How AI and empathy will redefine HR
We live in a data-saturated world. In fact, we’re generating data at such a rapid pace that around 90 percent of the world's data has been created in just the last two years. But the real power comes not from just collecting data, but understanding it—something I think about a lot as a Chief People Officer who also happens to work at a tech company.
Now, you can answer questions about everything from diversity and inclusion to employee engagement with the same scientific accuracy that business strategists use to analyze sales data. Many HR organizations have adopted the same enthusiasm for cutting-edge technologies as their peers in data science. You just have to be smart about the way you use this new technology and mindful of the places it falls short.
So how do software platforms, “artificial intelligence” and “big data” fit into HR—a field that, at its core, is defined by its deep emphasis on humans? The answer lies in combining the power of quantitative data with the authentic, qualitative information that is the nuance of HR decision-making.
What can AI do for HR?
Even the most human-centric aspects of HR can gain power from technology and from taking a scientific approach to measurement. In my recent guide, How to measure Diversity and Inclusion for a stronger workplace, I wrote about how simple, automated survey programs can give HR pros tangible insights about everything from who is interested in joining a mentorship program to whether female employees feel they’re compensated fairly.
In the process of writing the guide, I came across Charu Sharma, CEO of a company called NextPlay.ai, who developed an app that pairs employees with mentors within their organization based on their goals. The app creates a mentorship program for participants, including setting up meeting times and recommending conversation prompts.
NextPlay.ai gets at some of the core values that AI can offer: automation and categorization. Mentors and mentees don’t have to manually find times that work for them, or remember to keep up a cadence of meetings. Leaders don’t have to figure out who should mentor whom. These are simple, straightforward problems, and AI offers a clear solution.
A few more examples: Let’s say you want to send an automatic follow-up email to employees who left recently, or test whether certain job listings perform better than others, or even generate a list of potential employees with a specific skill set. There are solutions for all of those, many of which incorporate AI and machine-learning technology.
What can AI not do for HR?
Now, imagine you want to learn more about why employees are leaving. Data models can give you an accurate understanding of who left, general reasons why and potentially where they went, but they can’t provide a comprehensive understanding of their employee experience. How was their relationship with their boss? How was the integration of work and life? Did they feel like they belonged?
Questions about what people love about your culture, what they’re craving, and why they might choose to leave aren’t easy to determine with an algorithm. They’re textured, and often complicated, and require a human touch.
AI can have another, more insidious downside too: it can programmatize human biases. Algorithms that are taught to look for similarities in successful hires end up amplifying racial and gender biases by targeting candidates that conform to the majority. Facial recognition technology has been widely criticized for problems with racial and gender inequality—with some software incorrectly identifying black people as criminals.
As MGI chairman and director James Manyika does a good job explaining the shortcomings of AI in a recent interview with McKinsey:
"There are limitations that are purely technical. Questions like, can we actually explain what the algorithm is doing? Can we interpret why it’s making the choices and the outcomes and predictions that it’s making? Then you’ve also got a set of practical limitations. Questions like, is the data actually available? Is it labeled? But I’d also add a third limitation. These are limitations that you might call limitations in use. These are what lead you to questions around, how transparent are the algorithms? Is there any bias in the data? Is there any bias in the way the data was collected?"
In other words, sometimes AI can be a black box—and it’s always going to be limited by the mindset of the person (or company) who programmed it.
How to blend the power of algorithms with people-powered data
Often, the only time employees interact with HR folks is when they’re hired and when they leave. But if that’s your status quo, you’re missing a lot of opportunities to impact your employees' experience and your company culture. Too often, companies rely on the quantitative data to get them through, when a simple set of questions could give employees a forum to feel heard.
Earlier this year, SurveyMonkey released sentiment analysis—a feature that parses responses to open-ended questions in surveys and identifies them as positive or negative, so that users don’t have to read through and sort every one. We also have a word cloud feature that pulls out the most commonly-used words and phrases in those open-ended responses.
Features like these offer a high-level perspective of trends within the data, and also give users the power to dig in deeper by reading individual responses. That’s the most powerful type of AI for HR pros—solutions that let us do our jobs effectively and efficiently, but with full transparency into what’s happening. AI can tell us at a glance that comments are positive or negative, but it takes an empathetic human to dig in and understand why.
The World Economic Forum recently projected that 75 million current jobs will be displaced as a result of artificial intelligence—and that 133 million will be created as AI creates demand for technical skills and emotional intelligence to manage it. Artificial intelligence is most powerful when it’s tempered with real human emotion. In HR, this usually means starting conversations.
The automation aspect of AI gives us the opportunity to keep up a cycle of continuous learning by sharing surveys and other employee engagement materials in regular pulses. From there, it’s our job to approach that feedback with empathy.
In HR, AI can help us move fast, but human-centric technology will (hopefully) keep us from breaking things.
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