When an online store displays a “recommended for you” banner with products that exactly match your recent searches, it’s not magic. Behind this mechanism, an algorithm analyzes your clicks, your history, and your preferences to adjust the display in real time. This type of operation, driven by artificial intelligence, has become widespread in digital marketing to the point of changing how companies design their strategies.
European regulatory framework: what changes concretely for AI targeting
Most articles on AI in marketing mention the GDPR. But three recent European texts redefine the rules of the game in a much more precise way.
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The Digital Markets Act (DMA), fully applicable since March 2024, prohibits large platforms from combining personal data across their different services (advertising, analytics, social networks) without explicit consent. In practice, this reduces the amount of data available to feed automated targeting models.
The Digital Services Act (DSA) imposes a transparency obligation on recommendation systems and targeted advertising. Platforms must explain the criteria used by their algorithms. For a marketing team, this means that it is no longer enough to “let an AI-driven campaign run”: they must be able to justify the targeting logic.
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The AI Act, definitively adopted by the European Parliament on March 13, 2024, goes even further. Certain systems of customer scoring or advanced automated segmentation can be classified as “high risk” when they have a significant impact on individuals’ rights. A company using an algorithm to decide which prospects deserve a premium offer may need to comply with enhanced audit and documentation requirements.
These three texts combined push marketing teams to rethink their tools. It’s no longer about adopting the most efficient AI, but the one that remains compliant with the legal framework, and you can learn more on Intronaut about these impacts on the evolution of digital strategies.
Internal or open-source AI models: a fundamental trend in digital strategy
You may have noticed that some companies hesitate to send their customer data to external AI tools like ChatGPT or SaaS platforms? The reason is simple: every piece of data sent to a third-party service represents a risk of leakage.
To circumvent this problem, an increasing number of companies are deploying internal AI models, hosted on their own servers. Open-source models like LLaMA (Meta) or Mistral allow for running content generation, segmentation, or analysis algorithms without any data leaving the company’s infrastructure.
This approach has several concrete advantages:
- Customer data remains in a controlled environment, making compliance with the GDPR and the AI Act easier.
- The company can train the model on its own data (product catalogs, purchase histories, customer interactions) to achieve more relevant results than with a generic tool.
- The long-term cost decreases compared to SaaS subscriptions, especially for organizations that generate a high volume of content or analyses.
The trade-off: this strategy requires technical skills in data science and infrastructure. A small or medium-sized enterprise may not always have the resources to maintain a model in production. That’s why intermediate solutions exist, such as APIs hosted in Europe with contractual guarantees on data localization.
AI-generated content and SEO: where to draw the line
Producing articles, product sheets, or social media posts with AI has become common. The tool generates a first draft in seconds. But this ease creates a trap that many companies underestimate.
Content entirely generated by AI without human proofreading often presents the same flaws: generic formulations, lack of specific examples, predictable structure. Search engines, led by Google, evaluate content quality based on criteria of expertise and real usefulness to the user. A text that resembles dozens of other texts on the same topic has no reason to rank well.
The effective strategy is to use AI as a production accelerator, not as the final writer. For example, a marketing manager can ask the AI to structure an article outline on a given topic, then enrich each section with internal data, field feedback, or analyses specific to their sector.
Why does this distinction matter? Because user experience remains the dominant criterion in SEO. A visitor who finds a precise answer to their question stays on the page, shares the content, and returns to the site. A visitor who encounters a generic text closes the tab within seconds. AI does not replace industry knowledge; it allows for faster dissemination of it.
Data analysis and real-time customer engagement
Marketing data analysis existed long before AI. What changes is the speed and granularity. A traditional analysis tool produces a weekly report. An AI-powered tool detects a drop in engagement on an ongoing campaign and can adjust parameters in minutes.
Let’s take a concrete case. A brand launches an advertising campaign on social media. After a few hours, the algorithm identifies that visual A generates a significantly higher click-through rate than visual B among a specific segment. Without human intervention, the budget is automatically reallocated to the performing visual.
This type of real-time optimization also applies to marketing emails. AI can test multiple email subjects, various sending times, and adapt a campaign’s strategy during its execution, not after. The gain is not marginal: companies that leverage this capability see a significant improvement in their advertising return on investment.
Artificial intelligence is changing digital marketing strategies, but not in the spectacular way that some narratives suggest. The real changes occur in the details: regulatory compliance, infrastructure choices, content quality produced, campaign responsiveness. Tools are evolving rapidly, and so is the legal framework, and it is this dual constraint that defines viable strategies for the years to come.