By integrating an AI tool into your production processes, you have given your vendor a precise map of your operations. That data may no longer truly belong to you.
You may have already ceded more than you think.
An AI tool integrated into the production chain, a predictive maintenance assistant, a logistics optimisation system. With each deployment, data transited to external servers. Models were trained. Patterns were captured.
In most cases, contractual terms authorise the vendor to use this data to improve their general models. Your production data is potentially feeding the models your competitors use tomorrow.
In most cases, these situations are only discovered at the moment when it becomes costly : or impossible : to go back. The vendor has captured the value. Processes are embedded. The dependency is structural.
We reveal what you have actually ceded. We identify the invisible dependencies and the contractual conditions that structure them. Then we redefine, with leadership, what must be preserved, renegotiated or stopped.
01 · Reveal : You discover what you have actually ceded. Not an estimate : a precise map: data, systems, exact contractual conditions.
02 · Understand : You see what this really implies: exit cost, competitive exposure, what your vendor can do with your value.
03 · Decide : You take back control. Or you accept the dependency : but deliberately, with the conditions that make it strategically acceptable.
This intervention is the starting point of AI dependency mastery: see, understand, decide.
Most organisations do not lose their sovereignty suddenly.
They lose it silently : contract by contract, integration by integration.
Production data exposure to an AI vendor refers to the situation in which an organisation's operational data (production parameters, process data, anomalies, logistics flows) transits to external AI systems under conditions that allow the vendor to use it beyond the contracted service.
This is frequent and often unintentional. SaaS AI contracts typically include clauses authorising the use of input data to improve models. These clauses, rarely read at signing, can create an inverted dependency: the organisation thinks it is using a tool, but is actually contributing to improving an asset owned by the vendor.
Managing this exposure requires three levers: contractual audit (identifying data usage clauses), flow mapping (which data transits to which external systems), and a sovereign dependency policy (defining what can be externalised and under which conditions). This approach aligns with EU AI Act requirements on traceability and oversight of deployed AI systems.
The difference between the two can represent years of competitive advantage ceded without return. That is the starting point of our intervention.
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