How Endava is redesigning software delivery around AI agents

2026-06-04 · Source: OpenAI News · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, short

Summary

Endava, a global technology services company with over 25 years of experience, is fundamentally redesigning its software delivery and enterprise workflows by embedding AI agents. The company adopted OpenAI's ChatGPT Enterprise and Codex as its enterprise AI platform, aiming to integrate AI into daily work rather than just introducing new tools. This transformation led to DavaFlow, an AI-native delivery methodology that utilizes OpenAI technology across its entire lifecycle, from planning to deployment. Initially focused on software delivery teams, AI adoption expanded to Endava's 11,000-person global workforce, including legal, project management, and commercial teams, streamlining research, reporting, and internal application development. This initiative has accelerated software delivery, reduced manual work, and established AI fluency as a core expectation.

Key takeaway

For CTOs or Directors of AI scaling enterprise AI initiatives, prioritize embedding AI agents across all business functions, not just engineering. Your strategy should treat AI adoption as a fundamental behavior change, requiring active leadership use and a culture of experimentation. Start personally engaging with AI tools to understand their potential, then integrate them into everyday workflows to accelerate delivery and reshape operational models.

Key insights

Organizations can achieve AI-native operations by embedding AI agents as the primary problem-solving approach across all workflows.

Principles

• Treat AI adoption as behavior change.
• Leaders must actively use AI.
• Create space for experimentation.

Method

Endava's DavaFlow methodology integrates OpenAI technology across the entire software delivery lifecycle, from meeting preparation and business planning to product discovery, engineering, and deployment.

In practice

• Streamline legal research and documentation with AI.
• Generate governance reports and project summaries via AI.
• Develop internal apps like pricing tools without dedicated engineering.

Topics

• AI Agents
• Software Delivery
• Enterprise AI Adoption
• OpenAI
• DavaFlow
• Digital Transformation

Best for: VP of Engineering/Data, Executive, CTO, Director of AI/ML, Consultant

Related on AIssential

• AI Dev 26 x SF | João Moura: Building Recurring, Governed, and Embedded Enterprise Workflows — Enterprise agent adoption is a transformational problem requiring strategic planning, reusable components, and human-in-the-loop integration for success.
• TAI #203: OpenAI, Anthropic, and Wall Street Race to Build the AI Deployment Layer — Enterprise AI adoption requires significant hands-on integration and workflow redesign, not just model access.
• Six things have to be true before AI changes a workflow. Most companies have built two. — The strategic value in enterprise AI now lies in deep workflow integration, not just model access.
• Using Agents in Production: Past Present and Future // Euro Beinat — Large-scale AI agent adoption is primarily an organizational and cultural challenge, not just a technical one.
• People Moves: Noe Ramos, VP of AI Operations at Agiloft — Effective AI adoption requires disciplined evaluation, human alignment, and embedding AI into existing workflows.
• A New Way to Make AI Actually Work in the Real World — Effective enterprise AI deployment requires a systemic operating model, not just individual talent or technology.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by OpenAI News.