Agentic AI, an evolution of generative AI, automates complex processes. Promising but demanding, it requires reliable data, human supervision and strategy to create value.
Agentic artificial intelligence is now generating growing enthusiasm in the digital ecosystem. As a sophisticated extension of generative AI, it embodies a new technological frontier that could well shake up the cards in business process automation. However, if the temptation is great to want to quickly integrate these new tools, lucidity remains essential. Because agentic AI is not aimed at all processes or all companies.
A transformative potential… under conditions
The typical AI agent is an autonomous system that perceives its environment, interprets data, makes decisions in context, acts and learns. This feedback loop, which could be summarized as “perception – decision – action – adaptation”, opens up use cases inaccessible to traditional automation.
Sectors already engaged in digital transformation dynamics (industry 4.0, logistics, financial services, etc.) perceive in these agents an ability to streamline complex operations, increase organizational responsiveness or reduce inter-system friction. But these improvements do not happen overnight, the value of AI agents is only revealed over time, at the cost of a rigorous strategic approach.
What processes justify the adoption of AI agents?
Before investing, companies must ask themselves the nature of the processes they intend to improve. If these are repetitive, predictable and slightly variable, traditional automation is more than sufficient. Agentic AI deploys its full potential in complex environments, subject to numerous exceptions, evolving or poorly structured.
This is the case, for example, of supply chains subject to hazards, omnichannel customer journeys or even predictive maintenance systems, where decisions must integrate a diversity of data in real time, while taking into account conflicting objectives.
In these contexts, AI agents make it possible to optimize decision-making by simulating different possible scenarios, prioritizing expected results according to a logic of utility, and adapting their behavior over time thanks to continuous learning.
Essential technical foundations
However, integrating an AI agent into an existing system requires major technical prerequisites. Agents need a coherent, contextualized, and interoperable database. They also require human supervision, at least partial, in what are called Human-In-The-Loop (HITL) models, where human intervention intervenes to validate, arbitrate or supervise decisions.
A key tool to achieve this is Process Intelligence (PI). By creating a digital twin of operations, this technology provides agents with actionable, up-to-date data aligned with business objectives. It thus becomes the decision-making base allowing the agent to take relevant actions without departing from the defined operational framework.
Evaluate the value and time to return on investment
Another point of vigilance: the initial investments in agentic AI are substantial. Development, training, governance, recruitment of specialized skills — all this must be put into perspective with the expected value. The benefits, in the vast majority of cases, are neither immediate nor guaranteed.
Companies must model a profitability scenario that integrates not only economic ROI but also gains in agility, operational robustness or risk reduction. The time factor is crucial here: an efficient AI agent is built by iterations, in a logic of gradual increase in maturity.
Towards a redefinition of human roles
Finally, the emergence of AI agents is accompanied by a paradigm shift in the organization of work. Humans, long at the center of decision-making, are gradually becoming orchestrators. He is no longer the only one to decide, but he guarantees that the decisions made by the machines are aligned with ethics, strategic objectives and business rules.
This transformation does not mean the exclusion of humans, but their repositioning above them. It also requires acculturation of teams, support for change and transparency on the operating modes of agents to avoid black box effects or algorithmic deviations.
Choose wisely, deploy methodically
Agentic AI is today a promise with great potential, but it is also a demanding technology. It cannot be improvised, it must be anticipated. Its implementation must be based on a clear strategy, defined objectives and a solid infrastructure.
It is only under these conditions that agents will become something other than a technological mirage: a real lever of efficiency, resilience and differentiation for the company.
