Fractional AI Lead

Somebody senior has to own AI. For a few days a month, that's me.

I take the mandate for your AI innovation agenda, from spotting the opportunity to running it in production, and I lead the agentic AI engineer who builds it. Two days a week, on a monthly retainer.

Some companies call this role a fractional AI advisor. The difference is accountability: I am measured on what reaches production and what it earns, not on the advice.

The Difference

You get a lead and a builder

Advisory alone stalls at your existing engineering roadmap. So when the work justifies it, I bring an agentic AI engineer onto the team for the duration of the engagement: someone I have hired, whose standards I set and whose work I review.

It is deliberately temporary. The goal is production systems your own team can carry once we are done, not a dependency on me.

The Mandate

What I am accountable for

Executive-level AI strategy combined with hands-on leadership of delivery. Seven things, every month.

AI strategy & roadmap

A practical AI strategy tied to your business priorities, plus an opportunity backlog prioritized by expected impact, effort, risk and strategic value.

  • Business-first, not tool-first
  • Continuously re-prioritized
  • Owned, not handed over

Innovation project delivery

A focused portfolio of initiatives led from discovery and business case through prototype, production deployment and adoption.

  • Scope, owners and success criteria
  • Milestones with measurable outcomes
  • Production, not pilots

AI engineering leadership

I lead and mentor the agentic AI engineer, and set the standards: context and harness engineering, security, testing, CI/CD, observability, production readiness.

  • Standards before speed
  • Code review and mentoring
  • Hiring bar kept high

Architecture & technology decisions

Decisions on models, platforms, cloud architecture, data access, vendors and build-vs-buy, biased toward reusable components over isolated experiments.

  • Model and vendor selection
  • Build vs. buy, argued
  • Reusable components

Governance, security & risk

Pragmatic guardrails for data access, privacy, model usage, human oversight and production deployment, including how a vibe-coded app is allowed to reach production.

  • What AI may touch, and what it may not
  • Human oversight where it matters
  • Risks escalated early

Adoption & internal capability

Working with business owners to redesign processes around AI, drive adoption of what we ship, and transfer know-how so the capability outlives the engagement.

  • Process redesign with owners
  • Team training and new expectations
  • Know-how transfer by default

Executive reporting

A concise monthly view of the AI portfolio: what is being built, expected and realised impact, adoption, blockers, risks, spend and the next bets.

  • One page the board can read
  • Realised impact, not activity
  • Recommended next bets
The First Quarter

A 30–60–90 day plan

First 30 days
  • Map business priorities and processes
  • Audit the current AI, data and tooling estate
  • Build the opportunity backlog
  • Select the first 2–4 initiatives
  • Define engineering and governance standards
Days 30–60
  • Launch the priority projects
  • Establish the delivery cadence
  • Put reusable AI foundations in place
  • Measure early results
  • Refine the roadmap on evidence
Days 60–90
  • Move successful initiatives into production
  • Drive adoption with business owners
  • Report realised impact
  • Kill or redesign the weak experiments
  • Define the next quarter’s portfolio
How It Works

Operating cadence

Approximately 2 days per week, scheduled in advance

Direct collaboration with the CEO and leadership team

Weekly delivery session with the AI engineer and business owners

How success is judged

On delivered business outcomes rather than hours worked. Depending on the portfolio, that means:

  • Revenue or gross-margin uplift attributable to AI-enabled initiatives
  • Operating hours or external costs eliminated
  • Cycle-time reduction and throughput improvement
  • Adoption and sustained usage of what was deployed
  • Number and quality of initiatives moved from idea to production
  • Reliability, security and maintainability of the production AI systems
Investment

€3,000 – €10,000

per month + VAT

  • €3,000 for one day a week: strategy, backlog, technical direction and review.
  • €10,000 for two days a week with a dedicated agentic AI engineer delivering alongside.
  • Three-month minimum, then rolling.

The retainer buys ownership of the AI agenda and agreed capacity, not a bundle of freelance day rates. Large implementation scopes, third-party software and cloud costs, and additional engineering capacity are budgeted separately.

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Scope boundary

The fractional AI lead is accountable for strategy, prioritization, technical direction and delivery leadership. The role does not imply personally coding every solution, providing unlimited availability, or guaranteeing a predetermined financial return.

Material changes in scope or capacity are agreed separately. If the ambition is bigger than a retainer, the AI-Native Transformation Program is the right shape; if it is smaller, take ad-hoc consulting hours instead.

FAQ

Common questions

What does a fractional AI lead do?

Owns the company AI agenda without being a full-time hire: AI strategy and roadmap, a prioritized opportunity backlog, delivery leadership over the AI projects, architecture and vendor decisions, governance and risk, adoption inside the business, and a monthly report to leadership on realized impact and spend.

How is this different from a fractional AI advisor?

An advisor recommends. A lead is accountable for the outcome: the backlog, the priorities, the technical direction, the delivery, and the report at the end of the month. I also bring an agentic AI engineer onto the team, so the work gets built rather than queued behind your existing roadmap.

What does it cost?

€3,000–€10,000 per month + VAT, depending on capacity and scope. The lower end is one day a week of leadership; the upper end is two days a week with a dedicated agentic AI engineer alongside. Large implementation scopes, third-party software and cloud costs, and additional engineering capacity are budgeted separately.

Do you personally write the code?

Where it matters, yes: prototypes, difficult integrations, reviews. But the role is not to be your only builder. The retainer buys ownership of the AI agenda and agreed capacity, not a bundle of freelance day rates, and the agentic AI engineer carries most of the implementation.

What is out of scope?

The role does not imply personally coding every solution, unlimited availability, or a guaranteed financial return. Material changes in scope or capacity are agreed separately.

How long is the commitment?

Three months minimum, because nothing meaningful reaches production faster than that. Most engagements run six to twelve months, and several started as a single ad-hoc consulting block.

Let's build something that works.

Book a 30-minute call to talk through where AI can move the needle in your business.

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