Train your teams in AI: turn the tool into a reflex
Deploying artificial intelligence across your network does not mean it will be used.
Even when a tool is simple, fast, and capable of providing relevant answers, your teams may continue to email head office, call their network coordinator, look for a document on their computer, or ask their question on an internal messaging platform.
Why?
Because AI adoption does not depend solely on the quality of the technology. It depends above all on how it fits into work habits.
So, for AI to become genuinely useful across your network, it is not enough to train employees to write better prompts. You need to teach them when to use AI, how to interpret its answers, and how to help improve it.
And this training is not just for frontline teams.
Head office also has a central role to play.
Making AI available does not automatically create a new use case
The real challenge is not simply making AI available to your teams, but creating the conditions for it to become a genuine part of their work habits.
When a new feature is rolled out, a familiar phenomenon can occur: your teams keep using the methods they already know.
These habits are not irrational. They have simply developed over time. The arrival of AI adds another possible way to access information. But as long as your teams do not know exactly when to use it, the old habit will often still feel more natural.
The goal of training, then, is less to explain how artificial intelligence works than to answer a much more practical question:
“In my day-to-day work, when can this tool actually help me?”
Training in AI is not just about learning to write prompts
A large proportion of content about AI training focuses on writing prompts.
Knowing how to phrase a request correctly is useful. But in a network, it is only a small part of the picture.
A frontline employee does not need to become an expert in prompt engineering.
Above all, they need to learn how to interact effectively with the tool.
That involves several simple skills.
1. Knowing how to recognize situations where AI can be used
The first step is to identify relevant use cases.
- For example:
- finding a procedure;
- understanding an internal policy;
- checking a step in a process;
- finding the brand’s standards;
- knowing how to respond to a common situation;
- quickly accessing information in the network’s documentation.
Training should start with real-life situations.
The more closely examples reflect teams’ day-to-day work, the easier it is to understand the tool’s value.
2. Asking a sufficiently clear question
AI can only work with the information available to it.
For example:
“A customer wants to return a product purchased three weeks ago without a receipt. Which procedure should I follow?”
This question enables artificial intelligence to understand the context of the situation.
Training your teams therefore means teaching them to provide the necessary details to get a genuinely useful answer.
3. Knowing how to rephrase
The first answer is not necessarily the right one.
Employees must be able to clarify their request, add context, or rephrase the question.
AI then becomes less like a search engine and more like an interlocutor with whom they can gradually refine a request.
This skill matters because it prevents a common behavior: abandoning the tool as soon as the first answer seems incomplete.
Teams also need to learn to recognize AI’s limitations
A business-specific AI can be effective without knowing the answer to every question. And that’s normal.
If information is not in the knowledge base, if a procedure has not been documented, or if the situation requires a specific decision from head office, AI may reach its limits.
Training your employees also means explaining these limitations to them.
A missing or inadequate answer should not necessarily be considered a failure of the tool. It may reveal a gap in the network’s documentation.
This is where AI adoption starts to have a particularly interesting effect. Questions from frontline teams become a new source of information for head office.
At Cerca, if questions remain unanswered, Simon (our AI agent specialized in knowledge bases and tickets) identifies them and forwards them to the head-office teams so they can enrich the network’s knowledge base.
Every unanswered question can become an opportunity to improve
Imagine that several store managers regularly ask the same question.
There are two possibilities.
Either the answer is already documented but difficult to find.
In that case, the problem concerns the accessibility or organization of the information.
Or no clear answer exists.
In that case, the problem may be related to documentation or operations.
A procedure is missing.
A rule is ambiguous.
A specific case has never been formalized.
A step in the process needs to be clarified.
AI then serves not only to answer questions.
It also helps reveal areas of friction across the network.
Provided employees know how to flag an answer that is missing, unclear, or inadequate.
And head office knows how to make use of this feedback.
AI adoption is therefore also a training issue for head office
Successful deployment does not rest solely on frontline users.
Head-office teams also need to develop new habits.
The first is to analyze the questions being asked.
Which requests come up regularly? On what topics? At which locations?
Do certain questions arise after a new procedure, a sales campaign, or an operational change?
This analysis makes it possible to gradually identify the knowledge that needs to be strengthened. But not every question should lead to adding a new document.
Head office needs to learn to distinguish between several situations.
A question may reveal:
- missing information;
- outdated information;
- a document that is too difficult to find;
- a procedure that is poorly understood;
- an overly complex process;
- a need for training;
- a situation that genuinely requires human intervention.
This distinction is essential.
An effective knowledge base is not about accumulating more and more documents. It should provide useful, clear, actionable information when teams need it.
Maintaining knowledge becomes a new head-office routine
To remain relevant, a business-specific AI must be able to rely on relevant knowledge.
This means keeping content up to date over time.
New procedures, changes to offers, regulatory changes, new sales campaigns, new brand standards: the reality of the network is constantly evolving.
The document base must evolve along with it.
Head office’s role is therefore to organize a continuous improvement loop: Frontline question -> analysis -> knowledge update -> new answer available -> network informed.
This loop matters because it gradually turns AI into a living tool. Each interaction can help improve the next ones.
The network no longer has just a library of documents. It is gradually building knowledge that is accessible and continually enriched by the real needs of frontline teams.
To become a habit, AI must be part of daily routines
Even good initial training is not enough.
An employee may understand exactly how a tool works when it is launched and stop thinking about using it a few weeks later.
Adoption is built through repetition.
AI must therefore be tied to concrete, everyday situations.
Before sending a question to support: ask AI.
Before manually looking for a procedure: ask AI.
Before calling head office to find information that is already documented: ask AI.
This obviously does not mean that all human interactions should disappear.
The goal is to reduce repetitive requests and information searches so that head-office teams can spend more time on situations that genuinely require their expertise.
AI should take care of what it can.
People remain essential wherever their understanding of the context, experience, judgment, or ability to provide support is indispensable.
Head office’s example is crucial
It is difficult to ask frontline teams to adopt a new tool if head office itself continues to work exactly as before.
Adoption must therefore be visible.
When a question can be resolved with AI, network managers can encourage employees to use it rather than simply passing on the answer.
The change in behavior can be very simple.
Instead of replying:
“Here’s the document.”
Head office can reply:
“You can find this information directly in the assistant. Try asking it this question.”
This change may seem minor. But repeated over time, it helps create a new habit.
Some teams will need more support than others
Adoption will never be uniform from the outset.
Some employees will try AI immediately and explore a wide range of use cases.
Others will stick to their old habits for a long time.
That does not necessarily mean they are resistant to technology.
They may simply not have identified a sufficiently tangible benefit in their day-to-day work.
Head office’s role is therefore also to understand why some teams use the tool less.
Insufficient training?
Lack of confidence?
Questions misunderstood?
Inadequate answers?
Tool not integrated enough into routines?
Incomplete knowledge?
The answer will not always be to provide another general training session.
Targeted support focused on a few business-specific situations can be much more effective.
Measure adoption, not just deployment
A feature that has been enabled has not necessarily been adopted.
To understand whether AI is genuinely becoming part of everyday use, several indicators can be tracked:
- the number of active users;
- the frequency of questions asked;
- the proportion of locations that regularly use the tool;
- the most frequently searched topics;
- the volume of questions without a satisfactory answer;
- changes in the number of requests sent to head office;
This data helps identify where adoption is working and where it still needs support.
But the most interesting indicator is probably a change in behavior.
When an employee’s first reaction to a question becomes:
“I’ll ask the AI.” then the tool is genuinely starting to become part of the network’s habits.
AI adoption is above all a transformation in the way people work
The success of an artificial intelligence project is not measured by the number of features deployed.
It is measured by teams’ ability to use them at the right time and in the right way.
For frontline teams, this means knowing when to ask AI, how to provide the right context, how to verify an answer, and how to flag missing information.
For head office, this means listening to the questions being raised, continually improving the network’s knowledge, and supporting teams that encounter greater difficulties.
Training then becomes much more than an introduction to the tool.
It creates a virtuous circle:
better-trained teams ask better questions, these questions help head office improve its knowledge, and better knowledge gradually makes AI more useful for the entire network.
Only then does artificial intelligence stop being just another feature.
It becomes a genuine operational intelligence habit.
