Move AI from experimentation
to deployment, where it really matters.
For transformation leaders who have run experiment after experiment and now need to show results. The tests went down well, few were scaled up, and the committee expects a roadmap it can fund. We help you choose where to deploy, based on facts rather than demos.
“So where do we deploy now?”
The experiment on credit file analysis won over the pilot team. The committee now asks you to roll it out. But to which jobs, in what order, and for what real gain?
According to a preliminary report from MIT NANDA (2025), only 5% of the custom AI tools evaluated by companies reach production. The choice is too often made on the demo, rarely on real work. The details in what the studies say.
AI doesn't replace jobs, it changes tasks. The right question becomes: on which tasks is AI reliable, wanted by your teams and cost-effective? Here is how the tool answers it.
Three workstreams to scale up.
Quantify the gain, set priorities
Which jobs to start with, how much time AI returns to teams, and what it costs.
Quantify the transformation Your teamsDrive AI adoption without forcing it
Start from what your teams want to hand over to AI, and set a framework for the uses that already exist.
Drive AI adoption Your first stepA pilot on a few jobs
A first costed roadmap, on a few jobs in your company chosen together.
Discover the pilot« The gap between what AI can do and what it actually does in organizations is not a technical problem. It's a problem of oversight skills. »
Choose where to deploy, based on facts.
In 30 minutes, you describe your current experiments, we show you on a fictional company the roadmap the tool produces, and together we sketch what a pilot would deliver for you.
30 minutes on video with Skillberg, no commitment. No personal data required.
- MIT NANDA, The GenAI Divide: State of AI in Business 2025, July 2025 (preliminary report), p. 3