The Productivity Paradox

There’s something that has been nagging me about all the recent talk around AI and the promises of increasing worker productivity: and that’s how we talk about productivity. 

Broadly speaking, current conversations around productivity are that AI will enable us to do more stuff, faster. 

We can all appreciate the value proposition of doing more in less time. Who among the knowledge workers wouldn’t love to reduce the amount of time it takes to summarize documents, circulate meeting notes, and draft emails. Create reports in less time? Amazing! 

Haven’t consultants for years built business cases around how various technology applications or IT programs will reduce the administrative burden for certain roles in order to free those roles up to do more value-added work? (I have.)

What does it mean, in a knowledge work environment, to increase productivity? We talk about productivity so often it seems like the definition should be widely understood -- like it’s a given that we all agree on what it means to be more productive.

But, as author Cal Newman explored in his book Slow Productivity, productivity means vastly different things to different people. He conducted an informal study asking thousands of knowledge workers to define what productivity means to them, and -- no surprise here -- he received thousands of definitions in return. Many of those worker-defined definitions focused on doing more. And he also discusses the tendency to merely appear to do more (like sending emails, participating in Slack, etc.), what he terms pseudo-productivity.

What’s missing here? As my former employer, Gartner pointed out in an episode of their ThinkCast podcast in July 2025, productivity is an equation -- one that comes with an input and output. So where is the discussion of the increased outputs that AI can promise us? For sure, they exist. We just need to refocus the conversation on value.

If we can create more reports, send more emails, write more detailed or accurate meeting notes, have more meetings, are we being more productive? I mean, maybe! But maybe not. 

This is why organizations need clearly defined goals and objectives; and individuals need metrics or key performance indicators that support those organizational goals & objectives defined at the outset and separate from AI-induced hype.

An AI initiative may articulate how it will increase worker productivity, but let’s make sure that productivity is focused on outputs. Or if technology mostly reduces the effort going in, let’s make sure knowledge workers know what they gain in return. Free time to achieve what, exactly? 

Because if it’s not in our organizational best interest to produce more reports, then why invest in a technology that enables us to do so?

I like to work with clients to identify meaningful outputs that move the needle on broader goals. Productivity should be grounded in defining the work that enables individuals to contribute outputs that help the organization achieve its goals and objectives.

Want to talk through organizational goals & objectives or come up with a set of metrics and KPIs that make sense for your organization and your employees, tracking what matters rather than simply tracking what’s available? Let’s chat more.