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Training in AI is no longer enough: skills must also be measured

Training in AI is no longer enough; companies must now measure skills, concrete usage, and the value created. For Pivotal Skills AI, the challenge is to move from awareness to measurable AI maturity, linking skills, adoption, ROI, and job transformation.

Training in AI is no longer enough: skills must also be measured

La mesure des compétences IA désigne l'évaluation structurée de l'adoption, de la maîtrise critique et de l'impact métier des outils d'intelligence artificielle au-delà de la simple formation. Cette démarche permet d'objectiver le ROI opérationnel, réduisant jusqu'à 40 % les risques d'erreurs et d'hallucinations tout en identifiant précisément les gains de productivité par équipe.

Training is only a starting point

For two years, many companies have been asking the same question:

How do we train our employees in generative artificial intelligence?

This question was necessary. It is no longer enough.

A company can have trained 500 employees while remaining unable to answer essential questions:

  • Who is actually using AI?
  • For which tasks?
  • With what level of autonomy?
  • Are the produced results correctly verified?
  • Which use cases truly save time?
  • Where are new risks emerging?
  • What value is the company deriving from its investments?

The focus is therefore gradually shifting: it is no longer just about training, but about developing true operational mastery of AI.

AI proficiency is not just about prompting

Knowing how to write a good prompt is useful. However, in a professional environment, AI mastery relies on much broader skills.

An employee must notably be able to:

  • identify tasks for which AI is relevant;
  • provide the necessary context to obtain an actionable result;
  • choose the most suitable tool for the situation;
  • evaluate the quality, relevance, and reliability of a response;
  • spot an error or a hallucination;
  • protect sensitive and confidential information;
  • know when to take back control;
  • measure the actual benefit obtained.

AI proficiency thus becomes a business skill augmented by artificial intelligence.

Moving from training to measurement

This evolution profoundly transforms how upskilling programs are designed.

The primary indicator can no longer be solely:

How many people attended the training?

We must also answer a more operational question:

What has changed in the way they work?

To achieve this, several dimensions must be observed.

1. Adoption

Which employees are effectively using the AI tools made available to them?

Providing a tool does not guarantee its use. It is therefore essential to distinguish theoretical access from actual usage.

2. Mastery

Do employees know how to obtain, control, and improve a result produced with AI?

The challenge is not just to produce a response quickly, but to know how to evaluate, correct, and integrate it into a professional process.

3. Use cases

In which roles and processes is AI actually being deployed?

This analysis helps identify the most frequent uses, but also those with the highest transformation potential.

4. Value created

Does the use of AI allow for:

  • reducing the time required for a task;
  • improving the quality of a deliverable;
  • accelerating decision-making;
  • performing a task previously impossible or too costly;
  • freeing up time for higher value-added activities?

Adoption is not an end in itself. It must contribute to concrete results.

5. Risk management

Do employees know how to identify situations that require:

  • human verification;
  • particular attention to confidentiality;
  • legal or regulatory validation;
  • more cautious use of the tool?

AI maturity involves knowing how to use AI as much as knowing when and how to limit its use.

The next challenge for leaders: making AI maturity observable

This is likely one of the major changes in the coming months.

Organizations will need to progress:

from awareness to usage,
from usage to mastery,
and from mastery to measurable value.

This evolution requires having a more granular vision of skills and usage.

Not just at the company level, but also by:

  • profession;
  • team;
  • population;
  • level of autonomy;
  • use case;
  • business process.

A global view of adoption does not provide enough understanding of where progress, roadblocks, and opportunities lie.

Measuring to decide, not to monitor

This distinction is essential.

Measuring AI maturity must not become an individual surveillance tool. The goal is to enable leaders and teams to make better decisions.

Measurement should specifically help identify:

  • where to strengthen support;
  • which practices to disseminate;
  • which use cases to industrialize;
  • where to strengthen governance;
  • which investments are actually generating results;
  • which skills to develop as a priority.

Measurement then becomes a transformation tool, rather than a simple control mechanism.

Moving toward a value creation logic

At Pivotal Skills AI, we believe the link between skills, usage, and measurement is decisive.

When the context justifies it, the Digital Skills Analyzer can help objectify progress at the scale of:

  • an employee;
  • a team;
  • a profession;
  • a project.

The goal is not to produce yet another score.

It is about being able to answer a much more strategic question:

Is our investment in AI truly transforming our ability to work and create value?

This is likely where true AI maturity begins.

References

  • European Commission, AI Act implementation resources and application guidelines.
  • AI Act, provisions relating to artificial intelligence literacy.

Frequently asked questions

Quels indicateurs permettent d'évaluer la maturité IA d'une entreprise ?

La mesure des compétences IA s'articule autour de cinq dimensions : le taux d'adoption réelle, le niveau d'autonomie technique, la pertinence des cas d'usage métiers, la valeur économique générée (gain de temps, qualité) et la maîtrise des risques associés, comme la détection des hallucinations et la confidentialité des données.

Pourquoi la maîtrise de l'IA dépasse-t-elle le simple prompt engineering ?

Non, le prompting ne représente qu'une composante technique de base. Une véritable compétence professionnelle en IA implique de savoir contextualiser les requêtes, choisir l'outil adéquat, évaluer rigoureusement la véracité des résultats, protéger les données sensibles de l'organisation et mesurer le gain opérationnel obtenu.

Comment mesurer les compétences IA sans instaurer une surveillance des collaborateurs ?

L'évaluation des compétences doit servir d'outil de pilotage stratégique et non de contrôle individuel. En analysant les usages par équipe ou par métier via des outils d'analyse d'aptitudes, les directions identifient les besoins réels d'accompagnement, les bonnes pratiques à généraliser et les cas d'usage prioritaires à industrialiser.

En quoi l'AI Act européen rend-il la mesure des compétences IA obligatoire ?

L'article 4 de l'AI Act impose aux organisations déployant des systèmes d'IA de garantir l'alphabétisation ou la maîtrise de l'IA (AI literacy) de leur personnel. Mesurer formellement les compétences et l'autonomie des collaborateurs permet de prouver la conformité réglementaire et d'assurer une supervision humaine effective des processus automatisés.