AI Automation Consulting

Plain automation follows fixed rules: when this happens, do that. It is deterministic, cheap to run, and completely reliable when the input is structured and the logic is stable — a Zapier zap, a scheduled script, an accounting rule. AI automation adds a model that can handle the parts a rule cannot: reading an unstructured email and working out what it is about, summarising a messy document, classifying a request that does not fit a tidy category, drafting a reply for a human to approve. The important thing, which a lot of vendors gloss over, is that most useful automations are mostly rules with a small amount of AI in the one spot that actually needs judgement. Putting a language model in charge of a step a simple rule handles perfectly is slower, more expensive and less reliable than the rule. A good automation consultant spends as much time deciding where NOT to use AI as where to use it.

Three questions, in order. Is it high volume or high friction — does it happen often enough, or annoy enough people, that removing it frees real time? Is it stable — a process that changes every month is a moving target and the automation will rot faster than it pays back. And is the input consistent enough to be handled reliably — automation loves structured, repeatable work and struggles with genuine one-offs. If a task is rare, constantly changing, and different every time, it is usually cheaper to keep doing it by hand and automate something else. We map your candidate processes against those three axes during the audit, and a good number of them fail the test. That is a useful result: it stops you spending money automating something that was never going to pay for itself.

Often not, and we will tell you so. No-code tools like Zapier, Make and Power Automate are genuinely good, and for straightforward connect-A-to-B automations they are the right answer with no consulting fee attached. Where businesses hit a wall is a predictable set of places: automations that need real judgement on unstructured input, workflows that sprawl across five systems and become impossible to maintain, error handling and exceptions that the happy-path demo never covered, and the moment a business-critical process depends on a flow nobody documented and one person understands. That is where a consultant earns their fee — not building the simple stuff you can build yourself, but the harder integration, the AI decision points, and making the whole thing robust enough to trust in production. If your needs are genuinely covered by off-the-shelf no-code, keep your money.

It will, and designing for that is most of the engineering. A model that is right 95% of the time is not something you let run unattended on work that matters — the 5% has to go somewhere sensible. So we design the exception path first, not last. High-consequence steps keep a human in the loop, where the AI drafts or recommends and a person approves before anything commits. Low-confidence outputs get routed to a review queue instead of flowing straight through. Every decision is logged so you can see what happened and why. And we set up monitoring so a drift in quality shows up on a dashboard rather than in an angry customer email three weeks later. An automation that cannot tell you when it is unsure is not finished, it is just untested.

A single, well-scoped workflow is usually a few weeks from kick-off to something running in production, and it is priced as a fixed-scope project rather than an open-ended day rate. Multi-system automations that touch several platforms, need new integrations, or carry regulatory constraints run longer and cost more, mostly because of the integration and testing surface rather than the AI itself. Before any of that, the initial consultation is free and the AI Opportunity Audit sits at around $3,000 — that is where we map your processes, rank them, and tell you which ones are worth automating and which are not. The audit generally earns its fee at roughly 20 or more staff. A very small business with one or two annoying tasks usually needs a no-code tool and an afternoon, not a project.

We build it. That is the whole point of the way we work, and it is the main structural difference between us and a strategy firm that hands over a recommendations deck and leaves. The people who scope your automation are the people who integrate it, test it against your real data, wire in the exception handling and hand it over running. Building is also a discipline on the advice: a recommendation you have to deliver yourself is written far more carefully than one you will never have to make work. If you would rather take our scope to your own developers or another firm, the audit is written so you can — but most clients would rather we finished the job we diagnosed.