A salesperson spending two hours a week searching for the right proposal template, a support team answering the same technical question with three different phrasings, a manager improvising each onboarding due to a lack of framework: these situations waste time, coherence, and credibility. It is precisely on these operational frictions that a performance-oriented service academy can act, provided it goes beyond standard training to address the real business blockages.
Automation of repetitive processes and operational time savings
When auditing the daily tasks of a sales team or customer service, bottlenecks related to poorly configured tools or undocumented processes are almost always identified. A CRM with fields that do not match the actual sales cycle, a ticketing tool without automatic assignment rules: these are direct losses.
By structuring support around the automation of low-value tasks, we free up time for the interactions that matter. Specifically, this involves configuring workflows within existing tools, not adding an extra software layer. The goal is to make teams autonomous with their own tools, not to create dependency.
Feedback varies by sector, but teams that map their repetitive tasks before training their employees achieve more sustainable results than those that purchase a SaaS subscription without on-the-ground support.
For companies seeking a structured framework for this type of support, the services of Service Academy precisely cover this link between operational diagnosis and skill development.
AI training in companies: what the AI Act mandates since 2025
Since February 2, 2025, Article 4 of the AI Act requires suppliers and companies using AI systems to develop the AI proficiency of the relevant individuals. This is not about a generic e-learning module on ChatGPT, but a documented obligation that covers understanding the limitations of tools, risks of error, confidentiality, and responsible practices.
This regulatory requirement transforms AI training from a “nice to have” into a compliance obligation. The associated operational recommendations include keeping evidence of actions taken: affected audiences, materials used, certifications, and regular updates.
Moving from general awareness to a business logic
Organizations that derive real benefits from AI training are those that train on concrete use cases. A customer advisor does not need to understand how a neural network works. They need to know when to delegate a response to AI, when to take back control, and how to verify an automatic suggestion.
Training involves practical scenarios using real business tools, with clear delegation rules and a focus on complementary human skills (rephrasing, detecting inconsistencies, contextual judgment). A report from Le Nouvel Économiste published on October 9, 2026, documents the need to regularly update these trainings, as AI evolves too quickly for a static module to remain relevant for more than a few months.
Development of relational skills and customer performance
It is easy to invest in digital tools, but less so in the ability of teams to leverage these tools within a smooth customer interaction. A well-configured chatbot is useless if the agent who takes over does not know how to contextualize the previous exchange.
Working on relational skills at the team level is not limited to a workshop on active listening. It involves defining response standards by channel (phone, email, chat), practicing on scenarios derived from real customer verbatims, and measuring the gap between the service promise and the actual experience.
Concrete elements of a field-oriented program
- Analysis of negative customer verbatims to identify recurring journey breaks, channel by channel
- Construction of response templates tailored to the sector, tested in pairs before deployment
- Implementation of qualitative tracking indicators (not just NPS, but first contact resolution rate and rephrasing time)
- Monthly debriefing sessions on complex cases, with role rotation among participants
This type of program produces measurable effects when it is sustained over time. An isolated workshop rarely changes practices, whereas a cycle of several spaced sessions allows for anchoring.
Documenting and updating skills to remain compliant
The AI Act is not the only text pushing companies to structure their training policy. The underlying trend focuses on the traceability of learning: who has been trained, on what subject, with what materials, on what date, and when the next update is planned.
For a service academy, this means that value no longer resides solely in the content delivered but in the ability to produce usable documentation: certifications, evaluation grids, proof of updates. This represents a shift in posture for many training providers who operated on a “deliver and forget” model.
Content must be adapted to the experience level and context of use for each audience. Training a marketing director and a maintenance technician on the same AI module makes no sense, even if both use tools incorporating artificial intelligence.
Structuring a skills development pathway around these constraints requires initial work mapping profiles, tools used, and associated risks. This is where the academy approach makes sense compared to one-off training: it allows for building a framework that evolves with the company, not a static catalog that is renewed every three years.
