At Canvas 26, Miro’s annual conference for innovation and collaboration leaders, Endava’s Regional CTO for Europe and Global Head of Dava.X AI group, Joe Dunleavy, shared how the company built Dava.Flow™ — and what three plus years of AI transformation has actually taught them about change..
Challenge
When AI can generate code, what exactly is a software services company selling?
That’s not a rhetorical question. Three years ago, it was the live strategic problem facing Endava’s leadership, and the answer they landed on has reshaped how the company delivers, prices, and sells its work.
Endava is a global technology and consulting services company with 11,000+ people and around 60 delivery centers worldwide. For decades, they’ve operated on one conviction: technology and people are inseparable. When generative AI arrived, that conviction got tested fast.
Endava’s answer: context quality, not code generation. The insight that would lead to the creation of Dava.Flow™ was that the scarce resource in AI-assisted delivery isn’t technical output. It’s the structured understanding of what problem you’re actually solving, for whom, and under what constraints.
Solution
Dava.Flow™ is Endava’s proprietary AI engagement lifecycle methodology. Rather than layering AI onto existing delivery processes, it reimagines the entire engagement around one premise: get the context right first, then let agents work.
The methodology runs in four phases:
- Signal: qualifying the right problem. Before any AI agent writes a line of code, the work is anchored in a clear understanding of the business challenge, competitive context, and desired outcomes.
- Explore: structuring and enriching that context. This is where information from Signal gets synthesized into the structured inputs that give AI agents the guardrails they need. Joe Dunleavy was direct about why this phase is critical: “By the time you get to the agents writing code, they are working on a focused context. It’s a very managed, controlled environment. That’s where you get the higher quality of the output that is important to enterprise customers.”
- Govern: human oversight during delivery. Teams stay in the loop as AI agents do the technical work, iterating toward production-ready output.
- Evolve: monitoring in production. Telemetry and performance data feed back into Signal, creating a continuous loop rather than a one-time handoff.
Miro is embedded throughout. Endava built their entire Dava.Flow™ knowledge base and part of the delivery environment on the Miro canvas — from onboarding materials to the live working boards where client context gets captured and refined. The canvas serves as Endava’s context warehouse: the place where distributed teams collaborate on the information that makes the whole methodology work. Miro’s Sidekicks support the Signal and Explore phases specifically, helping teams surface insights from client conversations and structure them into the inputs that make AI output reliable.
“The entire criteria, the roadmap, all of what makes up Dava.Flow™ — we presented all of that information in Miro, in a way that people can actually use.”
Joe Dunleavy, Regional CTO for Europe and Global Head of Data.X AI at Endava
Impact
Dava.Flow™ is live and already winning work. Joe cited active programs with large enterprises, where the methodology is being used to scope, deliver, and evolve AI-enabled solutions.
The shift isn’t just about delivery speed. As AI handles more execution, Endava’s teams invest more time in problem qualification, context-building, and client partnership — the work that creates new value rather than automating existing processes.
Joe drew a sharp line between the two modes: “The world, at the moment, is obsessed with value capture — how do we get our ten, fifteen percent of automation? But that’s losing the point. The opportunity in AI is really strong collaboration and the creation of new things. Value creation is what we’re here for and excited to help deliver with our clients”.
That distinction is also changing how Endava sells, moving toward outcome-based models that reflect the new economics of AI-assisted delivery.
Bigger picture
Joe’s most candid moments at Canvas 26 weren’t about what’s possible.They were about what’s hard.
“The technology is ready. It’s the worst it’s going to be today — it’s only going to improve. The challenge of doing an AI transformation like this isn’t waiting for the technology to be perfect. It’s bringing people along on the journey with you.”
Joe Dunleavy, Regional CTO for Europe and Global Head of Data.X AI at Endava
As Endava continues to navigate that journey, here’s what they’ve found actually moves the needle:
- Tiered training works better than broad rollouts. Endava built a structured program, from apprentice-level onboarding to team-level practice, so that AI fluency could grow at different rates across different roles without leaving anyone behind.
- Champions networks matter more than mandates. Getting 11,000+ people to genuinely change how they work requires people inside each team who believe in the direction and model the new behavior day-to-day.
- Groups like Legal, HR, and leadership have to move together. Adoption stalls when operational functions aren’t brought along with delivery teams. The organizational infrastructure is as important as the technical one.
- Strong partnerships accelerate everything. “We don’t have to do this alone,” Joe said. “Where you’ve got strong partners, that’s the play.”
The methodology is the visible output. The training programs, champions, and cross-functional coordination are what made it real — and what any organization attempting a similar shift will need to invest in first.
