Leverage Artificial Intelligence in HR Processes Where It Matters Most

Artificial intelligence (AI) offers huge potential to boost HR process performance by improving operational efficiency, decision quality, and candidate and employee experiences.

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.

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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.

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.

Personalize the learning experience with AI

Providing the right learning experience continues to be a priority for HR functions when implementing AI. Gartner research reveals that L&D functions have increased spending by 16% in the past three years to keep pace with the needs of today’s digital learners.

And yet, these investments don’t seem to yield the desired results. According to Gartner research, only 37% of learning experienced in the past six months is applied on the job, and 60% of employees state that the learning they receive is irrelevant to their needs.

Rather than focusing on self-service learning platforms, organizations need to provide employees with more personalized learning experiences. AI-supported L&D enables this by leveraging data on an employee’s learning preferences as well as external skill trends to proactively recommend learning that addresses current development areas and future development needs.

AI can be leveraged to identify gaps in learning offerings by tracking employees’ learning successes

AI-based solutions help personalize the learning experience by recommending and assigning learning to each employee based on data — role, past completions, interaction with different kinds of learning content and formats, current skill needs and future career aspirations. In addition, AI can be leveraged to identify gaps in learning offerings by tracking employees’ learning successes, allowing for more targeted improvements in content and format.

As more employee data is collected and more employee learning interactions occur, AI-supported L&D will become smarter, providing better recommendations and ultimately making L&D more efficient and its outcomes more effective.

Only time will show the full potential of AI, but HR leaders who embrace AI-based solutions can drive their function’s journey toward more operational efficiency, better talent decision making, and improved employee and candidate experiences.

This article is based on insights that are part of an in-depth collection of research, tools, templates and advice available to Gartner clients.

 

Gartner for HR clients can read Leveraging Artificial Intelligence in HR Processes Where It Matters Most.

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