GDPR & AI Act 2026: How CRM solutions make data protection and AI fit
CAS team
7 minutes reading time
With the adoption of the AI Act in 2024, the European Union has created a uniform framework for the use of artificial intelligence. At the same time, the GDPR remains the central basis for handling personal data.
This increases the requirements for companies, especially in the interaction between data and AI. CRM solutions play a key role here: They bundle customer data, map processes and are increasingly becoming the basis for AI-supported applications.
For medium-sized companies in particular, a powerful CRM is crucial in order to implement regulatory requirements securely and at the same time exploit the potential of AI along the customer journey.
This article shows what CRM managers should look out for in 2026 and how they can future-proof their CRM.
What to expect in this article
GDPR - data protection remains central
The EU AI Act - AI in CRM gets clear rules
Consent management becomes strategic
Using AI features in CRM responsibly
Data minimization vs. Personalization pressure
Interfaces and third-party provider risks
Lifecycle management: Deletion concepts and data life cycle
Organizational and procedural measures
Checklist: 10 questions that CRM managers should ask themselves in 2026
GDPR - data protection remains central
In 2026, the General Data Protection Regulation (GDPR) will remain the authoritative basis for the handling of personal data. Legally compliant processing is still essential, especially in the B2B environment, where business contact data is frequently used.
For CRM managers, this means in particular documenting consent properly, carefully weighing up legitimate interests and consistently implementing the principle of data minimization. These requirements have a direct impact on central CRM functions such as lead scoring, tracking and profiling.
At the same time, the requirements for transparency and traceability are increasing. Companies must be able to prove at any time, the legal basis for data processing and how corresponding processes are mapped in the CRM system.
Practical check: GDPR in CRM 2026
- Are consents documented completely and in an audit-proof manner?
- Are legitimate interests clearly justified and regularly reviewed?
- Have you ensured that only necessary data is collected and used?
- Are all data-related processes in your CRM documented in a traceable manner?
The EU AI Act - AI in CRM gets clear rules
With the AI Act, the European Union is creating binding guidelines for the use of artificial intelligence for the first time - including in CRM. Typical use cases such as lead scoring, sales forecasts or automated customer interactions are therefore increasingly subject to regulatory requirements.
The focus is on risk classifications, transparency obligations and extensive documentation requirements. Companies must clearly explain how AI systems make decisions and what data is used for this.
Requirements regarding the explainability of AI results and the possibility of human oversight are also particularly relevant for CRM managers. Automated decisions must not be non-transparent or uncontrollable.
Consent management becomes strategic
With increasing regulatory requirements, consent management is evolving from a mandatory operational task to a strategic success factor. Companies must not only obtain consent, but also manage it in a differentiated, transparent and systematic manner.
Granular consents are required that make it possible to clearly separate different purposes of use. At the same time, first-party data strategies are gaining in importance, as third-party cookies are increasingly losing relevance. The CRM becomes the central instance for managing and documenting all consents.
Established procedures such as double opt-in are also coming under greater scrutiny and should be reviewed with regard to user-friendliness and verifiability. Seamless documentation remains an ongoing task that must be properly anchored in the CRM from a technical and organizational perspective.
Using AI features in CRM responsibly
The use of AI in CRM opens up new potential, but also requires clear guidelines. Before introducing AI-supported functions, companies should carry out a structured risk analysis and evaluate the respective use cases.
In addition, company-wide AI governance is becoming increasingly important. It defines binding rules for the use, monitoring and further development of AI systems in CRM.
A key aspect of this is ensuring human oversight: Automated decisions must remain verifiable and, in case of doubt, be able to be corrected by humans. CRM managers should also critically examine their technology providers, for example with regard to compliance evidence and regulatory standards.
Data minimization vs. Personalization pressure
A central area of tension in CRM will also remain in 2026: While marketing and sales are increasingly relying on a personalized approach, legislators are demanding consistent data minimization.
For companies, this means consciously weighing up which data is actually necessary and how it can be used sensibly. The aim is to create relevance for the customer without collecting or storing unnecessary data.
This is where the principle of "privacy by design" comes in: Data protection is integrated into processes and systems from the outset instead of being added later. If implemented correctly, this can not only reduce regulatory risks, but also create trust and thus become a competitive advantage.
Audit of existing CRM systems
A structured look at the existing CRM system is the first step towards harmonizing regulatory requirements and AI use. Many companies are already using AI-supported functions - often without full transparency about how they work and what data they are based on.
CRM managers should therefore systematically record which AI modules are already in use and where data is automatically enriched or processed. Equally important is the complete documentation of all data flows both within CRM and across system boundaries.
Practical check: CRM audit
- Which AI-supported functions are currently in use?
- Where is data automatically enriched or changed?
- Are all data flows fully documented and traceable?