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Have you ever been surprised by the amount of knowledge, skills and abilities your everyday colleagues have? Detecting these "implicit" skills is certainly one of the most exciting topics in the management of skills, and the one that will contribute the most to the evolution of the future conception of work. At the heart of these ongoing transformations, artificial intelligence represents a major tool for identifying skills useful to the organization.

AI contributes to the clarification of the company's employee levels through simple and transparent matching processes. Detecting the invisible skills is a source of enrichment for the company. Thanks to semantic analysis, AI can reveal, but also structure, and exploit skills that reveal the full potential of each individual.

What if the skills you need were already in your organization?

Using AI to detect implicit skills

A new field of HR investigation

Mobilizing skills to the right places in the organization has always been a core activity of human resources. Now all processes - recruitment, internal mobility, training - are linked to a broader talent management policy where detecting and valuing implicit skills , absent from job descriptions, is gaining momentum.

Based on natural language processing technologies, AI matches skills with different levels of reliability depending on several factors.

The more skills are expressed in an explicit and universal way, the more precise the results will be, as in the case of skills IT for example.

Semantic networks identify, extract and make sense of information to reveal the underlying skills of each profile.

What are the keys to detecting skills with AI?

Several factors influence AI to detect skills with relevance:

  • The initial training work
  • The amount of text and its form (structured or unstructured)
  • The standardization of the expression of the skill

A form of intelligence whose performance depends on machine learning, AI needs training data to recognize similarities and dissimilarities between several semantic fields.

The AI engine crosses unstructured qualitative information that is very time consuming for HR (resumes, career paths, job offers). The ability to automate the analysis of this data and the integration of business trends in a professional environment offers unprecedented development opportunities for the company. 

In other words: AI plays a monitoring role by capturing external weak signals of skills emerging to compare them with internal skills . It is therefore also a tool for anticipating key skills to detect jobs in tension and design future jobs.

The different forms of AI used in the detection of skills, the relevance criteria to watch for.
The different forms of AI used in the detection of skills and the relevance criteria to watch for.

Suggest skills to employees

The evaluation of skills implicitly thanks to AI comes from both internal and external data to the organization. Neural networks are the guarantors of the proper integration of any new data to classify it into intelligent categories.

Classify your skills in a permanent way thanks to our page "What are the different categories of skills in companies".

A permanent exchange with employees and the HRIS encourages the learning and enrichment of HR tools. HRIS and AI combine to form a transparent evaluation system which updates the existing and new skills databases on a daily basis. For each new skills proposal, employees, HR and managers will be invited to evaluate their reality and to choose whether or not to evaluate them in the mapping of skills of the organization. Active learning is a technology which, thanks to 4 interactions with the end user, allows to detect with 80% of veracity, a suggestion of skills, job offers or trainings.

The suggestion of skills on the Neobrain HR platform

What are the steps to using AI in managing skills ?

To use the full potential of AI in managing skills, we recommend 4 steps:

  1. Provide semantic instructions to the intelligence engine
  2. Propose to employees the skills recommendations
  3. Integrate skills into their profile
  4. Use the skills suggestions to propose a suitable career path

Moreover, learning algorithms observe all forms of textual data: it is not only skills, but also the undisclosed appetites, the implicit motivations that come to life with AI.

Evaluating skills immersed with AI makes both the employee and the company actors. Such a process takes into account the gaps between skills and the company's environment, and allows for better management of the strengths and areas for improvement of each employee. The skills thus valued become a living base to suggest new career paths to employees. 

Integrate skills implicit in the organization

To become aware of skills , which has not been proven until now, also requires to feed their integration and their development in the company. 

The semantic analysis engine provides suggestions for skills that have not been identified by HR actors. These new emerging assets can be the subject of training and will be the source of potential internal mobility.

The training

The algorithms synchronize the data from the individual interviews with the most relevant learning modules: an overview that accelerates the rise in skills through a coherent development plan for each person.

The company anticipates the support needs of its teams and trains to enhance their value. Faced with the rapid growth of skills , AI naturally suggests thinking outside the box. However, the employee acts in a concrete way on his evolution.

Beyond careers, AI promotes a different social link within the organization and with the external environment. The system promotes a learning culture that sets in motion all the actors of the company. Take the time to consider the"new opportunities for advancement" at skills.

Internal mobility

The skills implicitly detected offer the weapons to consider internal mobility as a real alternative to external recruitment. With AI, the organization visualizes every day with greater accuracy and depth two pieces of information contained in an Internal Talent Marketplace:

  • the potential of employees in the form of catalogs of skills
  • positions and missions visible and intelligently suggested to qualified profiles.

Semantic analysis makes it possible to connect all the assets of a company. Revealing individual skills helps to set the company's talents in motion.

As part of a global strategy, the company capitalizes on its talents. Job bridges make internal movements more fluid and facilitate transitions between different immediate or long-term job offers. Do you want to boost internal mobility? Here are the instructions for use.


AI must be configured according to the company's HR culture, as several parallel HR conceptions remain with regard to skills. The strength of a flexible editor such as Neobrain is to quickly take into account HR preferences, especially on a system of suggestions for new skills.

The advantage of AI in its understanding of skills is that it provides unexpected directions that promote agility and responsiveness. In intelligent articulation with human resources, the machine learning engine proves to be a particularly effective ally... and complementary.

Beyond being an indispensable ally of modern HR, AI produces an alignment of all skills with the company's competitiveness issues. 

Concerns about the use of AI in human resources remain, the technology must also evolve. We have identified and assessed these ethical risks and ongoing improvements in the page: "AI for Talent Management".