AI now belongs in the core of how an institution operates. A full in-house AI organisation, with the governance depth that regulated capital demands, is slow to hire, expensive to keep, and walks out of the building when its people leave. That leaves most institutions choosing between under-governed experimentation and doing nothing. Dartmouth offers a third path: a senior, accountable owner of the AI agenda, embedded at partner level and on demand, who carries the work from question to running system, then hands the capability to you.
The timing is not abstract. In Mercer's 2026 survey of 131 asset managers, 55 percent already run AI inside at least one investment process and another 27 percent are piloting. Grant Thornton, in the same season, found 78 percent of firms lack confidence they could pass an independent AI governance audit within 90 days. Most institutions now operate AI they could not evidence. The partner's first deliverable closes exactly that gap.
Assess
Where AI belongs, and, just as important, where it does not. We map the institution's governance footprint and the operating costs the platform is expected to reduce, and we say plainly which workflows are ready and which are not.
Govern
We compile your policy into Living Policy Architecture: the gate that decides what runs on its own, what escalates, and to whom. Governance is the first deliverable, not a later retrofit.
Build and run
We stand up and operate the runtimes that do the work, against your data, inside the boundary you declare. The partner is accountable for the system in production, not only for the advice.
Transfer
The compiled representation, the audit history, and the configuration are yours, in open formats, in perpetuity. The relationship is designed to leave the institution holding a capability it owns and understands.
Why a partner, and not a hire, a consultancy, or a vendor.
A partner carries undivided accountability for one agenda, brings the platform rather than learning on your time, leaves the institution owning the result, and is paid from outcomes rather than headcount.
| Fractional AI partner | In-house build | Consultancy | SaaS vendor | |
|---|---|---|---|---|
| Accountability | owns the agenda | yours to staff | the deck | the uptime |
| Time to value | weeks | quarters | months | licence day one |
| Who owns the IP | the institution | the institution | the firm | the vendor |
| Governance depth | compiled, audited | varies | advisory | bolted on |
| Cost shape | self-funding | fixed payroll | time and materials | per seat |
The first step is fixed in scope.
Engagements open with a readiness assessment: where AI already operates across the institution, sanctioned or not, what an examiner would find today, and which workflows carry the best return against the least risk, delivered as a board-ready report with a sequenced roadmap. From there, the partner seat runs on a weekly cadence. We hold a small number of seats at a time, by design.