For several years now, I have noticed a profound transformation in marketing teams. Companies are no longer simply looking for specialists confined to their respective fields. They are seeking profiles capable of navigating between raw data, automation tools, and global marketing strategies. This is what experts call hybrid profiles, and frankly, it has become essential in 2026. According to the “Growth & AI” barometer from Acsel, 95% of business leaders surveyed believe that AI is already contributing to their revenue, while 83% rank it among their top investment priorities. This AI revolution is progressing much faster than digital transformation itself. The positions of traffic manager, CRM manager, or data analyst that we knew yesterday are being restructured around cross-functional skills. I believe that understanding this evolution is essential for anyone wishing to remain relevant in digital marketing. This article explores the three key skills that companies are looking for in these new profiles, how schools are adapting to train them, and why this transition marks a major turning point in our industry.
📋 Summary
The Restructuring of Marketing Jobs in the Age of AI
For a long time, the organizational charts of marketing teams followed a very compartmentalized logic. On one side were traffic managers, on the other CRM managers, and elsewhere data analysts. Each in their specialty, each with their tools. But this monolithic approach no longer works. Marketing jobs are being restructured around much more cross-functional profiles. Valérie Dulieu, General Director of 3W Academy, a partner of the AI and data specialization of the MBA Specialized Digital Marketing & Business (MBADMB) at EFAP, confirms this: “These are profiles that are capable of managing data, content, automation, and tools.” This transformation 🔄 is not trivial; it reflects an economic reality: companies need people who understand the entire value chain.
This change is accelerating at a remarkable pace. According to Valérie Dulieu, “we are in an AI revolution that is moving much faster than the digital transformation of companies.” 42% of business leaders consider they have already reached an advanced stage of AI deployment. This acceleration creates an interesting tension: companies need talent now, but traditional training is not keeping pace. The profiles sought are neither pure developers nor strictly data scientists. They are rather mixed and hybrid data analysts, capable of understanding the sector and the business while providing KPIs, reports, and data visualization.
I must emphasize that this cross-functionality is probably not permanent. Once the market reaches a certain maturity, we will likely return to more precise jobs. But in the meantime, companies will seek cross-functional profiles capable of bridging multiple areas. The APEC study on AI in commercial marketing, published in March 2026, confirms this trend: 13% of executive job offers published the previous year in this field already mentioned AI, compared to 9% four years ago, thus occupying the second position behind IT.
Raw Data: The Fundamental Skill Often Overlooked
Among the three key skills identified by Valérie Dulieu, the first is also one of the least valued: knowing how to work with raw data. It’s almost counterintuitive, isn’t it? We often think that the most prestigious skills are those that involve creation or strategy. But the reality is different. Working with raw data means being able to clean it, qualify it, and understand what it really means. Why does a certain piece of data not make sense? Why does it need to be removed? These are questions that may seem tedious 📊, but they are actually fundamental business questions.
I find that many young professionals underestimate this skill. They want to jump straight to analysis, insights, and recommendations. But without a solid foundation of data cleaning and qualification, everything that follows is built on sand. Valérie Dulieu explains it well: “Working with data means being able to clean it, qualify it. You need to be comfortable with numbers and understand what they mean.” This fundamental skill is what separates true analysts from amateurs. It is also what makes these profiles so valuable to companies: they understand that the quality of data determines the quality of decisions.
What particularly interests me is that this skill can be learned. It is not a question of innate talent, but of rigor and method. Training that truly integrates this dimension, that makes you “get your hands dirty,” as Valérie Dulieu puts it, creates much more robust professionals. They understand the limits of their data, they know when to trust a metric and when to question it. It is this analytical rigor that makes the difference in an environment where data is becoming increasingly central.
Understanding AI: Beyond Technological Fascination
The second key skill is to understand how artificial intelligence works and its limitations. And this is where things get really interesting. With the arrival of AI agents, the supervisory role changes completely. It is no longer simply about supervising a task; it is about orchestrating an entire system. A manager will have to manage multiple AI agents, coordinate them, and orchestrate them. For this, one must understand what can be automated, what cannot, and when human intervention becomes necessary. This is a systemic understanding that few training programs really offer.
What fascinates me is the authority bias we have when faced with AI results. When a language model gives an answer, our brain tends to believe it. Studies clearly show this. That is why developing critical thinking regarding the results produced by AI has become an essential skill 🧠. Valérie Dulieu emphasizes this point: “When LLMs give an answer, it’s hard to tell ourselves that they might be wrong. Studies show there is an authority bias. It is really our job, as trainers, to encourage students to question and consider the possibility that AI can be wrong.” This critical thinking is what transforms a passive AI user into a true professional capable of using it strategically.
I personally recommend teaching how models actually work, their training, their biases, and their hallucinations. Future professionals must understand them for what they are: a set of technologies and algorithms, not an authority in itself. This perspective changes everything. It allows professionals to remain in control of their decisions rather than becoming dependent on AI recommendations. It is a decision-making autonomy that is becoming increasingly valuable in a world saturated with tools.
Ethics and Compliance: The Forgotten Skills
The third skill that Valérie Dulieu identifies is perhaps the most neglected: everything related to ethics and compliance. Shadow IT in marketing teams represents, according to her, “one of the biggest issues for young graduates.” And it’s true. I have seen too many cases where marketing teams used AI tools without really understanding where the data was hosted, whether it complied with European regulations, or what vulnerabilities it could open within information systems. This regulatory compliance is not just an administrative issue; it is an existential risk for the company.
The GDPR, cybersecurity, the protection of sensitive data: these are areas where marketers must have at least a basic understanding. There is no need to be a legal expert, but one must know how to identify what they master and what requires consulting the legal department or the IT department. It is a consciousness of limits that protects both the individual and the organization 🛡️. Valérie Dulieu explains it well: “It is something we raise awareness about a lot, so they can identify what they master and what requires consulting the legal department or the IT department of their organization.”
What worries me is that many AI and data training programs do not sufficiently cover this dimension. We focus on tools, techniques, and algorithms. But we often forget the ethical and legal implications. Yet, this is precisely what distinguishes a responsible professional from an amateur. Companies hiring these hybrid profiles are looking for people who understand that the power of AI must be accompanied by ethical responsibility. This is a key element of the value they bring.
How Schools Train These Hybrid Profiles
In response to these needs, schools must adapt. The partnership between EFAP and 3W Academy around the AI & data specialization of the MBADMB is an excellent example of this adaptation. The program is based first on a common core shared with other MBA specializations. It covers the entire marketing cycle: creation, acquisition, user experience, loyalty. About 300 hours to ensure that the profession is known and mastered. AI is already integrated into this foundation, with case studies and the organization of hackathons. It is a holistic approach to marketing that makes sense 🎓.
Next comes the specialization “AI & data” itself, which adds operational mastery. SQL, data visualization, web scraping, Python libraries, sentiment analysis, chatbot creation. Students learn to manipulate real tools on concrete cases. Valérie Dulieu summarizes the approach well: “We get our hands dirty, we do things for real.” The goal is not to train developers but to eliminate the fear of the technical aspect. Once you understand how it works, it becomes much easier to continue learning throughout your professional career. It is a technical confidence that opens doors.
The year of specialization concludes with a one-week bootcamp organized around a real business problem. Students choose their action axes and tools themselves, in order to produce a quantified recommendation that they must defend before a jury. It is a professional simulation that truly prepares them for the realities of the field. Evaluations are largely conducted orally, a deliberate choice to ensure that learners can explain their reasoning and their use of AI while addressing the needs of the invited company. It is this ability to communicate and justify their choices that makes the difference between a good professional and an excellent one.
Continuous Learning: The Key to Professional Survival
What strikes me most in 2026 is the speed of change. Tools are constantly evolving, best practices are transforming, regulations are updating. In this context, the ability to learn continuously becomes more important than current knowledge. Valérie Dulieu states clearly: “We must adapt as tools advance and continue to train throughout our lives, without waiting for the AI train to pass.” It is a learning mentality that must be cultivated from initial training.
I sincerely believe that this is the most important message to convey to young professionals. Schools can teach today’s tools, but tomorrow’s tools will be different. What really matters is learning to learn, especially in the age of AI. Valérie Dulieu adds a dark but realistic perspective: “There will be a gap that will widen between those who have embraced the revolution and those who have preferred to wait on the sidelines. And it will become increasingly difficult as technology evolves.” This urgency of adaptation is not a threat; it is an opportunity for those who seize it.
The hybrid profiles that companies are looking for are therefore those that combine three elements: a solid understanding of data, critical thinking regarding AI, and ethical awareness. But above all, they are people who know that they do not know everything and who are willing to continue learning. It is this intellectual humility coupled with ambition that creates the best professionals.
Conclusion
In 2026, marketing is no longer what it used to be. Hybrid profiles combining AI, data, and business skills are no longer an option; they are a necessity. I have seen this transformation unfold gradually, and I can affirm that companies investing in these profiles gain a real competitive advantage. The three key skills I have explored in this article – mastery of raw data, critical understanding of AI, and ethical awareness – form the foundation of these new professionals. What encourages me is that these skills can be learned. Schools like EFAP and 3W Academy show that it is possible to effectively train these profiles, even without an initial technical background.
The real challenge now is to maintain this learning dynamic throughout one’s career. Tools will change, regulations will evolve, best practices will transform. But professionals who have developed a true understanding of the fundamentals and a continuous learning mentality will remain relevant. If you work in marketing or are considering a career in this field, I encourage you to invest in these three skills. It is your best asset for navigating the marketing of tomorrow. To deepen your knowledge, I recommend checking out our guides on AI and its applications, as well as our resources on marketing automation and the best generative AI models.
📝 In Brief
- Hybrid profiles combining AI, data, and business skills have become essential in marketing in 2026, with 95% of leaders believing that AI contributes to their revenue.
- The three key skills sought are: mastery of raw data (cleaning and qualification), critical understanding of AI (limitations and biases), and ethical awareness and compliance (GDPR, cybersecurity).
- Training programs like the MBADMB from EFAP in partnership with 3W Academy combine a solid marketing core with operational specialization in AI and data, including a real business bootcamp.
- Continuous learning has become the most important skill, as tools and regulations evolve constantly, creating a gap between those who adapt and those who wait.
