Who is this for?

AI Labs work when they’re run like operating systems — not an IT or “innovation” side project. Led by a CAIO (or equivalent) and shared across tech, data, people, and business outcomes, they help teams make decisions faster, see constraints earlier, and drive adoption.

Scale value beyond pilots — turn one-offs into reusable, repeatable rollouts.

Govern without gridlock — prevent AI sprawl with safe sandboxes and early guardrails.

Focus scarce talent — build an internal bench instead of spreading experts thin.

Prove ROI fast — bring business and tech around a few high-impact bets, then drive adoption.

The problem

Most enterprises don’t have an AI adoption problem.
They have an AI fragmentation problem.
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AI pops up everywhere—tools, pilots, roadmaps—but rarely where money is made: inside real workflows, at scale, with trust.

Teams move fast and still stall because use cases aren’t tied to business goals or end-to-end operations. Data, governance, and integration limits surface late, then land as a blocker.
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Many initiatives also drift into internal productivity upgrades—faster emails, better summaries, smoother handoffs. Meanwhile the real upside comes from AI that changes the customer experience and the business model, not just the back office.
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The result is the pilot-to-scale gap: lots of demos, thin adoption, and no repeatable way to decide what to build, what to scale, and what to stop.
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AI Labs exist to fix this gap — not by building faster, but by deciding better.‍

The AI Labs Pillars

AI Facilitators

Internal champions who turn business goals into decision-grade AI opportunities—and run high-stakes discovery sessions with rigor and focus.

AI Discovery Pods

Small, cross-functional teams formed around one business goal to explore, test, and make a call on AI opportunities—fast, focused, and owned.

2-Day AI Labs

Short, structured discovery loops that combine problem framing and design sprints to produce tested AI agents or workflows—and a clear build / scale / stop decision.

How AI Labs Work

Step 1 - Training AI Facilitators

Build the internal capability first.
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AI Labs start by training a small group of AI Facilitators. They’re not engineers. They’re business, product, or transformation leaders trained in AI Problem Framing, Design Sprints, and decision facilitation.
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Their job is to run the 2-day Lab cadence with discipline—translating business goals into decision-ready AI opportunities and guiding teams to a clear build / scale / stop call.

A team of trained AI Facilitators who can run AI discovery end-to-end: prep & lead 2-day Labs
An AI Lab Operating Kit: playbooks, facilitation decks, agendas, worksheets, and templates.

Duration: 3 days (in-person)

Step 2 - Form AI Discovery Pods

Assemble the right team around a real business goal.
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For each AI opportunity, an AI Facilitator forms a temporary Discovery Pod. Pods bring together business, tech, data, and risk early—before decisions harden. Each pod is accountable for one thing: turning ambiguity into a clear decision.

Focused teams aligned on goals, constraints, and success criteria—early.
Time-boxed pods that pull in the right experts without disrupting day-to-day work

Duration: 1 week

Step 3 - Run a 2-Day AI Lab

Design, test, and decide—fast.
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Each pod runs a 2-day AI Lab
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Day 1: Condensed AI Problem Framing to anchor the opportunity in workflows, users, data, and business value.
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Day 2: Condensed Design Sprint to prototype and test AI agents or workflows with real users.

A tested concept and evidence to support a build / scale / stop decision.

Duration: 2 days

Step 4 - Decide and Act

Scale what works. Stop what doesn’t.
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Validated opportunities move into delivery or scale with confidence.
Weak ideas stop early—before cost, politics, and complexity compound.
Learnings feed back into the Lab system.

Fewer pilots, stronger bets, faster value realization.

Step 5 - Repeat as an Operating Cadence

From initiative to system.
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AI Labs become a repeatable enterprise cadence—run by internal facilitators, aligned with leadership priorities, and embedded into governance.

AI progress that compounds instead of fragmenting.

Duration: ongoing

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AI Labs

WHAT is an ai lab

An AI Lab helps enterprises design, test, and deploy AI agents and workflows that survive regulation, integration, and adoption constraints.

Who is this for?

The problem

The AI Labs Pillars

How AI Labs Work

Outcomes you can expect

AI Lab Operating KIT

Case Studies

Start with a Pilot AI Lab