AI-based solutions offer huge benefits for HR leaders as they seek to deliver high-value services with a limited budget, but HR leaders need to understand (now rather than later) where AI matters most so they can prepare for and take advantage of this rapidly evolving range of technologies.
“HR leaders need to reimagine their HR processes and identify ways to mitigate inefficiencies and unlock opportunities for more business-value,” says Seyda Berger-Böcker, Director Analyst, Gartner. “When deciding on HR process improvement initiatives, HR leaders must make AI-based solutions a central pillar to look at.”
HR leaders who embrace AI-based solutions can drive their function’s journey toward more operational efficiency
The most promising use cases for adopting AI-based solutions are in recruiting, skills management, and learning and development (L&D). Processes in these areas involve a high volume of time-consuming tasks that are still operated by human labor, rely on unstructured data that requires significant HR capacity to analyze, and involve complex decisions that are driven primarily by human judgment or intuition and have some degree of bias.
HR leaders who don’t start to make AI-based solutions a key priority in their HR process improvement initiatives risk missing the opportunity to further automate their processes for greater efficiency, make better-informed talent management decisions and increase the candidate and employee experiences.
Recruit fast and right with AI
As digitalization shifts the balance of power away from employers and toward candidates, recruitment costs are rising (costs per hire rose 18% between 2015 and 2017, largely driven by the rising cost of acquisition). The screening of applicants has become more arduous and time-consuming.
The Gartner Recruiting Efficiency Survey found that 25% of today’s candidates apply for 10 or more jobs; the average number of applications received for a single position rose 39% between 2012 and 2018. Additionally, recruiters must now weed through larger pools of poor-fit candidates — 72% of applications are considered low- to average-quality.
AI’s effectiveness in improving candidate fit also impacts indirect hiring costs, such as training
To address these challenges — and the manual nature of the recruiting process — recruiters can leverage AI-based solutions to assist in writing job posts and reach more diverse candidates. They can also leverage AI to engage with candidates and even to augment hiring decision making by analyzing and interpreting candidates’ responses and predicting candidates’ degree of fit and performance for current vacancies and other potential roles.
Leveraging AI capabilities in recruiting can reduce cost per hire by speeding up time to hire without sacrificing quality of hire or candidate fit. And because best-fit candidates generally have a longer tenure in an enterprise, AI’s effectiveness in improving candidate fit also impacts indirect hiring costs, such as training costs and the loss of institutional knowledge.
Read more: Essential Components of HR Process Governance
Capture skills shifts at the pace of change with AI
Gartner research found that 34% of employees agree they have learned an entirely new-to-world skill in the past three years, and that 19% of the skills they learned in the past year are no longer relevant today.
Unfortunately, according to a recent Gartner survey, 65% of heads of L&D agree they have greater uncertainty today than they did three years ago about which skills employees need. The consequences are already becoming apparent: Only one in five employees has both current and future skills preparedness.
Employee skill preparedness increases when HR leaders diversify skill identification inputs and the external market offers multiple sources of data that HR functions can use to diversify and clarify the picture around shifting skill needs. However, organizations struggle to analyze and make sense of all the unstructured big datasets and to effectively leverage them for improved decision making. Still, big data provides exactly the input needed to train the algorithms behind AI solutions.
AI can be leveraged both in skill identification — analyzing the availability of and demand for skills from a diverse set of internal and external sources and according to different categories, such as geography, industry, function and role — and also in skill shift prediction, where both internal and external market data is used to capture skill shifts and predict which skills are emerging, evolving and expiring. Capturing skill shifts and building skills at the pace of change is key to future successful organizational performance and will enable HR leaders to determine workforce planning and talent supply requirements more effectively.