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

This failure of measurement is the engine of the Solow Paradox. We pour millions into new platforms—be it ERP, CRM, or now, generative and agentic AI—expecting magic. But these systems are merely amplifiers. They amplify both the good and the bad. If you feed a generative AI model chaotic, redundant, and untrustworthy content, it will become a supercharged BSer, producing confident nonsense at an unprecedented scale and speed, aka the garbage-in, gospel-out (GIGO) paradigm.

Can AI Deliver on Its Promises?

This brings me to my concluding thought, one I’ve expressed before, but it bears relentless repetition. The current agentic/generative AI boom is highly unlikely to deliver on its stratospheric promises for the average organization. The technical hurdles and, more importantly, the data readiness challenges are vastly underestimated. Massive investments will once again fail to manifest in productivity statistics.

But within this raging hype cycle lies a colossal opportunity. There should be one clear group of winners emerging from the coming disillusionment: knowledge and information managers. The AI reckoning will force a long-overdue epiphany upon executive leadership: The value of technology is not inherent; it is contingent on the quality of the information fuel you feed it. Suddenly, the unglamorous, painstaking work of data governance, content hygiene, metadata modeling, and knowledge curation won’t be seen as a cost center, but as the most critical strategic investment a company can make.

For that to happen, for KIM to finally claim its rightful place as the bedrock of digital transformation, the world will need hard and convincing metrics. It’s time to move beyond measuring clicks and start measuring competitive advantage. The paradox isn’t going to solve itself.