Why the AI performance divide is widening, and what to do about it, with Prodoscore’s Sam Naficy

Sam Naficy is the CEO of Prodoscore, an employee productivity monitoring software that helps companies understand how work actually gets done across their teams. He’s set to speak at HR Brew’s upcoming summit, Talent 2030 Collective: Recruit, Retain, Repeat, on April 21 about how HR leaders can use people analytics to make sharper talent decisions and build more intentional workforce strategies. Before then, we had a chance to pick his brain about why the AI performance divide is already widening, and what today’s smartest organizations are doing about it.

The following has been edited for length and clarity.

If you zoom out, what’s the biggest shift happening in the talent landscape right now that HR leaders can’t afford to ignore?

The biggest shift is that AI is no longer just a productivity tool; it is a talent differentiator. We are seeing a clear performance gap between employees who use AI consistently and those who do not, and it is widening over time.

What is especially important is that AI is not just increasing output, it is making performance more consistent. Employees using AI are about 19% more productive on average, and those using it regularly can see gains of up to 32%. But what matters just as much is that AI is making performance more consistent. These employees show significantly less month-to-month variation, which is changing how organizations define strong performance.

At the same time, leaders are being pushed to rethink how work actually gets done. The shift now is toward more flexible, data-informed ways of working that reflect reality rather than assumptions.

Where do you see organizations falling short today when it comes to hiring, development, or retention?

A lot of organizations are still making talent decisions based on incomplete signals. They rely on job titles, performance reviews, or gut instinct instead of understanding how people are actually working day to day.

The issue is that disengagement is not sudden. It shows up gradually in behavior like reduced collaboration, inconsistent activity, lack of manager one-on-ones or stepping away from core tools. Without visibility into those patterns, companies miss the opportunity to step in early.

That also means they are not recognizing what strong, consistent performance actually looks like in a modern environment, especially as AI starts to change how work gets done and who is pulling ahead.

AI is showing up everywhere in the employee journey right now. Where do you think it’s actually adding value, and where is it being overhyped or misused?

AI is adding real value in how work gets done every day. Employees who use it consistently are not only more productive, they are also more consistent and predictable in their performance, with about 31% less variation month to month.

It also gives people back time. On average, AI adds close to an hour of productive work per day, which allows employees to focus more on higher-value tasks.

It gets overhyped when companies try to use it to replace human judgment. AI is only as strong as the data behind it. The real opportunity is using it to surface better insights so leaders can make smarter decisions.

What’s one thing HR leaders can start doing differently tomorrow to build stronger, more resilient teams?

Focus on consistency, not just output.

When you start looking at patterns in how people work, like collaboration, communication, and tool usage, you get a much clearer picture of performance over time. That makes it easier to spot early signs of burnout, disengagement, or even high performers who consistently deliver but are not always visible.

Leaders who use that insight to step in early and support their teams are the ones building more resilient organizations.

When you think about talent in 2030, what do you hope organizations have finally figured out?

I hope organizations move beyond measuring productivity as a simple output.

By 2030, the focus should be on understanding how people perform over time. That includes consistency, adaptability, and their contributions across teams. We are already starting to see that shift, with AI creating a measurable and growing performance divide across the workforce.

At the same time, leadership itself is evolving. Managers will need to lead both people and AI systems, balancing automation with accountability, trust, and performance.

The companies that win will be the ones that recognize that early and help their people adapt and keep up.