Fractional AI implementation team

A programme with a budget, a mandate and nobody who owns the architecture or the build. The decisions still get made. They get made by whoever is in the room: the loudest vendor, the team with the nearest deadline, or the last proof of concept that happened to work. The build waits for people who have not been hired.

Permanent hires fix it eventually. Two quarters of search, notice and ramp is the usual cost, and most of the decisions that shape the platform are taken before the team arrives.

If you are still working out what the missing role is, our guide to what an AI solution architect does sets out the decisions it owns and how it differs from a machine learning engineer or a data scientist.

A standing cadence, so the ownership is real rather than an advisory line on a slide.

• Architecture ownership

  We hold the target architecture and the trade-offs behind it, and we are answerable for them the way an employed lead would be.

• Hands-on implementation

  We build it. The platform, the integrations and the first production workflows are written by the people who drew the architecture, in your repositories and alongside your engineers.

• A weekly decision log

  Each decision, the options weighed and the reason for the one taken, written down and dated. It is the artefact that outlasts the retainer.

• Review of work in flight

  Technical review of what your teams and your suppliers are already building, so a problem is caught while it is still a design question. For a supplier that cannot hand over an audit report, the security review guide listed at the end of this page sets out what to ask for instead.

• Vendor and model decisions

  Which model, which platform, and where the build-versus-buy line falls, with the production evidence behind each choice stated rather than asserted.

• Enablement

  The point of the engagement is that it ends. Your engineers build with us from the first sprint, then take the platform, the log and the operating model and run them without us.

A working platform, an architecture and a decision record your own team can defend to a board, to an auditor or to the next supplier, and engineers who built all three with us.

A written handover, so the end of the retainer is a date in the contract rather than a cliff.

Whoever does the work, the first month should already show early evidence of that end state. Our guide to what the first 30 days of an AI implementation should deliver, listed at the end of this page, sets out what to see each week.

Two ways to add a team to an AI programme get confused. Staff augmentation adds engineers who work under your direction, in your backlog, for a term. A managed AI team takes responsibility for an outcome: it holds the architecture and the technical decisions, decides how its own people spend their time, and answers for the result. Both are sound ways to buy. Which one fits depends on whether someone on your side already owns the architecture and the delivery.

The two also combine. A managed team can set the architecture and review the design while augmented engineers add capacity under it, which covers both gaps at once. 1AYM offers both: this retained implementation team is the managed route, and Embedded Engineers on Contract is the staff augmentation route, with engineers inside your programme on a contract from three months. US clients can contract for either through 1AYM's US entity.

When the problem is already scoped, a fixed-scope engagement is cheaper and faster. A feasibility sprint settles what to build; an architecture and production build ships it. Both are priced against a defined output, which a retainer is not. If you already have a scoped job and need it resourced, 1AYM can staff it on contract from the collective of associates who work with the firm, held to the same standard as the rest of 1AYM.

How each shape moves the risk of an overrun, and the other lines an AI budget has to carry, is set out in our guide to AI implementation cost.

It is also the wrong shape when what is missing is full-time capacity inside your own team rather than a team that owns the outcome. That is a contract for embedded engineers, and it has its own page below.

Nor is it an executive seat. If what is missing is someone on the leadership team who answers for AI across the business, our guide to choosing between a chief AI officer and a head of AI sets out what each role owns and when to hire one.

If the question is still whether to hire at all, our comparison of hiring an AI engineer, contracting one and bringing in a team sets out how each route starts, who manages the work and who owns the code.