AI implementation
AI implementation that is still in use three months later
AI implementation is about getting AI into the way work is actually done, and the technology is rarely the hard part. I begin with one area, measure the effect and only expand if the numbers hold. After 400+ projects I know where it typically breaks.
- I usually reply the same day
- Written estimate before work starts
AI implementation
How I approach it
The first step is finding the work that gets done many times a week, follows a fixed pattern and annoys the people doing it. That is nearly always where the first win sits, and where people will actually adopt the solution.
Next we measure how long the task takes today. Without that number, nobody can decide afterwards whether anything improved, and the discussion ends up as one gut feeling against another.
Where AI typically gets adopted
- Customer service: faster replies by reusing knowledge from earlier cases, with drafts that only need a touch-up.
- Document handling: summaries and extraction from long documents, proposals and contracts.
- Internal tools: looking things up in handbooks, procedures and product data without asking a colleague.
- Integration with existing systems: AI features inside the programs people already use, rather than a new tab on the side.
Guidelines and staff
Many companies either have no AI guidelines or a memo nobody has read. Both end with people using AI anyway, just without it being agreed.
I help write something short and usable: what may be entered where, what needs checking, and who to ask. Two pages people actually read beat twenty on the intranet.
What you get
Concrete deliverables, not a report of recommendations someone else has to find time for.
- A map
An overview of where in the business there is genuinely something to gain, based on talking to the people doing the work.
- A measured baseline
A number for how long the task takes today and how often it happens, so the effect can be assessed afterwards.
- A first solution in production
One area covered in four to six weeks and used by real people, not a platform meant to do everything from day one.
- Guidelines
Two pages on what may go where and what needs checking, short enough to actually get read.
How it works
Four steps. You know what happens when, and you can stop after any of them.
- 01
Pick the process
Together we find the piece of work that repeats often enough for automation to pay off. The more contained the task, the faster the first version reaches production.
- 02
Set a baseline
We record how long the task takes today, how often it occurs, and how many errors slip through. Without those numbers, nobody can tell afterwards whether the solution was worth the effort.
- 03
Build against a test set
I collect real examples from your day-to-day and test every prompt and model change against them. That keeps quality stable when the solution is tuned later on.
- 04
Production with logs and a kill switch
The solution is wired into your existing systems with full logging and a switch that can turn it off. I document the setup and train someone on your side to maintain it.
Brands I've worked with
Frequently asked questions
- What does AI implementation mean in practice?
- Going from having access to AI to using it in daily work. It covers choosing the area, integrating into the systems people already use, training, and measuring whether it helps. The technology itself is rarely the bottleneck.
- What does AI implementation cost?
- Scope decides the price. The number of workflows, the amount of data cleanup and the need for integrations and training are the biggest items. Tell me briefly where you stand and I will reply the same day, with a concrete quote once the scope is clarified.
- How big do we need to be for this to make sense?
- There is no lower limit on headcount. What matters is whether a piece of work repeats often enough, and a ten-person company can easily have one.
- Can we start with a pilot?
- That is exactly how I recommend doing it. One area over four to six weeks, and a real decision afterwards about whether to do more. Large platform projects meant to cover everything from day one almost always end badly.
- What about GDPR and the EU AI Act?
- The guidelines we write together account for both: what may be entered where, and how output gets checked. For most ordinary uses the requirements are manageable. I am not a lawyer and will say so if you should involve one.
- Do you visit companies across the whole country?
- Yes, when it makes sense. Implementation involves people, so I prioritise meeting the team, especially at kickoff. Around Copenhagen that is easy, and for Aarhus, Odense or Aalborg we plan visits around the important milestones.
- Do we need our data in order first?
- Not all of it. But the data the first solution needs has to be retrievable and reasonably trustworthy. If it is not, the cleanup becomes part of the project and belongs in the timeline rather than arriving as a surprise.
- Can we use what we already pay for?
- Often yes. With Microsoft 365 or Google Workspace, AI features come with the subscription that cover a fair range of ordinary needs. I check that before proposing to build anything new.