You probably collected more performance data than ever this year. But did it help your people improve?
In this best-of episode of People Fundamentals, we revisit the standout insights from a year of conversations across the People Fundamentals podcast and the Betterworks EmpowerHR virtual summit.
The podcast season focused on data, but the themes that surfaced weren’t about dashboards or algorithms. They were about leaders who refused to wait for annual reviews to catch problems, the changes that occur when you pair metrics with the stories behind them, and why protecting time for managers to coach matters even more when AI has everyone feeling pressured to move faster.
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Make performance ratings a checkpoint, not a conclusion
Performance ratings can be useful at an organizational level, helping standardize decisions around compensation and talent planning.
The problem is when the rating becomes the destination at the individual level. If the conversation ends at the number, you’ve captured a moment in time without influencing what happens next.
The driver of improvement is what happens before the performance cycle closes: clear expectations, timely feedback, and manager-led coaching that shows up while priorities are shifting and outcomes are still flexible.
That’s why LivePerson redesigned performance around ongoing conversations, not end-of-year wrap-ups. Deanna LaPierre described moving away from a process where people simply completed and toward one that supports real growth: “We shifted from a process that felt like, hard to say this, an obligation to one that enables employees and managers to engage in meaningful discussions and foster real connections and focus on outcomes.”
Nick Haap at KeHE Distributors hit the same wall. When he looked closely at what their performance process actually produced, it wasn’t growth, but rather a number to justify pay decisions. Employees felt that, too. “People didn't look forward to it,” he said.
So he stopped treating the rating as the goal and started building the habits that strengthen performance all year: regular manager conversations on what matters most, clear alignment on next steps, and a process that feels like support, not a deadline. “That's what … set me down this path of transitioning from traditional performance management to performance enablement, which … is really geared towards unleashing individual potential.”
Numbers don’t tell you why
Performance and engagement data can tell you what’s happening, but it won’t tell you how work feels or the factors driving the trend. That’s where leaders get into trouble. When you act on signals without context, your next move can land as disconnected or unfair, even if the metric is technically correct.
Annie Cosgrove from Density sees the same dynamic in workplace analytics. Utilization data can reveal patterns — when space fills up, where traffic concentrates, which days peak — but it can’t explain the experience behind those patterns. “Knowing how much your space is used and when and for how long, and all of the good stuff that we can get from devices like sensors in the workplace, it can tell you a lot about how it's going, but it doesn't give you the whole story.”
The only way to get that fuller picture is by pairing quantitative signals with real employee perspectives, so you’re solving the right problem, not just reacting to the most dramatic-looking chart. “What's really important is pairing that sensor data … with qualitative insights, actually talking to your employees, figuring out the why.”
Don’t let AI amplify burnout
AI showed up in nearly every conversation, often framed as a way to move faster, do more, and elevate efficiency. But does that lead to better performance?
Not necessarily, notes Jeff Jolton, leader of Data and Insights in Spencer Stuart’s Leadership Advisory Services. For him, the difference comes down to whether we're using AI to enrich jobs or just enlarge them.
Enlargement means more tasks — more calls, more reports, more of what people already do. It burns people out. Enrichment adds autonomy, variety, and a sense of ownership. “When we tie a job to the vision, to the strategy, when we give people a sense of end-to-end ownership, that's when we start enriching the job and giving them something new to work on, something challenging, something to build on. Then, it's more exciting.”
Margaret Keane, CEO of Cisive, says for her the question isn't whether AI can cut costs. It’s about whether it delivers better outcomes. "If we're delivering a better product, a better solution, a faster solution for our partners, it's a win for them, which means it's a win for us," Margaret says. “That human element has got to be held high."
Trust is built in what you do next
Performance data can’t build trust on its own. People trust systems when they understand the purpose behind the data, see how it’s used, and watch leaders act on what it reveals. Without that transparency, participation becomes cautious, feedback gets filtered, and the system becomes a box-checking exercise.
Syed Ali Abbas from HelloFresh put it bluntly: Organizations often rush into major system rollouts with a financially focused use case before they’ve done the design work that makes adoption possible. “A lot of transformations fail because not enough attention is paid to the design stage. The ‘why are we doing this,’ ‘what exactly do we need to do,’ and what results do we want to achieve?’”
That’s why closing the feedback loop matters. Employees and managers trust what they can observe: what they heard, see changed, and what happens next. When input leads to visible action, adoption becomes real, and data starts driving better outcomes instead of just tracking activity.
The challenge isn’t collecting more data. It’s using data with intention and humanity so that it actually helps people grow.
People in This Episode
Nick Haap: LinkedIn
Deanna LaPierre: LinkedIn
Margaret Keane: LinkedIn
Annie Cosgrove: LinkedIn
Jeff Jolton: LinkedIn
Syed Ali Abbas: LinkedIn
