Say three agents went live last year. One drafts call notes into HubSpot, one triages the support inbox, one chases unpaid invoices in Xero. Twelve months later the person who built them has moved on, the inbox agent mislabels refunds, and nobody in the business can open the workflow without breaking it. That is the gap a fractional AI team fills: a small team that keeps building and owns what it built, a few days a week, without you hiring an in-house AI department.
A fractional Chief AI Officer, as one consultant describes the role, is "your AI decision-maker and translator", and "what they do not do is write code, unless they happen to have that background." That helps if your problem is policy. It helps much less when the refund label breaks on a Tuesday afternoon and someone has to open the workflow.
Fractional CTO vs fractional Chief AI Officer vs fractional AI team
| Fractional CTO | Fractional Chief AI Officer | Fractional AI team (us) | In-house AI engineer | |
|---|---|---|---|---|
| What you get | A part-time tech leader for all technology decisions | A part-time AI strategist: strategy, tools, governance | A team of 3-4 on weekly build cycles, monitoring and tuning, with a quarterly roadmap | One employed person |
| Who builds | Your engineers, directed by them | Not them, as a rule | The team builds and maintains | That one person |
| Typical cost | Quote-based; we cite no figure | One US practitioner reports $10K-$17K a month per client (self-reported) | £600/day; typically 3 days a week, about £7,800 a month | Salary, employer National Insurance, recruitment and a manager's time (our cost breakdown) |
| Best for (our view) | Product-led companies where AI is one part of the tech remit | Leadership that needs AI governance and vendor decisions before any build | SMEs with an ongoing backlog of agents and systems to connect | Companies where AI is the product and there is a manager to direct the hire |
The same consultant notes that "a fractional CTO owns all technology decisions including infrastructure, engineering teams, and product development", and that some now add AI strategy to the remit. A fractional AI consultant who also builds sits somewhere in between, but one person can only cover so much. The same limit applies to a lone fractional AI engineer.
What it costs
The fractional AI team is £600 per day, excluding VAT, for a team of 3-4 people. The typical retainer is 3 days a week, which is £1,800 a week and about £7,800 a month for the whole team.
For a fractional Chief AI Officer, the only number we have is one person's. Jon Cheney, a US fractional Chief AI Officer, says in an interview on Foundore's YouTube channel that he charges $10K-$17K a month per client. That is a self-reported US figure from one practitioner, not a market rate. Different currency, different market and a different engagement, so we do not compare the two directly.
An employed AI engineer is one full-time person, and you pay salary, employer National Insurance and recruitment, then find someone senior to direct them. Our cost of hiring an AI developer versus an agency works through the employee side in full.
We have no published figure for a fractional CTO that we would stand behind, so when you compare quotes, ask what each day produces: advice, or working code. The full list of engagement models is on our pricing and engagement models page.
What a week with the team looks like
The retainer runs on weekly build cycles. Alongside new builds, the team does ongoing monitoring, tuning and maintenance on the agents already in production, so they keep up as your business changes. The roadmap is reviewed with you once a quarter.
The work mixes async with live pairing alongside whoever in your team owns the process, so that knowledge stays in the business. Average client tenure is 14 months.
The ongoing part is the bit people underestimate. In an r/AI_Agents thread where the poster says they have built 25+ agents, the poster told a sceptical commenter: "The value isn't the prompt it's the implementation, reliability, and ongoing ownership." That is one builder's view, and it is why we sell days a week rather than a handover date: agents drift as your data and processes change, and someone has to own them after launch. Our LinkedIn AI SDR build is one example of a CRM-connected agent we have shipped.
Works with your stack
We build on the systems you already pay for.
- HubSpot. Agents can write call notes and extracted fields to contact and deal records. In an r/hubspot thread about setting up the Prospecting Agent, one commenter said enrollment criteria make or break these agents and suggested explicit do-not-enroll rules for recent unsubscribes, recent support tickets and existing opportunities. Writing and revising those rules is retainer work.
- Salesforce. Agents can log activity and update opportunity fields, with a person approving anything critical. The poster of an r/salesforce thread after Dreamforce '25 described Salesforce walking back unsupervised LLMs on critical processes.
- Pipedrive and Microsoft Dynamics 365. Agents can create and update deals, activities and notes.
- Microsoft 365, Teams and SharePoint. The agent can sit in Teams and answer from your SharePoint documents.
- Slack. Approvals and alerts can go to a channel so a person signs off before anything leaves the building.
- Xero. Agents can read invoices and bills for reconciliation and reporting.
- n8n, Make and Zapier. The glue: triggers, retries and routing between systems.
If you are still choosing a CRM, our comparison of HubSpot versus Salesforce for AI outbound covers the trade-offs.
Copilot Studio for Microsoft 365 shops
If your company lives in Teams, Copilot Studio is the right front end. The team builds the agent there and builds the parts Copilot Studio struggles with behind it: heavy data processing, external APIs and memory, connected through Power Automate and Azure Functions.
A builder on r/copilotstudio who made a timesheet agent for a small M365 company wrote up the same pattern. They found the docs thin and the generative answers weak out of the box, so they moved heavy processing into Azure Functions as HTTP connectors and kept the agent's YAML in GitHub. Their verdict was that it is "not ready for non-technical users", though orchestration, permissions and Teams integration made it worth the effort.
Version control is now supported directly. Microsoft's VS Code extension for Copilot Studio is generally available and lets you "version control your agent definitions with Git" and review changes through pull requests. We use it, so every change to your agent has a history and a reviewer.
Cost needs modelling before you commit. Microsoft says the Copilot Credits an agent consumes depend on its design, how often people use it and which features it touches: a generative answer is 2 credits, an agent action 5, and tenant graph grounding 10. Custom agents are switched off when a tenant reaches 125% of prepaid capacity. We run your expected traffic through those rates and set per-agent monthly limits, so you know the expected bill before the agent goes live.
When a strategist is enough
Not every business needs a team. The consultant quoted above makes the opposite case well: of the clients the author describes, "none of those people needed a full-time Chief AI Officer. They needed someone senior in the room two days a month." If your open questions are policy, risk and which vendor to pick, that may be the cheaper answer.
The national data cuts both ways. ONS figures from July 2026 show the share of UK businesses with 10 or more employees using AI rising from around 12% to around 35% since late 2023, but the number of AI technologies per business is "rising only modestly, from around 1.4 to around 1.6", and only 10% of adopters use AI extensively. Plenty of firms have one tool and no backlog. A team only pays off once you are building the second and third agent.
Who this is not for
If you have one well-defined job, such as invoice matching or call notes, you do not need a retainer. Take the free 30-minute audit and get a fixed-price custom build with a written proposal within 48 hours. The audit is for custom projects; it is not a route into the retainer.
It is also not for businesses that have not found a use for AI yet. Government research found a lack of identified use and limited skills were the most common barriers to adoption. And if AI is your core product, with enough full-time work and a manager to direct it, hire in-house. If you are comparing delivery partners rather than retainers, see what to look for in a UK AI automation agency.