Training your teams in AI: turning the tool into a reflex
Deploying artificial intelligence in your network does not mean that it will be used.
Even when a tool is simple, quick, and able to provide relevant answers, your teams can still send an email to headquarters, call their network facilitator, search for a document on their computer, or ask their question in an internal messaging system.
Why ?
Because the adoption of AI does not depend solely on the quality of the technology; it depends mainly on how it integrates into working habits.
Therefore, for AI to become truly useful in your network, it is not enough to train employees to write better prompts. They need to be taught when to use AI, how to interpret its responses, and how to contribute to its improvement.
And this training does not only concern the field.
The seat also has a central role to play.
Making AI available does not automatically create a new use case
The real challenge is not simply to make AI available to your teams, but to create the conditions for it to truly become part of their work routines.
When a new feature is deployed, a classic phenomenon can occur: your teams continue to use the methods they already know.
These reflexes are not irrational. They have simply been built over time.
The arrival of AI then adds a new possible path for accessing information. But until your teams know exactly in which situations to use it, the old reflex often remains the most natural.
The goal of the training is therefore less to explain how artificial intelligence works than to answer a much more concrete question:
Training in AI doesn't just consist of learning how to write prompts
Much of the content dedicated to AI training focuses on writing prompts. Knowing how to formulate a request correctly is useful. But in a network, this is only a small part of the topic. A field collaborator does not need to become an expert in prompt engineering. He or she must above all learn to effectively communicate with the tool. This involves several simple skills.
1. Knowing how to recognize the situations in which to use AI
The first step is to identify the relevant uses.
For example:
to find a procedure; ;
understand an internal rule; ;
checking a step of a process; ;
to restore the brand’s standards; ;
knowing how to react in a common situation; ;
access quickly to information present in the network documentation.
The training must start from real situations.
The more the examples correspond to the daily work of the teams, the easier it becomes to understand the value of the tool.
2. Ask a question that is clear enough
An AI can only work with the information it has available.
For example:
« A customer wants to return a product purchased three weeks ago without a receipt. What procedure should I follow?»
This question allows the articulatory intelligence to understand the context of the situation.
Training your teams therefore consists of teaching them how to provide the necessary elements to obtain a truly useful response.
3. Knowing how to rephrase
The first answer is not necessarily the right one.
The collaborator must be able to specify their request, add an element of context, or reformulate the question.
AI then becomes less of a search engine than an interlocutor with whom a request can be refined gradually.
This ability is important because it prevents a common behavior: abandoning the tool as soon as a first response seems incomplete.
It is also necessary to teach teams to recognize the limitations of AI
A business AI can be effective without knowing the answer to all the questions. And that’s normal.
If information does not exist in the knowledge base, if a procedure has not been documented or if the situation requires a specific decision from headquarters, AI can reach its limits.
Training your employees also means explaining these limitations to them.
An absent or insufficient response should not necessarily be considered a failure of the tool. It may reveal a lack in the network documentation.
It is here that the adoption of AI is starting to produce a particularly interesting effect.
Field questions become a new source of information for the headquarters.
And what if they never get any answers? Simon (our specialized knowledge base and tickets IA agent)) It identifies them and transmits them to the headquarters teams to enable them to enrich the network’s knowledge base.
Every unanswered question can become an opportunity for improvement
Imagine that several sales representatives regularly ask the same question.
There are two possibilities. Either the answer is already documented but difficult to find.
In this case, the problem concerns accessibility or the organization of the information.
In that case, the problem may be documentary or operational in nature. A procedure is missing. A rule is ambiguous. A particular case has never been formalized. An aspect of the process needs to be clarified.
AI is no longer solely used to answer questions. It also makes it possible to reveal the friction points in the network.
Provided that employees know how to report a missing, inaccurate, or insufficient response. And that the headquarters knows how to utilize these feedback.
The adoption of AI is therefore also a subject of training at the headquarters.
The success of the deployment does not depend solely on the field users.
The sitting teams must also develop new reflexes.
The first consists of analyzing the questions posed.
What requests come back regularly? On what topics? In which locations?
Do certain issues arise after a new procedure, a commercial campaign or an operational change?
This analysis gradually allows us to identify the knowledge that needs to be strengthened. But not all questions should lead to the addition of a new document.
The seat must learn to distinguish between several situations.
A question can reveal:
an information absent; ;
outdated information; ;
a document too difficult to find; ;
a procedure that is misunderstood; ;
a process that is too complex; ;
a need for training; ;
a situation that really requires human intervention.
This distinction is essential.
A well-functioning knowledge base does not consist of accumulating more and more documents. It must provide useful, clear information that can be used when teams need it.
Maintaining knowledge becomes a new routine of the office
To remain relevant, a business AI must be able to rely on knowledge that is itself relevant.
This requires updating the content over time.
New procedures, changes in offerings, regulatory changes, new marketing campaigns, new brand standards: the reality of the network is constantly evolving.
The documentation base must evolve with it.
The role of the headquarters therefore consists of organizing a cycle of continuous improvement:
Question of the terrain -> analysis -> update of the knowledge -> new answer available -> information to the network.
This loop is important because it gradually transforms AI into a living tool.
Each interaction can contribute to improving the following ones.
The network no longer has only a library of documents. It is gradually building up knowledge that is accessible and continuously enriched by the real needs of the field.
To create a reflex, AI must become part of daily routines.
Even good initial training is not enough. An employee may understand the operation of a tool perfectly when it is launched and not think about using it for a few weeks afterwards.
Adoption is built through repetition. Therefore, AI must be linked to concrete everyday situations.
Before sending a question to support: ask the AI.
Before manually searching for a procedure: ask the AI.
Before calling the headquarters to find information that is already documented: interrogate the AI.
This obviously does not mean that all human interactions must disappear.
The goal is to reduce repetitive tasks and the need to search for information so that the headquarters teams can devote more time to situations that truly require their expertise. AI should take over tasks that can be handled by it. The human remains present where their understanding of the context, their experience, their judgment, or their ability to provide support are indispensable.
The example of the siege is decisive
It is difficult to ask field teams to adopt a new tool if the headquarters itself continues to operate exactly as before. Adoption must therefore be visible.
When a question can be solved with AI, network managers can encourage employees to use it rather than simply relaying the answer. The change in behavior can be very simple.
Instead of answering:
«Here is the document.»
The seat can answer:
«You can find this information directly in the assistant. Try asking it this question.»
This change seems minor. But repeated over time, it helps to create a new reflex.
Some teams will need more support than others
Adoption will never be uniform from the outset. Some employees will immediately test the’IA And usage patterns will multiply. Others will long retain their old habits. This does not necessarily mean that they are resistant to technology. They may simply not have identified a sufficiently concrete benefit in their daily lives.
The role of the headquarters is therefore also to understand why some teams use the tool less.
Lack of training? Lack of confidence? Misunderstood questions? Inadequate answers? A tool that is not well integrated into routines?
Incomplete knowledge?
The answer will not always be to propose a new general training. Targeted support for specific work situations can be much more effective.
Measuring adoption rather than simply deployment
An activated feature is not necessarily an adopted feature.
To understand whether AI is truly being integrated into everyday life, several indicators can be observed:
the number of active users; ;
the frequency of the questions asked; ;
the share of installations that regularly use the tool; ;
the most sought-after topics; ;
the volume of unanswered questions; ;
the evolution of the requests addressed to the headquarters; ;
This data allows us to identify the areas where adoption works and those where it still needs to be accompanied by support.
But the most interesting indicator remains probably the change in behavior.
When the first reaction of a collaborator to a question becomes:
«I'm going to interrogate the AI.» then the tool really starts to become part of the network’s habits.
The adoption of AI is primarily a transformation of usage patterns
The success of an artificial intelligence project is not measured by the number of features deployed.
It measures the ability of teams to use them at the right time and in the right way.
For the field, this involves knowing when to ask the AI, how to provide the right context, how to verify a response, and how to report missing information.
For the seat, this means listening to the questions raised, continuously improving knowledge of the network, and supporting the teams that face more difficulties.
The training then becomes much more than just a presentation of the tool.
It creates a virtuous circle: Better-trained teams ask better questions; these questions allow the headquarters to improve knowledge, and better knowledge makes AI progressively more useful for the entire network.
It is on this condition that artificial intelligence ceases to be an additional feature.
It becomes a real operational intelligence reflex.
FAQ - Training your teams in AI: turning the tool into a reflex
Training teams in AI enables a tool that is available to be used in a real business context. Teams must, among other things, know when to use AI, how to formulate their request, verify the information obtained, and understand the limits of the tool.
The adoption of AI relies on concrete use cases, training tailored to teams, and gradual integration into daily routines. The headquarters and field teams also play an important role: they must support users, encourage good practices, and identify difficulties encountered in order to continuously improve usage.
Users do not need to become experts in artificial intelligence. They mainly need to learn to ask a clear question, provide the right context, rephrase their request if necessary, consult the sources, and know when human validation remains indispensable. For the headquarters, it is also necessary to be able to analyze the feedback from the field and maintain the knowledge used by the AI.
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