As HR technology evolves to be more user-driven and empowerment-focused, artificial intelligence (AI) and machine learning (ML) tools have become an innovative way to help leaders become more self-aware and to build better workplace relationships. As a matter of fact, according to Gartner, 17% of organizations currently use AI-based solutions in their HR departments and that number is expected to grow to 30% by 2022, with one analyst stating that the improvement in data-based decision-making, employee experience and cost savings due to these solutions is “remarkable.” Understanding the potential of these tools and positioning them properly within an organization can present a variety of opportunities. But to make that a reality, HR teams often must work to deconstruct preconceived notions around AI/ML, help users understand the value of these tools, and strategically integrate them into their existing people strategies.
Let’s explore some of the common questions related to the convergence of AI/ML and HR. What opportunities does this technology present for leadership empowerment? How do you communicate the value to your leaders? How can your team work to evaluate and assuage fears around data privacy? How do you set achievable, relevant goals for this technology? And, what are some best practices for rolling this out internally?
Questions and Opportunities for HR and the C-Suite
HR leaders always have plenty of amazing questions about the power of AI/ML leadership tools. For example:
- “How does this fit into our existing people strategy?” Forward-looking HR organizations often have a number of existing tools and programs in place, such as engagement surveys and performance management systems. They want to know how AI/ML platforms can complement and enhance these existing processes.
- “How does this work?” HR stakeholders may need help understanding how a tool works without going too deep into technical details.
- “How can we facilitate the rollout of this and support our users while they use it?” HR leaders want to know how much work they will need to put in to get their users using this tool. While engagement surveys usually require a lot of work from HR, AI/ML tools often require less.
These questions present an excellent segue into looking at the key opportunities AI/ML technology offers HR stakeholders. For example, it complements other HR efforts. AI/ML offerings can provide unique information, but often that information complements rather than directly replaces HR personnel or existing processes. It also scales easily. AI/ML-based tools can scale highly personalized experiences to a large number of employees at a fraction of the cost of more traditional high-touch HR offerings (like face-to-face coaching and training sessions). Your organization can do more with less. Furthermore, it can enhance HR’s ability to create organizational change. AI/ML platforms not only empower HR to make data-driven decisions, they are also increasingly offered directly to employees to create change at all levels of the organization.
Setting Goals & Deployment Best Practices
There are three crucial components to deploying an AI/ML tool successfully:
- setting goals,
- being transparent in communicating the tool to your employees, and
- including human touches in the onboarding process.
At Cultivate, we always talk to our strategic partners about how they can use Cultivate to achieve their broader personnel goals. These goals may include facilitating better remote work, improving manager trust, improving employee engagement and decreasing attrition. I suggest having these conversations early and often to ensure that you are setting goals that are achievable, appropriate for the technology solution you have selected, and in-line with your broader organizational priorities.
The second key component is transparency. AI/ML technology can be difficult to understand, and some users may have incorrect assumptions about how it works. Being as transparent as possible about what data the tool looks at, who has access to that data, why your organization is using it, and what the overall goals can help users feel more comfortable. Give users the agency not to participate if they aren’t comfortable, while educating them on benefits to opting-in.
Finally, including human touches can help to prevent misunderstandings and make users feel more comfortable. Create spaces in the onboarding process for users to ask questions. Webinars or in-person trainings can work well here. Also, be sure to provide education at an appropriate level for users that may not be familiar with AI/ML. Helping them understand the tool is also a helpful human touch.
Know How to Talk About AI
How you position an AI/ML tool in conversations with employees and other stakeholders can have a major impact on the success of your deployment. Be conscientious about word choice to help avoid misconceptions. For example, phrases like “tracking” or even “analyzing” can feed into negative assumptions about AI/ML. Emphasize that AI/ML can find and predict patterns and insights more efficiently, and often more accurately than a human; this helps employees and HR do their jobs better, not do their jobs for them. Put differently, AI can be an excellent partner to help employees and your organization win in the future of work. Making sure the tool is opt-in and designed to benefit employees directly (rather than just HR or the C-Suite) can help to underscore this message.
AI/ML technology has tremendous potential to scale and enhance the role of HR. It’s incredibly scalable and can complement existing HR strategies and tools well. If you follow these best practices for deployment and onboarding, you’ll be putting your users in the best position to get valuable insight from your newest AI/ML investment. And when more employees can access more information, or be offered more resources that were previously out of budget, the entire organization will benefit.
Margaret Tomaszczuk is the Head of Product and Customer Experience, partnering with Fortune 500 enterprises to scale leadership development globally. She’s been focused on building AI products and is passionate about promoting interdisciplinary thought in technology and AI, and ethical AI design.