We automate specific processes, not "the business". We pick one process, check whether the data allows it at all, and build the narrowest version that settles the question — before anyone commits a budget to the whole thing.
The difference is not that one is more modern than the other. It is what happens when reality does not match the rule.
Classic automation executes rules written in advance, and it is flawless at that: if an invoice carries its number in the same place as the last one, it will copy that number correctly a hundred thousand times. Meet an invoice laid out differently and it will either stop or copy the wrong thing — consistently, until somebody adds a rule.
AI automation handles that case: it recognises what a document is even when nobody has described that layout before, deciding on resemblance rather than pattern match. There is a definite price for this — the output stops being certain and becomes probable. An AI automation project that has not settled in advance what happens on a mistake is not a project ready to go live.
In practice the good solutions combine both: rules wherever rules suffice, because they are cheaper and predictable, and a model only where the rules cannot be written down.
A good first candidate meets five conditions at once. Missing one does not rule the project out, but it changes the scope — and that is better known before the quote than after it.
The data test takes one afternoon and is worth every hour of it: try to export the last hundred instances of the process as a single table. Not describe how you would — actually do it. Whatever you run into is the real scope of the first stage.
This is the decision with the largest effect on cost and on risk, and it is often taken by accident, late, during roll-out. In practice there are three options: a human approves every output, a human handles the exceptions, or oversight is statistical and consists of monitoring indicators.
We start with the first even when the third is the goal. Moving between them is far easier than retreating after an incident, and a few weeks of human approval produces quality data that cannot be obtained any other way. The full split of decisions — what we settle and what stays with you — is set out in our implementation methodology.
Automation is often sold as something that always pays for itself. It does not, and it is worth knowing when we advise against a project:
Three things drive the cost: how many systems have to be joined up, the state of the data, and the level of certainty required. Company size barely matters — a small business with one tidy process will pay less than a larger organisation running the same process across four tools.
The line most often left out is upkeep: per-operation fees, quality monitoring, and periodic retesting, because model providers retire versions and a solution built on a retired version has to be tested again. We break all of those components down in our note on what an AI implementation costs.
Automation nobody in the business understands degrades quietly: a document format changes, quality drops, and nobody notices for a quarter. Handover therefore includes explaining how the thing works, where it is unreliable, and how you would know it had broken.
If AI systems are already running in your business, note that the duty to support AI literacy among the people operating them comes from the AI Act itself and has applied since February 2025. What that means in engineering terms is set out alongside our AI Act readiness audit.
Automation is one stage of a wider implementation and follows the same rules: one process, a number measured before the start, the narrowest possible pilot, and parallel running before the switch. The full scope — from process audit through to upkeep — is set out on our AI implementation page. If you would rather work through the first steps yourself before we talk, our guide to how to implement AI in your company covers them.
Classic automation executes rules somebody wrote down in advance — if the invoice has this field in this position, copy it there. AI automation copes with cases where the rules are incomplete: the document is laid out differently from the last one, the customer message fits no category, the description is ambiguous. The price of that flexibility is that the output is no longer guaranteed, only probable — which is why the project has to settle up front what happens when the system gets it wrong.
Ones that recur often, follow reasonably stable rules, have input data in a machine-readable form, and fail in ways that are detectable and reversible. Most often they are text-based: qualifying and routing enquiries, reading documents, drafting replies for approval, filling in records in a system. A process run five times a month rarely justifies the cost of an integration, because that cost is largely fixed.
Cost is driven by the number of systems that have to be joined up, the state of the data, and the level of certainty required — not by the size of the company. The same process in a business with its data in one system, and in a business with that process scattered across mailboxes and scans, are two different projects and two different quotes. We break the components down in our note on what an AI implementation costs; a range quoted before anyone asks about your systems and data is a range pulled from the air.
In the projects we run, usually not — and not because that sounds better, but because the first option we recommend requires a person to approve every output. That person is the source of the quality data the system needs. The realistic early effect is shorter handling time and less copying work, rather than fewer roles. If headcount reduction is the goal, it is worth saying so at the outset, because it raises the level of certainty required and therefore the cost.
A single process with one integration and legible data is a matter of weeks. A process touching several systems, with data that needs tidying and with oversight requirements, is a matter of months. The single longest item in the schedule is frequently obtaining access to the systems the solution must read from — so start requesting it in the same week you pick the process.
Describe it in a few sentences. We will tell you whether we see sense in it — including when the answer is that it is not worth doing.
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