What is one risk or one reward of utilizing AI in HR?
To help you understand the risks and rewards associated with using AI in HR, we asked HR professionals and business leaders this question for their insights. From setting realistic expectations to considering litigation risks, there are several risks and rewards to consider when implementing AI in HR.
Here are eight risks and rewards with AI in HR:
- Set Realistic Expectations
- Increase Efficiencies
- Minimizes Repetitive Tasks
- Consider Litigation Risks
- Evaluate the Validity and Accuracy of Data
- Balance AI Insights with Expectations
- Check for Biased and Unbiased Insights
- Unlock Billions in Latent Value
Set Realistic Expectations
One of the major risks of utilizing AI in HR is thinking that it will be a magic fix when what is really needed is creating sound HR practices that mitigate bias while being strategic in nature to achieve specific people's goals. While AI can help sort through large quantities of data, it is only as good as the data that goes into it. For example, using AI to help in hiring won’t be very useful if there are not clear and detailed job descriptions tied to successful performance in those roles.
Vivian Woo, Culture Amp
Increase Efficiencies
AI has many benefits but one reward, in terms of HR and talent acquisition specifically, is that it can help to make the hiring process more efficient. AI-enabled technologies can automate routine tasks, which gives recruiters more time to spend on strategic business decisions. The level of automation AI can bring is something that shouldn’t be taken for granted, though. Automation goes hand in hand with human interaction; you can’t have one without the other. Because AI can save a lot of time in the hiring process, it means recruiters can spend more quality time with candidates further in the candidate journey, ensuring they feel engaged and excited about the opportunity ahead.
Lesley Taylor, WilsonHCG
Minimize Repetitive Tasks
Utilizing AI in HR definitely brings faster processes and results, allowing the team to minimize some repetitive tasks. On the other hand, AI is still too dependent on keywords. Therefore, those results might actually lack accuracy and reliability, and there’s always a chance of great candidates being overlooked.
Ihor Shcherbinin, DistantJob
Consider Litigation Risks
One of the most significant risks of using AI in HR is legal risk. We’ve seen a substantial increase in litigation surrounding HR algorithm bias, screening, hiring, and discrimination. Many states are already beginning to introduce legislation restricting the ways that you can use AI in HR. Predictive analytics has its merits, but you could potentially end up running into problems without careful monitoring.
Andrew Greenberg, ContractRecruiter
Evaluate the Validity and Accuracy of Data
Whether we’re talking about Artificial Intelligence (AI) broadly or Machine Learning (ML) specifically, we must understand that any artifact of either is a model or approximation of something in the real world. If it’s not, then we’re not talking about AI. The error rate is never 0%. To truly eliminate bias from AI, we’d need a way to perfectly identify all of the factors (called features in AI) that describe the thing in the real world that we want to model; collect enormous amounts of error-free data to train a model; and then, with 100% accuracy, predict something. Today, despite all of the advances in AI, that is an impossible goal. Will it ever be achieved? The short answer is no. This is important because you, a buyer of HCM software that claims to be AI-driven, must be able to evaluate the claims made by vendors and challenge those claims when they are misleading or false (like bias-free AI).
Frank Ginac, TalentGuard
Balance AI Insights with Experience
AI holds tremendous promise for HR, enabling the function to understand the connection points between vast amounts of data. While the benefits of AI are real, HR professionals need to be mindful that what we learn from AI needs to be tempered by good HR judgment. As professionals, we need to balance what we learn from AI with what we know of the organization’s strategic direction, market pressures, talent challenges, and anticipated changes within the organization. It is imperative that HR professionals provide valuable context to data and AI to enable leaders to make good data-based decisions about people.
Danielle Wanderer, Perceptyx
Check for Biased and Unbiased Insights AI methodologies are the foundation for many HR software programs out there. The beauty of them is they can provide a non-biased analysis of candidates using standardized questions. However, as easy as it is to use these tools, HR professionals should still use their training in spotting errors. For example, in the same expectation of a non-biased analysis, a simple "computer glitch" can result in incorrect information, or worse, unintentional bias caused by coding mistakes. No system is perfect, and human insight will remain the best detector of error.
Michelle Jimenez, Fromer Eye Centers
Unlock Billions in Latent Value
HR requires realistic integration of the tangible Human Connectivity (i.e., EQ, Soft Skills) elements to quantifiable and generally accepted hard-skill metrics. It will be a force multiplier for work teams and organizations and unlock billions in latent value by providing real insights to real Human Connectivity measurement.
Christine Wzorek, White Label Advisors
Brett Farmiloe is the Founder and CEO – and currently CHRO - of Terkel.io, a platform where business leaders can answer questions related to their expertise and get published in articles featuring their
insights. Brett is a #SHRM22Influencers and has also been a keynote speaker at several state SHRM conferences around the topic of employee engagement.
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