AI Services
AI integration consulting without the hype
AI integration consulting answers one question: where would AI genuinely pay off in your business, and where would it waste money? We audit your product and operations, rank the opportunities by return, and pilot the best one before you commit widely.
Who it is for: Leaders under pressure to adopt AI who want a grounded plan, and product teams weighing where AI features belong in what they sell.
Everything in this service
- Audit of workflows and product for AI opportunities
- Feasibility and return ranked case by case
- Build, buy or skip recommendation for each
- A piloted first project with success criteria agreed upfront
- Team training and data-handling guidance
What to expect
- A ranked list of where AI pays in your business
- One pilot proven with real numbers before wider spend
- Money not wasted on AI theatre
How ai integration consulting actually works
Auditing where AI genuinely pays back
Most AI adoption fails at the first step, which is choosing what to work on. Projects get picked because a competitor announced something, because a board asked, or because a demo was impressive. None of those are reasons connected to your economics, and a project chosen that way tends to end as a pilot nobody cancels and nobody uses.
A useful audit starts from where time and money currently go. Which tasks consume the most hours across the business. Which of them are repetitive. Where do backlogs form. Where do errors cost you. Where do customers wait. Then, separately, which of those involve reading or writing language, working through unstructured documents, or classifying things a person currently reads and sorts. The overlap between those two lists is the shortlist, and it is usually short.
Each candidate then gets scored on the same four questions: how much value would a good version create, how hard is it to build, how bad is a wrong answer, and would the people involved actually use it. That last question kills more projects than the technical ones. A tool that saves twenty minutes but requires a team to change how they work will lose to the status quo, and knowing that before you build is worth the audit fee on its own.
- Start from where time, cost and delay already sit
- Filter for tasks involving language, documents or classification
- Score value, difficulty, cost of error and likely adoption
- Expect a short list, not a long one
- Adoption risk sinks more projects than technical risk
Build, buy or skip, decided with numbers
Every opportunity has three honest answers and one of them is skip. Buying makes sense for common problems that a mature product already solves, where you gain from someone else maintaining it and improving it. Building makes sense when the workflow is specific enough that no product fits, when per-seat pricing has grown beyond the cost of ownership, or when data cannot go where the product requires. Skipping makes sense far more often than vendors suggest.
The comparison should be done with real numbers rather than instinct. For buying, that means the licence cost at your actual seat count in two years rather than today, the configuration effort, and what happens to your data and process if you leave. For building, it means the build cost, the ongoing maintenance, and the internal ownership that a custom system requires. A build with no named owner inside the business is a future problem regardless of how well it works at launch.
The uncomfortable answer is often that the process should be fixed before anything is automated. If work is slow because approvals sit for three days, an AI tool that drafts the document faster changes nothing. Sequencing the process fix first is cheaper and produces a bigger result, and it is the recommendation we give more often than clients expect.
- Compare licence cost at future seat counts, not current ones
- Count maintenance and internal ownership in any build case
- Check what leaving a vendor would cost before committing
- Fix the process first when the delay is not the task itself
- Treat skip as a real and frequent answer
Data handling questions to ask any vendor
Vendor selection turns on questions that are easy to ask and revealing to hear answered. Is our data used to train models, and is that stated in the contract rather than a marketing page. Where is it processed and stored. How long is it retained, including in logs. Who at the vendor can see it. What happens to it if we cancel. Can we get our data and our configuration out in a usable form.
Then the operational ones. What is the uptime commitment and what happens when it is missed. How much notice is given before a model is changed or retired. Is there a way to pin a version. How does pricing change with volume, and what does it look like at ten times our current usage. Are there sub-processors, and who are they, because your data may be travelling further than the vendor name suggests.
We help you ask these and interpret the answers, and we would send anything that becomes a question about your legal obligations to your own advisers rather than answering it ourselves. What we can say is that vague answers are themselves an answer. A vendor that cannot describe its data handling clearly in a sales conversation will not describe it more clearly in an incident.
- Get training exclusion in the contract, not on a web page
- Ask about retention in logs, not just in the main system
- Check notice periods for model changes and retirements
- Model pricing at ten times your current volume
- Ask for the sub-processor list and read it
Designing a pilot that can actually fail
A pilot is only useful if it is capable of producing a no. Most are not. They are demonstrations with a friendly audience, a curated dataset and no agreed standard, and they conclude with everyone feeling positive and nothing being decided. Real pilots have success criteria written down before the work starts.
The criteria should be specific and few. What accuracy standard must be met, measured how, on what sample. What time or cost saving would justify continuing. Who uses it, for how long, and how their experience will be captured. And critically, what result would mean stopping. Agreeing the stopping condition in advance is what prevents a pilot from turning into a permanent state of nearly working.
Keep the scope small and the timeframe short. One team, one task, a few weeks, real data and real users. That produces a genuine answer faster than a six-month programme, and it costs little enough that stopping is not politically difficult. Where a pilot succeeds, the same work becomes the first stage of the rollout rather than a throwaway. Where it fails, you have learned something specific about your data, your process or your team for a fraction of what a full build would have cost.
- Write success and stopping criteria before starting
- Use real data and real users, not a curated demonstration
- Keep it to one team, one task and a few weeks
- Measure against an agreed standard, not general impressions
- Make stopping cheap enough to be politically possible
Training and adoption, where most value is won or lost
A tool that works and is not used produces the same return as a tool that does not work. Adoption is not a communications exercise at the end of a project. It is a design constraint from the start, and the people who will use the system should be involved in scoping it rather than presented with it.
Practical training is short, specific and repeated. People need to know what the tool is for, what it is not for, how to tell when the output is wrong, and what to do about it when it is. That last part matters most. A team that has been told the system is reliable and then finds an error loses confidence in everything it produces. A team that has been told plainly where it struggles, and given a way to flag problems, keeps using it and improves it.
Watch for the quiet signals. If usage drops after the first fortnight, if people keep a parallel spreadsheet, or if one enthusiast is doing all the work through the tool while everyone else avoids it, the issue is fit rather than familiarity. Those signals are worth acting on early, because a tool that has been abandoned once is much harder to reintroduce than one that is adjusted while people are still trying it.
- Involve the eventual users during scoping, not at launch
- Teach where the tool fails, not only where it succeeds
- Give people an easy way to flag a wrong result
- Watch usage after two weeks, that is when drop-off shows
- Parallel spreadsheets mean the tool does not fit the work
What AI is genuinely good at today, and what it is not
Honest advice starts with capability. Current systems are strong at working with language and unstructured material: summarising, drafting, extracting fields from documents, classifying free text, translating, answering from a supplied set of sources, and turning a rough version of something into a tidy one. Those capabilities are reliable enough to build on when the output is checked.
They are weak where people most want them to be strong. Arithmetic and precise reasoning over numbers should go to systems built for that. Anything requiring facts the system was not given will produce confident invention. Consistency across many runs is not guaranteed, which matters when a process expects the same input to give the same output. And judgment involving people, risk or relationships remains a human responsibility, not because of a technical limit alone but because accountability has to sit somewhere.
The pattern that follows from this is assisted work rather than autonomous work in most business settings. The system does the gathering, the drafting and the first pass, and a person decides. That keeps the majority of the time saving while leaving responsibility where it belongs, and it is where the reliable returns are today.
- Strong on language, summarising, extraction and classification
- Weak on arithmetic, precision and unsupplied facts
- Output varies between runs, which some processes cannot accept
- Keep judgment and accountability with people
- Assisted workflows return most of the value with far less risk
Sequencing a programme rather than buying tools
Businesses that get value from AI tend to do it in a sequence rather than a purchase. The first stage is usually giving the team good general tools and clear rules for using them, because that produces immediate benefit and teaches everyone where the technology is useful. It also surfaces the specific tasks worth building for, from people who do the work.
The second stage is one grounded, narrow system built on your own content or data, where quality can be measured. That is where confidence is earned internally. The third is integration into a process, which is where the returns get larger and where the engineering discipline matters: error handling, monitoring, ownership, documentation. Skipping to the third stage first is the most common expensive mistake.
Throughout, keep a short written policy on what staff may and may not put into external tools, who approves new tools, and what has to be reviewed by a person before it goes to a customer. That is not bureaucracy for its own sake. It is what allows you to say yes to experimentation without discovering later that client data has been pasted into something nobody vetted. We help write it, and we would have your own advisers check anything with legal weight.
- Start with good general tools and clear usage rules
- Then build one narrow grounded system with measurable quality
- Integrate into processes only once reliability is proven
- Keep a short written policy on tools and data
- Review anything customer-facing before it ships
A clear path, step by step
- 01
Audit
We map your operations and product for the tasks AI genuinely does well today.
- 02
Rank
Each opportunity scored on return, effort and risk, with skip recommendations stated plainly.
- 03
Pilot
The strongest case built small, with success criteria agreed before a pound is spent.
- 04
Roll out or stop
The pilot’s numbers decide. What works scales, what does not gets dropped.
Why choose us for this
We build AI systems ourselves, so advice comes from practice
We are paid for judgment, and "do not do this one" is common advice
Pilots over decks: proof beats projection
Common questions
Do we need our data sorted before starting with AI?
Less than the hype suggests. Many high-return uses, like support automation and document processing, need only the content you already have. Where a use case does need cleaner data, we tell you the real cost of getting there first.
Build our own AI or buy an existing product?
Buy when a good product exists for a common problem. Build when the workflow is specific to you or the per-seat pricing outgrows a one-off build. We compare both with numbers for each use case.
Explore related work
Want this for your business?
Book a free visibility call and I will tell you honestly whether I can help.
How this is delivered
One person leads every project. Where a job genuinely needs a specialist, I bring in people I have worked with before and manage them, so you get one point of contact and one invoice rather than three suppliers blaming each other.
- You talk to the person responsible for the work, not an account manager
- Specialists are briefed and managed by me, and their work is checked before it reaches you
- One contract, one invoice, one place to chase