AI Consulting
AI consulting services that end in a working system
AI consulting is the work of deciding what a business should automate, in what order, and whether the data supports it — then proving it with something that runs. A good engagement starts with a readiness assessment, picks one use case tied to a number the business already tracks, checks the data before the model, and ends with a system in production and a team that can run it. If it ends with a slide deck, it was not consulting. It was a report.
What does an AI consultant actually do?
Most AI projects stall on process and data, not on the model.
An AI consultant maps the work a team repeats, finds where judgement is applied to the same inputs over and over, and works out which of those tasks a model can take on safely. The job is selection and sequencing. Any competent engineer can wire up an API; the value is in knowing which problem is worth solving first and which one will quietly fail.
- Map the repeated work and its real cost
- Score candidate use cases on effort against payback
- Check the data exists, is clean and is owned
- Choose build, buy or leave alone
- Define what “working” means before anyone builds
What is an AI readiness assessment?
A short, structured check across data, process, people and risk.
A readiness assessment answers one question: if you spent money on AI next month, would it survive contact with your business? It looks at whether your data is accessible and accurate, whether the process is documented enough to automate, whether a named person owns the workflow, and whether you could tell that the system had gone wrong.
- Data: accessible, accurate, and yours to use
- Process: repeatable and documented, not tribal
- People: an owner for every workflow
- Risk: approval gates, audit trail, a way to switch it off
- Output: a ranked list, not a maturity score
How the work runs· for applied ai & automation
Diagnose. Select. Build. Hand over.
Four stages, each with something to show at the end of it. No stage depends on you having already decided what to build.
Diagnose
Readiness across data, process, people and risk. Output is a ranked list of candidate use cases with the evidence behind each.
Select
Pick one use case tied to a number you already report. Agree the baseline and the threshold that would make it a failure.
Build
Ship it into production with evaluation and approval gates from day one, not retrofitted after the pilot goes well.
Hand over
Your team runs it. Documentation, evaluation set, escalation path, and a review date to decide whether it earns its keep.
Which AI solutions pay back first?
The boring ones: frequent, well-defined, clear inputs and outputs.
Payback comes from volume and repetition, not sophistication. The work that pays back first is the work a person does many times a week to a predictable recipe. Anything that needs a human to weigh context, take responsibility, or read a room stays with the human and gets a better tool instead.
- Enquiry triage and routing
- Extracting structured data from documents
- Assistants answering from your own content
- Lead scoring against your own closed-won history
- First drafts of routine, repeated writing
How much does AI consulting cost?
Priced three ways: hourly, fixed project, or retainer.
Cost is driven by scope, how ready your data is, and whether the engagement stops at advice or carries through to a running system. Published 2026 rate guides such as Groovy Web’s set out the going ranges by model and region. Judge any quote by modelled payback rather than by day rate.
- Hourly suits discovery and second opinions
- Fixed project suits a defined build with a clear finish
- Retainer suits ongoing evaluation and iteration
- Data clean-up is a line item, not a rounding error
- Ask what happens if the use case fails its threshold
AI consulting or an AI automation agency?
Advice without a build stalls. A build without strategy drifts.
A consultancy tells you what to do; an automation agency builds what you ask for. Each fails in a predictable way on its own. Strategy-only engagements end in a document nobody implements. Build-only engagements produce working software for a problem that was not worth solving. The useful version is one team doing both, accountable for the outcome.
- Consulting: what to automate, and why
- Automation: building it and keeping it running
- Together: one team accountable for the number
- Choose by the outcome you need, not the label
- See applied AI & automation for the build arm
What you get
Deliverables, not decks
Every engagement produces artefacts your team keeps and uses. None of them is a slide deck nobody opens again.
AI readiness assessment
Data, process, people and risk, scored, with the evidence behind each finding.
Ranked use-case shortlist
Candidates scored on effort against payback, with the ones to avoid and why.
Business case for use case one
Baseline, target, failure threshold, and what it costs to find out.
Working system
In production, against real work, with approval gates where they belong.
Evaluation set
The tests that prove it works, which your team can re-run whenever it changes.
Handover pack
Documentation, escalation path, owner, and a date to review whether it earns its keep.
What do you get at the end of an engagement?
A running system, and the evidence that it works.
The test of an AI consulting engagement is whether anything is different once the consultant leaves. That means production access, not a sandbox; an evaluation set your team can re-run; and a named owner who understands the failure modes. Everything else — the strategy, the roadmap, the workshop — is scaffolding around that.
- A system running against real work, not a demo
- The evaluation set used to prove it works
- Documentation written for your team, not for us
- An escalation path when the model gets it wrong
- A review date to decide whether it still earns its place
How do you know the AI is actually working?
By the number you agreed before anything was built.
Every use case gets a baseline and a threshold at the start: the number it should move, measured how it is already measured, and the level at which the project is called a failure. Agreeing that up front turns the stop-or-continue decision into a measurement instead of an argument between people with different memories of the kick-off.
- One number, taken from reporting you already trust
- A baseline measured before the build starts
- A failure threshold agreed in writing
- An evaluation set that catches quality drift
- A review date with a real option to stop
How do you keep AI governable?
- Approval gates on anything that writes, sends or spends
- An evaluation set and an audit trail from day one, not after the pilot
Fit
A good fit and a bad one
Good fit
- You have a number you already report and want moved
- The work is repeated often enough to be worth automating
- Someone will own the workflow after handover
- You would rather have one system running than five pilots
Not a fit right now
- You want AI adopted because the board asked for AI
- The data lives in someone’s head or a shared inbox
- Nobody can say what a good outcome would look like
Questions
Questions we get asked
They decide what to automate and in what order, check that the data supports it, and make sure the result reaches production. The deliverable is a working system and a team that can run it — not a strategy document.
A readiness assessment takes days, not months. Selecting a use case and agreeing its baseline takes a week or two. A first build in production is typically measured in weeks, depending on how clean the data is and how many approvals the workflow touches.
No, but you need to know its state. The readiness assessment tells you which use cases your current data can support and which need clean-up first. Skipping that step is the most common reason AI projects stall after the demo.
It is priced hourly, as a fixed project, or on retainer, and the range varies widely by scope and region — published 2026 rate guides set out the going figures. The number worth comparing is modelled payback against the number you are trying to move, not the day rate.
Consulting decides what should be automated and why. An automation agency builds what it is asked to build. Engagements that stop at advice tend not to get implemented; engagements that skip the advice tend to build the wrong thing well.
You do. Handover includes documentation, the evaluation set used to prove it works, an escalation path, and a review date to decide whether it still earns its place.
That is agreed in advance. Every use case gets a baseline and a threshold that would make it a failure, set before anything is built, so the decision to stop is a measurement rather than an argument.
Proof
AI consulting in practice
Case studies from the same practice. Client names are used only where we have permission.
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Read the case studyA free diagnostic session: we look at the work your team repeats, what your data can currently support, and which use case is worth doing first. You leave with a ranked list whether or not you work with us.
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Fifteen minutes with a senior on the Applied AI & Automation pod. We tell you what we would look at, what it would cost and whether it is worth doing — including when the answer is no.