how AI can help skills based hiring and upskilling.

Skills based hiring is becoming a strategic imperative as AI and automation continue to transform work. The ideal skill sets for jobs in many industries are changing faster than ever, challenging employers to rethink traditional qualifications – and create upskilling paths to keep their workforce agile.

But while AI is driving this shift, it can also be part of the solution. AI tools can focus on skills assessments in the hiring process and personalize skills development paths for internal teams. This helps streamline hiring, tailor growth opportunities to worker skill sets, and create a more diverse, agile workforce.

Below is an overview of a few key AI strategies that are helping employers stay ahead of the hiring curve – and some important considerations for AI implementation in skills-based hiring.

how AI can transform skills based hiring practices.

From job descriptions to candidate matching and interviews, AI can help employers reinvent each step of the hiring process to shift the focus to skill sets. Below are just a few ways employers are using AI to rethink traditional approaches to hiring.

write skills based job descriptions

Skills based hiring is about more than just removing credential requirements, since each credential is a stand-in for unstated skills. To create an effective job description, employers need to replace credentials with the skill sets they represent.

Fortunately, generative AI tools can help suggest new skills to add in lieu of credentials. Employers can start by writing prompts that explicitly state the nature of the job, and ask what credentials could be replaced with skills. For best results, it’s a good idea to include information such as:

• The role you’re hiring for and key responsibilities
• Required skills and qualifications
• The tone of voice you’d like to use
• The structure and length of the job description

Once the right skills are in the job description, AI can help brainstorm strategic ways to assess them. It’s also important to note that while AI can make suggestions, hiring managers still need to evaluate them with a critical eye to align skill descriptions with the specific needs of their organization.

emphasize skills when screening resumes

An estimated 99% of Fortune 500 companies use AI screening tools to increase efficiency in the hiring process. However, these tools may not be calibrated for skills based hiring, and risk reinforcing a more traditional emphasis on qualifications and years of experience.

Employers using AI resume screening tools should update parameters regularly to make sure the system is screening for core skills. For example, instead of searching for a four-year computer science degree, AI tools can search for coding proficiency in specific languages or familiarity with code libraries.

AI screening tools can also create more equity in hiring when they’re adjusted to omit names, ages, and other demographic identifiers. Instead, they can zero in on skill sets to surface candidates with skills that best match what employers are looking for.

automate interviews from skills-based assessments

Skills assessments can be an important way to reduce reliance on resumes altogether in favor of more objective performance evaluations. These tests can go beyond technical ability and test for attributes like verbal reasoning, attention to detail, critical thinking, and personality traits.

Once you understand the skills you’re looking for, choosing a few assessments that focus on core skills can help identify strong candidates. Ideally, these assessments should be targeted, brief, and have clear outcomes to signal success.

For high-volume hiring, AI agents can walk candidates through each step of the screening process, identify candidates with assessment scores that align with hiring needs, and automate interview scheduling. Short skills assessments save time for both employers and candidates, as in-person interviews can focus on candidates who have skills that match the role’s requirements.

improve candidate matching

AI can also assess a new candidate’s potential with predictive analytics. By comparing candidate resumes and skills assessments to the historical patterns of your top performers, AI can spot subtle cues that indicate whether or not the new candidate will be a good fit.

Skills inference engines can aggregate outcomes based on a list of identified skills – but rather than relying on individual keywords, they can make inferences about skills based on similar language and related outcomes. For example, a skills inference engine could analyze a behavioral response to a question about conflict resolution and make inferences about conflict resolution and emotional intelligence skills.

When it’s used for screening resumes, a skills inference engine can help expand the talent pool and reduce bias in early-stage screening. When paired with assessments, it can help pinpoint which candidates could help cover skills gaps in your workforce.

offer timely, informative candidate feedback

Feedback on interview assessments can offer a lot of value for both employers and candidates. It can help keep lines of communication open with candidates who just aren’t a good fit for now – and help candidates understand how they can improve.

Most employers don’t have time to offer feedback on screening assessments, but Unilever’s unique AI implementation helps deliver valuable feedback to each candidate. Their screening tool includes gamified assessments for each candidate, with automated feedback on candidate results and how they could improve to be successful in the future.

In an interview with Bernard Marr & Co., Unilever’s Chief of HR Leena Nair said, “It’s an example of artificial intelligence allowing us to be more human.”

AI for personalized upskilling strategies

72% of employees say that learning and development opportunities are important at work, yet upskilling strategies can be difficult to implement. The more diverse skill sets are, the more likely it is that employees will benefit from personalized training rather than one-size-fits-all training initiatives. Fortunately, AI can help.

enable faster, more efficient upskilling strategies

Used internally, AI-powered skill assessments do more than just audit capabilities – they can empower employees with personalized career paths that align individual goals with organizational needs. From there, it can offer adaptive learning that adjusts content delivery based on employee progress.

For example, PwC’s Digital Fitness solution is a mobile-friendly app that offers digital skills assessments across the organization. Once employees complete the assessments, the app recommends short learning modules in a variety of formats tailored to employee skills gaps. This helps ensure employees aren’t spending time on internal training that covers something they already know.

AI-enabled training can cover a wide range of topics, from management skills to coding skills. This gives employees a tangible way to achieve their career goals that’s tailored to fit their learning style – and the flexibility to choose when they’re ready to take the next step.

support continuous employee development

Meaningful feedback helps support employees to do their best work – but annual performance reviews can be ineffective and time-consuming. HBR reports that an estimated 70% of multinational companies are moving towards continuous feedback to help workforces stay agile.

AI assessment tools can help give employees real-time feedback that supports them to do specific tasks. For example, it can suggest resources for a project, more efficient workflows, or spot mistakes early on in project development. It can also help employees adopt a mindset of continuous learning and growth as they spot skills gaps they can work on.

Progress dashboards can help boost employee motivation by making growth tangible. When learning is a constant, visible journey, each step is an accomplishment that can boost employee confidence to keep going.

ethical considerations for AI-enabled hiring and upskilling.

With any AI implementation, it’s important to remember that AI tools are only as unbiased as the data they’re trained on. If historical hiring patterns or workforce data reflect systemic inequalities, AI risks perpetuating these biases at scale.

For example, AI training is the most sought-after learning and development opportunity for Baby Boomers in the workforce – yet more Gen Z workers have received AI training. Left unchecked, AI upskilling systems could prioritize recommendations based on historical participation patterns and route fewer AI training opportunities to older employees.

Some important strategies to mitigate AI risk include:

• Transparency, including auditing AI outputs for demographic disparities
• Context-aware design to train models on diverse datasets that include nontraditional career paths
• Human oversight that positions AI as a tool, not a final arbiter

Some regulatory frameworks like the EU’s AI Act now mandate accessibility, requiring companies to disclose AI use in hiring and prove that the systems they use don’t discriminate. Ultimately, ethical AI is a commitment to continuous improvement – as Nair at Unilever notes, the goal is to “be more human,” which relies on systems that amplify equity.

AI and the future of skills based talent strategies

The shift to skills based hiring and development isn’t just a trend. When adaptability defines the competitiveness of workforces, it’s a crucial development in talent management. AI tools can help unlock unprecedented precision in skills-based hiring and upskilling to create more future-proof recruitment and talent management strategies at scale.

AI doesn’t just help forward-thinking organizations future-proof their workforces. It also helps them redefine what career paths look like in a world where skills, not pedigrees, drive progress. Consult our expert talent advisors to learn more about how skills-focused approaches are changing the world of work.