Excel has been a résumé staple for decades. AI is the newest addition to the "must-have" list. On their own, each is useful. Together, they're something employers are actively paying more for — and the data backs that up, not just the sales pitch.

Here's what the numbers actually show, and what building this combination looks like in practice.

Two separate trends are converging right now, and it's worth looking at them plainly rather than taking anyone's word for it.

AI skills carry a real, growing wage premium. PwC's 2026 Global AI Jobs Barometer — based on more than 1 billion job ads across 27 countries — found the wage premium for AI skills climbed to 62% in 2026, up from 57% in 2025 and just 25% in 2024. For comparison, a master's degree is associated with roughly a 13% wage premium and a bachelor's degree around 8%. That said, the same research is honest about a catch: a majority of employers aren't yet attaching formal bonuses or pay bumps to AI skills specifically — the premium mostly shows up in who gets hired and promoted, not a guaranteed raise for everyone who lists "AI" on a résumé.

Excel remains one of the most consistently requested skills in the market, and it isn't being displaced by AI — it's being layered with it. Coursera's 2026 Job Skills Report lists Excel formulas among the fastest-growing data skills employers are screening for, alongside things like SQL and data visualization. The pattern across labor-market research is consistent: the highest salaries don't go to people with just Excel, or just AI — they go to people who can move between the two.

Excel alone signals you can organize and calculate. AI alone, without a home for the output, is a novelty. Put them together and you get something employers actually need: someone who can take messy real-world data, structure it in Excel, and use AI to analyze, summarize, or model it faster than someone doing it manually — and faster than someone who only knows the AI tool but has nowhere to put the results.

This is why job postings increasingly list both, even for roles that aren't technical. A marketing coordinator who can pull a campaign report and ask Copilot in Excel to summarize what's driving performance is doing work that used to require a dedicated analyst. That's not a hypothetical — it's the exact kind of task PwC and Coursera's data show is reshaping which roles get hired and promoted first.

It's less dramatic than it sounds, and that's good news for anyone worried they're starting from zero.

Reporting. Instead of manually building a monthly report, you organize the raw data in Excel and ask AI to summarize trends, flag anomalies, and draft the narrative section.

Forecasting. Copilot in Excel can run Python-powered statistical forecasts on data you've already structured — no statistics background required, but you still need the data in usable shape first.

Decision support. When your manager asks "why did this number move," you're the person who can open the sheet, ask the right question, and have an answer in minutes instead of promising to "look into it."

Communication. AI can help turn a dense spreadsheet into a one-paragraph summary for someone who isn't going to open the file — a skill that matters as much as the analysis itself.

None of this requires becoming a data scientist. It requires being comfortable enough in Excel to structure information, and comfortable enough with AI tools to ask useful questions of it.

You don't need both skills at expert level to benefit — you need enough of each that they reinforce one another.

If you're stronger in Excel already, the fastest next step is learning to ask AI tools questions about your existing spreadsheets rather than building every formula by hand. If you're more comfortable with AI tools generally but shaky on Excel fundamentals, the fastest next step is the reverse: solid footing in tables, formulas, and PivotTables so AI has clean structured data to work with in the first place.

This is genuinely good news for professionals who've been in the workforce for a while. You likely already have the harder half of this combination — Excel fluency built over years of actual work — and the wage-premium data shows employers are rewarding the AI layer on top of that experience, not replacing it. The gap to close is smaller than it feels: it's not "learn everything from scratch," it's "add one new layer to a skill you already have."

Pick one recurring task you already do in Excel — a monthly report, a budget review, a data cleanup — and try doing it with AI assistance for the first time. Notice where it saves time and where you still need to double-check its work. That single rep is a more useful starting point than reading another list of trends.