Deployment evidence

AEVA in Production

Two enterprise deployments. Real governance challenges. Measurable outcomes. AEVA was not designed in a consulting firm — it was designed in boardrooms, delivery rooms, and post-mortems where the same failure patterns repeated across sectors.

MANUFACTURING

Yamaha Motor Solutions India

2025 — First Live AEVA Deployment

Situation

Shadow AI proliferation detected across 5 departments before any policy existed. Employees openly using ChatGPT via personal mobile devices on uncleared production data. No data security governance, no output validation, no organisational visibility. Leadership, meanwhile, continued projecting an image of purposeful AI adoption in external communications. The gap between projection and reality was structural.

Challenge

Contain the Shadow AI risk without destroying delivery momentum. Govern a 300+ person operation where unsanctioned AI usage had spread across multiple teams simultaneously.

AEVA elements applied

Precision Backlog Refinement · Shadow AI Proliferation Mitigation · Increment Delivery Charter · Enterprise AI Visibility

Approach

1
Conducted first AI tool inventory across all 5 departments — surfacing the full scope of unsanctioned usage before attempting governance
2
Deployed Increment Delivery Charter — sanctioned tools, data classification boundaries, output accountability assignment per Increment
3
Introduced Precision Backlog Refinement with Functional-Technical AC Taxonomy — creating process-level immunity where Shadow AI output must comply with Functional AC to pass Feature Clearance regardless of which tool generated it

400+

hours of delivery capacity recovered

departments brought under governance framework

1st

live enterprise deployment of AEVA framework

uncontrolled AI tool usage remaining after governance implementation

Shadow AI is not a technology problem. It is a governance vacuum. The moment you create a clear, fast, low-friction path to sanctioned AI use — adoption of unsanctioned tools drops immediately. The technical fence cannot be made high enough. Process-level immunity is the only durable solution.

AVIATION

Etihad Airways · Dubai Airports

Multi-Organisation Programme

Situation

200+ person aviation transformation programme. Agentic delivery workflows introduced mid-programme. No governance alignment layer. Multi-organisation complexity — airline and airport authority with different risk appetites, regulatory frameworks, and internal governance cultures.

Challenge

Establish a single governance model accepted by two independent organisations without requiring either to change their internal policies.

AEVA elements applied

Alignment Architecture (Dimension 04) · Velocity Preservation · Identity Crisis Mitigation · Financial Model Application

Approach

1
Applied AEVA financial model to reframe per-project cost vs annual team cost — creating commercial alignment between organisations on delivery investment
2
Deployed Identity Crisis mitigation for senior practitioners whose delivery authority was challenged by AI-assisted workflows — reframing from "I build" to "I govern what AI builds"
3
Established shared AI governance language between airline and airport authority — Increment Delivery Charter as the contract between organisations

500+

hours recovered through structured governance

200+

person programme under governance framework

independent organisations aligned to single governance model

108%

revenue growth achieved in delivery portfolio (Coforge)

In multi-organisation programmes, governance is the contract between parties. AEVA's approach gave both organisations a shared language for AI risk — without requiring either to change their internal policies. The framework adapted. The organisations did not have to.

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