problem.
The Unified Customer View isn't the destination — it's the hack. It's how you get teams to stop fighting over deal pipelines with 20 stages and start aligning around what a customer actually looks like. You don't have to connect everything. You have to get humans moving on the connections that matter — because humans are the channels of all the shadow data, and they need a reason to move.
You don't have to connect everything. You have to get humans moving on the connections that matter.
Go deeper 2 episodes
Metrics Gap Part 1 — March 31, 2026
Erin Wiggers demos AI ROI measurement across three dimensions: AI-Assisted Revenue Closed, Revenue Protected, Hours Saved. The Measurement Trap explicitly named.
Metrics Gap Part 2 — April 14, 2026
The confidence-reality gap. 87% of data leaders say infrastructure is AI-ready; 42% say it's their biggest blocker. Compounding data quality debt.
Your data is incomplete — and bad — in ways you haven't named.
Shadow data, unstructured data, missing context, plain wrong data — four 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. And the data that did make it in is often actively wrong — missing pieces, problematic entries, untouched since the last person who half-knew what it meant left. Picture the boss who sees one thing out of context and makes 17 wrong assumptions about why you did or didn't do something. Now picture that boss every Monday morning. The way out is to make it hard to assume things — make the facts dead simple to find. If they're in black and white, hitting you in the face, it takes one click. A grumpy boss has fewer excuses to cherry-pick.
Make it hard to assume things. Make the facts dead simple to find.
In production
A dedicated anchor episode for Theme 2 is in production. The recap episode walks the framing in full — start there.
Watch the recap →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. Conceptually we call it the AI Orchestrator. In the field, what's actually emerging is a RevOps role — someone whose actual job is to understand how all the systems interact and give every other team the information they need to operate in their lane. Without that owner, you're asking the services team or the sales team to be aware of a ten-lane highway while they're trying to stay in their one lane. That's a trap.
The AI Orchestrator is the role most organizations don't know they're missing.
What's emerging in production
“What we're seeing play out is more of an emphasis on a RevOps role. This is the number one thing you need to focus on, because the rest of it is not going to come along until you acknowledge that this is different and requires a different model.”
— Erin Wiggers and Trisha Merriam, May 6, 2026
Go deeper 3 episodes
Leaders Before AI — March 3, 2026
Chris names the AI Orchestrator role. What leadership looks like in an AI-ready company. The "go figure out AI" anti-pattern.
Governance Architecture — April 21, 2026
Five governance patterns from VFT's production system: gateways, delegation hooks, enforcement that survives context compaction, model tiering, Corrective Action Reports.
Context, Access, Scope — April 22, 2026
Governance is putting agents in a position to succeed — building architectural constraints that make wrong behavior impossible rather than merely discouraged.
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.
Agents are the result of the foundation, not the substitute for it.
AI readiness is organizational, not technical.
Leadership behavior, psychological safety, cohort learning instead of "go figure out AI."
Anybody can show up and start learning AI in front of everybody else. That creates the safe space for everybody else to start learning. Cohort learning is the only paradigm that actually works here — because the only way to learn AI is by building with it. The old model of taking a course and answering questions has been turned on its head. The role pairing that works: maybe a chief people officer and an AI leader working together. Somebody needs to be in charge of asking how do our humans work with AI? — not how does AI work? Everybody is focused on the second question. The first one is what determines whether anything sticks.
The only way to learn AI is by building with it. Cohort learning is the paradigm that works.
Watch the Recap
Trisha Merriam, Chris Carolan, and Erin Wiggers walk the throughline across all five themes — for people who haven’t seen any of it, and for those who saw a single episode and want a map.
Hosts
Trisha Merriam
Chris Carolan
Erin Wiggers
Coverage
All five themes from six months of weekly conversation.
Best for
First-time viewers and anyone wanting the full map of the series.
The Anchor Episodes
Eight of the fourteen episodes form the navigational backbone of the series. Each is tagged with the themes it anchors. The full episode list lives on the show page.
Data Readiness Through Empowerment
Bill Barlas. Series origin point — readiness as an organizational property, not a technical one.
Open episode →
Leaders Before AI
The AI Orchestrator role named. What leadership looks like in an AI-ready company.
Open episode →
12 Complexity Traps in Builder's Order
Chris walks the 12 Traps in the sequence he actually resolved them. B2B Trap and SaaS Trap as foundational.
Open episode →
Metrics Gap — Part 1
Erin Wiggers demos AI ROI across three measurement dimensions. The Measurement Trap explicitly named.
Open episode →
Metrics Gap — Part 2
The confidence-reality gap. 87% say infrastructure is ready; 42% say it's their biggest blocker.
Open episode →
Governance Architecture
Five governance patterns from VFT's production system. Gateways, delegation hooks, enforcement, tiering, CARs.
Open episode →
Context, Access, Scope
Putting agents in a position to succeed — architectural constraints that make wrong behavior impossible.
Open episode →
5 Themes Recap
Trisha Merriam, Chris Carolan, and Erin Wiggers walk all five themes. The map for new visitors.
Open episode →
Which Gap Hurts Most Right Now?
Pick the one statement that lands hardest for your organization today. We’ll point you at the episode that addresses it directly.
Which of these five gaps is most acute in your organization right now?
Pick the gap that hurts most — we’ll point you to the right starting episode.
Two Ways Forward
The conversation continues weekly. The conversation about your organization can start whenever you’re ready.
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Weekly conversations with practitioners and operators on what AI data readiness actually requires. Free, ongoing.
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One conversation about the gap that hurts most in your organization. We’ll map it, name what’s underneath, and point at the smallest next step.
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