27.09.2021

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AI and HR: Why use NLP techniques? (part 2)

In the same way that Google Translate relies on semantic analysis technologies to translate texts from one language to another, thanks to NLP techniques, it is possible to translate jobs and training into skills. What are the advantages of this translation? What HR issues can be addressed by using Natural Language Processing?

 

 

1. Recruitment

 

The use of Natural Language Processing has two main value-added applications in recruitment: it optimizes the entire process by using skills as a common thread throughout the various stages, and it saves time by obtaining shortlists of candidates that focus on the elements that are decisive for the positions to be filled.

 

Optimizing the recruitment process with NLP

 

Translating jobs into skills using NLP techniques allows HR teams to be more precise when writing job descriptions. The skills associated with each position are thus better defined and more objective. Recruiters can then target their search for candidates by focusing on the precise skill requirements identified through the technologies. Then, armed with this information, they can optimize the preparation of interviews by relying on advanced knowledge of the skills to look for in candidates. The entire recruitment process becomes more objective and efficient.

 

Related: Skills-based hiring: a solution to talent shortages?

 

 

Obtaining shortlists of candidates using NLP

 

Natural language processing techniques can also be used to analyze the content of resumes. From the textual elements present in this key document of the recruitment process, it is, therefore, possible to identify the candidates’ skills.  Depending on the position, other criteria may also be relevant, such as whether the candidate has obtained specific diplomas or certifications or where they are located.  Therefore, Semantic analysis technologies can be configured to identify whether or not candidates meet the desired characteristics and select the most relevant profiles according to the determining criteria for the positions to be filled.

 

 

2. Learning and Development

 

Translating training content into skills using NLP techniques represents two significant advantages for training providers: better knowledge of learners’ needs and the ability to analyze their offer in order to optimize it.

 

Offer adapted training content based on NLP

 

When training content is translated into skills, it is easier to match them with the needs of learners. Whether in the case of upskilling or reskilling, having a standard reading key makes it easier to identify the pedagogical resources that will help professionals improve their skills. Training organizations can therefore offer training courses that are truly adapted to employees and companies’ needs and maximize the learners’ experience by suggesting truly relevant content.

 

Related: How do you bridge a skill gap in companies?

 

 

Streamline and optimize training catalogues with natural language processing

 

Using natural language processing to analyze the content of training catalogues, makes it possible to identify the skills covered, duplications and potential gaps in relation to the needs identified by learners and client companies. This inventory allows training organizations to obtain an overview of their entire offer, to make the necessary decisions to optimize it in an informed manner.

 

Related: Why and how to rationalize your training catalogue?

 

 

3. Career management

 

NLP can enrich career management within organizations by opening up possibilities and highlighting the most appropriate training to support professional development.

 

Identify the most relevant career paths using Natural Language Processing

 

Natural Language Processing allows us to highlight the proximity between jobs thanks to analyzing the skills associated with them. The technology can thus propose the most relevant career paths for employees based on their skills at a given moment. NLP also opens up the field of possibilities in this respect. If HR know their organization’s jobs well, technology can generally take career management a step further and create opportunities by offering skill-based options that employees may not have thought about.

 

Selecting the proper training to support professional development using NLP

 

Natural language processing makes it possible to facilitate the link between jobs, skills and training. Using NLP capabilities, it is possible to identify the skills that need to be acquired to move from one profession to another, and determine the training courses that will allow the skills to be upgraded to transition between the two professions. The use of technology not only saves time and efficiency for employees who wish to evolve professionally, but also for HR and training organizations.

 

 

4. SWP

 

In the context of SWP, natural language processing techniques allow organizations to take stock of their existing resources and anticipate the impact of macro trends on their structure.

 

Taking stock of existing resources with NLP

 

NLP techniques enable the mapping of skills available to employees of organizations by translating all their job titles into skills. This approach allows HR and leaders to obtain an overall vision of the resources in place within their organization.

 

 

 Anticipating the impact of macro trends on organizations using AI

 

Key studies are emerging on the impact of macro trends on jobs and skills. Automation and remote work, for example, have been shaking up organizations for several years. In the context of SWP approaches, anticipating these changes is fundamental in enabling organizations to define action plans to be implemented. To do this,  teams in charge of the projects can rely on NLP techniques to translate the results of these studies and apply them to the jobs and skills of their organization. HR and leaders, therefore,  have vital data to plan actions adapted to their organization’s situation.

 

Related: Dealing with skills obsolescence

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Illustration credits: https://www.istockphoto.com/fr/portfolio/robuart