HR Must Build on These Skills to Leverage AI Use Cases

Published: September 19, 2023 | by Natasha K. A. Wiebusch, Marketing Content Manager at Brightmine

The future of artificial intelligence (AI) in HR is bright, but HR isn’t ready — at least not yet.

According to a Paychex survey, over 75% of HR leaders are preparing to implement artificial intelligence (AI) in the next 12 months. And 65% of HR leaders believe AI will have a positive impact on HR over the next two years.

However, in August, a study by Mercer found that no leaders would invest in AI financially in a significant or moderate way this year. Only 6% plan to invest in 2024, and almost half were unsure about their plans through the following year.

So why won’t leaders commit? They’re still learning about their new HR partner and exploring use cases. To be fully prepared, HR leaders must not only identify the best use cases, but they must also ensure their teams have the necessary skills to leverage those use cases.

Demystifying AI

Before skill-building, however, leaders must ensure that their team understands what AI software is. With a solid understanding of AI technology, HR teams will be better prepared to select the best AI use cases.

Generally, an AI software program is a program that can mimic human decision-making by performing cognitive functions, such as reasoning, learning or problem solving. Today, what we have is called artificial narrow intelligence (ANI), which is a program (or machine) designed to complete a specific task.

To build AI-powered software, companies use analytics. However, not every software actually achieves AI. “It’s an all bourbon is whiskey but not all whisky is bourbon situation,” explained Erica G. Wilson, an attorney at Fisher Phillips and the Vice Chair of the firm’s Artificial Intelligence Team, “All AI is powered by analytics, but not all software that uses analytics is necessarily AI.”

The difference between analytics and AI is in their capabilities. For example, while analytics can flag inconsistencies in data, AI can understand what those inconsistencies mean, take (or recommend) remedial action and learn from the data provided.

AI use cases

According to Brightmine Senior Global Analyst, HR Strategy and Insights Laci Loew, “AI is impacting the future of work, and HR and business leaders need to make big strategic shifts to keep up.”

Specifically, AI is poised to free employees from the mundane, transactional tasks that have historically crowded their schedules. HR leaders, in turn, will need to redirect employee efforts and time towards higher value work. “To succeed,” adds Loew, “leaders should get comfortable with AI augmenting — not replacing — humans and ditch legacy top-down ways of leading; embrace co-creating work and AI workflow opportunities with employees; and pivot the approach of how productivity is measured from quantifying employee output to evaluating human performance.”

Implementing this level of change can be daunting, as leaders need to begin prioritizing what employee work should change first. They can get started by selecting the top use cases for the HR team. Some top use cases to consider include, but are not limited to:

• Analyzing and grading candidate interviews.
• Customizing the employee learning experience.
• Intelligent compensation recommendations and feedback.
• Learning content generation.
• Policy and document generation.
• Automating task management.
• Reviewing and parsing resumes.

When identifying use cases, in addition to considering the skills their employees have or need, leaders should consider:

• Budget.
• Current HR technology in use.
• Department structure.
• Long- and short-term strategic goals for AI.
• Teams’ current time expenditure.

Building AI skills for HR

Though identifying use cases is key to adopting AI, without the proper skills, it’s not enough. To properly leverage AI’s best use cases, HR leaders must prepare their teams by building on the following skills:

Data literacy

AI’s lifeblood is data. Lots of it. If an AI software solution does not have sufficient data, or if it has been trained on poor or biased data, it’s likely to make mistakes, create biases and underperform.

To use AI effectively, HR team members must be data literate to understand how the AI they’re using is ingesting, using and learning from data. They must also be able to identify potential problems with the AI’s data.

Critical thinking

Similarly, HR team members must be able to verify AI outputs, identify errors or bias, evaluate the quality of AI’s work, and abide by established safeguards for using AI. All of these tasks require astute critical thinking skills.

Systematically, team members should be able to perform or facilitate audits of the AI systems they use for potential bias and overall output quality. In some jurisdictions, AI audits are already required (e.g., New York City), and others are very likely to follow suit. Carrying out audit responsibilities will also require critical thinking skills.

Ethics

HR team members must use AI ethically. More specifically, they must be able to identify and manage ethical dilemmas that may arise in the course of using AI. Without these skills, the organization is also more likely to expose itself to liability.

Ethical use is particularly important when an organization allows AI access to personal identifying information. For example, if an AI software solution has access to employee or candidate demographic data, team members who monitor the software must understand that AI bias often targets historically underrepresented groups, why this is problematic, and how to remediate the issue. To this end, employees should also exhibit sufficient cultural awareness to identify biases.

A solid understanding of ethics and cultural awareness will also help team members guard against their own internal biases, which may be exasperated by the automated decision-making AI offers.

Agility

As has been made clear by recent developments, AI technology will change HR professions – from talent recruiters to benefits specialists — in significant ways. By partnering with AI, HR professionals will see their job duties, schedules and goals change over time.

These professionals must be able to remain agile to successfully partner and evolve with AI. When employees are agile, they’re better able to learn new skills, adapt to change and leverage creativity to solve unique or new problems.

Collaboration

One of AI’s greatest assets is its ability to increase productivity by removing the burden of low-value tasks from employees. Employees will be able to spend their new-found time on high-value tasks, which tend to require more soft skills. As a result, employees must be prepared to collaborate and communicate more with their team members and other stakeholders as they push more strategic projects forward.

For example, instead of spending time analyzing data and creating models, which will be done by AI, a workforce data analyst will spend their time discussing options with stakeholders, building consensus and thinking strategically.

Human-centrism

Ironically, in a world that is so quickly becoming automated by technology, HR must exhibit human-centric skills. Human-centrism is the practice of understanding and prioritizing people, including their individual experiences, values, feelings and needs. Human-centric skills include emotional intelligence and conflict management, among others.

In the framework of HR, human-centrism means understanding when AI should and should not be used. For example, though AI can automate all communications with prospective candidates, an HR manager may ask themselves whether it should.

HR team members must have empathy and understanding to recognize when AI, though efficient, may not achieve HR’s goals or align with the organization’s values.

Conclusion

AI will certainly make its mark on HR. Though Mercer’s study found few HR leaders willing to make big investments in AI this year, these sentiments will almost certainly change as leaders gather the knowledge they need to build a solid AI strategy. To prepare their teams, leaders can’t stop at use cases. Employees need the right skills.