AI in Business: From Experimentation to Adoption – How Training Ensures Successful Deployment!

Today, the adoption of AI by a majority of companies is a given; the real question is how to implement it effectively. We spoke with Ikram Bouchikh, who oversees the Digital Technologies Training Offer at Lefebvre Dalloz Compétences. She shared insights into the main challenges to corporate AI adoption, the dangers of deploying it without a clear framework or strategy, and the keys to effective job-specific training.

AI is widely adopted in businesses, yet often lacks a proper usage framework. What do you consider the main obstacle?

The challenge isn’t the absence of AI applications but the lack of a strategic and professional framework. AI is being deployed, but without a strategy, or at best, with only a short-term strategy.

In the medium to long term, this results in tools that no longer fit, and employees experimenting on their own, without guidance or training. Departments deploy licenses without coordinating with legal, IT, or business units. There is no clear usage doctrine, nor is there validation of the data used to power these tools.

Today’s most striking phenomenon is “Shadow AI.” Employees don’t wait for official decisions; they’re already using AI in their daily routines, sometimes without informing their superiors. In some companies, more than one in two employees already uses generative AI tools at work.

The problem is that AI is being used without strategy, framework, or support.

What are the risks of lacking proper oversight when integrating AI across various company services?

There are three levels of risk.

The first is legal risk, involving sensitive data flows, non-compliance, and uncontrolled data sharing. Over time, this can lead to serious breaches and leaks.

The second risk is strategic: each market solution is connected to a public database, which could have reliable or utterly erroneous information.

Making strategic decisions based on unvalidated data exposes businesses to undetected errors and loss of credibility.

The third risk is human: it leads to a lack of responsibility among employees, creates excessive dependency, and ultimately devalues their skills.

Poorly managed AI deployments can create more chaos than productivity, as adoption is already widespread. What’s lacking is the framework.

Do you have an example where AI integration was counterproductive in a business?

I’ve worked with companies that gave all their employees access to a generative AI tool without any training. It was a free-for-all: do whatever you want.

The result was inaccurate content being shared externally, time wasted on corrections, and internal tensions between enthusiasts and skeptics. While the tool was efficient, the problem lay in the lack of support, acculturation, and methodology.

Another common mistake is the “One shot” approach, where a company conducts a one- or two-day training session without follow-up or reinforcement. The effect is dramatic at the moment but fades within weeks if nothing is structured behind it.

Giving access to AI without proper training is like giving a Ferrari to someone without a license and telling them go ahead, drive. They’re heading for a crash.

What are the concrete benefits of adopting AI training tailored to specific jobs and uses, rather than focusing on tools?

Training focused solely on tools is a strategic mistake, as tools evolve constantly, sometimes weekly in our field. However, the profession and its needs remain. HR does not use AI in the same way a legal department might. For HR, AI might help in drafting job postings or analyzing resumes. For finance, it can speed up reporting or data consolidation. For marketing, it becomes a lever for content production and optimization. Each department has specific needs that should be contextualized and personalized to their real-world situations.

Instead of training for specific tools, we train to solve job-specific problems with AI: when to use it, how to use it, and who takes over afterward. The tool can be replaced, but the skill of using it is lasting.

How do you concretely assist companies in maximizing their chances of truly adopting AI?

At Lefebvre Dalloz Compétences, we support companies by offering AI training paths designed for specific professions, based on real use cases. We start from their concrete needs, create practical cases, and introduce AI where it genuinely addresses these needs.

We structure support in three stages:

• an initial awareness phase,
• operational training,
• and later, an enhancement phase to consolidate practices.

The goal is not to train for a tool but to integrate artificial intelligence into daily practices in a concrete and secure way, so teams can autonomously transition from one tool to another. We aim to develop this strategic usage skill.

Ikram’s advice

We recommend creating a network of AI ambassadors within teams. These key profiles, trained in depth, spread best practices, identify blockages, and help foster a lasting culture.

Ikram Bouchikh

Head of Digital Technologies Training Offer, Lefebvre Dalloz Compétences

Without clear management endorsement and defined governance, training remains an isolated event. AI must be part of a corporate strategy, not just a training plan.

What indicators do you track to determine if AI is truly adopted and utilized daily by a company?

Most often, the chosen KPI is the number of licenses deployed. This drives me crazy (smile). It’s an indicator of budget, not performance!

It’s a common mistake: confusing budget deployment with actual adoption. Just because a tool is available doesn’t mean it transforms practices. The real indicators must be defined and measured by immediate managers: recurring use cases, measurable reductions in time for certain tasks, quality improvements, and increased team autonomy.

If AI doesn’t change daily work habits, it’s not adopted. Either the activity doesn’t really need it, or it’s poorly deployed, and the model needs revising.

With AI evolving rapidly, how do you anticipate future training needs to prevent today’s processes from quickly becoming obsolete?

We must avoid the classic mistake: do not train for a static tool, as there is no magical solution that endures.

At Lefebvre Dalloz Compétences, we develop adaptability within teams by fostering critical thinking about results, evaluating model limitations, understanding legal, human, and budgetary risks, and having a methodology for testing new tools without being locked into one.

AI is now a fundamental skill like Excel or PowerPoint. It is no longer an innovative topic reserved for a few experts but a competency expected in most roles. When a team is already trained in AI, switching tools becomes smooth and simple. It’s like with social media: if you master one, you can switch to any other.

The key skill of tomorrow is not knowing how to use a specific AI tool, but deciding when, how, and which one to use.

AI is not just a technological project; it’s a project for transforming skills. Adoption is already underway. What will make a difference tomorrow is the ability of organizations to structure, supervise, and provide ongoing training.

Train in AI with Lefebvre Dalloz Compétences