AI Agency: The CDO at the Heart of Business Strategy

Agentic AI is gradually establishing itself in France, transforming artificial intelligence from a generative tool into an autonomous action actor. This evolution places the Chief Data Officer (CDO) at the heart of corporate strategy, necessitating a solid framework of trust for these new technologies.

For the past eighteen months, the focus on artificial intelligence (AI) has primarily concerned its capabilities to produce: whether generating images, drafting memos, or synthesizing data. However, a new phase is opening for French companies, where AI is moving from mere content generation to action implementation. The emphasis is no longer solely on proposal but on execution.

We are witnessing the emergence of agentic AI: systems that no longer just generate content but operate autonomously to achieve defined objectives. These agents can, for example, negotiate rates with suppliers, reorganize a supply chain in the event of weather disruptions, or adjust a marketing campaign in real-time based on sales data.

For the CDO, this evolution is not merely an extension of responsibilities but a profound and structural transformation of their role. The speed of this transformation in France is particularly noteworthy: 55% of French companies have already deployed some form of agentic AI, and 16% plan to do so within the next six months. This means that within a year, nearly three-quarters of organizations could rely on autonomous systems capable of making operational decisions. The question is no longer whether the CDO should be interested in this technology, but rather how quickly they can adapt to it. Indeed, agentic AI does not just use data; it exploits it, acts accordingly, and depends on it to function effectively.

This new reality profoundly redefines the role of the CDO. They are no longer just the guardian of static assets, ensuring the cleanliness and compliance of data within the data warehouse. They are now the architect of a more dynamic organization, capable of integrating operational autonomy. At the heart of this transformation lies what is termed a "trust context": a framework where autonomous agents can intervene safely, thanks to a precise definition of boundaries, permissions, and the meaning of data. In this controlled environment, systems can act autonomously without constantly seeking human validation.

From "Data Manager" to "Action Coordinator"

Traditionally, a CDO's performance was evaluated through indicators focused on data quality, governance, and compliance. Key questions revolved around their accuracy, protection, traceability, and the ability to demonstrate their origin and the accesses that had been made to them to regulatory authorities. These issues, while fundamental, were primarily retrospective: they aimed primarily at securing and structuring an informational asset.

The rise of agentic AI profoundly transforms this approach. Data are no longer just resources consulted occasionally; they become continuously mobilized data for decisions made in milliseconds, without human intervention. A stock management agent no longer waits for a report: it analyzes sales, checks supplier lead times, and places orders. A marketing agent no longer depends on a brief: it adjusts campaign budgets based on observed performance, sometimes from one day to the next.

In this context, having "clean" data is no longer sufficient. They must be enriched, contextualized, and immediately interpretable by systems. An agent must not only recognize a customer ID but also understand its segment, payment history, or applicable pricing rules. The data must be explicit, meaningful, and ready to be activated.

This change marks a major transition. The CDO becomes the architect of the information flow between agents, the coordinator of a distributed digital workforce. They must ensure that marketing, logistics, and financial agents rely on a common definition of key concepts, whether it be a "customer," a "pending order," or a "profitable transaction."

This evolution also calls for a technical transformation. The era of massive data lakes, where raw data was dumped waiting for humans to extract value, is coming to an end. CDOs are now building true "inter-agent knowledge graphs": structures that represent not only entities but also their relationships, usage rules, and the operational intent that connects them. This is no longer the equivalent of a dictionary; it is the transmission of context, a native understanding of the business environment.

The success of this system relies on a foundation of trust: to allow agents to act autonomously and in compliance, the provenance of the data must be verifiable, their meaning unambiguous, and their access rights strictly regulated and enforced. When this context is firmly established, human intervention is no longer required for every micro-decision. Agents operate within their authorized perimeters, safely and in total compliance.

Avoiding the Pitfalls of Autonomy

Entrusting operational responsibilities to autonomous agents naturally carries significantly greater risks than those associated with previous generations of AI. An hallucination produced by a chatbot may be embarrassing, but an erroneous financial transaction initiated by an autonomous agent can trigger a real crisis. This difference in impact illustrates the significant elevation in the level of vigilance required in the face of these new systems.

While 57% of French companies still consider data quality and recovery as major challenges, deploying agentic AI on fragile foundations does nothing to resolve these difficulties: it simply automates existing dysfunctions. An agent making decisions based on incorrect data does not improve performance; it amplifies errors with the speed characteristic of automated systems. The "trust context" must therefore rely on data that are not only clean but truly impeccable.

The scope of security is also evolving significantly: according to the Digital Trust Digest, 69% of cybersecurity professionals now consider the inherent vulnerabilities of AI agents to be a more serious threat than their potentially malicious use by human actors. The risk model has transformed: it is no longer solely about preventing the exploitation of a tool by a malicious third party, but about controlling the behavior of the tool itself, which can become dangerous due to an error, misconfiguration, or an unforeseen interaction with another agent. The risk is no longer limited to external attacks; it now includes internal failures that could be mistaken for an intrusion.

The question of dependency also arises: 60% of French companies rely on, or plan to rely on, external providers for their AI agents. This raises a major governance issue. Once data leaves the internal trust context to join a third-party ecosystem, who assumes responsibility? How can it be ensured that an agent operated by a provider located in another jurisdiction adheres to your internal policies and French regulatory requirements? The role of the CDO now extends to managing and supervising external data necessary for secure and compliant interactions with partners and suppliers.

The modern CDO finds themselves at a complex crossroads. On one side, the imperative of innovation, speed, and efficiency that autonomous agents can offer. On the other, the need for trust, control, and certainty regarding the relevance of the organization’s decisions and compliance with its obligations. The CDO is no longer just the guardian of data security; they are also the facilitator of autonomy. They must create the conditions that allow agents to act freely, not by loosening controls, but by integrating them so intrinsically into the data themselves that freedom and compliance become one.

Successfully navigating this transition requires governance integrated at the very heart of the data, impeccable quality, and a technical framework where every relationship and every usage right is explicitly defined. Organizations that will benefit from agentic AI are those capable of delegating action without delegating responsibility. This delicate balance is now becoming a strategic pillar of performance and business transformation.