AI Services
AI Consulting & Strategy
The useful question is not "how do we use AI". It is which repetitive, high-volume task in your business has a clear enough rule set that a machine could do most of it.
AI consultancy establishes where AI would genuinely help your business and where it would not. The Nexclick measures the gap before recommending anything, produces a written assessment you own, and will say plainly when there is nothing worth building — which is a cheaper finding than discovering it afterwards.
Is this you?
What usually prompts the call
- Your board has asked what your AI strategy is and you would prefer an honest answer to a fashionable one.
- You have run a pilot that impressed in a demo and stopped being used by month three.
- Vendors keep pitching you AI products and you cannot tell which claims are real.
- You suspect there is something worth automating and cannot identify what.
What we do
The actual deliverables
Things that appear on an invoice, not adjectives.
- Find the repetitive, high-volume tasks
- Where the same decision or action happens hundreds of times with a reasonably clear rule set. These are where AI pays. General capability upgrades are where pilots go to die.
- Measure the gap in real numbers
- Call tracking, ticket categorisation or process timing over two to four weeks. Turning "we miss a lot of calls" into a number changes the conversation and sometimes ends it.
- Cost each opportunity honestly
- Build cost, running cost at your volume, and the internal change effort. Running cost is the one that gets left out and it is the one that scales.
- Assess your data and systems
- Whether the information a system would need actually exists, is accessible, and is consistent enough to be useful. Frequently the real blocker.
- Vendor and build-versus-buy assessment
- Where an off-the-shelf product does it, we will say so. Building what you can buy is one of the more expensive mistakes available here.
- A sequenced plan
- What to do first, what to defer, and what not to do at all. Ordered by return against effort rather than by which sounds most impressive.
- Governance and risk position
- What data would leave your systems, under what lawful basis, and what you would need to show a regulator or an enterprise client.
Decision tree
Is this a real AI opportunity, or a pilot waiting to fail?
Most AI projects that quietly die failed one of these at the outset. Run any idea you are considering through it — several branches point at something cheaper that would have worked.
01The task happens hundreds of times a month with a fairly consistent rule set
Genuine opportunity. Measure the volume and the current cost, then compare. This is the shape that works.
02The underlying process is broken or undocumented
Fix the process first. Automating a broken process gives you a broken process running faster and harder to correct.
03The data it would need is scattered, inconsistent or does not exist
Data groundwork first. That work is worth doing regardless of whether AI follows, which makes it an easy recommendation.
04An off-the-shelf product already does this well
Buy it. Building what you can buy is one of the more expensive mistakes in this category, and the product will improve without you paying for it.
05The task needs judgement, empathy or accountability
Not a candidate for automation. It may be a candidate for assistance — a model that drafts and a person who decides.
06The volume is genuinely low — a few times a week
The return will not cover the build or the running cost. Note it and move on to the next candidate.
07You want it because competitors mention AI
That is a marketing position, not an operations project. Say so internally rather than building something to justify it.
How it works
Step by step, with timeframes
Timeframes are typical rather than guaranteed, and they assume we get account access and approvals when we ask.
- 01Week 1
Understand the business
What the operation actually does, where the volume is, and where people spend time on things they find tedious. Usually the most informative conversation.
- 02Week 1–4
Measure candidate gaps
Real data over a fortnight or more on the two or three most promising areas. An impression is not a business case.
- 03Week 4–5
Cost and assess
Build cost, running cost, data readiness, vendor options and change effort for each candidate.
- 04Week 5–6
Recommend and hand over
A written plan with a session to talk through it, including an explicit list of what not to pursue.
What you get
Reporting and ownership
- A written assessment you own, usable with any supplier including one that is not us.
- Real measured figures for each candidate gap, not estimates.
- Running costs modelled at your actual volume, not at a demo tier.
- A named list of things we recommend not doing, and why.
- A governance and data position you could put in front of an enterprise client.
Tools and platforms
- Call tracking and ticket analysis
- Process timing and observation
- Commercial model API pricing calculators
- Data audit tooling
- Vendor evaluation frameworks
Timeline
How long this actually takes
Five to six weeks, of which two to four are measurement. The measurement is the part clients want to skip and the part that makes the recommendation worth anything. On outcomes: a meaningful share of these engagements conclude that the best available opportunity is smaller than the cost of building it. That is a legitimate result and a far cheaper one than a pilot nobody uses. We would rather deliver that conclusion than a project, and the fee is structured so it does not depend on the answer.
Pricing model
Day rate
Fixed price for the assessment, or a day rate for ongoing advisory. No obligation to buy delivery from us afterwards, and no commission from any vendor we assess.
Questions
AI Consulting & Strategy questions
Where do most businesses actually find value in AI?
In narrow, repetitive, high-volume tasks — calls answered out of hours, first-line support, document handling, lead triage. Not in general capability upgrades. The pattern is consistent: the projects that survive have a measurable gap and a specific rule set, and the ones that die were an initiative.
Why do you insist on measuring first?
Because a business case built on an impression is not a business case. "We miss a lot of calls" becomes a different conversation once it is a number — sometimes it is larger than anyone thought, and sometimes it is small enough that building anything would be wasteful.
Do you take commission from AI vendors?
No. Where an off-the-shelf product is the right answer we say so and take nothing for it. An adviser with a financial interest in the recommendation is not an adviser, and you should ask this question of anyone assessing vendors on your behalf.
What if the honest answer is that we should not do anything?
Then that is the deliverable, and it is a legitimate one. It costs the price of an assessment rather than the price of a pilot, an internal rollout and eighteen months of a system nobody opens. The fee does not depend on the conclusion.
How do we evaluate the vendors pitching to us?
Against your measured baseline rather than their demo. Ask what happens on the cases their demo does not cover, what the running cost is at your volume, what data leaves your systems, and what happens when it gets something wrong. Most pitches thin out considerably under those four.
Is this different from an AI readiness assessment?
Related. Readiness scores your data, processes and appetite — whether you are in a position to do anything. Consultancy identifies and costs the specific opportunities. Businesses with obvious candidates go straight to consultancy; those unsure whether they are ready start with the assessment.
Last reviewed 28 July 2026.
Tell us what you are trying to fix
A 20-minute call, no pitch deck. The Nexclick will tell you what we would do, roughly what it costs, and whether we are the right people for it.