Endava and AI agents: how software delivery is changing

Endava: timeline of a quiet transformation

Endava isn't a name that often pops up in Italian SME conversations. However, it's one of the most significant digital engineering companies globally. Headquartered in London with operations across Europe, Latin America, and North America, the company manages complex projects for clients in the finance, healthcare, and retail sectors.

During 2025, Endava launched a structured program for adopting AI agents. The stated goal was clear: to reduce software time-to-delivery without sacrificing quality. The program rolled out in progressive phases, starting with high-volume use cases and low decision-making complexity.

Therefore, the starting point was not the most advanced technology available. It was identifying the real bottlenecks in the development process. This methodological choice is perhaps the most useful lesson for anyone looking at the case from the outside.

The core tools: ChatGPT Enterprise and Codex

The case study published by OpenAI explains in detail the tech setup used by Endava. At the core are two tools: ChatGPT Enterprise and Codex.

ChatGPT Enterprise has been integrated into developers' daily workflows. In particular, it has found application in generating technical documentation, writing automated tests, and reviewing existing code. Codex, on the other hand, has been used to accelerate code production in specific languages, reducing the time spent on repetitive tasks.

In addition to this, Endava has developed specialized agents for vertical tasks. For example, an agent dedicated to migrating legacy codebases, another for automatically generating technical specifications from business requirements. So, this is not a generic use of AI: it's a targeted orchestration of specific capabilities.

According to the estimates reported in the case study, some phases of the development cycle saw time reductions in the range of 30-40%. These numbers are consistent with what was observed by McKinsey in their studies on dev productivity with generative AI .

Who won and who had to adapt

In every transformation like this, there are winners and those who have to reinvent their role. In Endava's case, the line is drawn clearly.

Senior developers benefited the most from automation. In fact, freed from low-value tasks, they could focus on architecture, critical review, and design decisions. Their qualitative output increased in a measurable way.

Conversely, junior profiles have experienced a more complex transition phase. Tasks traditionally assigned to entry-level developers — writing boilerplate code, basic documentation, manual testing — have become the domain of AI agents. Therefore, Endava had to redesign onboarding and training paths for this group of professionals.

Finally, project managers saw the nature of their work change drastically. Managing task dependencies became partly automated. As a result, their value shifted toward AI process governance and exception handling.

The cultural dimension: what the numbers miss

We at SHM Studio we feel the coolest part of the Endava story isn't the tech. It's how they organized things.

The company has invested significantly in building an AI-native culture. This has meant widespread training programs, internal communities of practice, and — an often overlooked aspect — a governance system that defines when agents can act autonomously and when human supervision is needed.

Similarly to what we've seen in other enterprise settings, the main risk wasn't resistance to change. It was surface-level adoption: using AI tools without tweaking the underlying processes. Endava chose the longer, but sturdier path. As a result, they locked in results that stand the test of time.

This approach is supported by research from Harvard Business Review on building an AI-ready culture , which points to governance and training as the big keys to success for enterprise rollout.

SHM Studio's take: what this means for Italian SMEs

The Endava case is enterprise by definition. However, the dynamics it describes are also relevant for smaller companies. Italian SMEs operating in B2B or digital retail contexts are facing similar choices today, on a different scale.

First off, the AI agents approach doesn't need big-corporation setups to work. Tools like AI solutions already available on the market allow you to automate specific workflows—from content management to lead qualification—with affordable investments.

Secondly, the culture lesson is directly transferable. A 50-person company that adopts AI tools without redefining roles and responsibilities will get marginal results. On the other hand, a company that integrates AI into existing processes with method and training can gain significant competitive advantages.

So, a small business shouldn't kick things off by picking software. They need to map out high-volume tasks that don't need much heavy thinking—just like Endava did.

Real-world uses: from dev workflows to marketing tasks

The logic of AI agents applied by Endava to software development has a direct counterpart in digital marketing and communication processes. In these areas too, there are high-volume, structured, and repetitive tasks that lend themselves to smart automation.

For instance, writing SEO articles, running online ad campaigns, and putting together routine reports are great spots for AI agents to take over with just a quick human check. Our services for SEO and Digital marketing already integrate these logics into operational workflows.

Furthermore, content generation for LinkedIn campaigns and Google Ads directly benefits from assisted automation. This is not about replacing human strategic judgment, but rather speeding up the operational phases following decisions.

Similarly, the Copywriting professional is shifting towards a hybrid model: the pro sets the strategy, tone, and goals; the AI agent drafts content and variations; the pro reviews and tweaks it. This setup is already up and running in plenty of global agencies.

The next moves: what we expect in the next 18 months

The Endava case anticipates a direction that will become mainstream by 2027-2028. Some trends are already identifiable with sufficient clarity.

First of all, agent specialization will increase. Today, AI agents are relatively general-purpose. Later, we'll see vertical agents for specific industries — legal tech, healthcare, manufacturing — with reasoning skills tailored to their domains.

Plus, agent governance is turning into a must-have skill. Companies that are setting up rules and review processes right now will have a massive edge down the road as these tech setups get trickier.

Finally, the integration between AI agents and platforms of web development and CMS will become standard. Companies that currently manage their digital presence manually will find themselves at a competitive disadvantage compared to those that have automated recurring operational processes.

To dive deeper into this or see how to bring AI into your team's workflow, you can contact the SHM Studio team or explore related articles in our Blog .