Endava is rethinking how software gets delivered with AI agents at the core

If you're curious about how a large technology services company actually puts AI to work—beyond the hype and pilot projects—Endava offers a useful example. The company, which builds software for clients across finance, healthcare, retail, and other industries, has been weaving AI agents, ChatGPT Enterprise, and Codex into its day-to-day operations. In this article, we'll look at how these tools support software delivery, which workflows they automate, and how Endava is shaping an AI-native culture across its teams.

Why an AI-Native Approach Matters

Endava's goal isn't simply to bolt AI onto existing processes. Instead, the company is working toward becoming what it describes as AI-native—meaning AI is treated as a normal part of how work gets done, not an optional extra. For a services firm that ships software for hundreds of clients, even small gains in speed or quality compound quickly across thousands of projects.

This mindset shapes everything from how engineers write code to how project teams handle documentation, testing, and client communication. Rather than treating AI as a standalone tool a few enthusiasts use on the side, Endava encourages widespread adoption with training and clear guardrails so employees can use these systems confidently and safely.

ChatGPT Enterprise Across the Workforce

One of the cornerstones of Endava's approach is ChatGPT Enterprise, which gives employees a secure, company-approved way to use a powerful language model in their daily work. Because it's an enterprise-grade offering, it comes with the data protections and administrative controls a company handling sensitive client information needs.

Teams use ChatGPT Enterprise for a wide range of tasks: drafting and summarizing documents, analyzing requirements, preparing meeting notes, brainstorming solutions, and answering questions about unfamiliar technologies. The value here isn't just individual productivity—it's consistency. When everyone has access to the same capable assistant, knowledge spreads more evenly across the organization, and junior staff can ramp up faster.

Codex and AI Agents in the Development Workflow

For engineering teams, Endava has turned to Codex to accelerate the core work of building software. Codex can help generate code, suggest fixes, and handle repetitive implementation tasks, which frees developers to focus on architecture, design decisions, and understanding client needs.

Beyond coding assistants, Endava is also exploring AI agents—systems that can carry out multi-step tasks with less hand-holding. These agents can take on workflow automation that used to require manual effort: gathering information, coordinating steps in a delivery pipeline, or handling routine tasks that don't need deep human judgment. The idea is to let people spend their time on the work where human creativity and client understanding actually matter.

It's worth noting that agentic AI is still maturing across the industry. Companies like Endava tend to start with well-scoped use cases, prove value there, and expand gradually as confidence and tooling improve.

Building the Culture, Not Just the Stack

Tools alone don't create transformation. A significant part of Endava's effort goes into culture: helping tens of thousands of employees understand what AI can and can't do, and giving them practical training rather than abstract theory.

This includes establishing responsible-use practices—being careful with client data, reviewing AI-generated output before it reaches customers, and being transparent about where AI was used. By setting these expectations early, the company reduces risk while still moving fast.

There's also a shift in mindset around roles. As AI takes over more routine tasks, the emphasis moves toward skills like problem framing, quality judgment, and client collaboration. Employees are encouraged to see AI as a collaborator that amplifies their work rather than a threat to it.

What Other Companies Can Learn

Endava's experience points to a few practical takeaways for organizations considering a similar path:

• Start with secure, enterprise-grade tools. Data protection is non-negotiable when client work is involved.
• Prioritize adoption over experimentation. Broad training and easy access beat small pilots that never scale.
• Automate workflows, not just tasks. The biggest wins come when AI handles whole processes, like parts of the delivery pipeline, rather than isolated steps.
• Invest in people. Cultural readiness and clear guidelines determine whether AI initiatives stick or fade.

None of this requires exotic technology. ChatGPT Enterprise, Codex, and emerging agent frameworks are available to most organizations—the differentiator is disciplined, company-wide execution.

The Road Ahead

Endava's journey with AI is ongoing. As agents become more capable and development tools grow more sophisticated, the company plans to deepen how AI supports software delivery—from early requirement gathering through testing and maintenance. For now, its story illustrates a clear pattern: pairing powerful tools with deliberate cultural change is what turns AI from a buzzword into measurable business impact.

For teams watching from the outside, the lesson is simple. The tools are ready. The harder—and more rewarding—work is building the habits, training, and trust that let people use them well every day.