AI Already Knows How We Work. That’s Exactly Why the System Needs to Change

A recent Reuters report revealed how major tech companies are increasingly tracking employee behaviour and output to train AI systems.

Beyond the immediate privacy concerns, the story highlights something bigger: we’re approaching a defining moment in how creative and knowledge work evolves. Because the moment people know they’re being observed, they change how they work. What gets captured isn’t natural behaviour. It’s performance under scrutiny. And that creates a fundamental problem, not just ethically, but operationally.

As Andy Berg, CEO at Rhapsody, puts it: “You’re not getting clean data. You’re getting performance data, and that’s a pretty expensive mistake if accuracy is the goal.”

There’s also a deeper human impact. When people feel monitored instead of trusted, decision-making changes. Risk-taking shrinks. Creativity narrows. And in industries where judgment, instinct, and taste are the real differentiators, that erosion matters.

Right now, two very different approaches to AI are emerging. One attempts to capture existing workflows in granular detail and train systems to replicate them at scale. The other asks a more important question: What if those workflows were never the right starting point in the first place?

At Rhapsody, we believe AI’s real value doesn’t come from accelerating outdated systems. It comes from redesigning them entirely. The opportunity isn’t simply to make each step faster. It’s to rethink how those steps connect. Reducing friction, removing unnecessary handoffs, and bringing decision-making closer to the work itself.

Take retouching as an example. Traditionally, AI gets layered onto the existing process. A designer sends a PSD with comments. A retoucher opens the file in Photoshop, now with AI helping complete tasks faster. The workflow remains intact. The gain is incremental. We approached it differently.

At its core, the “brief” is simply feedback on an image. So instead of improving the file exchange, we asked a more fundamental question: Why does the handoff exist at all?

The result was a shared, browser-based canvas where designers annotate directly onto the image, while retouchers, human or AI, work within the same environment in real time. No versioning. No back-and-forth. No parallel workflows trying to stay aligned. In this case, AI didn’t just speed up the process. It made large parts of the process unnecessary. That’s a fundamentally different outcome.

The same principle can be seen elsewhere. Tesla didn’t build separate systems for human drivers and Autopilot. It designed a single interface where both operate together. The future of creative operations works the same way. Not humans over here and AI over there but shared systems designed for collaboration between both. Because in creative work, output was rarely the real bottleneck. The constraints have always been time, cost, complexity, and the structures required to move from idea to execution.

AI changes that equation, but only if companies are willing to rethink the systems surrounding it. What AI still doesn’t replace is judgment.

Knowing what’s worth making. What needs refining. What carries emotional value. What deserves attention.

If anything, those decisions become even more important as the volume of possible output explodes. The current wave of employee-monitoring AI won’t be the last example of its kind. It’s simply an early signal of one possible direction for the industry. The more important question is whether that direction becomes the default.

Because the companies that thrive long term won’t be the ones that extracted the most from their people. They’ll be the ones that trusted them enough to build something better together.