80% of enterprise data products are never used. This observation illustrates a paradox: despite significant investments in structuring, validating and governing data, its use often remains limited.
Too frequently locked away in rigid and complex systems, data does not flow well between teams.
A new generation of tools is changing the game: artificial intelligence agents. These autonomous digital assistants converse with the user in natural language, identify useful data, transform it, and present it at the right time, in the most suitable format. They no longer merely automate: they make data access more flexible, proactive, and contextual.
Companies have never had as much data: dashboards, automated reports, APIs, or predictive models.
Yet, much of this wealth is never tapped into by those who need it most.
Business teams struggle to find the right sources, understand technical terms, or combine multiple datasets to answer a specific question.
Result: a widening gap between data's potential and its real-world value, with a frequently disappointing return on investment.
Current architectures were designed for stable needs. As soon as demand changes, they show their limitations.
Discovering Data Products requires mastering tools reserved for specialists.
The customisation remains basic: filters, settings… but the user still has to know where to look and how to phrase their request.
Access to the data remains passive: it must be requested in order for it to respond.
These constraints are hindering adoption and causing frustration for both business departments and data teams.
Unlike traditional systems, the AI agent acts like a genuine Data assistant.
It understands a question formulated in natural language, grasps its context, searches for the right information, and assembles it to formulate an actionable answer.
Its strengths:
AI agents are transforming data catalogues into truly interactive spaces.
The user converses with the agent like a colleague: they ask a question, validate a proposal, and receive not only the result but also explanations about the calculations, the origin and reliability of the data.
The agent can also automatically suggest complementary indicators (margins, forecasts, risks) depending on the context.
This conversational approach makes data access more intuitive and reduces errors and time losses associated with manual searches.
Do you want to make your Data Products more accessible and truly used?
With AI agents, data no longer waits to be requested; it comes to the user.
Before a meeting, an agent can prepare a report, propose recommendations based on business thresholds, or automatically trigger alerts in case of anomalies.
According to the person's profile, they choose the most relevant format: a graph, a table, or a textual summary.
The information becomes clear, contextualised, and immediately actionable.
Businesses adopting these agents find measurable gains. Data products are used more, as access to them becomes simple and seamless. The time between a question and an actionable answer drops from several days to a few minutes. Decisions are based on contextualised, more reliable data. Costs decrease thanks to the reduction of duplication, bespoke development, and repeated maintenance.
Implementing AI agents requires overcoming four key challenges.
First, interoperability the agent must be able to connect to all existing systems (catalogues, databases, APIs and BI platforms) without creating new silos.
Next, the governance must evolve to frame the autonomy of agents and verify the compliance of their recommendations.
The security of sensitive data remains a priority, requiring encryption, strong authentication, and fine-grained access management.
Finally, Organisational change must be accompanied The business and data teams require dedicated training and iterative feedback experiences to adopt this new way of working.
Tomorrow, the most mature organisations will possess a living, proactive data estate:
AI agents are redefining the relationship between a company and its Data Products, shifting from a static asset, consulted on an ad-hoc basis, to an intelligent, responsive, and conversational system.
At JEMS, we are convinced that this evolution embodies the next major step in the data journey. Our philosophy is based on the reasoned industrialisation of intelligence: progressively deploying agents within pilot domains, establishing augmented governance, and ensuring sustainable business adoption. By integrating agentification today, organisations can finally fully leverage their data assets and build a “data-conscious” culture, where Data no longer sleeps, but actively works to create value.
