Most conversations about AI transformation ask whether the technology can do the work. We debate accuracy, cost, reliability and how quickly companies can move from pilots to production. I expect the technology to keep improving. I’ve become more interested in what happens when the answer is yes.
In Migrating Scarcity, I argued that when technology makes one thing abundant, the constraint moves somewhere else. My new book follows one of those moves into the workplace.
Consider an insurance company where AI now resolves 61 percent of claims, up from 43 percent six months earlier. Customers get answers faster, and cost per claim is down. Then a manager looks one row lower in the operating report. For the claims that still reach a person, average handling time is up 28 percent.
The employees haven’t suddenly become less productive. The work reaching them has changed. AI is taking the straightforward claims, leaving humans with a greater concentration of difficult ones. The 28 percent increase is real, but comparing it with the old number can lead management to the wrong conclusion.
There is a less obvious problem. Those easy claims were doing more than generating transactions. They were how new adjusters learned the business, how managers developed confidence in someone’s judgment, and how experienced people noticed patterns no single file revealed. Remove that work and you change how the organization develops expertise, not just what it costs to process a claim.
We’ve heard plenty about AI eliminating entry-level jobs. I’m interested in what happens to the careers that remain. We assume people will move to higher-value work, but that assumes they already have the judgment to do it. If AI removes the work that used to teach them, where does that judgment come from?
These questions became the basis for my new book, The Corner Case: Where Human Value Moves When AI Does the Easy Work. A corner case is the situation that doesn’t fit the pattern. As AI absorbs the patterns, more of the human role becomes handling the cases that don’t, and that is the work people now have fewer chances to learn.
I chose to explore the questions through a fictional insurance company called Northstar. Maya Rao runs its claims operation. Daniel Cho is a veteran adjuster whose experience has made him indispensable. Leah Bennett is earlier in her career, trying to establish herself in a business where the path that produced Daniel’s expertise is changing underneath her.
The company is succeeding at automation. Its leaders are competent, and the technology is delivering much of what was promised. But success creates decisions that are harder than the original technology choices. Maya, Daniel and Leah see those decisions differently, and each has good reasons for doing so.
I won’t say much more about what happens at Northstar. I’d rather readers encounter the choices themselves. The book combines the story with research and practical playbooks for leaders and individuals. It also makes a claim that can be tested instead of asking readers to accept a fictional company as evidence. If AI removes work that helped people learn, and nothing replaces that learning, comparable new groups should take longer to earn independent authority, perform worse on unfamiliar cases, lean harder on senior colleagues, or need more rescuing when the AI is uncertain or wrong. Much routine work teaches almost nothing, and if that is true in your organization, the effect won’t appear. The point is to look before deciding.
I come to this from two perspectives. I spend much of my professional life helping companies make these bets on AI, which means I have a commercial interest in these questions being taken seriously. I’m also the father of a daughter preparing to enter a labor market that may look very different from the one I entered. I believe strongly in the potential of this technology, but I don’t think enthusiasm should exempt us from examining what happens when it succeeds.
The Corner Case is not another attempt to predict how many jobs AI will eliminate or create. I wanted to examine something that gets much less attention: what happens to expertise, responsibility, ambition and trust when the work around which they developed starts to disappear.
I suspect executives and people earlier in their careers will read the same story quite differently. If you’ve led a transformation, I’d like to know whether Maya’s choices ring true. If you’re earlier in your career, I’d like to know whether Leah’s do. Reply and tell me where you disagree, or send this to someone on the other side of your org chart and compare notes.
The Corner Case: Where Human Value Moves When AI Does the Easy Work is now available on Amazon.
