A structured system installed inside your organisation — so your teams can decide which AI bets are worth building, stop the ones that aren't, and own the capability entirely by the end. Every idea that doesn't survive the process is a six-month pilot that never happened.
Two years of AI investment. Tools deployed. Training run. Experiments launched across teams.
And yet nothing is connecting — no shared prioritization, no clear path from experiment to production, no straightforward answer when leadership asks what the investment is actually delivering.
Bottom-up got the organization moving. It didn't get it deciding. There's a gap between AI strategy and AI execution - the point where opportunities should be defined, validated or stopped - and most organisations have nothing in place there. No process. No ownership. No method for making the call.
The AI Lab is built for organisations that have moved past the question of whether to invest in AI — and are now facing the harder one: why isn't it adding up?
The next ELT review is coming and you need visible results to point to.
Leadership is asking for ROI and you need logic and facts, not a progress update.
Business and tech are running separately — and the collaboration problem keeps showing up in every prioritisation meeting.
Ideas keep getting resourced without proper evaluation — everything gets the AI sticker.
AI adoption, governance, and specific use cases are all running in parallel — and nothing is connecting them
You need a capability that stays inside the organisation when the external partner leaves
Small, cross-functional teams, assembled around specific AI opportunities. They form, do the work, and disband. Temporary by design.
Trained internal facilitators who orchestrate the work. They are the connective tissue between business goals and AI decisions — accountable for the quality of AI decisions and outcomes.
Structured, time-boxed workshops purpose-built for AI.
→ AI Problem Framing 1-day
turns a vague mandate into validated use cases.
→ AI Workflow Sprints 2-4 days
redesign internal workflows around AI
→ AI Design Sprints 2-4 days
build and test customer-facing AI products.
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Every AI Lab that runs makes the next one faster. Use cases get validated or stopped before serious resources are committed. The internal AI Facilitators get sharper with every session. And leadership has a live view of what the system is producing — decisions made, use cases in development, ROI from what reaches production.
Faster decisions. Better bets. A capability that compounds.
A real business challenge, the right cross-functional team, a structured process. DSA facilitates the full Lab — one day of AI Problem Framing to define and validate the use case, followed by an AI Workflow Sprint to redesign the workflow and test a working prototype with real employees. By the end, your organisation has a validated use case and has experienced what a well-run Lab actually feels like. That's often the most important output of the first one.
1 challenge · 1 AI Problem Framing · 1 AI Workflow Sprint
Your internal people learn to run Labs themselves. A five-day in-person program covering the full methodology: AI Problem Framing on day one, AI Workflow Sprint on days two and three, AI Design Sprint on days four and five. They leave with the full facilitation toolkit — methodology, playbooks, agendas, and the AI Copilot — ready to run Labs without DSA in the room.
Up to 15 participants · 5 days · In-person
Newly trained facilitators don't fly solo immediately. For each of the first three Labs, DSA is present for preparation, in the room during the session, and available for debrief afterwards. This is where real capability transfer happens — the methodology gets adjusted to fit the organisation, the facilitators start owning the process, and the metrics dashboard gets built alongside so leadership has a live view of what the Lab is producing from day one.
3 Labs · DSA present throughout
Turner Construction came with a real problem. 11,000 employees. Hundreds of active job sites. Decades of operational knowledge locked inside people's heads, with no reliable way to surface or scale it. Leadership knew AI was part of the answer — they just didn't know which part.
DSA ran an AI Problem Framing session with Directors and VPs, facilitated AI Workflow Sprints across three teams with three specific use cases, and trained Turner's own innovation team to run the methodology independently. Read more about it.
What Turner built from there:
400+ custom AI applications built by their employees
70,000+ hours of annual capacity unlocked
A running internal AI Lab operating model
How the AI Lab works in practice — and what we've learned installing it inside large organisations.
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