The agentic AI transforms the company. It is divided into four levels of maturity, from the basic chatbot to the multi -aged ecosystems, with a return on investment equivalent to 40% of gains
In a context where artificial intelligence stands out as a competitive advantage, 84% of CIOs believe that AI will have such a decisive impact on the company that the Internet had. In all sectors, IA agents are already collaborating with the teams: for the qualification of leads, the piloting of Marketin campaign or, personalized recommendations. However, a lot of CIOs still wonder: where to start, how to industrialize the AI agents, and, how to measure their real impact.
This tilting requires a paradigm shift: integrating agents into the core business processes, connecting them to the right systems, securing their access and organizing their supervision. They can relieve teams, accelerate operations, and make the company more fluid, more reactive, more intelligent. But so that this rise in power holds its promises, it is still necessary to structure it.
An agency maturity framework was thus defined to guide the CIOs: it is based on 4 progressive levels, each providing measurable benefits, provided you activate the right levers.
Level 0: fixed rules and repetitive tasks (chatbots and co-pilots)
At this stage, automation is based on static rules. Chatbots and co-pilots are limited to providing pre-recorded responses or extracting simple information. This is the case of assistants backed by knowledge bases or FAQ, in customer or HR services, capable of unclogging contact channels on recurring requests (holidays, reimbursements, schedules). The switch to agentic AI occurs when a reasoning is introduced: recommendation of an action or autonomous execution. To succeed in this transition, it is necessary to go beyond decision -making trees, to secure a harmonized data source and to choose cases of use at low operational risk. This first step must demonstrate rapid benefits, both in efficiency and in user experience.
Level 1: Information research agents
Agents are starting to actively assist users. They identify, meet and offer useful information without acting in their place. In a banking context, for example, an agent can make a personalized investment recommendation from customer criteria, while letting the advisor to validate the operation. This hybrid level increases efficiency while maintaining humans at the center. To evolve towards the next step, it is essential to structure governance, improve the quality of data sources, and measure the gains obtained in terms of satisfaction, time saved and precision in decision -making.
Level 2: Simple orchestration, unique area
Agents access greater autonomy, orchestrating simple workflows in an isolated field: automated processing of an insurance reimbursement file, validation of an order form in the supply chain, followed by a computer incident on the internal support side. To speed up, you must make a strategic choice: multifunction agent or specialized agents depending on the use case. Architecture must be able to cash in complexity, with robust API connectors, fine management of access rights and suitable supervision. This level makes it possible to de-pilot the functions, without yet crossing the milestone of interoperability.
Level 3: Complex orchestration, multiple domains
At this stage, the agents operate at the crossroads of several trades. They orchestrate processes that mobilize data from different systems: CRM, ERP, logistics platforms, etc. In the context of large retailers: synchronize stock levels, provide replenishments, trigger recovery campaigns and adapt real -time promotions according to local sales performance. Success depends here on enhanced governance, evolutionary architecture and fluid communication between agents. This inter-domain orchestration also requires reinforced supervision mechanisms, with complete traceability of the actions undertaken.
The integration between agents then becomes decisive: the most advanced systems adopt a logic of native interoperability, made possible thanks to protocols such as the Context Protocol (MCP). This standard allows each agent to instantly connect to the necessary resources, without duplication of treatment or control conflict. At the same time, the emergence of agent-to-agent interactions (A2A) marks a new step: the agents are now able to dialogue between them, to delegate tasks and to dynamically align around a unified supervision. This ability to cooperate fluid in distributed environments constitutes a key marker of operational maturity.
Level 4: Multi-agent orchestration
The agents reach their maximum maturity: autonomous, interoperable, capable of cooperating within complex ecosystems. This level opens the way to value chains controlled by the AI - predictive maintenance integrating land, HR and logistics in the industry; Fluid coordination of health care paths. The impacts go beyond productivity to affect overall performance, loyalty and emergence of new economic models. This requires advanced governance, adapted indicators and an ethical framework constantly evolving.
The promise of agentic AI is no longer theoretical: companies that deploy large -scale IA agents find up to 40% productivity gains, especially in customer support, sales and operations. This figure illustrates the power of intelligent automation when orchestrated with method and vision.
