Nick Takkenberg, seated at a dark wooden desk with a laptop and a notebook

Nick Takkenberg · entrepreneur and innovation adviser

How to innovate with AI.

Innovation rarely fails on ideas. It fails on decision making. I help boards actually reach decisions with AI, and I bring that same story to the stage.

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Theoretical or practical

There are two kinds of AI speaker.

The one

Speaks about everything AI can do. Impressive demos, big numbers, a room that walks out full of enthusiasm. On Monday nothing has changed.

The other

Builds it himself, runs into the walls, and shows what did and did not work. Including the things that failed. That is where I sit.

The framework from the talk

Seven steps your room takes home.

Seven steps from observing to scaling. At GO / NO-GO the decision falls: carry on, adjust or stop.

OBSERVE
CONTEXT
RIPPLE
CORE
PROBE
GO / NO-GO
SCALE

The talk

What a room walks away with.

Where do you start

Not with the tool. With the question you have not made sharp yet. I show you how to get that one out in the open first.

Where AI does work

And where it does not. With real examples from my own companies, including what went wrong.

How you decide

A framework you can use on Monday to work it out yourself: carry on, adjust or stop.

Working principles

What I build on.

Clarity before speed

We take the time to get clear on what is actually going on.

Context is everything

No context, no innovation. First look at how the system works.

Simplicity as strength

We look for the simplest solution that works.

Experiment smartly

Small steps, fast learning. An experiment is a design too.

Decide with confidence

Better a decision that has been made than an analysis that keeps running.

Scale what lasts

Impact first, then scale. Scaling what does not work yet only makes it bigger.

Who Nick is

An entrepreneur who builds it himself.

I am an entrepreneur and innovation adviser. Founder of Beacy and co-founder of Takkenberg & Westerveld. My ground is the fuzzy front end. The stage where the problem is still vague and the wrong choice gets expensive.

I work with owners and boards who are willing to innovate. From law firms to mechanical services companies. The sector differs, the question does not: you have to decide while you are not yet sure.

I am not an analyst. I build to find out whether something holds up. Write down the assumption, make something small, put it in front of real people, then look at what the evidence says. Sometimes that comes back as “I was wrong”. Then I stop, and that is exactly the point.

Innovation and AI belong together, but they are almost always organised apart. The innovation side does not know what is technically possible, the AI side does not know which problem it solves. That is where my work sits. You will not get a report for the drawer from me, you will get a decision that has been made.

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What I am building