May 2, 2024

The State of AI in HR, in Six Charts

The State of AI in HR, in Six Charts

More human resource departments are adopting AI in HR platforms like SkyHive for talent management–but not nearly as fast as their clients elsewhere in the enterprise are adopting AI in their work, according to the annual Stanford AI Index.

The Stanford University AI Index is the benchmark annual study for how AI is being adopted around the world. The report covers a wide range of ways this technology is changing the world, from investment and productivity to regulation and public attitudes.

Given how fast technological change is moving, it’s hard to keep up with the state of HR technology. SkyHive has selected some specific data from the Stanford index that shed light on where AI in HR stands today–and where it may be going in terms of talent management, skills-based workforce management, and employee retention.

AI in HR can cut costs and increase revenue

In a McKinsey survey cited in the report, four in 10 HR managers surveyed said AI cut their costs 10%, and most of those (26%) said it reduced costs by 20% or more. In addition, six in 10 surveyed said AI increased their revenue by 10% or more.

What strikes us at SkyHive is that other departments are seeing gains as big or even bigger. For the HR staff, that means your stakeholders elsewhere in the enterprise are going to be knocking on HR’s door asking for more workers with artificial intelligence skills. In manufacturing, for example, 55% of managers said they were seeing cost savings and 66% said they were seeing a revenue increase. So skills-based workforce planning is going to be essential if HR teams are to meet demand for AI-skilled manufacturing workers.

[Figure 4.4.7] Some 40% of HR managers said using AI in HR reduced costs, while 60% said it increased revenue.

HR teams aren’t using AI at the same rate as other departments

Overall, product development (26%), marketing and sales (25%), and service operations (24%) reported the greatest use of AI tools. In specific industries, like product development in tech, media, and telecom, the rate was much higher (44%).

By contrast, AI in HR was only reported in 9% of industries on average, with the lowest adoption in healthcare (5%) and the highest in tech (14%).

[Figure 4.4.4] Only 9% of human resource departments report using AI talent management systems. By contrast, 26% in product development groups use AI and 25% in marketing and sales.

Most AI hiring is for data engineers and machine learning engineers

Most of the hiring for AI roles is for data engineers (36%), AI data scientists (31%), and machine-learning engineers (31%). Financial services and tech companies reported the highest hiring rates for machine learning engineers.

[Figure 4.4.6] Financial services and tech companies reported the greatest appetite for artificial intelligence talent.

Where can hiring managers find AI talent? Think globally

The U.S. continues to lead the world in artificial intelligence investment and startups, but when it comes to talent acquisition, more and more global workers are including AI skills in their profiles.

A LinkedIn analysis included in the Stanford report showed U.S. workers are 2.2 times more likely than the global average to say they have AI skills. But workers in India with LinkedIn profiles are even more likely to say this (2.75 times the global average). 

[Figure 4.2.14] Workers in India, the United States, and Germany are twice as likely to claim artificial intelligence skills than the global average.

Many of these AI roles either can be done remotely or could easily migrate. The United States still attracts more AI talent than it loses, but nations like the United Arab Emirates and Switzerland attract even more workers. Israel, India, and South Korea have all been losing more AI talent than they attract. Israel and South Korea also have more AI workers to lose: when you calculate the concentration of talent, these countries have far more AI workers per capita.

[Figure 4.2.19] The United States is still a net winner in AI talent migration, but small countries like Luxembourg, Switzerland, and the UAE do even better in talent attraction.

Can AI improve HR productivity? Yes, if you use it correctly

One of the Stanford report’s main findings is that AI makes workers more productive and leads to higher quality work. There are multiple studies cited about AI’s work in transcription, legal analysis, and call centers.

But the report also cited one study by a Harvard Business School researcher that flagged a potential misstep for HR teams. 

The study asked professional recruiters to evaluate resumes and find the candidates with the best computational skills. The recruiters who used AI performed better than those who did not. But there was also a quality difference. Some recruiters were told they were using “good AI” that performed well, while others were told they were using “bad AI” that was known to make errors. Those using the “bad AI” performed better. The researcher speculated that recruiters using the “good AI” became complacent while those with the “bad AI” looked harder at the results.

[Figure 4.4.24] Recruiters using AI in a talent acquisition test performed 0.60 points better than those who didn’t. But those who used “good AI” performed 1.06 points worse than those who used “bad AI.” So the context of your talent management system matters.

Does that mean companies are better off buying buggy, second-class talent management systems than good ones? Of course not. It shouldn’t surprise anyone that if you tell people a system may make mistakes, they’ll act more cautiously. 

But this finding does mean that context matters in talent management. 

SkyHive has always argued that there is an “art and science” of skills-based workforce planning. The science isn’t just about making faster and better choices–although well-built AI should assist in that. It’s about identifying skills people didn’t even know they have, and making it possible to offer better insight into closing skill gaps. The art isn’t just sorting through a resume pile but in crafting a skills strategy that enables workers and employers to adapt to the future of work.

The participants in the Harvard Business School study were only asked to rate candidates, based on one particular skill. The study didn’t involve anything as complex as skills transformation, with a broad knowledge of the skills in demand and company goals. Nor did they have the comprehensive knowledge of the AI-powered workforce management platform that would come from a multi-stage implementation process, in partnership with the vendor. It’s those complex, high-value tasks that will bring the most benefit to companies, and demand the most from both HR staff and AI tools like SkyHive.

Skills transformation is the real value of AI in HR. If you’re ready to find out more about it, and how SkyHive can support you, find out more today.

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