Q&A with Matt Cloke, Chief Technology Officer, Endava, on what it takes to scale AI across an enterprise and the lessons learned from embedding AI into collaboration across a global workforce.
Tell us a bit about you and your role
I’m the Chief Technology Officer at Endava. I lead our technology strategy and delivery capability globally and my focus is straightforward: taking what technology makes possible and turning it into something that works reliably in the real world.
We’re at one of those inflection points in technology. AI isn’t just another tool – it’s changing how software is built, tested and run. Tasks that once took days can now be completed in minutes. That’s exciting, but it also raises an important question: how do you move faster without losing control?
My role is to ensure we harness that acceleration responsibly – embedding AI into real delivery environments in a way that is governed, measurable and aligned to business outcomes.
How can embedding AI into collaboration tools help scale AI beyond pilots?
In our experience, AI pilots fail because they sit outside the core workflow. A team might experiment with a model or an assistant, but if it’s not embedded into how decisions are made and work is structured, it remains a side experiment. Scaling happens when AI becomes part of the operating rhythm – not an add-on.
When AI is integrated into collaboration environments, it can help teams clarify intent, structure requirements, generate artefacts and maintain alignment in real time. That reduces ambiguity – and ambiguity is often the hidden cost in Digital Transformation programmes.
For us, scaling AI isn’t about having more tools. It’s about redesigning how work flows so humans and intelligent systems operate together safely and predictably.
You’ve mentioned redesigning the operating model for the AI era. What does that look like in practice?
At Endava, that thinking led us to develop Dava.Flow.
Rather than treating AI as a feature or a bolt-on capability, Dava.Flow is about redesigning how work moves from idea to outcome. It connects four stages of change – identifying what matters, shaping clear intent, delivering in controlled increments and continuously improving once live – into a single, visible flow.
The important shift is this: when AI can generate code, tests or analysis in minutes, the bottleneck is no longer effort. It’s clarity and governance. Dava.Flow focuses on making decisions explicit, structuring work so both people and intelligent systems can act on it safely and ensuring every change leaves an evidence trail.
It’s not about replacing humans or introducing more process. It’s about evolving how delivery works when machines participate alongside humans. The result is faster progress, but with stronger accountability and clearer measurement of impact.
Back in February you partnered with Miro to bring collaborative AI workflows to Endava’s global operations. How does Miro’s AI support faster change management and compliance?
We have found that collaborative AI tools are powerful when they don’t just generate ideas but also help structure and trace decisions. In workshops, for example, AI can synthesise inputs, surface patterns and produce consistent artefacts almost instantly.
The real advantage is that this shortens the time between exploration and execution. What used to take weeks – capturing insights, documenting decisions, aligning stakeholders – can often be compressed to days.
But the compliance angle matters just as much. When decisions are visible, structured and traceable, governance becomes part of the workflow rather than a separate review process. That’s how you move faster without sacrificing accountability.
What impact has integrating AI into team collaboration had on performance?
The biggest shift has been clarity and cycle time. AI can accelerate coding and testing, but the real performance gains come earlier – in defining what needs to be built and ensuring alignment before execution begins.
We’ve seen measurable reductions in idea-to-value lead times, along with improvements in release quality and predictability. When intent is clearer and artefacts are structured from the start, rework decreases significantly.
There’s also a mindset shift. Teams move from tracking effort – hours, tasks, velocity – to tracking outcomes: impact delivered, reliability improved, time saved. That changes the conversation from activity to value.
How does Endava use AI to train teams to work confidently with agentic tools?
In our experience, confidence builds when there is clear structure behind the tools.
We focus on three things:
- Fluency – helping people understand how agentic tools work and where they add value.
- Judgement – knowing how to validate outputs and challenge assumptions.
- Governance awareness – understanding that AI outputs must be reviewed, traceable and safe.
We use hands-on project environments rather than theoretical training. Teams learn by working with AI in real delivery contexts, with clear guardrails in place.
As people see that AI can accelerate parts of their work without undermining quality or accountability, confidence builds naturally.
How do you see AI shaping the future of consulting at Endava?
AI is reshaping consulting at a much deeper level than simple technology adoption. It’s changing both what clients are asking for and what they’re willing to pay a premium for.
Over the past two years, many organisations have experimented with AI. They’ve built prototypes, launched assistants and tested automation. What they’ve discovered is that generating output is relatively easy. The harder challenge is making AI reliable, governable and commercially meaningful at scale. That’s where expectations have shifted.
Clients now want three things: speed, yes – but also predictability and evidence. They want to know not just that something can be built quickly, but that it will stand up to audit, reduce risk and deliver measurable business value. In other words, the conversation is moving from “What can AI do?” to “How do we run our business differently because AI exists?”
That forces consulting firms to evolve. It’s no longer enough to advise on use cases or deploy tools. We have to help organisations rethink how decisions are made, how work is structured, how accountability is defined and how outcomes are measured. AI compresses cycle times, which means governance, clarity and operating discipline become even more important – not less.
So the real shift isn’t just technological. It’s operational and economic. The firms that succeed will be those that can combine acceleration with assurance – helping clients move faster while increasing confidence.
For us, that’s exactly why we invested in evolving our own delivery model through Dava.Flow. It gives clients a practical way to translate AI’s potential into structured, governed execution – not just experimentation.
And the organisations that embrace that shift early, thoughtfully and responsibly, will build a structural advantage that compounds over time.
