Internal strategy workshops systematically miss the attack surface of your own business model. Not out of incompetence, but because nobody in the room has a genuine interest in breaking the company apart. That is exactly the perspective AI-DisruptMe provides.
The controlled-attack method
The method comes out of the Y Combinator ecosystem and was originally developed to help startups identify outdated business models. AI-DisruptMe transfers it to established companies. External teams with no loyalty to the status quo build AI-powered business models with a single goal: serve the incumbent's customers better, cheaper or more conveniently.
The difference from a classic competitive workshop is fundamental. In the workshop, the company's assumptions get discussed and confirmed. In the controlled attack, they get attacked. What survives was actually stable. What falls was only thought to be stable.
Typical findings
Three patterns repeated across the projects run so far. First: customer interfaces that can be fully automated by AI agents were often the most expensive asset at the incumbent. Second: data silos considered an internal competitive advantage were reconstructed surprisingly fast by external AI models using publicly available data. Third: regulatory moats became less valuable than assumed once AI-native compliance processes were factored in.
None of these patterns is universal. What matters is that they get tested in the specific controlled attack against the specific company. General trends do not substitute for a specific analysis.
What happens afterwards
At the end of a DisruptMe project there is not a report but a list of concrete attacks that would work, and a second list of counter-measures. The counter-measures are prioritized by effort, impact and urgency. Management then decides which of them to actually implement. The value of the project comes not from the uncovering itself but from informed non-action where the attack is harmless, and from fast action where it would be fatal.