Is Skills-based Hiring the Future for Talent Acquisition?

The Challenges Facing TA Today

In a world with dwindling talent pools and increased competition for skilled talent, you might expect to find Talent Acquisition thriving. The reality could not be more different and recruitment has probably never before faced so many existential threats. These include:

• Intensifying reputational damage due to consistently poor candidate experiences caused by malfunctioning processes
• Failure to adapt quickly enough to automation and AI opportunities and pressure from vendors in this area trying to sell incomplete products as the finished article
• Inability to access new talent pools due to shrinking workforces and an unwillingness from managers to consider different talent pools or profiles
• In-built inequities and biases in the process
• A lack of proper science behind hiring decisions or, at least, a lack of confidence in the tools
• Ongoing cost pressures on support functions resulting in strategic TA leaders being replaced by less experienced, operational leads
• Unpredictable markets leading to stop-start and reactive hiring

So, what can be done differently to enable TA to rebuild the lost confidence and thrive once again, in a world where candidates expect a consumer-grade experience for every interaction?

The fact that hiring is as much an art as a science means that there will always be some human participation required to drive it, however AI and automation present exciting possibilities for co-piloting the recruitment process as well as solutions to many of the ongoing challenges listed above. In order to be able fully to leverage these opportunities, however, we need to find a new lens through which we can view both the roles we do and the skills and capabilities which are necessary to carry them out effectively.

Skills are likely to be the beating heart which links and drive many of our corporate functions in the future.

A Skills Based Approach

With contingent, contractual, freelance and fractional work becoming more common, we can start to see the importance of defining job roles by more empirical and "relevant" measures such as output, performance and required skills rather than traditional guidelines such as job title and years of experience. If we go further and align our business strategy to the skills needed to realise it, we can assign criticality and value to skills specific to the organization (current and future) and build our Rewards strategy around it - this is pay-for-skills. In fact, skills are likely to be the beating heart which links and driving many of our corporate functions in the future, such as TA, TM, C&B, L&D.

By placing more emphasis on the specific skills and capabilities a candidate possesses, rather than education background or previous roles they have done, say, we are able to access new talent pools of candidates (both internally and externally), find better matched individuals against the job requirements, challenge entrenched beliefs and biases and eliminate archaic pay gaps based on things like gender. Ultimately, this means making better hires and creating wider and fairer opportunities. So far, so good, however it is important to consider the not insignificant challenges to making this work.

Challenges

On the surface, the roadmap to implement a skills-based structure might look pretty simple; identify and roll out a skills taxonomy across your business, assign skills with levels/ratings to your employees and map your jobs against the skills framework. When you then need to fill a role, you can simply match best-fit candidates (both internal and external) by skills to your job role skills requirements. A talent marketplace provides internal opportunities again linked to skills. And these skills matchmaking processes can be driven proactively by AI, identifying opportunities for employees that they might not necessarily be aware they could do but for which they are well suited. Employees can view the skills roadmaps which form part of the future business strategy and plot and drive their own skills development accordingly. All that then remains is to foster a culture change towards non-linear careers, lifelong learning supported by project assignments and managers happy to promote the development of their teams and the success of the company over their own individual outputs and drivers. Simple, right? Not so.

Skills and capabilities are still extremely subjective. For instance, what are considered strong Excel skills by someone in HR or Marketing might not be strong for someone in Finance. So, from the start we have a challenge of definition and a lack of precision for the very structure that was supposed to provide this. But at least Excel skills can be measured through testing. How about soft skills (which are going to become even more critical as AI and automation subsumes hard skill tasks and functions)? Teamwork, innovation or resilience, for instance, are harder to measure and - while personality assessments exist - there is no globally accepted norm data around these. Moreover, an AI sourcing tool which sifts CVs to match skills is reliant on every 'good' candidate being canny enough to list the right skills out in their resume. Or, even more likely these days, that the generative AI tool they used was appropriately directed to do so.

There is also the challenge of maintenance. A skills taxonomy, even in a small organization, is a complex matrix of thousands of ever-changing and fluctuating ontological components. As so many talent decisions and processes would become based on this tool, it is critical that the data is maintained and updated regularly in order to remain effective. While this can be vendor supported of course, each company will need its own weightings and calibrations constantly checked and rechecked in order to keep the descriptions and requirements current and correct.

Conclusion

While skills-based hiring offers many solutions to the ongoing challenges faced by Talent Acquisition today, implementing and maintaining something this complex presents a real challenge, particularly with the limited resources each organization can deploy. Also, the lack of any global regulation or definition over skills and the ways candidates are presented means that even the most advanced AI tools would today likely be simply entrenching (different) biases into the process. In the future we will likely see more global alignment and accreditation for skills and capabilities, and I am sure we will then see the gates open and a full-blooded charge towards skills based AI supported hiring. Until then, however, the death of the recruiter is greatly exaggerated.