Investing in AI? Make Sure You're Buying Business Value, Not Just Technology

Artificial intelligence can help with a well-defined task, but a product demonstration rarely shows the full cost of using it in everyday work. Subscription fees may be visible; implementation, data preparation, staff time, integration, checking, security and exit costs are easier to overlook. Before approving a tool, decide what business problem it should solve and what evidence would justify keeping it.

A central-bank warning is not an SME buying rule

The Bank of England's Financial Policy Committee said in its September 2026 record that rising AI-related borrowing broadens financial-market exposure to developments in AI. That is a financial-stability observation about investment and funding across the sector. It does not mean that every AI supplier is about to fail, or that a small business should avoid useful software.

For a buyer, the practical question is narrower: could the supplier's finances, pricing or product direction affect a process your business depends on? A provider may be capable today but change its terms, raise usage charges, discontinue a feature or rely on another cloud or model supplier. Treat continuity and exit as part of the buying decision, not as an afterthought.

Write the business case before the product shortlist

Start by describing the current work. How long does it take? What does delay or rework cost? Which errors matter? Name the person who owns the process and agree a measurable improvement, such as fewer hours spent on a repeatable task, a shorter response time or a lower rate of avoidable corrections. Set a baseline first; otherwise a pilot can produce an impressive anecdote without showing whether the business is better off.

Then estimate total cost of ownership over a period that matches the commitment. Include licences and consumption charges, configuration, integration, data cleanup, security review, training, human checking, support, storage, contract minimums and the time needed to move away. Compare the result with a simpler process change or existing software. AI may be useful, but it should not win merely because it is new.

Test the data and supplier boundaries

Before staff upload business information, identify what the service receives, where it is processed, how long it is retained and whether it is used to improve a shared model. Check which accounts can use the tool, whether company information can be separated from personal use and how access is removed when an employee leaves. Review the supplier's data-processing terms and the systems or sub-processors on which the service depends.

Ask what the service does when it is unavailable, produces an unreliable result or changes its model. Can staff complete the task manually for a short period? Can you export your prompts, configuration and business records in a usable format? Which integrations must be rebuilt? Who owns the output and what contractual limits apply? The answers should fit the importance of the process: a low-risk drafting aid does not need the same continuity plan as a system that supports customer, financial or operational decisions.

Evidence to request before approval

• A short pilot plan with a baseline, a named owner, success measures and a review date.
• A complete cost estimate covering licences, usage, setup, support, staff checking and exit.
• A data-flow summary showing what information is sent, where it goes and how long it is kept.
• The service's uptime, support, change-notification and data-export terms, with dependencies identified.
• A record of user access, approval for integrations and a rollback or manual-workaround procedure.

If an IT provider recommends the purchase, ask them to explain which parts they have independently assessed and which statements come from the supplier. A useful review should identify assumptions and unresolved questions instead of presenting a vendor claim as a tested result.

Run a controlled pilot, then decide

1. Choose one bounded task with low consequences if the output needs correction.
2. Use approved test data and limit access to the people who need to take part.
3. Measure quality, time, ongoing cost and the effort needed to check results.
4. Test how the process works when the tool is unavailable or gives a poor answer.
5. Continue, change or stop based on the agreed measures, and record the reason.

A trial that does not meet its target is still useful if it exposes the true costs before a longer commitment. Altitude IT's existing guide to common AI adoption problems covers the people and process side; for AI-specific consultancy, see Altitude AI.

Can you demonstrate the return on your AI investment, or are you simply adding another subscription?

Sources and further reading

• The reported AI debt concerns and business implications
• Bank of England: Financial Policy Committee Record, September 2026
• NCSC: Thinking carefully before adopting agentic AI
• Assessing the suppliers and services that hold business data