Agentic AI: Concrete Examples and Enterprise Use Cases

There is no shortage of definitions of agentic AI. Concrete examples, however, are. Most of the resources available on the web remain at the conceptual level: autonomy, action loop, tools, memory, definition… This is useful for understanding, but it is not enough to make a decision.

This article takes the opposite approach: explaining to you across six sectors, with six real-world use cases, what agentic AI concretely does.

What these examples have in common

Before diving into the details, I want to explain what these cases have in common. They come from different sectors but all share the same logic: in each, the agent takes over a repetitive, multi-step task, freeing up humans for what truly requires their judgment.

Three constants systematically appear:

• Human value is in the decision, not in the execution. Humans decide on what is complex, ambiguous, or strategic, and the agent executes.

• Data quality determines the quality of the agent. A well-designed agent on poor data produces poor actions. This is the non-negotiable prerequisite for all deployments presented here.

• The agent does not replace expertise, it amplifies it.

Use case #1: Customer service and autonomous ticket resolution

The problem before

The volume of incoming tickets exceeds the capacity of the teams. Delays grow longer, causing support agents to spend most of their time on simple, repetitive requests (resets, product questions, standard refunds…), at the expense of complex cases that truly require their expertise.

This can lead to customer dissatisfaction, team burnout, and wasted human added value on low-stakes tasks.

What the AI agent does

Upon receipt of a ticket, the agent takes over immediately:

• It analyzes and categorizes the ticket: request type, urgency level, customer concerned, history of previous interactions.

• It searches the knowledge base for the response or procedure adapted to this category of request.

• For simple cases, it resolves autonomously: sends the reply, triggers the refund, resets access.

• For complex cases, it escalates intelligently by transferring the ticket to the right human agent, with the context already structured, without the agent having to re-read everything from the beginning.

The measurable result

• First-contact resolution rate significantly increased on simple requests

• Average resolution time reduced

• Human agents repositioned on high-relational-value cases and complex situations

Use case #2: Field sales and the preparation and follow-up of business visits

The problem before

A traveling sales representative spends a significant portion of their time outside the store on administrative tasks: retrieving visit histories, identifying priority points of sale, synthesizing current alerts, writing reports…

This non-visit time is not insignificant. Moreover, records validated from memory, often in the evening or the following day, frequently turn out to be incorrect or incomplete, which degrades CRM data quality for everyone.

What the AI agent does

The agent intervenes at three key moments in the sales representative's day:

Before the visit: it analyzes available CRM data (history, current goals, active alerts, results of latest visits) and automatically generates a personalized brief per point of sale. The representative arrives prepared, without having spent 30 minutes looking for information.

During or just after the visit: the representative dictates their observations aloud, or enters their field records on mobile. The agent automatically structures this data into a formatted report.

At the end of the day: it updates the point-of-sale files in the CRM, records the actions taken, plans the next steps.

We have detailed the specific use cases of agentic AI in supermarkets (retail sector) in another article.

The measurable result

• Visit preparation time reduced

• Report completion rate increased

• CRM data is fresher and more reliable, which improves the quality of the agent's recommendations for subsequent visits

Use case #3: Trade marketing and monitoring the execution of trade plans in points of sale

The problem before

A promotional campaign is planned, briefed, and validated. It is then supposed to be executed in hundreds of points of sale simultaneously. But the reality on the ground can be different: missing POS displays, poor shelf placement, incorrect price, promotion not set up.

Verification is manual, via records and your sales representatives. Feedbacks from the field can therefore be slow. By the time the discrepancy is detected, the campaign is often already well underway.

What the AI agent does

Field sales representatives upload photos of shelves during their visits. The agent takes over:

• It analyzes the photos using visual recognition and compares them with the reference planogram or promotional brief.

• It automatically style detects discrepancies: missing POS display, incorrect facing, non-compliant price, promotion not of present.

• It generates a targeted alert for the sales representative or regional manager, indicating the corrective action to take.

The measurable result

• Execution rate of trade plans measured in real time, no longer post-mortem

• Corrections possible during the campaign, not after

• Field execution data available and structured for the performance review

Use case #4: B2B Insurance and automated contract renewal

The problem before

The B2B contract portfolio is large. Deadlines are numerous and manual follow-up cannot cover everything: some reminders arrive too late, others do not arrive at all. Contracts expire without any action being triggered, not due to lack of will, but due to lack of capacity.

What the AI agent does

The agent continuously monitors portfolio deadlines and automatically triggers the right actions at the right time:

• At 90 days before expiration: first personalized contact, adapted to the client profile and claim history.

• At 60 days: reminder if no response, using a different angle.

• At 30 days: automatic escalation to the human advisor if the client still has not responded, or if the contract value exceeds a defined threshold.

Each message is generated according to the precise context of the client.

The measurable result

• Renewal rate increased

• No contract expires without an action being triggered

• Advisor administrative workload reduced since they intervene only where their value is real

Use case #5: B2B reseller networks and activation of inactive resellers

The problem before

In a reseller network, not all partners are equally active. Some gradually drop off: less frequent orders, declining volumes, rising support tickets. This drop-off is rarely detected in time and reminders, when they occur, are often generic and not very effective.

What the AI agent does

The agent continuously monitors drop-off signals in the CRM:

• It detects resellers at risk: no orders for X weeks, significant drop in volume, recurring support tickets.

• It generates a personalized follow-up based on the reseller's profile, history, and the types of products they usually sell.

• It proposes a sales action adapted to the context: targeted discount, invitation to a training session, triggered call from the reference sales rep.

The measurable result

• Measurable reactivation of dormant accounts

• Better network coverage without additional workload for sales teams

• Drop-off data available for managerial steering of the network

Use case #6: Cash & carry and out-of-stock management and priority restocking

The problem before

In cash & carry - just as in supermarkets - an out-of-stock situation can be detected late. Sometimes, it is the foodservice (RHF) sales representative on a visit who notices it. By then, the damage is done: by the time the product is reordered and delivered, the brand has lost sales.

What the AI agent does

The agent does not wait for the out-of-stock to warn:

• It monitors stock levels in real time by SKU and by zone, without human intervention.

• It calculates the velocity of each product: the pace at which it sells, over what period, and with what seasonal variations.

• It cross-references this data with current orders to detect tensions before they become stockouts.

• When the critical threshold approaches, it automatically triggers alerts to the suppliers concerned and initiates priority restocking.

The difference with a classic alert system lies in the fact that the agent does not wait for the stock to reach zero before reacting.

The measurable result

• Product availability rate improved

• Reduced sales loss on high-velocity items

• Teams repositioned on supplier relation and arbitration decisions, not on manual stock monitoring

What these six cases reveal about the actual deployment of agentic AI

Three cross-cutting lessons emerge from these examples.

1. The agent always intervenes where humans lack time, not competence. It is not expertise that is lacking in these situations, but the capacity to process a high volume of repetitive tasks with the same rigor every time.

2. The limiting factor is never the technology, but the quality and structure of the data available as input. An agent deployed on a poorly maintained CRM or unstructured data will produce disappointing results, regardless of how sophisticated the underlying model is.

3. The right starting point is not the most ambitious one. In all these cases, deployment began on a restricted and well-defined scope, before being expanded. This is the right sequence.

To understand how these agents technically work, I invite you to read our article on how an AI agent works?

These six examples cover different sectors but demonstrate the same thing: agentic AI is not a futuristic project reserved for large tech giants. It is an operational reality, deployed today for concrete, measurable tasks.

Our advice: start with the most repetitive, best-documented, and least-risk task in case of error. The rest will follow naturally.