Data, AI and compliance: moving from pilot to scale
Your data and your use of AI create value only if they are reliable, governed and compliant. We build the strategy, deploy it within your existing tools, then steer the results with you.
The stakes
Many organisations have launched AI pilots that work in a demonstration and stop there. The reason is rarely technological. Data is scattered, its quality is not measured, responsibilities are undefined, and nobody knows who decides to put a use case into production.
The regulatory framework has tightened in parallel. The nLPD applies in Switzerland, the GDPR applies as soon as you process data of individuals in the European Union, and the EU AI Act introduces obligations graded by the risk level of each system. For an executive committee, the question is no longer whether to address this, but in what order and to what standard.
The stakes are therefore twofold: not to slow innovation with compliance experienced as an obstacle, and not to expose the company through uncontrolled uses. Data sovereignty adds to this: where is your data hosted, who can access it, and which dependencies on third-party providers are you willing to accept. These choices engage the responsibility of management and call for a clear, documented and proportionate answer.
What we build
We map your critical data, define owners and simple quality rules, then put measurable controls in place. The aim is data your leadership can rely on to make decisions.
We define roles, acceptance criteria for a use case, the register of systems and the rules for human oversight. Governance stays light, adapted to your size and your sector.
We translate the nLPD, the GDPR and the EU AI Act into concrete requirements for your teams: impact assessments, information to individuals, supplier management, traceability. They fit into your existing processes rather than sitting on top of them.
We select use cases by value and risk, prepare the organisation to receive them, then deploy them with tracking indicators. Cases that do not stand up to comparison are stopped early.
Where AI helps
AI helps detect duplicates, anomalies and inconsistencies at a scale where manual checks would take too long. Defining the rules and naming the owners is a matter of organisation and common sense.
AI can speed up the inventory of processing activities and the preparation of documentation. Risk trade-offs, acceptance decisions and accountability remain human and carried by management.
AI adds value on repetitive tasks, search across large volumes of documents and forecasting, provided the use case is well framed. Team adoption, training and clear processes do the rest.
Targeted results
- →Critical data identified, owned and controlled
- →An AI governance framework approved by management
- →Documented, proportionate compliance with the nLPD, GDPR and EU AI Act
- →Use cases in production, followed through clear indicators
Industries
The industries where this pillar is most often needed.
Insights
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