The Role of AI in Skills Management and Career Pathing

Skills management is foundational to career pathing and internal mobility. But managing the sheer breadth of data involved in effective skills management can be overwhelming — especially for large organizations.

With an AI-driven skills database, HR and TA professionals can save time and gain visibility into the skills that are already available internally to meet future needs.

To find out how AI can help you better manage skills and unlock internal mobility, watch this Phenom presented session here — or check out key insights below!

Discover employee skills to unlock internal mobility

First, organizations need to analyze teams’ existing skills and experience to form a baseline of skills in the AI system. TA and HR managers can collect skills data from several sources:

Employee profiles with up-to-date information on skills, experience, and job history
Resumes
Employee assessments
Manager and co-worker validation
Referrals

How AI augments the skills profile

Once all relevant information is cataloged in the system, AI can get to work. It does this by automatically separating job skills into various categories:

Semantic skills. AI analyzes skills available in an employee’s profile and identifies additional skills that may be related. “If you have a skill in Java, for example, then we have the ability to assume you know Beacon or some other programming language,” Sumita Mehta, former Phenom product manager, explained.

Correlated skills. The AI engine can also identify skills related to specific job titles, Mehta explained: “If you say you are in sales, then … we can also correlate that with negotiation skills, for example. So we can build a more robust skills profile just from your job title and how recently you’ve had specific jobs.”

Categorizing skills. AI sorts skills into three categories: technical skills, soft skills, and industry-related skills. Technical skills are those needed to perform specific job tasks. Soft skills are transferable skills gained throughout the course of someone's professional experience, like problem-solving and teamwork. Industry-based skills correlate with established categories. For example, someone working in healthcare most likely has additional skills related to that field.

“These are the different ways that we can use AI and put some intelligence into this so that even after you create your profile, AI can help augment that profile and use it in different places,” Mehta said.

Multilingual ontology. Global organizations hire across different countries and multiple languages. But “a lot of the ontologies that are out there are very much focused on single languages, primarily English,” John Deal, Director of Knowledge Management at Phenom, said. “They depend on machine translation to work within that framework ... and I’ve seen that machine translation doesn’t quite get you there all the way.”

Phenom AI helps preserve context, which is critical in creating ontology and taxonomy — and Phenom has created native language versions of skills ontology to provide better matching across different languages.

Leveraging an AI-driven skills base to optimize TA functions

Once you have a baseline and greater visibility into your skills inventory, AI can help optimize and streamline your TA process. In this application, AI works as a recommendation engine to help employees move closer to their career aspirations. Here’s how:

Improved sourcing insight and visibility. This is an area where all that manual work of establishing the skills base really pays off — it helps form a common language around employee skills, making it much easier to identify internal skills gaps and guide sourcing efforts.

“By having that common language, it helps to set a base to decide where you should be sourcing – should you be looking internally at upskilling, or should you be looking outside?” Deal said. Although you may not be able to fill every position with an internal employee, this insight provides recruiters with the information they need to find best-fit candidates for current and future open roles.

Resource: Quadient's Key to Internal Mobility & Creating an Agile Workforce That Lasts

Simplified matching with Phenom AI Fit Score. A fit score is a dynamic score that categorizes, ranks, and recommends candidates for open positions based on their skills, experience, and geographic location — and it plays a key role in discovering the right talent.

Although data fields such as preferences and other employee information come into play, skills are the primary component driving AI-assisted candidate recommendations. “Skills is the main data element we use to match people, allowing them to see jobs they may not have seen otherwise,” Mehta said. “So if they have skills that are not obvious [but that AI associates with their profile], we can use those to suggest other kinds of jobs as well.”

Proactive career pathing and succession planning. TA professionals can also use AI-powered skills management to forecast potential skill deficits that can be averted with proactive career pathing and upskilling. “It answers [the question]: how do you proactively prepare your people for your needs when you’re pipelining or looking at future skill requirements for the organization?’” Deal said. “That’s where the idea of career pathing comes in.”

Just as building the skills base requires hands-on effort, career pathing also begins with manual work, including:

Examining the organization’s job roles
Coming up with job descriptions and skills needed
Developing hierarchies to build the career path
Establishing required learning to get to the next level

“Most large organizations probably don’t have a fully fleshed out job hierarchy for each and every person,” Mehta said. “We have the [unique] ability to take a company's data of people skills, as well as how they moved within the organization, to create career paths on the fly … that highlight lateral moves that could be really good for your career.”

Closing skills gaps through learning, gigs, and mentoring

Once the career path has been established, it's time to invest in your employees to help them achieve their goals while alleviating hiring costs and filling critical roles.

To help accelerate their journey and support upskilling opportunities, be sure to consider:

Recommending LMS courses on new topics that foster employee development
Offering gigs, or short-term projects, in different departments that provide a hands-on learning experience
Establishing mentoring relationships with senior employees who have had a similar career path

Career pathing and internal mobility is all about “using AI as a recommendation engine, not just for jobs, but also for learning opportunities, gigs, [and] mentoring [to help] you grow in your career,” Mehta said.

Resource: The Definitive Guide to Employee Experience

Like any application of AI, the technology only gets better with time and increasing amounts of data. Deal noted, “AI learns as companies hire into positions ... it can continue to refine career paths as it learns more and more from actual activity, such as a promotion or new hire perspective.

Another invaluable benefit? TA teams can leverage AI for succession planning. With the application of AI, “companies can [begin to] understand who can move into roles internally and whether it makes more sense to go outside [the organization] based on the talent they have,” according to Deal.

These types of AI-powered insights are critical to both the current and future hiring and retention success of enterprises — especially those with high-volume demands.