Fractional AI Program Director

Who this is for

Organizations running more than one AI initiative, without a single person accountable for whether any of them reach production.

• Several departments are building or buying AI separately, with no shared platform or governance.
• Security and data governance are being consulted after design rather than during it.
• Vendors are proposing architectures nobody internally is equipped to challenge.
• The people who need to deliver do not report to whoever is sponsoring the work.
• You need this capability directed now, but not a permanent executive hire.

What the role does

1. Hold one architecture instead of several

   A governed platform built once carries every subsequent workflow. Ten point solutions produce ten governance conversations, ten cost models and ten integration surfaces. Establishing the gateway, the routing, the masking, the logging and the metering turns each new use case into an application problem rather than a policy problem.

2. Set the technical bar and keep it

   Decisions get made against a working prototype rather than a vendor's slide. Where a use case needs proving, I build the prototype myself so the specification handed to engineers has already been demonstrated.

3. Work through teams that do not report to me

   The sponsoring department owns the outcome. The engineers own the build. The role owns the design, the standard and the sequence. That separation is what makes the work survive after the engagement ends, and it is how all ten workflows in the case study were delivered.

4. Bring security and governance in during design

   Risk tiering and human-in-the-loop controls are decided before anything is built, which avoids both failure modes: a blanket approval requirement that kills adoption, and an ungoverned system that eventually produces an incident.

5. Govern the vendors

   Vendor and contract governance is a thirty-year discipline here, not an adjacent interest. Specifications are written to be procured and delivered by other people, through to a procurement-ready bill of quantities.

6. Report in numbers executives can act on

   Adoption, volume, feedback and cost per request, metered rather than estimated. The reason a figure like $0.015 per query exists at all is that the instrumentation was there from the first day.

What you get

• A single accountable direction for AI delivery across departments.
• One governed reference architecture rather than several incompatible ones.
• A use-case sequence, with acceptance criteria per use case.
• Risk tiers and controls applied consistently across the portfolio.
• Executive metrics covering adoption, volume, feedback and unit cost.
• Working prototypes where a decision needs one to be settled.

Time commitment, duration and commercial terms are agreed per engagement, and depend on how many workflows are in flight and how much of the platform already exists.

What this is not

It is not staff augmentation, and it does not replace your engineering team. Production code is written by your engineers or your integrator, working from specifications a prototype has already proven. It is also not a permanent role. If a single workflow is the whole problem, the AI Pain-Point Prototype Sprint is a smaller and better-fitting starting point.

Why this works

Over roughly two years this role put ten AI workflows into production across nine business functions at a large research university, every one shipping into a department that did not report to the program, on a platform sustained in production since October 2024 and serving 30,761 users.

Read the case study

Start

A useful first message describes how many AI initiatives are in flight, who is sponsoring them, and where the coordination is breaking down.