Enterprise AI Consultation
Most enterprise AI stalls in adoption, not technology. I have crossed that gap, from first pilot to measurable organisation-wide value, and I bring that execution record directly to your leadership team.
- Gavin
The gap is not in technology.
It is in execution.
AI adoption is near-universal. The ability to translate that adoption into measurable business value is rare. Most organisations have run the pilots and acquired the tools; few have crossed the harder gap of embedding AI into how the organisation actually works.
External partnership with the right advisor roughly doubles the odds of transformation success versus internal build alone — not because internal teams lack capability, but because the pattern recognition and execution precedent simply do not exist inside most enterprises yet.
Sources: McKinsey Global Survey 2024; MIT NANDA 2025
Services
AI Readiness Assessment & Roadmap
A structured evaluation of your organisation's AI posture — technology, data, people, and governance — producing a clear, sequenced roadmap to measurable value. An honest picture of where you are and what to do next.
Independent AI Architectural Review
A candid, outside-in review of an existing AI programme, platform, or major initiative. Identifies structural risks, missed leverage points, and the decisions that most affect long-term cost and capability.
AI Adoption & Scaling Programme
Sustained advisory on moving from isolated pilots to embedded, organisation-wide AI practice — covering the pipeline, governance, and culture change that separates high performers from the rest.
AI Enablement Platform
Advisory on building or maturing the internal platform that lets your engineering teams adopt AI tools safely, consistently, and at scale — an inner-source capability, not a vendor dependency.
Agentic SDLC Transformation
Guidance on transitioning the software development lifecycle to agentic workflows — where AI agents handle routine tasks end-to-end, freeing senior engineers for high-judgment work.
Fractional Chief AI Architect
Ongoing embedded advisory at the executive level. A trusted counterpart for your CIO or CTO — present in the decisions that matter, without the overhead of a full-time hire.
Case Studies
Enterprise AI Enablement Platform
Situation
AI tool sprawl across a 9,000-engineer organisation. Teams were building similar capabilities independently — no shared foundation, no governance, no way to leverage organisation-wide learnings.
Approach
Designed and built an inner-source AI platform using a CNCF-inspired maturity model (Sandbox → Incubating → Graduated). Included a shared LLM gateway, a skills registry, an MCP registry, and a developer portal.
Result
A single governed platform serves the entire engineering organisation — reducing duplication and enabling quality and cost controls at scale.
Agentic ALM — 100% Agent-Driven Operations
Situation
A platform engineering team managing 160+ repositories and 80+ cloud accounts faced growing operational overhead. Routine tasks were consuming senior engineering time.
Approach
Designed and implemented a fully agentic application lifecycle management system. AI agents handle the complete operational loop — from issue triage to infrastructure queries — with a confirm-before-mutate safety model for consequential actions.
Result
100% of routine ALM operations now handled by agents. Senior engineers focus on high-judgment work. The safety model has maintained a zero-incident record on destructive operations.
Spec-Driven Development — Field Study
Situation
Informal claims about AI coding productivity varied widely and were rarely supported by controlled evidence. Leadership needed data to make resourcing decisions.
Approach
Conducted a structured field study measuring productivity across multiple languages and task types, with and without AI-assisted specification-driven development.
Result
10–40× productivity improvement measured across task types. The study also documented a failure case in Go — where the approach did not generalise — because honest evidence matters more than a clean story.
Experience
Enterprise software architect with over three decades of experience shaping large-scale, complex technology environments.
A trusted advisor to enterprise customers across multiple technology waves—from client-server to big data to cloud-native, and now AI/GenAI—I specialize in separating signal from noise and translating emerging trends into practical, high-impact solutions.
Most recently Chief Software Architect at a $4.5B global telecom software company, where I led the launch and scale of an AI transformation program beginning in 2023.
Operating model
Independent — no pyramid, no delivery team beneath me
Selective — a small number of concurrent engagements
Referral-driven — not a marketing operation
Past Industries
Telecommunications
Aerospace
Banking
Insurance
Wholesale
Retail
Government
Railway
Shipping
Catering
Manufacturing
Engineering & Construction
Past clients
