AI Opportunity Audit

Start with the work, not the technology

The reliable way to find AI opportunities is boring: list what your team does over and over, then ask which of those things a machine could do acceptably. Businesses that start from "what can AI do?" end up with an impressive demo and no change to their operating costs. Businesses that start from "what do we repeat?" end up with something dull that saves money every week.

How to read the savings ranges

The monthly figures attached to each opportunity are broad market ranges, not a quote and not a forecast for your business. They are there to sort opportunities by rough magnitude, so you can tell a £500-a-month problem from a £4,000-a-month one before investing time in either. Your actual number depends on your volume, your wage costs and how much of the task can genuinely be handed over.

For a figure specific to you, take the top one or two results from this audit into the ROI calculator and use your own numbers.

Which opportunities are usually worth doing first

• High volume, low stakes. Something done fifty times a week where a mistake is cheap to catch. This is where automation earns trust.
• Clearly bounded. Tasks with a definite start and finish automate far better than judgement-heavy work that meanders.
• Already documented. If someone has written down how the task is done, you are most of the way there. If nobody has, that is the first job — and it frequently pays for itself even if you never automate anything.

The trap in the results

Selecting eight opportunities and pursuing all eight is the most common way this exercise goes wrong. Automating one process properly returns more than starting five and finishing none, because the value only arrives when the old way of working actually stops. Pick the single highest-leverage item, ship it, verify the saving is real, and only then move to the second.

Once you have chosen, the use case generator will map your industry and constraints onto specific implementations worth considering.