The Generalization Trap
One desirable outcome of an AI project is that it’ll save time. But there is a trap here.
The decline of specialist support
Over the last few decades, advances in computers and software have meant that many tasks can now be done by anyone, while before, they’d be done by specialized support staff. Using a computer, anyone can
put together slides for a presentation,
fill out a complete expense report, and
book travel and accommodation for a business trip,
so why bother having graphic designers, administrative assistants or travel agents on staff?
AI tools promise to vastly expand what anyone is capable of. That means the trend of shifting more work away from specialist support roles will likely continue. But unless AI makes that work effortless, we should think twice before performing such a reassignment. Here's why.
The world’s highest-paid assistant
A brilliant professor I knew once lamented that she sometimes felt like “the world’s highest paid administrative assistant” due to the vast amount of administrative work she had to do in addition to her actual duties of supervising graduate students, teaching classes, and conducting award-winning research.
Of course, she’s perfectly capable of using her computer to file her expense reports, book conference travel, fill out this or that form, and whatever else would have been handled by her assistant had the university provided one.
But whenever she’s doing that type of work, she’s not prepping classes, coaching students, or conducting research, which is (you’d assume) what the university hired her to do.
Like universities, most organizations have a core value-creating activity undertaken by a specific type of employee: researchers, software developers, writers, etc. Other roles exist to support them, but they don’t create value themselves. You’d think that, therefore, organizations would try to maximize the amount of value-creating work done by their value-creating employees, and ruthlessly eliminate anything that distracts from that. Instead, they focus on eliminating the supporting roles by shifting the responsibility of the supporting tasks onto the value-creating employees.
Supercharged Support
Instead of using AI to eliminate support roles and have everything handled by your core employees, think about using AI to make your support roles that much more effective. I’m convinced that in most cases this makes the most economic sense. If you’re a research institute, the value you create is directly related to the amount of time your researchers can fully dedicate to research. Anything you can take off their plate that’s not research is an economic win.
The organizations that thrive with AI won't be the ones that eliminate the most roles. They'll be the ones that amplify the right ones.
