Section 1 – Demystifying
The question isn’t whether your organization can deploy AI agents. The real question is where it should do so first.
This is precisely the point at which most agentic initiatives go off the rails. Not because the technology isn’t ready, but because the first investment choices are misaligned with the real impact expected.
Many organizations oscillate between two extremes: wanting to automate everything at once or, on the contrary, multiplying micro-cases of use with no strategic scope. The trap is not only to aim too high. It’s also to aim too small.
In this article
Why the first use cases are almost always the wrong ones
The first use cases proposed almost always start from the team’s immediate perimeter. Employees think about their daily irritants. Managers are looking to speed up what already exists. Technical teams assess what can be done quickly. Everyone acts in good faith, but everyone thinks locally.
Result: the first use cases optimize visible, local tasks, but leave processes intact. We automate a step, improve an interface, speed up a validation. The experience improves. The process, however, remains unchanged.
These false productivity signals are seductive:
- we save a few minutes here and there;
- teams find the tool useful;
- the demonstrations are convincing.
But these gains remain marginal if they do not change the structure of work. They improve individual comfort without transforming collective performance. The bias is normal. It becomes risky when it guides investment decisions.
Another frequent symptom of a bad use case is copying and pasting. When an agent simply produces an answer that a human then has to copy into a system, this is not automation. It’s an interface improvement. Copy and paste almost always indicates a lack of real integration.
The systematic presence of a human in the loop often signals a useful learning phase, but is not in itself a strategic lever. As long as the agent cannot act in the systems, the value remains limited.
Agents create real impact when applied where decision making is repetitive and costly, where information is fragmented, where error is risky, and where variability undermines performance. The best use cases combine informational complexity, contextual reasoning, sufficient volume and measurable impact. This is where agentique becomes a lever for transformation.
Taking a step back then becomes essential: we need to think in terms of complete processes, not isolated tasks. The right question is not “which step can we automate? but “which process do we want to improve, stabilize or transform?
Three main categories of agentic use cases
Not all use cases are created equal. It’s useful to distinguish clearly between them.
1- Individual productivity (self-service) – Individual acceleration
Personal agents, assistants, ad hoc decision support. Useful for adoption and organizational learning. Limited organizational impact if these uses remain isolated.
These use cases play an important role: they familiarize teams with AI and bring opportunities to light. But they should not be confused with transformation levers.
Examples: contextual writing and synthesis, meeting preparation, rapid data analysis.
2- Team automation – Stabilizing a team
An agent that standardizes access to information, coordinates tasks and reduces dependence on key experts. This is often where the first structural gains appear. We begin to reduce variability, accelerate workflows and improve operational continuity.
Examples: internal support (knowledge agent), ticket qualification and routing, task coordination
3- Transformation of critical processes – Change the way the company operates
Agentic solution integrated into systems, capable of acting, deciding and orchestrating at scale. This is where the strategic value lies… and the complexity. These use cases require architecture, governance and integration choices, as well as a more sustained effort to manage change. They change the way work is done, not just the speed at which it’s done.
Examples: pricing/promotion decisions, demand and inventory management, handling complex files.
Confusing these categories leads to poor investment decisions. Treating an individual productivity case as a transformation project leads to disappointment. Treating a transformation lever as a mere tool leads to under-utilization.
Aligning use cases with concrete business objectives becomes essential. A good agentic use case must be linked to a measurable lever: cost reduction, service level improvement, revenue increase, risk reduction. Without this alignment, agentique remains a technological exercise.
Focus on skills, not on increasing the number of agents
Another common trap is to replicate existing processes identically. This leads to the creation of ultra-specialized, rigid agents, incapable of adapting to the context. In the medium term, the organization ends up with thousands of agents who are difficult to maintain, govern and develop.
This logic may seem effective in the short term. It becomes unmanageable at scale. Some organizations run the risk of ending up with tens of thousands of frozen agents, each based on a process variation, with no capacity for evolution.
Rather than multiplying agents, we recommend moving towards a limited number of very powerful agents, capable of accessing multiple systems and data sources. These agents become execution platforms. Users no longer create new agents. They createskills in these very powerful agents to meet their specific needs.
The shift is a major one: from a logic of duplication (one agent per use case) to a logic of composition (one central agent + reusable skills ).
A sustainable approach therefore involves structuring these skills:
- reasoning;
- data access;
- integrations;
- decision rules;
- ability to act.
These skills can be combined, adjusted and reused according to context, without having to recreate a new agent each time.
A few well-designed central agents, enhanced by a set of modular skills, create far more value than thousands of fixed agents, not to mention the reduction in maintenance required. The aim is no longer to reproduce over-strict processes, but to build an adaptable capability, capable of evolving with operational reality.
Prioritizing without stifling innovation in the field
Choosing the right use cases doesn’t mean blocking experimentation. It means establishing a clear structure:
- exploration;
- which deserves to be industrialized;
- and what must remain local.
This structure protects the organization from complexity and uncontrollable chaos, while allowing innovation to emerge where it is relevant. It enables rapid learning without creating an accumulation of isolated agents that are difficult to orchestrate.
A good agentic use case is measured by its real organizational impact. Not by the quality of the demo. Not by the speed of implementation. But in its ability to modify a process, stabilize a decision or sustainably improve performance.
TL;DR
- – Not all agentic uses are created equal. The first ones identified are rarely the right ones to transform a process.
- – Prioritize where the impact is measurable and systemic, not where the demo is easy.
- – A few well-designed agents built around reusable skills create more value than a multiplication of isolated agents. The aim is not to automate everything from the outset. It’s to invest first where agentiics really changes the way work is done.
