Beyond the Resume: Why Validating Skills Matters

In a world clamoring for skills visibility, a key challenge has flown under the radar: ensuring their authenticity. As diverse as the skills themselves, so too is the unique journey an individual takes to acquire them.

So what is today’s gold standard for skills validation? On this episode of Talent Experience Live, we unveiled AI’s role in the process compared to conventional managerial affirmations and peer endorsements. In a conversation with John Deal, Phenom’s Senior Director of Product Marketing, we explored how you can navigate the intricate landscape of skills-forward technology with confidence and clarity.

Read on for highlights or watch the full episode below.

What prompted the shift toward a skills-based economy?

In the current talent shortage, organizations must get more granular to fill roles quickly. That means looking beyond job titles and requirements for candidates with singular skills needed for a position.

Considering transferrable skills can help broaden talent pools. Think of a baseball player with a strong throwing arm. Could that person become a quarterback? “That’s really what’s driving this — you need more granularity in order to fill roles faster, and we can’t just depend on what [a candidate’s] last job was,” Deal said.

How critical is skill origin?

Does it matter if the candidate has a degree in a related field, or experience working in the industry? Ideally, every hiring manager would find a candidate who embodies that perfect combination of market knowledge and skills, Deal said.  

But in a highly competitive talent market, you need to focus more on the skill itself rather than whether it was gained through a college degree or a past role in the same industry, he added. Today, job candidates can gain transferrable skills in various ways that might not appear on a traditional resume.

“There’s so many different ways you can learn and acquire knowledge,“ Deal said. “I think that the origin of a skill has become less important. It’s more, how do you know if it’s valid or not? Just because they have a bachelor’s degree in computer engineering, does that mean they really know how to do Javascript?”

Let’s talk about traditional skills validation methods.

When it comes to skills validation, traditional methods like collecting the perspectives of managers and peers are still critical. 

Manager validation is important, of course (assuming that the manager has spent time evaluating the employee’s performance and holding regular one-on-ones). Peer validation brings the perspective of a colleague who’s worked alongside the individual and seen their skills in action. 

“It has to be data from multiple sources, even from the person themselves,” Deal said, alluding to the need for candidates to self-reflect on their skills and abilities.

What is the role of AI in collecting and validating data? 

A system that’s powered by AI brings accuracy and efficiency to a skills-based hiring approach — and it saves the humans involved from having to analyze an overwhelming amount of data. The machine can quickly sort through large datasets of skills that are collected from multiple sources — such as skills data from assessments, projects, online courses, endorsements, or performance reviews — without missing a single skill or correlation.

“I don’t think you can do it without the AI because you need enough data to be statistically relevant. And then as you start to grow that dataset, it becomes impossible to manually collate everything,” Deal said. “The machine will be able to take the different sources of data, weigh those different sources, and be able to give you an indicator of how valid a skill is and what level of proficiency they might have.”

Phenom’s AI-powered skills ontology database includes roughly 400,000 skills. Sounds like a lot — until you start considering granular-level skills, like flying a certain model aircraft, having a black belt in jiu-jitsu, or the huge range of programs and applications under the IT umbrella, and the specific skills required to support them.  

AI-supported skills ontology systems can give organizations the granular level of understanding that they need regarding:

• Skills available from the current internal workforce

• Skills needed for current and future staffing needs

• Skills available in their talent pools

Related: Demystifying Skill Ontologies: Your Roadmap to Clarity

Can AI recommend skills associated with specific job roles?

Yes! The machine will recommend skills that it identifies as related to various job roles based on data from user profiles and resumes. This can help guide managers in making endorsements, users in self-reflection, and the machine in inferring related skills.

“Moving toward skills doesn’t mean moving away from roles,” Deal clarified. “Moving toward skills just means enhancing role information.”

What are some challenges of using skills validation technology? 

Rolling out new processes and technology typically meets some resistance. Shifting to skills-based hiring and implementing an AI-driven system for skills validation is no different. 

With a platform that lets users self-rate their skills, people may question whether the organization is introducing the risk of people “gaming the system.”

To some extent, yes, this can happen, Deal said. However, thoroughly communicating the goals of skills technology, and the benefits of correct usage can encourage buy-in and effective participation.

“I think it leads to better self-reflection, better endorsement, and better engagement in other upskilling activities,” Deal said. “I think that the biggest thing is communicating the strategy, why you’re moving toward skills, and how people can engage in that strategy effectively.”

What are the benefits of using AI-powered skills technology? 

Using an AI-driven skills ontology platform can help TA teams unlock faster hiring and better retention, with specific benefits for multiple talent stakeholders:

Recruiters and hiring managers: Uncovering skills that are needed for a job role at a granular level — and additional transferable skills — gives recruiters a bigger talent pool to draw from, Deal explained. Recruiters who use Phenom have Ideal Candidate Matching functionality, which identifies the top skills correlated with successful hires, guiding future candidate selection.

Employees: AI helps employees discover how their skills dovetail with internal job openings, and also can guide resume-building by identifying additional skills that match their current job roles.

Candidates: When candidates share information on a career site that uses AI for skills, they can receive job recommendations that match a comprehensive skill set rather than just their job title. It also gives them better transparency into what the job role would entail at that particular organization.

Can the platform differentiate between hard skills and soft skills? 

Phenom AI does accommodate both, and uses various methods of validation, according to Deal. Assessments are used for hard skills (e.g., technical skills) and soft skills, like empathy and interpersonal skills. 

People can endorse both categories of skills on user profiles. The system also can infer skills based on the job role, the type of organization, or volunteer work.

Final thoughts?

“The data is important, but being able to action the data is equally important,” Deal said. This is why validation is so essential: “If you have an experience where your employees, their peers, managers, and the HR team can participate in moving people along in the organization versus analyzing all the data on the back end, that’s the difference.”

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