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StrategyAIJanuary 26, 2026

๐—ช๐—ต๐—ฎ๐˜ ๐—ถ๐—ณ ๐—บ๐—ผ๐˜€๐˜ "๐—”๐—œ" ๐—ต๐—ฒ๐—ฎ๐—ฑ๐—น๐—ถ๐—ป๐—ฒ๐˜€ ๐—ฎ๐—ฟ๐—ฒ ๐—บ๐—ฒ๐—ฎ๐—ป๐—ถ๐—ป๐—ด๐—น๐—ฒ๐˜€๐˜€?

Not wrong. Not right. Just... saying nothing at all.

On any given day, my feed serves up headlines like: "AI will eliminate 40% of white-collar jobs by 2030" ...followed immediately by... "AI adoption stalls as companies struggle to find use cases"

"AI is transforming market research!" ...right next to... "AI-generated insights lack the depth of human analysis"

๐—›๐—ผ๐˜„ ๐—ฐ๐—ฎ๐—ป ๐—ฎ๐—น๐—น ๐—ผ๐—ณ ๐˜๐—ต๐—ฒ๐˜€๐—ฒ ๐—ฏ๐—ฒ ๐˜๐—ฟ๐˜‚๐—ฒ ๐—ฎ๐˜ ๐—ผ๐—ป๐—ฐ๐—ฒ?
They can't. Unless... they're all talking about completely different things while using the same word. Try a simple experiment: take any headline about "AI" and replace it with "software" or "computers."

"Companies are adopting computers at record pace"
"Software will eliminate 40% of jobs"
"Is your company ready for computers?"

Suddenly, the statement becomes meaningless. Because of course companies use computers. Of course software changes how we work. The statement tells you nothing about which software, for what purpose, with what impact.

"AI" has become a container term so broad it's analytically useless. It lumps together:

- A chatbot answering FAQs
- An algorithm detecting credit card fraud
- A recommendation engine suggesting products
- A generative model writing marketing copy
- A computer vision system inspecting factory parts

These have about as much in common as a calculator and a video game... both software, but knowing that tells you nothing useful. No wonder the headlines contradict each other. They're not actually about the same thing.

๐—ง๐—ต๐—ฒ ๐˜ƒ๐—ฎ๐—ด๐˜‚๐—ฒ๐—ป๐—ฒ๐˜€๐˜€ ๐—ถ๐˜€๐—ป'๐˜ ๐—ฎ๐—ฐ๐—ฐ๐—ถ๐—ฑ๐—ฒ๐—ป๐˜๐—ฎ๐—น. "AI" sounds transformative and inevitable. Specificity invites scrutiny. And scrutiny reveals that the answer is almost always: IT DEPENDS.

It depends on which technology. Which tasks. Which implementation. What quality controls. What human oversight.

The real questions worth asking are specific: Which tasks can large language models perform reliably in your context? Where does generative AI add value versus introduce risk? What quality controls does automated analysis require in your workflow?

Next time you see contradictory "AI" headlines, assume they're talking about different things and that the word "AI" is doing more to obscure than illuminate.

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