The Productivity Paradox: Why Your AI Investment Won’t Pay Off Without KM

If you are a regular reader of this column, first of all, thank you. Secondly, you will be well aware that I have a certain fondness for IT “laws”—those unofficial, often cynical, rules of thumb that reveal more truth than any vendor’s glossy brochure. Moore’s Law on processing power is the famous one, with Amara’s Law on overestimating short-term tech impact trailing behind. But the one we should be citing constantly in today’s climate of AI euphoria is the Solow Computer Paradox, named for Robert Solow, the Nobel laureate economist who famously observed, “You can see the computer age everywhere but in the productivity statistics.”

This paradox highlights a yawning chasm that should embarrass our industry: the stark disconnects between the double-digit productivity gains promised by technology investments and the stubbornly flat, single-digit—or even negative— gains realized by the organizations that deploy the vendors technology in the real world. Something is profoundly amiss.

Now, the easy and predictable route for this column would be to launch a broadside against the hype merchants and marketing machines that fuel this cycle. But while their culpability is a given, I want to pivot to a more fundamental and, frankly, more critical issue: How do we actually measure productivity, specifically in the realms of knowledge and information management (KIM)?

At its most basic, productivity is a simple ratio: output divided by input. But in the messy, human-centric world of KIM, both factors are notoriously slippery to quantify. What is the “output” of a well-managed knowledgebase? It's not a widget. It’s the reduction in time a senior engineer spends answering the same question for the 10th time. It’s the avoidance of a regulatory fine because the correct procedure was instantly findable. It’s shaving off weeks from a new hire’s journey to competence.

The KIM profession has, of necessity, clung to a set of convenient, system-level metrics. We report on search success rates, time to find information, and content usage views. These are not worthless, but they are dangerously incomplete. They measure activity, not outcome. They tell you that your intranet is being used, but not if it’s making anyone decisively better at their job. These are the metrics of a function that is struggling to prove its value in a language the C-suite understands.

Simple Productivity Ratio

The metrics that truly matter are far more operational and strategic: a reduction in product development cycle time, a decrease in customer service handle time, an acceleration in employee onboarding, and measurable cost avoidance from prevented errors or rework. These are the numbers that move the needle for a business. Yet, they are seldom tracked in relation to KIM initiatives. Why? Because it is incredibly hard work. It requires deep collaboration with business units, meticulous before-and-after analysis, and a willingness to isolate the impact of information quality from a dozen other variables. It’s far easier to just report on intranet page views and call it a day.