A small professional services firm is pitched a specialist AI contract review tool at £350 a month per seat. The demo is persuasive. It extracts key clauses, flags non-standard terms, and formats a summary in house style. The founder asks one question afterwards: could we do this with the ChatGPT Teams subscription we already have?
She was right to ask. Many AI purchasing decisions turn on exactly this question, and the answer tells you more about the product than any vendor presentation will.
What choice are you actually facing?
Two paths open up once you decide AI is worth trying. You can buy a specialist or “AI-native” product built around a particular workflow or sector, or you can subscribe to a general-purpose model such as GPT-4, Claude, or Gemini and build light processes around it yourself. Both are legitimate approaches. The question is which one matches what you actually need, and the vendor pitching you is not the most objective source of advice.
Fifteen per cent of UK businesses were using at least one form of AI in 2023, according to ONS data, rising to 68% among large firms. Adoption among smaller businesses lagged well behind. Part of that gap is awareness, but part of it is this buying decision landing before owners have had a chance to develop a clear framework. Worth knowing before you commit: AI software typically takes 21% longer to procure than conventional SaaS products, according to Vendr’s procurement data, because purchases attract additional security and legal review. That delay is not wasted time. Use it.
When does a specialist AI product earn its place?
A purpose-built tool justifies the premium when you need AI embedded inside a governed, repeatable workflow rather than a one-off capability. If your use case requires audit trails, sector-specific integrations, role-based access controls, or documented compliance features, a specialist product typically provides what a general-purpose model and a shared account cannot. Scale and regulatory exposure are the two clearest signals that you are in this territory.
Consider a financial services firm producing client suitability reports. The FCA has confirmed that firms using AI in regulated activities remain fully accountable under existing Consumer Duty and governance rules. A general chatbot session leaves no log, no version control, and no approval routing. A specialist tool built for that workflow does. The AI capability may be similar between the two. The governance scaffolding around it is not. The same logic applies in legal, health, and HR contexts. Where outputs influence decisions about individuals, and where a regulator may one day ask how that decision was reached, the operational layer around the model matters as much as the model itself.
When is a general-purpose subscription enough?
For many knowledge work tasks, a direct enterprise subscription is sufficient. Drafting communications, summarising documents, writing first-draft proposals, generating marketing content: these are tasks where text quality matters more than workflow integration. A ChatGPT Teams or Claude account at around £25 to £30 per user per month often delivers as much as a specialist wrapper at five times that price, because many of those wrappers are a user interface built on the same underlying model.
A 2023 Boston Consulting Group study of 758 consultants found that access to GPT-4 improved task completion speed by 25% and output quality by 40% on suitable tasks. Those gains are available through a direct subscription, without a specialist vendor in the middle. The question to ask yourself is whether your use case needs repeatability, governance, and integration, or whether it needs speed and quality on a task your team already understand well. Enterprise accounts from OpenAI, Anthropic, and Google do not use your inputs to train future models by default, which handles one of the most common data concerns without any additional configuration. A simple internal usage policy and basic logging on top of that is sufficient for many small services firms to get started safely.
What does it cost to get this call wrong?
Both errors carry real costs. Over-buying means paying for AI features that do not change how the business runs, typically a mark-up for a product that wraps a commodity model in extra interface. Under-buying is often more serious. The BCG consultant study found that AI worsened performance on complex tasks when consultants over-relied on plausible-sounding outputs that turned out to be wrong.
Vendr’s procurement analysis found that many SaaS vendors have bolted AI onto existing products as a marketing move rather than because the feature materially changes outcomes. For a small business without a structured review process, relying on AI for high-stakes outputs without a human check carries genuine legal or reputational weight.
The regulatory picture sharpens this further. ICO fines for UK GDPR breaches can reach £17.5 million or 4% of global annual turnover. The ICO fined Clearview AI £7.5 million in 2022 for processing images of UK residents without a lawful basis, demonstrating a willingness to act aggressively on AI misuse. Buying a tool that processes personal data without a lawful basis, a Data Processing Agreement, and a Data Protection Impact Assessment where required creates direct regulatory exposure. The NCSC separately warns that connecting third-party AI APIs extends your attack surface, with prompt injection and data exfiltration as documented risks that require active management.
What to ask before you sign anything?
Five questions cut through vendor presentations quickly. They are drawn from UK regulatory guidance and procurement experience, and they apply whether you are evaluating a specialist product or deciding whether a direct subscription is sufficient. Getting clear answers before you sign tells you more than any demo.
First: does this product do something I cannot reasonably do with a direct subscription to GPT-4, Claude, or Gemini plus a basic internal process? If the honest answer is no, you are paying a mark-up for a user interface.
Second: where is my data stored and processed? UK data residency matters for UK GDPR compliance. Ask for a list of sub-processors and a Data Processing Agreement before signing.
Third: does the vendor use my inputs to train or fine-tune their models? Request a contractual guarantee that they do not. If this cannot be confirmed in writing, it belongs in your decision.
Fourth: can I run a time-boxed pilot with clear success metrics before committing to a long contract? A vendor with a genuinely useful product will say yes without hesitation.
Fifth: what are the exit terms? Data portability, contract length, and switching costs are worth reading before signing, not after. The CMA’s 2024 review of foundation model markets flagged concentration risk among a small number of providers as a real concern for downstream business users. Where you can, choose tools with clear exit provisions.
The practical test before any of this: spend an afternoon on your specific task using a direct subscription to the general model the vendor is likely wrapping. If the output meets your standard, you have your answer. If it falls short because of governance, integration, or audit trail gaps, you know exactly what you are buying and why it costs more.
