Native AI Profiles: Revolutionizing Data Adoption in Business

The Persistent Failure of Data Projects

Nearly 70% of data projects fail, and it's not due to a lack of training. The problem lies in a structural gap between data and decision-making. Native AI profiles could be the solution to bridge this gap. For over 20 years, organizations have been investing in building indicators and installing business intelligence tools, followed by training programs for their business teams. However, these efforts often result in partial and sporadic adoption of the tools, leading to a series of iterations with data teams to converge on the actual needs. This approach ultimately overwhelms the teams, despite their commitment. It is time to view artificial intelligence not merely as a technical addition, but as a native profile integrated into the data platform, embedded in user processes and practices.

A Problem Rooted in Traditional BI

Chief Information Officers (CIOs), data analysts, and business directors are well aware of this pattern: the data team builds a warehouse, models the indicators, configures the dashboards, and then hands the tool over to the business teams. Complications arise at this point. A chasm forms between data production and its operational use. Companies then implement a change management program to support the adoption of these new tools and indicators. But as time goes on, the gap between data and business use widens. After 12 to 18 months of training, less than 30% of trained teams are autonomous, and 70% of data projects fail to generate sustainable business value. This blockage is not pedagogical; it is structural.

The Adoption Wall

Change management often starts from an implicit assumption: if teams do not adopt data, it is because they lack something, such as training, motivation, or further explanation. Acting on the right lever would be enough to promote data ownership. This reasoning is not incorrect, but it misses the real issue. A sales director who no longer opens their dashboard or explores their indicators has not given up on managing their activity through data. They have given up on the cost it incurs. Between the question they ask one morning (why are my sales in population X dropping in department Y) and the answer that data could provide, there is a technical and cognitive distance that no training can sustainably reduce. Their only recourse remains to consult a data expert, again and again. The bottleneck does not disappear; it shifts.

Native AI Profiles as Infrastructure for Use

Change management can address the symptom of resistance, but it cannot resolve the cause: the distance between data and decision-making. The paradigm shift is radical: moving from observation to business use does not depend on humans, but on the data & AI platform. An AI profile, unlike a generalist AI agent, is an intelligence embedded in the processes and language of a specific function (sales, finance, HR). More than just responding to a request, it understands the context in which it arises. Empowering business users involves modernizing the data environment with a unified platform that covers the entire data lifecycle, within which AI profiles are natively integrated at every stage (not as an overlay) to assist the user throughout their exploration and analysis work. They do not merely guide the user along the path from data to decision; they eliminate much of it.

Present at the Right Place, at the Right Time, and Exactly Tailored to Business Use

Designed as an extension of the business, these AI profiles continuously support the user, at the right moment and in the right context. Tailored to fit the role of each user (sales, CFO, or others), they assist them in their real use cases as they arise. They engage in dialogue, refine their intentions, illuminate their choices, and instantly execute explorations and analyses, even testing their intuitions and enabling them to take action. All of this occurs within the company's data platform, in on-premise mode, without any data leaving its sovereignty perimeter. By relying on the foundations of data teams, they absorb technical complexity and streamline the entire journey. The result: a shift from slow, iterative exploration to smooth, continuous management, where the user progresses directly from question to decision.

A Shift in Posture and Sovereignty

This approach represents a shift in posture more than in technology. Because each AI profile is dedicated to a specific use, embedded in the processes of a function, and present at every stage of the data lifecycle—from production to exploration, from catalog to decision—the user no longer needs to be trained: they build their understanding themselves, through their questions, explorations, and decisions, alongside the right AI profile. This vision is even more distinctive given that the issue of sovereignty is central for public organizations and large French and European groups. Deploying native AI profiles, in on-premise mode, within a sovereign data platform, without transit to third-party infrastructures or dependence on American hyperscalers, offers an alternative that dominant players in the global market cannot replicate. By unifying governance, exploration, and action, we no longer train users on data: we finally make data usable.