AI May Not Make Your Experience Obsolete. It May Finally Make It More Valuable
Susan expected the call to last 10 minutes.
“I need another pair of eyes,” Michael said.
They had worked together years earlier, when Susan still spent her days in conference rooms reviewing marketing campaigns and trying to understand why customers didn’t always behave the way research reports said they would.
At 67, she had left corporate life behind, but she still accepted occasional consulting assignments when something sounded interesting.
Michael’s sounded simple.
His company was preparing to launch a new service. His team had used AI extensively to develop the campaign, and almost everything was finished.
“Just tell me if you see anything we’ve missed.”
Susan poured herself a cup of coffee and opened the files. Then she stopped.
Had AI Just Done Her Old Job?
The campaign was good. Not good for AI. Good.
The positioning was clear. The headlines were strong. There were customer profiles, emails, social posts and landing-page copy.
Work that once might have occupied Susan’s team for weeks had apparently taken a fraction of the time.
She looked at her coffee.
“Well,” she said to the empty room. “At least I still make this better.”
For a moment, though, the joke wasn’t particularly funny. Susan had spent 35 years learning how to do much of what she was seeing on the screen. AI had apparently spent part of Tuesday.
She called Michael. “Remind me why you’re paying me?”
He laughed. “Because I don’t trust it.”
“That makes two of us.”
But that wasn’t quite true. Susan didn’t distrust AI. She used it herself. What bothered her was harder to identify.
The campaign looked right. It simply didn’t feel right.
What Was Susan Seeing That AI Had Missed?
Experience in the age of AI may matter because experienced people sometimes recognize the question that hasn’t been asked.
Susan went back to the customer profile. The campaign targeted owners of established small businesses. Many had employees, customers, systems and routines that already worked reasonably well.
Yet the campaign kept promising them the same things: Transformation. Innovation. Growth.
Susan frowned. She had known hundreds of business owners during her career. Very few had ever arrived at work thinking, You know what I need today? More disruption.
She opened AI herself.
“What do these customers want?”
The answer came immediately: growth, innovation, efficiency and competitive advantage.
Perfectly reasonable.
Then Susan changed the question.
“What are they afraid of losing?”
The answers were different.
Control. Trusted employees. Customer relationships. Systems that already worked.
Susan lifted her coffee.
“There you are.”
What Had 35 Years Actually Taught Her?
Susan’s experience had not given her a secret marketing formula. It had taught her where to look when something didn’t make sense.
She remembered a software company convinced customers wanted more features when what they really wanted was fewer mistakes.
She remembered a financial-services client talking enthusiastically about returns while its customers worried about losing money they already had.
And she remembered an expensive campaign everyone in the conference room loved. Customers didn’t.
The situations were different. The lesson wasn’t.
People often tell you what they want. Their behavior tells you what matters.
Susan called Michael again. “I know what’s wrong with your campaign.”
“Finally.”
“You’re selling them the future.”
“Isn’t that generally the idea?”
“Not if they’re worried you’re going to break the present.”
There was silence.
“Say that again.”
Did Susan Need to Compete with AI?
No. That turned out to be the wrong competition.
Susan worked with Michael’s team for the next two days. They didn’t throw away the AI-generated material. They changed the assumption underneath it.
Instead of promising to transform the customer’s business, the campaign began talking about improving what wasn’t working without disrupting what was.
Then they went back to AI.
It researched. It summarized. It drafted. It produced 20 new headlines. Susan rejected 17. Everybody seemed happier with the new arrangement.
Something else had changed too. Susan stopped measuring herself against how quickly the technology could produce things.
Of course it was faster.
She couldn’t produce 20 headlines in seconds. She couldn’t search thousands of documents instantly either.
Why would she want to compete with that?
A more interesting question had emerged.
What could Susan see because she had spent 35 years watching real people make real decisions?
Is Experience Becoming More or Less Valuable?
AI will change work. Some tasks people spent years learning will become faster, cheaper or unnecessary. Women over 60 are not somehow exempt from that change.
There is reason for concern. AARP’s 2026 research found that 64% of adults 50+ worry about AI replacing human jobs, while familiarity with AI is nevertheless increasing.
But perhaps Susan had been confusing two things. There was the work she had learned to perform. And there was the judgment she accumulated while performing it.
AI might reduce the value of some of the first.
What if it made the second easier to see?
Late in the project, one of Michael’s younger team members asked her a question.
“How did you know the first campaign was wrong?”
Susan thought about failed launches, focus groups, brilliant ideas nobody bought and terrible ideas everybody loved.
She could have given him a long answer.
Instead, she shrugged. “I’ve met customers.”
He laughed.
Susan did too.
But she knew there was more truth in that answer than either of them had expected.
What Does Experience in the Age of AI Really Mean?
Perhaps experience in the age of AI is becoming less about having all the answers and more about recognizing which answers deserve another question.
When Michael called after the revised campaign had been tested, the response was encouraging. He wanted Susan involved through the launch.
She closed the call and looked at the AI window still open on her laptop. For years, she had thought her expertise was largely what she knew. Now she wasn’t so sure.
Maybe much of it was knowing what to notice. The machine had produced answers almost instantly. Recognizing which answers deserved to be trusted had taken Susan 35 years.
And, she would probably insist, a decent cup of coffee.
Susan’s experience raises a much bigger question. As experienced people leave the workforce just as businesses race to adopt AI, are companies losing the very judgment they need to make AI useful? I explore the emerging “experience recession” on Next Cradle.
Author’s note: The characters and events in this article are fictionalized to illustrate a common experience among women navigating change after 60. Any resemblance to actual individuals is coincidental.
Over to You:
Has AI made you question the value of something you spent years learning to do? Or has using it helped you recognize abilities that you had stopped thinking of as expertise?
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