The client receives two deliverables…
Both solve the problem they were hired to solve. Both are accurate and useful, and lead to the same business results. The client is happy with the work and does not see a noticeable difference in the results.
Then they find out that one delivery took 20 hours to create and the other takes 20 minutes. Now the questions start to arise:
- Was AI involved?
- Should faster delivery cost less?
- Is the graduate somehow less skilled because he found a way to work more efficiently?
Interestingly, most of us have completely different reactions to AI depending on which side of the profession we sit on. We love using AI to save ourselves time, but many are uncomfortable when they become customers and discover that AI was used to create something they bought.
I recently conducted a LinkedIn survey asking a simple question: If the result is good, do we really care how it’s done?
The answers emphasized something I had been thinking about for a long time. The biggest objections people have to AI tend to have little to do with quality.
It’s a time counter to a false value
I think part of the discomfort comes from the fact that we’ve spent decades associating value with effort.
- The long hours felt important.
- Working quickly sounds suspicious.
- Struggling feels like a skill.
The harder something seems, the easier it is to justify its price.
The story is about a ship’s engine that stopped working. After many attempts to fix it, the owners brought in an engineer with decades of experience. He checked the engine, tapped it once with a small hammer, and the machine roared back to life.
His invoice was $10,000.
The owners were furious and wanted the building written off.
Answer:
- Hammer tapping: $2
- Knowing where to touch: $9,998
The debate as to whether this is a true story or just a happy myth that people like me use to justify value based values. Whether the story is true is almost irrelevant. The course does.
People don’t pay at the tap. They pay for the technology behind it.
That’s what makes AI such an interesting topic. It forces us to face a question that many of us have avoided for years:
- Are we paying for technology or virtual effort?
Those are not always the same.
An important objection
To be clear, not all AI arguments are bad. I certainly had no problem sharing my opinion.
In fact, I think some of the strongest arguments against AI have very little to do with how quickly something is created.
These are all valid concerns, and thankfully none of these concerns have much to do with how long it took to create the deliverables.
It is a question of trust.
- Can your output be trusted?
- Can the recommendation be protected?
- Would someone stand up confidently after work if asked six months from now?
Because if something goes wrong, no one blames the AI. The employee is responsible. The consultant is responsible. The company is responsible.
That’s why I’ve always found the quality debate to be the most interesting part of the conversation. The most important question is not whether AI was involved. That the result is reliable enough for someone to put their name behind it.
Evaluation of the effect
The more I think about AI, the less interested I become in what it was used for.
Instead, I find myself asking a different set of questions.
- Was the result accurate?
- Was it helpful?
- Was it better than the other?
- Would you be willing to stand behind it with your name, reputation, and credentials on the line?
If the answer to all of this is yes, should we really care about how it is produced?
I suspect that’s where a lot of people are uncomfortable because it takes the talk away from the tools and back to the results.
The irony is that this is also where people become more important, not less.
The future is not machines compared to humans (I know, the movies “The Terminator” and “I, Robot” will not be the same). It’s people who use AI versus people who don’t. The premium will not come from refusing to use AI. It will come from judgment, taste, decision making, communication and accountability.
AI can accelerate execution, but humans still decide what to build, what to publish, and what risks are acceptable. More importantly, people are still responsible for the outcome.
The people who are defeated by AI will not be the ones who use it. They will be the ones evaluating the effort while everyone else is measuring the results
This post originally appeared on the author’s website and is republished here with permission.
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