AI Strategy & Roadmapping
A sequenced roadmap with named owners and honest estimates — not a slide deck that sits in a drawer.
View approachAI Strategy & Digital Transformation
Turn AI ambition into systems that ship.
I help organizations define, build, and govern AI systems that hold up in production.
20+ years at the intersection of data, cloud, and AI — working with organizations that carry real data estates and a compliance obligation.
Not sure where to start?
AI Readiness Assessment
A fixed-fee engagement that ends in a decision you own — scored use cases, an honest read on your data, and a sequenced roadmap.
Fixed fee · 2–3 weeksWhat it covers →Already underway?
Contract & embedded work
If the plan already exists and the constraint is senior capacity, we can skip the assessment entirely. I join in-flight teams as an embedded engineer or architect.
Weekly or monthlyHow it works →Four engagement shapes in total, including fractional leadership and fixed-scope projects — see all of them.
Services
Where I work
Strategy through to the data underneath it — and the engineering practices that keep both honest.
Data Foundations & Governance
Lineage, quality, and governance built to carry AI workloads — the layer most AI programs discover too late.
View approachAI Governance & Compliance
Governance mapped to NIST AI RMF, ISO 42001, and the EU AI Act — written by someone who has built the systems being governed.
View approachAI Platform & Application Development
Production-ready AI systems that integrate with your existing infrastructure — evaluated before launch, observable after it.
View approachAI-Augmented Engineering
AI pointed at your delivery process — feature decomposition, commit-level quality gates, and documentation that stays current.
View approachCourse & Curriculum Development
Technical curricula built as engineering systems, so a course can be corrected in an afternoon instead of rebuilt every few semesters.
View approachWhy this practice exists
Most AI initiatives fail on the data, not the model
An organization runs a promising pilot, gets a good result, then stalls for eighteen months. The post-mortem blames the model, the vendor, or the pace of the technology.
It is almost never any of those. It is that nobody could trace where the data came from, nobody measured its quality, and nobody decided who was accountable when the system was wrong. The pilot worked because someone hand-picked the inputs — which is exactly what production will not do for you.
More about how I workWho I work with
Organizations with real data estates and a compliance obligation — where governance, data quality, and engineering discipline all matter at the same time.
- Insurance
- Healthcare
- Financial services
- Education
- Regulated manufacturing
Twenty years at the intersection of data, cloud, and AI, working with organizations from first use case through to enterprise-wide programs.
Not sure where your AI program should start?
That is usually the right first conversation. Tell me what you are weighing and I will tell you honestly whether I am the right fit.
Get in touch