What is a fractional Chief AI Officer?
A fractional Chief AI Officer is a senior AI operating executive who works with your company part-time, typically on a quarterly engagement, rather than as a full-time hire. The role is not advisory. A fractional Chief AI Officer owns the operating layer directly: the visibility infrastructure, the workflow embed, the governance and adoption discipline, and the transfer to an internal owner. For companies between 20 and 500 employees that need cross-functional AI leadership without the timeline or cost of a full-time executive hire, the fractional model closes the gap.
Why do most AI initiatives fail to produce measurable productivity?
The failure is not at the model level. The models work. The failure happens in the gap between leadership intent and daily workflow. Strategy decks describe what AI will do. Implementation handles how AI will do it. Neither addresses whether people will actually work differently. That gap is the operating layer. It requires someone to own it directly, measure it weekly, and embed AI into the specific workflows that change unit economics. Without operating layer ownership, AI spend compounds and productivity does not.
How do you measure AI ROI?
AI ROI measurement requires three observable inputs: adoption, proficiency, and workflow yield. Adoption measures whether people are using the tools at all. Proficiency measures whether they are using them at a level that produces output quality above the baseline. Workflow yield measures whether the workflows where AI is embedded are producing measurable cycle time reduction or unit cost reduction. We build visibility infrastructure that makes all three observable on a weekly cadence, tied to the dashboards the executive team already uses.
What does AI adoption failure look like?
The most common signal is uneven productivity. Some teams show real gains. Others show none. The difference is rarely a matter of technology. It is a matter of operating discipline. Adoption failure looks like: licenses deployed but usage concentrated in a small fraction of the team, pilots that worked in the test environment but did not survive contact with daily workflow, and a CEO who has become the de facto transformation function because nobody else has the cross-functional authority to drive it.
What is an AI operating layer?
The operating layer is the set of systems, disciplines, and ownership structures that sit between AI strategy and daily workflow. It has four components. Visibility infrastructure so the executive team can see what is actually happening with AI on a weekly cadence. Workflow embed so AI is changing the two or three processes that move unit economics, not running as isolated pilots. Governance and adoption infrastructure so the gains are defensible under board, regulatory, or enterprise customer scrutiny. And internal ownership transfer so the systems continue running after the engagement ends.
What size companies do you work with?
We work with companies between 20 and 500 employees, post product-market fit, that are either funded or profitable enough to invest in operating capacity beyond technology spend. The engagement model is designed for founder-led companies where the CEO is currently the de facto owner of AI transformation and needs an operating partner to install the layer underneath them.
How long does an engagement take?
Every engagement is structured on a quarterly cadence. The illustrative 90-day arc runs as follows: visibility infrastructure live within the first 30 days, two priority workflows in pilot with defined reliability thresholds by day 60, governance and internal ownership in place by day 90 with a roll-forward plan agreed. Some engagements extend beyond 90 days. The transfer plan is agreed at the start, not at the end.
Do you work with companies outside Washington DC?
Yes. Client engagements have included companies in enterprise SaaS, adtech, government affairs, and commercial space. Work is conducted remotely with on-site engagement where the operating situation requires it.