The True Cost of AI Automation for a 200-Person Financial Services Firm

The single most useful conversation we have with mid-market financial services COOs and CTOs is about cost, not because cost is the most interesting part of an AI automation project, but because most of the cost figures in the public domain are wildly miscalibrated for a 200-person firm. They are either consumer figures (“ChatGPT costs £20 per user per month”) or large-enterprise figures (“the firm invested £8 million in their AI programme”). Neither is useful for planning a real engagement at a mid-market FCA-regulated firm.

This piece walks through what AI automation actually costs to design, build, run, and govern in a typical 200-person UK financial services context, a wealth manager, an advisory firm, a broker, a specialist insurer, or a similarly sized regulated business. The numbers below are ranges drawn from real engagements; they will land you in the right ballpark.

The four cost layers

AI automation cost has four distinct layers, and the proportions matter. Most cost surprises come from underestimating one of them.

1. Discovery and design. Before a model is selected, the workflow has to be understood as it actually runs. The Evolve Workflow Audit typically lands at £18,000-£28,000 for a single workflow at a 200-person firm, depending on the number of stakeholders and the complexity of the operating model. For firms that want a wider scan, the full AI Readiness Assessment is a sensible upgrade. This layer is a fixed-fee engagement; the output is what the rest of the cost is planned from.

2. Build and integration. The cost of building the automation, integrating it with your existing systems, and standing up the eval harness, observability, and rollback path. For a single well-bounded workflow at a 200-person firm, say, automating first-pass suitability report drafting from structured client data and adviser notes, this typically lands between £25,000 and £80,000. The wide range reflects the integration scope. A workflow that lives entirely inside one system, with a clean API and well-organised data, is cheaper. A workflow that touches the CRM, the document management system, and the email archive, and where the data needs cleaning before it is useful, is more expensive.

3. Inference and infrastructure. The running costs of the model and the supporting infrastructure. For most mid-market deployments using the firm's own AWS Bedrock or Azure OpenAI tenancy in UK regions, this lands between £200 and £800 per month for a single workflow at typical mid-market volumes. Surprisingly modest, model inference has fallen dramatically in price since 2023. Where firms get caught is on log retention and observability infrastructure, which can add a similar amount again, but is the kind of cost the regulator will expect you to be paying.

4. Governance and ongoing operation. The costs that are most often left out of business cases. Quarterly re-evaluation of the eval harness against new edge cases. Monitoring dashboards reviewed by the second line. Periodic refresh of prompts and curated test sets as the underlying business evolves. For a single deployed workflow, budget £8,000-£20,000 per year for governance and minor improvements; for a portfolio of three or four workflows, the per-workflow cost falls because much of the eval and monitoring infrastructure is shared.

What that adds up to

For a typical 200-person UK financial services firm starting with one well-scoped AI automation workflow, the year-one all-in cost lands somewhere between £60,000 and £130,000. That covers discovery, build, the first year of running costs, and the first year of governance. The next workflow added to the same eval and monitoring infrastructure typically costs 60-70% of the first one, because the foundations carry over.

For comparison: the human-time equivalent of automating a first-pass suitability drafting workflow at a typical wealth manager, assuming six advisers each saving roughly six hours per week of report drafting time, translates into roughly £180,000-£250,000 per year of returned adviser capacity, depending on adviser cost. That is the case the business is built on.

Where firms blow the budget

Three patterns repeatedly. None are technical; all are scoping problems.

Wrong workflow. The most expensive failure mode is an automation pointed at a workflow that should not have been automated, or that should have been a different shape of automation. The Workflow Audit prevents this; firms that skip it because “we already know what we want to build” are the ones that find themselves rebuilding twelve months later.

Underestimated data work. The build cost ranges above assume the underlying data is in roughly the state the discovery suggests. Where data needs cleaning, deduplicating, or restructuring before the automation can use it, that work has to happen and it has to be budgeted. Most firms underestimate it on the first project and right-size it on the second.

Governance retrofit. Firms that build the automation first and bolt on governance later spend two or three times what they would have spent designing it in. The FCA, the ICO, and the firm's second line all expect the same artefacts; building those as part of the original engagement is much cheaper than reverse-engineering them under audit deadline pressure.

How to scope the first project to land well

The pattern that lands best for mid-market firms: pick one well-bounded workflow with clear quality criteria and recoverable errors. Run the Workflow Audit. Build, eval, and pilot. Take the learnings into the next workflow with the same governance scaffolding. The compounding effect after three or four workflows is substantial, because the per-workflow build cost falls as the surrounding infrastructure carries over and the firm's confidence in scoping increases.

The wrong move is the opposite, committing to a multi-workflow programme before any single workflow has been delivered into production. The cost of that approach scales linearly with scope and the risk of the first workflow being the wrong one is highest.

For a deeper look at the patterns we see most often in financial services, see the AI automation for financial services page. For the question of when an AI automation should instead be an agentic AI deployment, and what the cost differential looks like for multi-step agentic systems, the natural next read is Agentic AI explained: a UK operator's guide.