In some of my trainings, we play the Ball Point Game.
Participants form teams and pass balls to each other – following a set of simple rules – to score as many points as possible.
At first, they try things out, improve a little – and are quickly quite satisfied.
Then they hear that other groups have achieved ten times as many points.
Some teams take that as motivation and start to rethink everything: “What would we have to change to get there?”
Others don’t believe it – or stick to their current approach.
What’s fascinating: most teams that take the leap achieve significantly more.
Some get close to the 10x goal. A few even reach it.
I believe we might be at a very similar moment with artificial intelligence – not with balls this time, but with code, text, images, video, and more.
Between excitement and skepticism
This week, Timon forwarded me a compelling blog post by Harper Reed. In it, he describes his personal learning journey with AI tools in software development – across nine stages.
He starts with basic prompts and ends up with a setup where he runs 3–5 parallel AI sessions, switching between terminals – watching the AI write code.
In his conclusion, he writes:
“Nobody can see this because they didn’t go through the journey to get here. But those who have are agreeing and sharing their own tips around the journey, and debating the destination.
Henrik Kniberg – known to many from his “PO in a Nutshell” video – has also shared how AI is changing his work. In a recent post, he’s seen eating fresh strawberries while an AI codes for him.
And then this line from him:
“My productivity doubled five times – that’s a 32x increase.”
And this is where a real tension emerges:
- Many AI results are not high quality. It hallucinates, makes things up.
- Often, a flood of mediocre content is generated – which can dilute rather than raise the bar.
- Especially in content spaces (like blogs), this can even be harmful when quantity overtakes quality.
- And: The less I know about a subject, the more impressed I tend to be when the AI produces something seemingly usable. This can lead to conflict between domain experts and managers in organizations.
Still, I believe: AI is here to stay.
Much like the internet in the 90s – overhyped at first, yet impossible to ignore today.
And I want to leave room for the possibility that this really is a Ball Point Game moment – and we’re right in the middle of it.
What I’m working on
Right now, I’m learning a lot from developers about how they use AI – and I’m exploring how AI can support Product Backlog Management: structuring requirements, writing more clearly, slicing more effectively. The same works also for Product and Company Strategy.
I also find the potential of fast documentation and easier processing of input from many stakeholders or workshop participants incredibly promising for facilitation of work with larger groups.
If that sounds interesting to you – or if you’re on a similar path – feel free to reach out. I’d love to exchange thoughts and let you know once something more concrete takes shape.