Skill management

AI-driven learning journeys for employee development

Faced challenges

Telekom AG was looking for new opportunities to develop its workforce in selected future-proven skills supported by an efficient data-driven approach. The idea was to automatically create learning journeys for the employees that would cover future skills and trends. The question to HRForecast was how Telekom can benefit from AI technologies to create target skills based on market data and incorporate them into the learning journeys. The target of the project was to provide a lean, engaging, and motivational learner experience.

„With the data-driven approach of HRForecast, we were able to define necessary skill profiles and training recommendations in an automated, efficient way. With their support, we were able to better concentrate on alignment with experts, stakeholder buy-in as well as on a program guiding and supporting the learners.“

Thomas Berthold, Head of Expert Qualification Management, Deutsche Telekom AG

Questions to be answered:

How can Telekom optimize talent development and training according to future job requirements?

How can Telekom optimize development costs by integrating available learning platforms and contents into the talent development activities?

How can employees be motivated to close their skill gaps and obtain future skills for a target job role?

How does a clear and structured learning path for the employee look like?

Project approach

1. Determination of the target skills for the learning journeys

Using AI algorithms, macro-economic insights to make future skill levels and paths tangible were provided. Global market data was used to detect changes in job requirements, such as evolving skills and new technologies. Telekom received future-oriented job profiles that served as target profiles for the learning journey.

2. Data crawling & matching

Various training sources (e.g. learning platforms, massive open online courses) were crawled to identify suitable training providers. Through automatic extraction and analysis of training content (descriptions, titles, targets, skills, formats, etc.) a best-fit matching of the learning path requirements derived from the future profiles against the offerings of the providers was done.

3. Learning journey curation

Trainings were filtered according to costs, rating, format, duration, and proficiency levels. Furthermore, white spots of training content were detected. A shortlist of relevant learning contents to identify the right matches for the Telekom training requirements was provided.

Key insights & value-adds for Telekom

Through automated market screening of changes in the job requirements the client received future-oriented job profiles that served as target job profiles for the learning journeys for the employees
Using our AI technology, it was possible to screen and select the most suitable contents from different learning platforms for the defined learning journeys. Such parameters as skills to be trained, the skill level, rating, duration, and number of course graduates were considered during the evaluation of the trainings
The learning journeys provided a clear vision for the employees, which skills will be needed for the target job roles and a transparent path, how the required skills can be build up and how much investment is required to close the skill gaps (time and costs)