# The real cost of building an AI team vs using an AI consultancy Building an internal AI team costs more than most CFOs expect. Compare the real costs of building vs consultancy and learn how to make the right call. Published: 2025-06-25 Author: Rodan Analytics Most senior leaders underestimate what it actually costs to build an internal AI capability. Not because they are careless — because the visible costs are only part of the picture. The mistake organisations at this stage typically make is treating AI hiring as a straightforward headcount decision. They benchmark a few data scientist salaries, add a machine learning engineer, maybe a head of AI, and present a business case. What they do not account for is the time to productivity, the infrastructure investment, the cost of wrong hires in a market where genuine talent is scarce, and the opportunity cost of building slowly while competitive pressure does not pause. This article will give you a clear-eyed comparison of both approaches. Not a polemic against internal teams — there are good reasons to build one, at the right stage. But the financial and strategic logic is more nuanced than most CFOs see at the point of decision. By the end, you will know which questions to ask, what the true cost comparison looks like, and how to structure the decision for your specific situation. ## The build path: what the numbers actually look like Start with the headline salaries. A credible head of AI in London commands £150,000 to £200,000 base. Senior machine learning engineers sit at £90,000 to £130,000. Data engineers, who are essential but often forgotten in early headcount plans, add another £70,000 to £100,000 each. You need at least two or three of them before the function is viable. Add employer NI, pension contributions, equity or bonus expectations in a market where tech talent benchmarks against FinTech and hyperscaler packages, and you are at 1.3x to 1.5x base salary in total employment cost. That gets you to a realistic first-year team cost of £600,000 to £900,000 for a minimum viable internal capability. Before a single model goes near production. Now layer in the hidden costs. Cloud infrastructure for training and inference. MLOps tooling. Data labelling. The six to twelve months a new team needs to understand your data landscape, your systems, your commercial context. The wrong hire — and in this market, a third of senior AI hires do not work out within eighteen months — costs you the recruitment fee, the salary burn, and the delay. A consumer goods business at £800m revenue recently went through this exercise. They hired a head of AI, two data scientists and a data engineer in Q1. By Q3 they had a functioning data pipeline and a proof of concept. By Q4 they were revisiting the technology stack because initial infrastructure choices did not scale. Twelve months in, they were at the starting line. That is not a failure of ambition. It is the normal trajectory of building from scratch. ## The consultancy path: what you are actually buying A consultancy engagement is not cheaper than an internal team on a like-for-like basis over a long enough time horizon. That is not the right comparison. What you are buying is time compression and risk transfer. A consultancy with genuine delivery experience brings a team that has already made the expensive mistakes — on someone else's engagement. They arrive with tooling, frameworks and pattern recognition built across dozens of prior deployments. They do not need twelve months to understand what good looks like. They need two or three weeks to understand your specific situation. For a mid-market firm, the practical implication is this: a well-scoped consultancy engagement can deliver in four to six months what an internal team takes twelve to eighteen months to produce, with lower capital at risk. The cost structure is also different in a way that matters to a CFO. Consultancy spend is largely operating expenditure. Headcount is fixed cost with severance exposure. In an environment where AI use cases are still being proved out, the ability to step up or step back without restructuring risk has real balance sheet value. That said, the consultancy path has genuine weaknesses. Institutional knowledge walks out the door. Internal capability does not accumulate at the same pace. And if your AI ambition is a permanent, central part of how you compete — not a series of use cases but a structural differentiator — then long-term dependency on external resource is strategically fragile. ## The decision framework: build, buy or blend The right answer for most organisations at this revenue scale is not a binary choice. It is a sequenced approach. Use this as a starting decision framework: - **Define the use case horizon.** If you have one to three defined use cases and the rest is exploratory, a consultancy engagement is almost always more capital-efficient. If you have fifteen defined use cases and a roadmap that runs for three-plus years, the internal build case strengthens materially. - **Assess your data maturity.** An internal AI team cannot perform without clean, well-governed data. If your data infrastructure is immature, you will be paying senior AI salaries to do data engineering work. Fixing data foundations through a targeted engagement first is usually the right call. - **Test with a paid diagnostic before committing to either path.** A structured four to six week diagnostic — scoping your data assets, identifying your highest-value use cases, mapping your infrastructure gaps — costs a fraction of a wrong hire and gives you the information you actually need to make the build-versus-buy decision. Rodan's diagnostic engagements are designed exactly for this moment. - **Plan the transition.** If you intend to build internal capability over time, the consultancy phase should be structured to transfer knowledge, not create dependency. Insist on documentation, joint working and clear handover milestones. Any consultancy that resists this is not aligned with your interests. - **Revisit annually.** The economics of AI talent and tooling are shifting. A decision that was right in 2023 may not be right in 2025. Build in a review cadence. ## Where organisations at this scale most commonly go wrong There are two failure modes that recur at the £500m to £1.5bn revenue range specifically. The first is premature hiring. A board decides AI is a priority, a CDO or CTO is tasked with building capability, and the reflex is to hire. Headcount feels like progress. What it actually produces is an expensive team without a clear mandate, building proofs of concept that never reach production because the organisational infrastructure to deploy AI does not yet exist. The second is underspecified consultancy. A firm brings in an external partner without a clear brief, a defined success metric or a governance structure that connects the engagement to commercial outcomes. The consultancy delivers technically. The business does not change. The spend is written off and the conclusion drawn — incorrectly — is that AI does not work for firms like this. Both failures are avoidable. They share a root cause: the decision was made before the problem was properly defined. A private equity-backed distribution business avoided this by running a scoped diagnostic before committing to either path. The diagnostic identified one use case — dynamic pricing — with a clear twelve-month ROI case, and two use cases that looked attractive but could not be supported by the current data infrastructure. They built the pricing capability with external support, hired one internal data engineer to own it ongoing, and deferred the rest. That is the outcome a disciplined process produces. ## The question that reframes the whole decision The build-versus-buy question is really a question about where you are in your AI maturity journey and what your highest-value use cases actually are. Most organisations at this stage do not yet have clean answers to either. That is not a criticism — it reflects how rapidly the landscape has shifted. If you do not yet have those answers, the worst thing you can do is make a large, slow, expensive commitment — whether that is a five-person internal team or an eighteen-month consultancy retainer — before you have the information to scope it properly. The right first move is a diagnostic. Understand your data assets. Identify your use cases by commercial value. Map the gap between your current infrastructure and what each use case requires. Make the build-versus-buy decision from a position of clarity, not urgency. The cost of moving without that clarity is not just the money. It is the eighteen months you lose while a competitor who moved more deliberately is already in production. Book a diagnostic with Rodan to get the information you need before you commit. --- **Meta description:** Building an internal AI team costs more than most CFOs expect. Compare the real costs of building vs consultancy and learn how to make the right call. 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