Lots of pilots, little use
There are plenty of ideas, but AI isn't used systematically in day-to-day work. After the demo, it fizzles out.
AI & Governance
AI adoption for SMEs
By now, most companies have tried something with AI. A pilot, a trial, an enthusiastic colleague who got started with it. And then? Usually, not much happens. We make AI part of how people work every day—with buy-in, working agreements, training and follow-up that doesn’t stop after the first month.
30 minutes, no obligation. We first determine whether the issue lies in use cases, adoption, governance or process design. No additional AI project if that is not the solution.
Practical and results-drivenNot a strategy document. Working agreements that take effect on Monday.
Fifteen years of experienceIn manufacturing, wholesale, services, and e-commerce.
Structured approachFive steps, with an owner for each one.
Adoption by team
exampleAdoption rate68%+12% compared with last quarter
Active use cases37of the 54 proposed
Next step11teams up next
| Team | Status | Use cases | Progress | Next step |
|---|---|---|---|---|
| Sales | In use | 8 | 92% | Scale |
| HR | In training | 6 | 65% | Complete training |
| Finance | In training | 7 | 60% | Documenting how we work |
| Service | Stuck | 5 | 25% | Build support |
| Operations | Yet to start | 4 | 10% | Choose use cases |
please note
Service is held back by lack of buy-in, not by technology. More training won't help.
A pilot is not adoption.
Only if Monday
if it still happens, it counts.
Only if Monday
if it still happens, it counts.
Why AI adoption often stalls
Five causes. They almost never occur alone.
No clear way of working
It’s unclear when and how AI may or must be used. As a result, it remains optional and everyone does it differently.
Resistance or uncertainty
Employees don't know what it means for their work, or fear for their roles. No one says it out loud, but it slows everything down.
No ownership
No one feels responsible for adoption. It slips to the bottom of the priority list.
No follow-up
Initiatives disappear without measurement, feedback, and adjustment. There’s no point when anyone asks: is this still working?
What we take care of
Not sure which model fits a use case? Use the AI Model Advisor to compare models by task, cost, speed and governance.
Five components. Together, they make the difference between a successful demo and a habit.
Choose use cases
We start with the work that’s taking up time now, not with the technology.
Bring teams along
Support doesn’t come from an email; it comes from showing people how it’s done.
- Someone in each department who wants to do it
- Champions who lead by example
- Explanation of what's changing
AI working agreements
Governance that works instead of getting in the way.
- Roles and responsibilities
- Which data is allowed and which isn’t
- When a person reviews it
Training and adoption
Practice with your own proposals, emails, and lists.
- On the shop floor, not in a meeting room
- Do, practice, embed
- No demo slides
Measure and improve
If you don’t measure it, you don’t know it.
- Measure usage and impact
- Collect feedback
- Quarterly adjustments
This is how we approach it
Five steps. The first is the Fresh Perspective — the conversation starts with your own figures instead of assumptions.
1Fresh LookWe map opportunities, risks and requirements. An online assessment plus a day observing your work.
2Define use casesTogether, we choose the applications with the most value and the least resistance.
3Configuration and way of workingWe set up processes, agreements and tools in a practical way.
4Adoption and trainingWe train teams on the job, using their own work as the material.
5Measure and improveWe measure, learn and adjust to keep it working.
Something for every role otherwise
Adoption usually stalls because for one party it is a goal and for the other it is a burden. That's why a different answer per role.
Executive team
- Steering AI adoption
- Clear picture of value and risk
- Governance and compliance in place
MT
- AI as an accelerator for existing goals
- Make better decisions with data
- Control over what's in progress
Team leaders
- Practical tools for the team
- Higher productivity and quality
- Support that works
Employees
- AI that helps with everyday work
- Less routine, more meaningful work
- Training and support
What this isn’t. No eighty-slide AI strategy. It's an approach to bringing AI into the day-to-day work of an organization with 3 to 250 employees — where no one has an AI department and everyone is already stretched. The biggest gains usually don't come from a smarter tool, but from agreeing who uses it and when.
What you safeguard
Adoption is only successful if it continues to work without anyone monitoring it. You establish seven things for that.
Processes
Clear steps, checklists, and quality criteria—so the work doesn’t depend on who happens to be doing it.
Policy
AI policies, guidelines and risk management documented and easy to find.
Prompts and templates
Approved prompts and formats available centrally, instead of in everyone's personal notes.
Data and context
Reliable data and sources, ready for reuse.
Roles
Owner, sponsor, and users: everyone knows what’s expected of them.
Monitoring
Dashboards and reports for management and accountability.
Documentation
Decisions, lessons learned and changes properly documented.
How you can tell that it works
Five signs. If you don't measure them, you won't know it has fizzled out until someone asks.
72%
Adoption rate
share of employees who use it weekly
48
Active teams
teams with at least one running application
36
Working agreements
processes with documented rules
4,2
Hour per week
time saved per person, measured rather than estimated
8,6
Satisfaction
of the people who work with it every day
Why this makes a difference. Most AI programs don’t end with a decision, but with silence. There’s no point when someone says: this didn’t work. It simply disappears from the agenda. These five figures are the moment it comes to light—early enough to do something about it. Tracking them is part of Bedrijfsgeheugen Continu.
What this affects
Adoption is not separate from the rest. These eight pages cover the components you encounter along the way.
The AI Act
What applies now and what has been postponed, and which risk class you fall into. To the AI Act →
AI governance
Register, ownership, approvals, and audit trail. To AI governance →
Data sovereignty
Where does your data go with ChatGPT, Claude or Copilot? To data sovereignty →
Business case
Benefits, costs, risks and three scenarios, all substantiated. To the business case →
For executives
Decisions, risk overview and governance. To business leaders →
Implement responsibly
Privacy, security, testing and working agreements. To implementation →
Proof of concept
A pilot with a hypothesis, measurement, and a decision at the end. To proof of concept →
AI workshops
Eight tracks, from the basics to building, data and automation. To the workshops →
AI in business intelligence
What works and what doesn’t when you put AI to work on your numbers. To AI in BI →
AI in data engineering
Documenting, designing checks, and writing code — and where the boundary lies. To data engineering →
Want to find out where the problem is for you first? Take the AI scan — five minutes, and you’ll have a figure per task. You can compare your result with other companies via the benchmark. Or measure with the Completion Index or whether what you started last year is still running.
Background in the blog: what the EU AI Act means for SMEs and seven tasks you can automate with Microsoft Copilot.
AI only helps when it becomes part of your daily work.
Start with the free online Fresh perspective: thirteen components, approximately six minutes. You immediately see where you stand with data and AI, and how you compare to your industry.
Free online scanNo obligationsConcrete next steps
Ask this site
Ask your question about AI, the AI ActEuropean law that sets requirements for those who develop or use AI. Determines which risk category your application falls into and what you must document for it., connections or digitalization. The answer comes from the pages of this site, with references to where it is located.
Figures and context: AI use in SMEs, why AI projects fail and preserve knowledge when employees retire.
Want to know exactly what you need to be able to do?
Adoption is about people. The foundation beneath it is about AI capability model: 86 capabilities across nine layers, with a nine-question scan that puts your result in your portal.
View the model →The way it works now
- The appointment is in one colleague's mailbox
- Data is manually retyped from one system to another
- The person who knows is on vacation
- No one can see afterwards why this decision was made
As it is
- The agreement is stored with the client, findable for everyone
- Once entered, information flows through to the rest
- Work continues, even if someone is absent
- Every decision has a source, an owner, and a date
drag with your finger
What if Peter leaves?
Two out of five rules on this page disappear. That's how it feels when knowledge isn't part of the company.
What is your next step?
Free self-assessmentTwelve click questions, three minutes, immediate score and your biggest risk.
Fresh LookBusiness case, benchmark and priority. Settled once we build after this.
What it costsFixed price per package, no price per user, no hourly billing.
From the fieldWhat changed at companies that started with this.
Also good to know: frequently asked questions · security and data · terms and conditions · partners · research and figures · templates · what's changing · where you start
Where does the process break down in practice?
AI tools are available, but employees do not yet use them effectively. Which task, guidance and quality checks will make a difference?
Direct answer
Start with a concrete business problem and a measurable outcome, not with an AI tool. Arrange data, ownership, risk and human control before you scale up.
What we do
We connect analysis, business intelligence, data and execution so you don't get stuck in advice or loose tooling.
How it works
- We clarify the current situation, data and the real bottleneck.
- We determine the smallest working solution, owner and measurable outcome.
- We implement or prioritize the next step and verify the result.
- We secure what works and stop or fix what doesn't work.
What you get
A concrete analysis, priorities, decision criteria, an executable next step and visibility into what needs to be built, integrated or secured next.
For whom and whom not
Right for Dutch SMB organizations that want to solve a concrete process, knowledge, data or AI problem. Not for organizations looking only for a generic tool demo or unproven AI promise.
Costs and choice
Price and scope depend on the chosen next step. View the current fixed packages and pricing logic at pricing for digitization.
Risks and concerns
We work step by step, make assumptions and ownership explicit, use only the minimum necessary data, and maintain human control over decisions and execution.
Frequently asked questions before you decide
Does everything have to be done at once?
No. The preference is one clearly defined problem with a measurable outcome, then scale up.
What does it cost?
The current pricing and package logic is on the pricing page; custom work is only chosen if the scope really requires it.
How do you prevent a trajectory without results?
By defining the problem, owner, success metric and stop criteria in advance, and doing readback and verification after each step.
How we assess your situation
We map your current process, systems, exceptions and costs. Only then do we advise whether an off-the-shelf solution, a custom integration or another approach makes sense.
From insight to execution
This page supports the following concrete solution.
Continue in Powerhouse
Choose the next step that fits what you are viewing now.