Customer & Marketing
To take advantage of artificial intelligence (AI) in digital marketing, companies need to move beyond isolated experiments and build a clear roadmap. BearingPoint points out that without a strategic vision and cross-functional coordination, AI initiatives run the risk of bogging down after the pilot phases. Marketing departments therefore need to structure their approach, from initial audit through to industrialization. This fourth part proposes a detailed roadmap tailored to companies in 2026.
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Start with an information systems audit: data quality, completeness, formats, compliance (RGPD), access. Analyze the infrastructure (data lake, data warehouse, APIs). Identify grey areas, silos and risks. Examine the organization’s ability to collect and process data in real time.
The audit must also focus on internal skills: capabilities in data science, analytical marketing, AI project management, development. Afges stresses that digital transformation is not an IT project, but must be driven by the company’s culture and values. Assess your teams’ commitment to AI and identify training needs.
Take stock of existing initiatives (chatbots, scoring, automations), identify their impact and level of maturity. According to BCG, only 22% of companies have gone beyond the proof-of-concept stage, and only 4% are creating substantial value. This diagnosis enables you to prioritize your efforts.
The vision must be driven by general management and implemented by marketing, digital, IT and the business units. Define business objectives (revenue growth, cost optimization, customer loyalty, customer experience) and the indicators that will enable success to be measured. Afges reminds us of the importance of governance in aligning culture, objectives and investment decisions.
Take trends into account: mass adoption of AI (1 in 6 people use it), rise of autonomous agents (17% of AI value in 2025, 29% in 2028), emergence of generative engines and decline in organic click-through rates (-34.5%). Integrate GEO (Generative Engine Optimization) objectives to ensure future visibility.
Classify use cases according to their potential value (impact on sales or customer satisfaction) and feasibility (data availability, technical complexity, regulatory risks). Prioritize use cases with high value and moderate feasibility: e-mail personalization, predictive segmentation, product recommendation, marketing task automation.
Quick win projects: automated production of posts, FAQ chatbots, lead scoring. They require little data and quickly demonstrate their value.
Core business projects: hyper-personalization, optimization of advertising campaigns, recommendations. They require voluminous data and good governance.
Innovative projects: autonomous agents orchestrating campaigns, multimedia content creation, marketing supply chain optimization. They offer competitive advantages, but require investment and ethical control.
Classic SEO is evolving into GEO. Integrate projects aimed at optimizing content for generative engines: complete guides, well-structured FAQs, semantic schemas, multi-platform presence (site, social, podcasts). This enhances visibility in AI Overviews.
Collect data from different sources (CRM, ERP, web analytics, social networks). Unify customer identifiers for a 360° view. Clean up duplicates and correct errors. Ensure RGPD compliance.
Choose the right architecture: data lake to store raw data, warehouse for structured data, BI platforms for analysis. Integrate ETL/ELT solutions to orchestrate data flows. Opt for tools to orchestrate ML/LLM pipelines (Kubeflow, MLflow, LangChain). For governance, define data repositories, access policies and responsibilities.
Establish an ethics committee to validate use cases, monitor bias and ensure compliance. Risks related to data protection, intellectual property and model consistency must be identified and managed. Model documentation (training data, limits) must be accessible to stakeholders and compliant with the future AI Act.
Alignment with needs: the chosen solutions must cover priority use cases.
Interoperability: compatibility with existing systems (CRM, ERP, CMS).
Security and compliance: certifications, management of sensitive data.
Scalability: ability to manage growing volumes and integrate new models (multimodal, agents).
Support and community: accessibility of resources, quality of support.
For ambitious projects, collaborate with consulting firms, publishers or startups. BearingPoint, for example, supports companies in defining and deploying AI strategies by combining management consulting and technological expertise. Partnerships help accelerate adoption and reduce risk.
Choose an initial use case and set up a multidisciplinary team (marketing, data science, IT, legal). Define a limited scope, a short timeframe (3 to 6 months) and precise KPIs. For example: launch a support chatbot for a customer segment; measure the reduction in response time, the satisfaction rate and the impact on cross-selling.
Evaluate the results, identify any difficulties (data quality, inadequate model, internal resistance) and draw lessons for the future.
Standardize processes: model lifecycle management (MLOps/LLMOps), versioning, monitoring, continuous deployment. Establish best practices for prompt engineering and agent workflow design. Ensure model maintenance (retraining, drift control).
AI must be integrated into the tools used daily by teams (CMS, CRM, planning tools). For example, an AI assistant can be integrated into the CRM to suggest actions, or into the content management tool to automatically optimize tags and text structure.
Organize training sessions tailored to different profiles: marketing (using co-pilots, interpreting results), data/IT (deployment, monitoring), management (reading KPIs, decision-making). Set up an ongoing acculturation program and internal “champions” to help teams adopt AI.
Define a dashboard with quantitative and qualitative indicators: number of leads, acquisition cost, sales generated, customer satisfaction, model error rate. KPIs should reflect the objectives set up front (growth, cost reduction, NPS). Also track AI-specific indicators: agent response time, co-pilot utilization rate, hallucination rate.
Regularly analyze results and adjust your roadmap: improve prompts, train models on more relevant data, diversify use cases. ELLEVATE reminds us that we need to relearn how to read KPIs: the question is no longer “How many visitors?” but “How many decisions triggered?”.
Stay abreast of technological developments (new models, multimodality, autonomous agents) and regulatory developments (AI Act). Participate in ecosystems, conferences and communities. Continuous innovation is a key success factor.
Data protection and intellectual property are major concerns. Afges notes that 73% of respondents cite data security as the main risk of AI, and 50% mention intellectual property issues. Implement strict access management policies, encrypt sensitive data and negotiate intellectual property clauses with your model providers.
Draw up an ethical charter defining values, rules for AI use, transparency towards users and non-discrimination. Monitor biases in data sets and models. Implement procedures to correct deviations.
Anticipate the impact on employment: 36% of companies estimate that at least 10% of jobs will be automated within the year. Set up retraining and skills enhancement plans. Communicate on the purpose of AI projects and involve employee representatives in governance.
AI agents and multimodality represent the next waves of innovation. Adapt your roadmap to integrate agents capable of orchestrating complete workflows (marketing campaigns, reporting, competitive intelligence) and multimodal models that analyze images, videos and sounds. Future-built organizations are already allocating a significant proportion of their budgets to these technologies.
Open source platforms (such as Mistral AI) offer greater flexibility and sovereignty. They make it possible to train models on in-house data and avoid being locked into the ecosystems of the giants. In France and Europe, public and private investments are multiplying to create sovereign alternatives. Keep an eye on these initiatives and test local solutions.
Integrate sustainability and social impact into your AI projects. Measure the energy consumption of models, favor low-carbon infrastructures, reuse generated content to limit the waste of resources. Social responsibility will become a competitive advantage.
Building a roadmap for AI in digital marketing requires reconciling ambition and pragmatism. In 2026, the maturity of tools, the evolution of behaviors and the rise of AI engines will require a profound adaptation of digital strategies. By following the ten steps outlined – diagnosis, vision, prioritization, data foundation, technologies, pilots, industrialization, measurement, risk management and innovation – marketing departments will be able to leverage AI to create sustainable, responsible value. AI is not an end in itself, but a means of enhancing human creativity, improving the customer experience and anticipating tomorrow’s needs.