Customer & Marketing
In the space of just a few months, generative AI (GenAI) has crossed the threshold from experimental gadget to strategic driver of digital transformation. It can produce text, code, images, video or synthetic data, ushering in a new era of intelligent automation and autonomous agents. Eulidia sums up this transformation: generative AI is becoming a major strategic lever for accelerating digital transformation and boosting competitiveness, but its success depends on the quality, governance and structuring of data. The number of concrete uses is exploding: document automation, business co-pilots, assisted software development, advanced analysis and anomaly detection. Organizations are entering a new era marked by intelligent automation, autonomous agents and the rise of sovereign AI platforms.
This chapter analyzes the key role of generative AI in digital transformation: its mechanisms, impacts on organization and marketing, opportunities and risks, and best practices for adopting it responsibly.
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Generative AI is based on large language models (LLMs) trained on huge volumes of structured and unstructured data. These models learn to spot patterns, anticipate responses and produce coherent content. Three key elements underpin their effectiveness: self-supervised learning, the ability to process several types of data simultaneously (text, image, audio) and the power of cloud infrastructures. They are capable not only of answering questions, but also of generating original content (articles, scripts, code, images) and coordinating sequences of actions through agents.
Generative models are increasingly integrated into autonomous agents that orchestrate multiple tools. An agent combines an LLM, search, memory and execution functions to accomplish complex tasks: creating a marketing campaign, analyzing a market, writing a report. Future-built companies are investing heavily in these agents: they accounted for 17% of the value created by AI in 2025, and should reach 29% by 2028. Marketing will benefit from these agents to automate content generation, personalization and real-time steering.
One of the most tangible impacts of generative AI is time savings. Eulidia points out that decision-making cycles are becoming shorter: business units are producing faster, with less friction; IT teams are automating time-consuming tasks such as documentation and testing; and business departments have assistants capable of explaining, comparing, summarizing and recommending. Digital economies are already evolving: shorter development cycles, accelerated analysis and the creation of new offers in a matter of weeks.
For marketing departments, this means that a quarterly editorial plan can be generated in a matter of hours, campaigns can be tested in real time and optimized automatically, and budgets can be allocated based on performance predictions. Generative AI acts as an impact multiplier; when backed by a solid data strategy, it becomes a transformative lever capable of amplifying operational performance and informing decisions faster.
GenAI makes it possible to move from segmentation to mass personalization. Brands can automatically generate emails tailored to each prospect, personalized site content, explainer videos or conversational chatbots. By 2028, BearingPoint estimates that more than 80% of marketing campaigns will be hyper-personalized thanks to AI, and that more than two-thirds of content will be generated by AI tools. This evolution implies a more intimate relationship between brands and their customers, but also the need to control editorial consistency and message relevance.
Generative AI is revolutionizing information retrieval. Conversational engines provide direct answers, summarize sources and reduce the number of clicks to websites. Evergreen Media observes that Google’s AI Overviews reduce organic click-through rates by an average of 34.5%. Language models handle longer queries and aggregate sub-searches to produce a synthetic answer; in Europe, Google deployed AI Mode in October 2025. These changes are forcing companies to adopt Generative Engine Optimization (GEO ) – content optimization for AI engines – and strengthen their brand to stay visible.
Marketing departments can create “content factories” powered by generative AI. This includes the generation of articles, video scripts, LinkedIn posts, product sheets or FAQs. LLMs also enable multilingual rewriting and synthesis of existing content. Editorial teams become curators and verifiers rather than manual producers. Production times are reduced and editorial consistency is improved.
Marketing co-pilots support teams in their day-to-day tasks: they analyze campaign performance, suggest budget adjustments, generate reports, suggest A/B tests and answer employees’ questions. They facilitate the onboarding of new employees and democratize access to marketing expertise.
Generative AI and predictive AI combine: models generate natural language recommendations based on behavioral predictions (propensity to buy, churn, customer lifetime value). Dashboards become interactive: a decision-maker can ask “What are the three segments most likely to buy this product next month?” and immediately receive a reasoned response. In B2B sales, more than half of all sales will be made via conversational interfaces by 2028.
GenAI-powered chatbots and voice assistants answer questions, solve simple problems and refer customers. Multimodal models can analyze images or audio files to provide contextualized answers. In the service sector, generative AI can be used to create interactive technical support, video instructions or automated diagnostics.
GenAI helps R&D teams generate concepts, visuals and prototypes. In fashion and design, AI suggests new styles; in industry, it generates 3D models. In marketing, it enables offers to be co-created with customers via conversational interfaces or co-innovation platforms. These rapid innovations require a framework to protect intellectual property and respect data confidentiality.
LLMs can produce plausible but incorrect content, known as hallucinations. These errors can damage brand reputation or mislead customers. It is crucial to implement editorial controls, train models on quality data and impose human validation.
Another risk is bias. Models reproduce the biases present in training data, which can lead to discrimination. Governance must include the detection and mitigation of biases, the diversification of datasets and the transparency of algorithms.
The risks of data leakage, prompt injection attacks and intellectual property infringement increase with the use of LLMs. The Eulidia article reminds us that robust governance and secure architectures (such as Retrieval Augmented Generation – RAG) are needed to control these risks. The European regulatory framework (AI Act) imposes a classification of high-risk systems and transparency obligations. Marketing departments need to work with legal experts and CISOs to comply with these requirements.
Generative AI automates tasks, creating concerns about employment. Afges reports that 36% of companies expect at least 10% of their jobs to be automated within the year. Marketing departments need to anticipate these changes, redeploy resources to higher value-added tasks (creativity, strategy, customer relations) and invest in training and retraining. Acculturation programs help to develop skills and maintain employee commitment.
Model quality depends on data quality. Before deploying GenAI, data must be structured, duplicates cleaned up, access rights managed and governance defined. Eulidia recommends consolidating foundation data and training teams before moving on to industrialization.
Launching generative AI on all fronts is risky. It is preferable to target one or two processes where the potential impact is high and the complexity limited: FAQ generation, assistance in creating briefs, catalog translation. The successes achieved will serve as proof of value and help convince stakeholders.
AI agents are powerful, but require supervision. Collaboration between humans and machines guarantees the relevance, ethics and originality of content. The final decision must remain human; the agent must serve as an assistant that accelerates and expands team capabilities.
As with any digital project, you need to define success indicators and regularly measure the impact of the models: correct generation rate, error rate, productivity, customer satisfaction, ROI. Models need to be re-trained according to feedback, market developments and regulations.
Generative AI is a powerful driver of digital transformation. It accelerates cycles, hyper-personalizes experiences and creates new growth opportunities. However, it requires companies to strengthen their data foundation, govern risk and train their teams. Organizations that manage to integrate GenAI responsibly and strategically will become more agile and innovative. Marketing departments have a key role to play in orchestrating this transformation and turning AI into a competitive advantage.