What Changed at Endava
Endava, a global technology services company with over 25 years of experience, has made AI central to its mission. But adopting AI wasn’t just about introducing new tools—it required rethinking workflows, leadership behaviors, and team collaboration. Matthew Cloke, CTO of Endava, shared in an interview with OpenAI how the company embedded AI across the entire organization, redesigning software delivery around agents.
“AI has had a fundamental impact on Endava over the past couple of years,” says Cloke. “We really had to answer the question of how to be a relevant organization in the new AI world.”
That mindset led Endava to make OpenAI its enterprise AI platform, giving employees access to ChatGPT Enterprise and Codex. The goal wasn’t simply adoption—it was making AI part of the flow of everyday work.
“To be AI-native at Endava, it’s about thinking about AI to solve the problem first,” Cloke explains. “It’s the first thing you do rather than the last thing that you do.”
How It Works: AI Embedded in the Delivery Lifecycle
Endava’s transformation began inside its software delivery teams. As developers experimented with AI-assisted coding and agentic workflows, they quickly realized the bottleneck was no longer engineering output. Requirements gathering, business analysis, planning, and stakeholder coordination all needed to move faster too.
“We started to challenge how quickly we could produce requirements and how quickly we could produce the right business solutions for our clients,” says Cloke.
Today, OpenAI technology is embedded throughout the entire DavaFlow lifecycle—from meeting preparation and business planning to product discovery, software engineering, and deployment. Cloke states, “There isn’t a part of DavaFlow that doesn’t use OpenAI technology.”
Adoption didn’t stop with developers. Legal teams began using AI to streamline research and documentation workflows. Project managers used Codex to generate governance reports and summarize engineering progress. Commercial teams replaced spreadsheet-heavy planning exercises with lightweight AI-generated applications.
In one internal pricing discussion, employees skipped spreadsheets entirely and instead built a single-page pricing app teams could interact with immediately. “It changed the conversation completely,” Cloke says.
AI agents have also become embedded in day-to-day operations. Leadership teams use agents to summarize projects, automate communications, manage inboxes, and coordinate work asynchronously.
Practical Use Cases and Implementation
Endava’s experience offers concrete examples for product builders:
- Accelerated software delivery: Integrating AI agents into engineering workflows sped up delivery.
- Cross-functional adoption: AI expanded beyond engineering into legal, finance, and operations teams.
- Reduced manual work: AI-assisted workflows cut manual reporting and coordination.
- Empowered non-engineers: Teams built internal tools without dedicated engineering support.
- AI fluency as a hiring criterion: AI fluency became part of hiring and promotion expectations.
For product builders, the key is to identify bottlenecks beyond engineering. If your team’s constraint is requirements gathering or stakeholder coordination, consider how AI agents can assist. For example, use AI to draft requirements from meeting notes, generate status reports, or create lightweight internal tools that replace spreadsheets.
Limitations and Trade-offs
While Endava’s results are impressive, there are trade-offs. The transformation required a deliberate cultural shift, not just tool adoption. Cloke emphasizes that AI adoption is a behavior change, not a software rollout. Leaders must actively use AI to drive organization-wide adoption. Creating space for experimentation—even when outcomes are imperfect—is essential.
Another limitation is that AI is not a silver bullet. The bottleneck simply moved from engineering to other areas, requiring continuous workflow redesign. Also, non-technical teams need to be brought in early, not later, to ensure alignment. Hands-on experience is the fastest way to overcome skepticism, but it requires time and investment.
Key Takeaways for Product Builders
Endava’s journey provides actionable lessons:
- Treat AI adoption as a behavior change—not a software rollout. It’s about changing how people think about problem-solving.
- Leaders must use AI themselves—to model the behavior and drive adoption.
- Create space for experimentation—even when outcomes are imperfect.
- Bring non-technical teams in early—so they can contribute to workflow design.
- Make AI part of everyday workflows—not a separate initiative.
Cloke’s advice for organizations still early in the journey is straightforward: start using the technology personally. “The future arrived,” he says. “You just have to lean into it.”
What’s Next: Orchestration and the Operating Model
Endava sees the next phase of enterprise AI centered around orchestration—combining models, agents, workflows, and human expertise into integrated systems that reshape how organizations operate. “We’re really excited about the workflows that can be created by combining these tools,” says Cloke.
From reasoning models and Codex agents to automation and enterprise-scale collaboration, AI is becoming more than a productivity layer. It’s becoming the operating model itself. For product builders, this means designing products that can orchestrate AI agents and human workflows seamlessly.
The key question isn’t “should we use AI?” but “how do we redesign workflows so AI is the first thought?” Endava’s example shows that the answer lies in embedding AI into every part of the delivery lifecycle, from requirements to deployment, and making it a core part of the company’s culture.
Sources
AI-assisted summary compiled from the sources above, reviewed by a human before publishing.