When I review AI candidate applications at TTGC, I'm not looking for the skills LinkedIn tells you to put on your profile. I'm looking for the skills that map to the work I actually need someone to do. Those are not always the same.

Here's what the data shows employers want — and what I, specifically, look for when I'm hiring.

What the research says

The World Economic Forum's Future of Jobs Report 2023 surveyed 803 companies on the skills they expect to grow in importance through 2027. The top six rising skills were:

Notice how many of those are cognitive and behavioral skills, not technical. The WEF data and what I see in hiring agree: the skills employers most need are not the ones that look most technical on a resume.

The seven skills I actually look for

1. Practical AI tool fluency

Not "I know how to use ChatGPT." More like: "I've built this content workflow using Claude as the drafter, Midjourney for visual moodboards, and a vector database for our brand voice library." Specificity. Recipe-level detail. Show me you've actually used these tools on real work.

2. Clear thinking about quality

AI tools produce a lot of mediocre work quickly. The valuable hire is the person who can tell mediocre from excellent and articulate why. This is editorial judgment, and it doesn't come from a course. It comes from reading widely and shipping work.

3. Domain expertise the AI doesn't have

If you understand healthcare patient acquisition, that's defensible value an AI can't replicate cheaply. If you understand luxury watch buyer psychology (we have Jacob & Co. as a client — this matters), that's defensible value. Combine domain expertise with AI tools and you're a force multiplier.

4. Documentation and communication

You'll spend more time documenting workflows, writing briefs, and communicating with stakeholders than actually using AI tools. Clear writing is non-negotiable. If your cover letter is unclear, your work output will be unclear too.

5. Pattern recognition for "is this output good enough"

This is the meta-skill of working with AI. Models will produce confident-sounding nonsense. The skill is noticing when the output looks right but isn't. This requires deep domain knowledge plus the discipline to verify rather than ship.

6. Cost-benefit instinct

When should you use AI? When should you do it manually? When does the cost of validating AI output exceed the cost of just doing the work yourself? Strong candidates have instincts here. Weak candidates use AI for everything indiscriminately and then have to redo half their work.

7. Adaptability with evidence

I ask every candidate to walk me through a recent time they had to learn a new tool quickly. The good answers describe stumbling, debugging, and figuring it out. The weak answers describe taking a course. The data backs this: WEF reports "resilience, flexibility, and agility" as a top-six growing skill for a reason.

Technical skills that still matter

For more engineering-leaning AI roles, here's the realistic technical layer above the seven cognitive skills:

For non-engineering AI roles, ignore most of this and focus on the seven cognitive skills above.

What employers don't care about as much as you think

What employers do care about, with intensity: can you show me work you've shipped? Can you describe it specifically? Can you explain what you'd do differently?

The hiring summary

If you want to maximize your hireability, focus on demonstrable evidence of the seven cognitive skills, layered with practical tool fluency. The technical skills matter but they're table stakes for engineering roles and largely unnecessary for non-engineering roles.

The candidates who get hired are the ones who can show me work. Everything else is supporting evidence.

Sources