From the first spark to embedded practice: we guide organizations through the AI transition with people as the starting point.
You start where you are. Each phase builds on the one before it and ends with something tangible. Not a process that only pays off at the very end. Alongside the phases run two tracks, Management and Governance, which you can bring in whenever your organization needs them.
Management and Governance run alongside all four phases.
No technology explainers, no prompts. First the question that matters: why would we want this, when do we use it, and what does it give us.
Five themes, each with two sides: what becomes possible, and what it asks of you. They cover imagination, humanity, mindset, what AI already does and your role in it. At Mindset comes the question it all comes down to. Three doors, one choice: wait it out, use it, or partner with it.
Every theme ends with a question to the room. Not with an answer.
Format. Half a day, of which ninety minutes is the conversation itself. The rest is preparation and alignment. Twenty people at most, led by Alf himself. A conversation, not a presentation, and that is why twenty.
For boards and middle management there's a separate route in, with the same themes and different questions. You'll find it under the Management track.
What it produces. No test, no certificate. Spark sets things in motion. The measurable part starts in Play.
From inspired to doing. Not thinking about AI one more time, but taking it in hand: playing, trying things out, training, until it becomes normal.
Where possible, Play starts with a deep dive into your sector: what is going on, what is possible, where it goes wrong. We build that per assignment. Not a template with a different logo on it.
After that people get to work themselves, in two ways.
Online, in their own time and place. A game with short missions and immediate feedback. Wherever and whenever it suits.
In the classroom, in groups of twenty at most. Training such as AI as a team of colleagues, and a training on prompting.
What it produces. At the end everyone hands in something concrete from their own work: an opportunity to improve something, an agent they built, a way of working they set up. Not AI taking over the work, but someone working with AI. That is the proof that they walked through the third door.
The improvements identified in Play are the raw material here. With guidance they are worked out into real proposals: what it is, who leads it, who works on it, what it produces, how feasible it is, where the risk sits.
We guide the business case and the ROI along the way. Not as arithmetic for its own sake: together with the proposal itself and a working test, that is what the committee bases its choice on.
Those proposals go to a committee from your own organization: management, operations, tech and compliance, for example. They choose, not us.
What gets through is tested further: do the assumptions hold in practice, can it scale, and is it actually allowed? That last question we test against the frameworks from the Governance track. Then a final round, pitched with something working alongside it.
What it produces. Two or three use cases chosen and tested by your own organization. Not a list of ideas. Proposals with an owner and a decision behind them.
What was chosen in Create goes into practice here: validate, experiment small, scale up, embed. You build and implement it yourselves, with your own team or supplier. We are mainly there at the start and at the end: at the question of whether it is right, and at the question of whether it lasts.
Alongside that we set up the Lab: a fixed process that takes the next AI solution through the same route. Roles, a recurring cycle, and governance built in rather than bolted on. Whether it lasts depends largely on how it's led; that's what the Management track is about.
Anyone who wants to go beyond AI alone can widen this to innovation as a whole, a separate and heavier program.
What it produces. Use cases that are in structural use, and an organization that can run the next round itself.
What your people do is changing, and that asks something of the people who lead them: seeing what shifts in the work, and steering on that. Steering on the adoption of a tool is easier to measure, and it tells you far less.
This track runs alongside the four phases. We work with leaders at two levels. With the board and executive team, the conversation is about value: when doing the work gets cheaper, what will your clients still pay for? With middle management, it's about the team: what do you actually know about how your people's work has changed? That is where a strategy lands, or where it stalls.
Same vision, same five themes as Spark, with different examples and different questions at each level. No technology explainers; that isn't where a leader's challenge lies.
Concerns and resistance are real, and we treat them that way. First we listen and try to understand, and only then do we work out together what to do instead.
What it produces. Leaders who know what is changing in their people's work and steer on it. And a clear answer to what the new way of working should deliver for you.
Once AI is part of the work, questions come up that matter from the very first phase: what may be done with which data, and who decides? For some applications, the EU AI Act turns those questions into obligations.
This track runs alongside the four phases, and weighs more heavily the further you go. At Validate it's about whether an assumption holds; at Experiment it must already be clear what's allowed with which data; at Scale, governance effectively decides whether you may scale at all. And at Embed it becomes part of how you work.
We set the frameworks: compliance, ethics, and what regulation asks of you. Where specialist knowledge is needed, we bring it in from our circle of specialists.
Governance deserves attention in every AI transition, which is exactly why it isn't something to tidy up at the end. Whether you do it with us is up to you; you can also take on this track by itself, at any point.
What it produces. Frameworks your people can actually work with, and an organization that can explain what it does with AI and why.
Hundreds of professionals there go through Spark and Play.
Spark runs in groups of twenty at most, over and over, until everyone has been. Ninety minutes per group, no presentation. A conversation that ends with questions instead of answers.
In Play there is first a deep dive built specifically for their sector. Then the game, online and in their own time, and the classroom training in those same groups of twenty.
At the end everyone hands in something from their own work, and that is assessed. Not on how clever the application is, but on one question: does this person use AI as a partner, or do they let it take the work over.
From those groups, some go on to Create. Not everyone is a creator, and they do not need to be. Those who are take the next step.
What that produces: hundreds of people who not only know what AI can do but have worked with it themselves, and a stack of concrete starting points from their own practice.
How long the whole thing takes depends on how big your organization is and where you start. We determine that together.
After that you will know whether this fits you.
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