Scenario library
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Filter by industry, size and AI maturity to find dilemmas close to the reality your leadership team lives in.
11 of 11 scenarios
The AI agent recommends letting an employee go
Your HR agent has analysed performance data and proposes ending three employments. The reasoning looks sound.
AI is taking over the tasks we train our juniors on
An analyst skill that took three years to learn can now be done in an afternoon. So how do you build tomorrow's seniors?
Your AI agent has done wrong by a customer
Thursday morning you discover an automated process sent the wrong notice to 1,200 customers overnight.
We know we 'should be doing something with AI' — but what?
Pressure from the board, customers and employees. You've heard the talks, read the reports. And there you sit.
Shadow IT on steroids — when Johan's tool wants to scale
An employee built a tool that saves hours every week. Three other teams want it. Nobody knows where the data lives.
The competence paradox — when half the team pulls ahead
Half the analytics team produces three times as much with AI. The other half is struggling. The gap widens every week.
The manager as bottleneck — when approvals pile up
Every week someone shows up with a new prototype that 'just needs sign-off'. You can't keep up, you don't grasp every detail — but saying no feels like killing engagement.
Those who have time to have time — when life circumstances decide who develops
Three people on the team have become AI stars. The other nine have barely touched the tools. The difference? Who has slack at home.
The bill arrived before the results
Your AI spend is now roughly a quarter of your profit. The effect on the bottom line can't be evidenced.
They're spending their time on the wrong things
You can see what AI could take over. Your team sees something else.
Everyone uses it. Nobody can say what it gives.
Eight in ten use the AI tools every week. The adoption target is met. The business metrics look like last year's.