The uncomfortable part isn’t that AI can write code, or draft a marketing campaign, or produce a surprisingly decent strategic plan. The uncomfortable part is that these used to be proof you were valuable, and now they’re table stakes. This has led a lot of founders to ask the wrong question: “If AI can do my job, what’s left for me?”. The better question is: Who decides what the AI should be doing in the first place?
AI is becoming remarkably good at generating options – ask it for a go-to-market strategy and you’ll get one. Ask for pricing ideas, positioning, hiring plans or software architecture and you’ll probably get something usable. Recent research even suggests large language models can generate and evaluate strategic alternatives at levels comparable to entrepreneurs in structured settings.
But businesses don’t fail because they couldn’t think of enough options – they fail because they picked the wrong one. Every company has constraints AI doesn’t fully understand. A founder who’s burning through cash should make different decisions than one preparing for acquisition. A company protecting a premium brand shouldn’t use the same marketing strategy as one chasing volume. The decision isn’t simply “Which idea is best?”, it’s “Which trade-off makes sense for us?”
The same thing is happening in software. AI can write thousands of lines of code in minutes, it can also produce thousands of lines solving the wrong problem. Experienced engineering leaders are finding that their value is shifting away from typing code and toward defining requirements, reviewing architecture and deciding whether the software should exist at all.
Marketing is following the same pattern. Creating content is becoming cheaper every month, but knowing what deserves to be said remains a human issue. If every competitor can generate fifty LinkedIn posts before lunch, the advantage isn’t publishing fifty-one – it’s understanding your customers well enough to publish the one they’ll actually remember.
This changes what expertise looks like. For years, expertise meant having answers. Increasingly, expertise means asking better questions.
- What customer are we actually trying to serve?
- Which metric matters here?
- What are we willing to sacrifice to get this outcome?
- What assumptions are we making?
AI rarely struggles to answer, but it does struggle to know whether the question itself is worth answering.
At Sigma Growth Specialists, we’ve found that the companies getting the most value from AI aren’t the ones with the fanciest prompts, but they’re the ones with the clearest thinking. Good operators use AI to explore possibilities faster – they don’t outsource judgment, because that’s the one thing competitors can’t download.
Stop measuring your value by how quickly you can produce work, and instead measure it by whether you’re solving the right problem. The future belongs to people who combine AI’s speed with human judgment about priorities, trade-offs and consequences. AI is compressing execution. It is making discernment more valuable, not less.
