When Answers Become Cheap, What Becomes Valuable?

As a parent, I sometimes wonder what kind of world we are preparing our children for.

For several generations, a university degree, a good office job and a respectable distance from manual labour were widely accepted signs of success. It wasn’t a foolish idea. I’m just not convinced it will work quite the same way for the next generation.

I find myself asking a similar question about organizations. Companies are investing heavily in artificial intelligence, reconsidering the skills they need and trying to anticipate which roles will change or disappear. There is understandable urgency behind these discussions, although I sometimes wonder whether we are still looking at the future through categories inherited from the past.

Take the mechanic.

When I was growing up, a car workshop was a place of grease, dismantled engines and tools scattered across workbenches. A good mechanic could recognize a problem by listening to an engine, noticing an unusual vibration or remembering something similar from a repair years earlier.

Today, many workshops look remarkably different. Diagnostic software, sensors, electronic systems and specialized equipment have changed the profession. A mechanic may spend considerable time interpreting information before picking up a tool.

And yet, we still need mechanics.

A diagnostic system can identify a fault, but an experienced mechanic may recognize that the fault is merely a symptom of something happening elsewhere. The diagnosis can be correct. The component may genuinely be failing. Replacing it might still clear the warning light without solving the underlying problem.

What interests me is what happens after that discovery.

Does the workshop record what the mechanic learned? Does someone review the diagnostic procedure? Will another mechanic recognize the same problem when it appears six months later, or will the investigation begin all over again?

The technology helps identify the problem. Experience helps interpret it. But the workshop becomes more capable only when what was learned changes how the work is done.

I have encountered variations of this situation throughout my career, and the hardest ones rarely involve wrong answers. They involve correct ones.

In one organization I found thirteen different sources for postal codes, data that should have had one. Each had been built by capable people to solve a real problem, and each of them worked. Every team saw postal codes through its own need, not as one piece of data that differed only in how it was used. So every team built its own. The organization had paid for the same answer thirteen times, and every answer was right.

Organizations are full of knowledgeable people who understand their work and often know what needs to change. Yet sometimes responsibilities overlap so extensively that nobody knows who can make a decision. Sometimes a process works perfectly well within one department while creating difficulties for another. Sometimes people use the same number but attach different meanings to it, without realizing that they disagree.

These situations are rarely explained by a lack of intelligence or effort. The people involved may be excellent at their jobs. The difficulty lies in how their work connects, how decisions are made and what happens to knowledge once it has been acquired.

This is where artificial intelligence can change something real.

We are developing tools that can analyze enormous amounts of information, identify relationships, recover knowledge, support decisions and make sophisticated capabilities available to people who previously had limited access to them.

Imagine a mechanic who can consult not only a manufacturer’s technical documentation but also the accumulated experience of hundreds of colleagues, including unusual failures and the solutions that eventually worked. Or an organization able to recognize that a problem appearing in one department is closely related to something another team encountered months earlier.

AI can help make those connections. It can reduce the effort required to find relevant information and allow people to spend more time understanding what it means and deciding what to do.

But making knowledge available is only part of the challenge.

Someone must still have the responsibility to act on what is discovered. Processes need to allow that action to happen. People need to understand how their decisions affect others. And the organization needs a way to learn from the results, including when an apparently sensible decision turns out to be wrong.

Without those conditions, even valuable insights can disappear into another report, another meeting or another system that nobody quite knows how to use.

More answers can also make an older difficulty harder to see. Organizations can spend enormous effort answering questions that nobody has stopped to question. A correct answer to the wrong question is still the wrong answer. It is only more convincing.

This is why I wonder whether philosophy, of all disciplines, might enjoy an unexpected moment of relevance. Philosophers have spent centuries examining assumptions, questioning familiar explanations and asking what we mean by things we believe we understand.

I don’t expect companies to start replacing their management teams with philosophers. Although, after attending certain meetings, I can see the appeal.

But there is a difference between having the information needed to answer a question and recognizing that the question itself may be wrong. And there is a further difference between recognizing it once and building an organization capable of questioning its assumptions the next time. The first is a matter of asking better questions. The second is a matter of learning: allowing what we discover to change how the organization works.

When I think about the world our children will inherit, I cannot tell them which profession to choose. I am not even certain that many of today’s professional categories will mean the same thing twenty years from now. I have a similar uncertainty about organizations investing in AI. The technology will keep changing what people can do.

When answers become cheap, what becomes valuable is recognizing when a correct answer fails to solve the real problem, and ensuring the organization does not have to discover that failure all over again.

Perhaps the question is no longer how much organizations can know, but how much better they can become because of what they know.