Use Case Intelligence RSS Search use cases Search
OpenAI Commercial and Professional Services Global

Endava

Application Refactoring · IT Operations Automation

Endava needed to remain relevant in an AI-centric market by redesigning software delivery and cross-functional workflows, not just adding point tools to existing engineering processes.

Read the original story Explore all use cases
11,000-person global workforce
Expanded AI adoption beyond engineering into legal, finance, and operations teams

Reported impact

Business outcome

Endava accelerated software delivery, reduced manual coordination work, spread AI usage beyond engineering, and established AI fluency as an expected behavior across hiring, leadership, and promotion decisions.

  • Productivity Improvement
  • Scalability
  • Speed to Market

Solution

Endava made OpenAI its enterprise AI platform and rebuilt delivery around agentic workflows rather than limiting AI to coding assistance. The transformation started in software-delivery teams, where developers using ChatGPT Enterprise and Codex discovered that faster engineering exposed new bottlenecks in requirements gathering, planning, business analysis, and stakeholder coordination. That led to DavaFlow, Endava’s AI-native delivery methodology, which applies OpenAI technology across the full lifecycle from meeting preparation and business planning to discovery, software engineering, deployment, and operational coordination. The rollout then expanded into legal, project management, finance, operations, and commercial teams, where Codex helps create governance reports, summarize engineering progress, and even replace spreadsheet-heavy planning with lightweight interactive applications. Leadership teams also use always-on agents for project summaries, communications, and inbox coordination, making AI fluency part of daily work and career expectations across an 11,000-person workforce.

Data flow

Meeting context, requirements, business plans, project updates, and engineering tasks move through DavaFlow into ChatGPT Enterprise, Codex, and background agents. Outputs become software artifacts, governance reports, planning apps, or summaries that teams across Endava review and use in client delivery.

Solution architecture

4 components · 2 layers
  1. Serving
    • ChatGPT Enterprise Provides the enterprise AI surface Endava employees use across delivery, planning, and collaboration workflows.
  2. Orchestration
    • Codex Generates code, reports, summaries, and internal apps across engineering and adjacent business functions.
    • DavaFlow Defines the AI-native delivery methodology that embeds OpenAI technology across the full software lifecycle.
    • AI agents Automate project summaries, communications, inbox management, and other asynchronous coordination tasks.

Architecture clues

  • Commercial teams replaced spreadsheet-heavy exercises with AI-generated single-page apps
  • DavaFlow spans meeting preparation, business planning, discovery, engineering, and deployment
  • Leadership teams run agents to summarize projects and coordinate work asynchronously
  • Project managers use Codex for governance reports and engineering summaries

Evidence from the source

"As Endava rolled out AI across its 11,000-person global workforce"
"Project managers started using Codex to generate governance reports and summarize engineering progress."
"That realization led to the creation of DavaFlow, the company’s AI-native delivery methodology."
"There isn’t a part of DavaFlow that doesn’t use OpenAI technology."
Transparency Evidence & methodology 75/100 reference value · 87% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value75/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact0/25
    0 metrics describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence87%
  • Source authority20/25
    The main evidence is an official vendor-published customer story.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    4 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity8/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. OpenAI

    Endava customer story

    First-party
    https://openai.com/index/endava-frontiers/
    Source type
    Vendor-published customer story
    Published
    Oct 8, 2026
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page Oct 8, 2026 · OpenAI · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.

Related

Similar use cases

See all like this
OpenAI May 28, 2026 Endava Commercial and Professional Services Global Endava uses Codex to scale senior engineering expertise across its full delivery lifecycle. Application RefactoringIT Operations Automation Codex OpenAI May 7, 2026 Simplex Commercial and Professional Services Asia-Pacific & Oceania Simplex wanted a repeatable way to measure and scale AI-driven software delivery across design, implementation, testing, and operations instead of relying on ad hoc developer experiments. 17% less time needed for internal integration testing Application RefactoringIT Operations Automation ChatGPT Enterprise · Codex · Codex CLI OpenAI Oct 9, 2026 Asana Software / SaaS North America Asana needed to cut the cost and latency of its StackAI browser agent so it could offer customers more capable models for browser-driven workflows without unsustainable operating expense. $0.47 average estimated model cost with the optimized GPT-6.1 Sol workflow Application RefactoringIT Operations Automation Codex · Command · GPT-6 Astra