Most In-Demand Skills Employers Want Right Now (2026 Data)

Employers say they can't find the skills they need, and job seekers say they can't figure out which skills employers actually want. Both are partly right. The lists of in demand skills you'll find online often mix survey opinions, course marketing, and old data, so they contradict each other and rarely tell you what to do next.

This guide pulls together the most recent hard evidence: LinkedIn's Skills on the Rise rankings, GMAC's 2026 survey of corporate recruiters, NACE's employer research, BLS employment projections for 2025 to 2035, and 2026 job posting analysis from Indeed Hiring Lab and Lightcast. Where the sources agree, you can treat the signal as strong. Where they disagree, you'll see why.

You'll also get a practical method for finding the skills a specific employer wants, tables that show weak versus strong ways to signal each skill, and before-and-after resume rewrites. The goal isn't to learn everything on the list. It's to pick the two or three skills that matter for your next role and make them impossible to miss.

What makes a skill "in demand"#

"In demand" sounds simple, but different sources measure different things. A skill can appear in thousands of job posts and still be easy to hire for. Another can appear rarely but go unfilled for months. Before you act on any list, know which kind of demand it measures.

Three signals of real demand#

Look for skills that show up strongly on at least two of these three signals:

• Volume: how often employers ask for the skill in job postings or name it in surveys.
• Growth: how fast that demand is rising compared with other skills or occupations.
• Value: whether people with the skill get hired faster or paid more than people without it.

A skill with high volume but no growth, like basic word processing, is a minimum requirement rather than an advantage. A skill with growth and value, like AI literacy right now, is where you get the most return on your time.

How to read the major skills reports#

Each major data source has strengths and blind spots. This table compares the ones used throughout this guide.

The most in-demand skills right now#

When you line up the 2025 and 2026 data, the same cluster of skills keeps appearing. The table below consolidates them, shows which source points to each, and suggests the kind of proof that convinces employers.

Where employer demand rose fastest in 2026#

GMAC's 2026 Corporate Recruiters Survey, which surveyed 621 hiring managers and recruiters across 39 countries, mostly at Fortune 500 companies, measured how much demand for key skills changed from the previous year.

Notice the mix. Two of the five fastest-rising skills are technical, and three are human. GMAC reported that communication, problem-solving, and adaptability were the human-oriented skills in highest demand. Employers aren't choosing between tech skills and people skills. They want both in the same person.

In-demand skills by job function#

The market-wide list becomes more useful when you translate it into your own function. The pairings below reflect how the rising skills show up in day-to-day work. Treat them as starting points to test against real postings in your field.

The pattern holds across rows: one tool-based skill that raises output, plus one people skill that keeps work moving across teams. If you can show both in the same bullet, you're signaling exactly what the data says employers want.

AI literacy: the top in-demand skill for 2026#

AI literacy tops almost every current skills ranking. LinkedIn's Skills on the Rise 2025 placed it first for the US, based on 2024 growth in profile skills, hiring success, and job posting demand. Large language model development and application also made the list at No. 10.

The posting data backs this up. Lightcast analyzed more than 1.3 billion job postings and found that postings requiring AI skills advertise about 28% higher salaries, nearly $18,000 more per year, and that 51% of those postings in 2024 were outside IT and computer science, according to Lightcast's July 2025 report announcement. PwC's 2025 Global AI Jobs Barometer also found skill requirements changing 66% faster in AI-exposed jobs than in other roles, per PwC's release.

AI exposure is lifting advertised pay#

A September 2026 analysis from Indeed Hiring Lab found that median advertised pay in the most AI-exposed occupations rose about 46% since 2021, compared with 25% in the least-exposed occupations. The most exposed groups included software development, IT, data and analytics, marketing, and banking and finance.

Indeed is careful about what this means. These are advertised salaries, part of the rise reflects a shift toward more senior postings, and the link to AI is described as consistent with, not proof of, employers competing for AI-adjacent skills. Still, the direction is clear: in roles that AI touches, pay hasn't collapsed. It has risen.

The entry-level warning inside the data#

The same Indeed analysis contains a harder finding for new graduates. In the most AI-exposed occupations, the share of postings labeled entry-level fell from 29% in 2021 to 10% in 2026, while the senior share rose from 22% to 47%.

If you're early in your career and aiming at an AI-exposed field, that changes your strategy. You need to look less like a trainee and more like someone who can contribute on day one, which means projects, internships, and AI-assisted work samples that show output, not just coursework.

What AI literacy looks like at each level#

"AI literacy" means different things for different roles. Use this table to aim at the level your target job actually needs.

For wording that works on a resume, see AI skills to put on your resume. To understand how employers use the same tools on your application, read how companies use AI in hiring.

Data skills employers want in every function#

Data analysis is the second-fastest-rising skill in GMAC's 2026 survey, up 17 percentage points. It's also tied to one of the fastest-growing occupations BLS tracks: data scientists are projected to grow 35% from 2025 to 2035.

But you don't need to become a data scientist to benefit. Employers increasingly expect marketers, operations staff, HR professionals, and managers to pull numbers, read them correctly, and make a recommendation. The skill in demand is interpretation: turning data into a decision.

The data skill ladder#

Climb one rung at a time. Each step is useful on its own, and most non-technical roles only need the first three.

• Spreadsheets: pivot tables, lookups, and clean formulas that someone else can follow.
• SQL: pulling your own data instead of waiting for an analyst.
• Visualization: dashboards and charts that answer one question clearly.
• Statistics: knowing when a difference is real and when it's noise.
• Programming and modeling: Python or R for automation, forecasting, and machine learning.

If you're moving into a data role, the data analyst resume guide shows how to present projects. If you're adding data skills to a non-data role, the guide to computer skills for a resume shows how to list tools at the right level.

Technical skills with the strongest long-term outlook#

Skills surveys tell you what employers want this year. BLS projections tell you where job growth is expected over the next decade. For context, BLS projects total US employment to grow 3.5% from 2025 to 2035. The occupations below are projected to grow many times faster.

Skill clusters behind the fastest-growing jobs#

The occupations on the BLS fastest-growing list group into four skill clusters. Each one points to skills you can build now.

Healthcare skills#

Healthcare dominates the growth outlook. BLS projects healthcare support occupations to grow 13.3% and healthcare practitioners 8.0%, together accounting for almost one-third of new jobs over the decade. Clinical skills require licensure, but healthcare operations, scheduling, billing, compliance, and health data roles also benefit. See the registered nurse resume guide for how clinical skills translate to a resume.

Cybersecurity and data protection#

Information security analysts are projected to grow 21%. Demand comes from every industry that stores sensitive data, which is nearly all of them. Certifications, hands-on labs, and incident response experience are the usual entry points, and the cybersecurity resume guide covers how to present them.

Skilled trades and energy technology#

Solar photovoltaic installers and wind turbine service technicians are among the fastest-growing occupations, and industrial machinery mechanics are projected to grow 18%. These roles reward mechanical troubleshooting, electrical knowledge, and safety certifications, usually learned through on-the-job training or technical programs rather than a four-year degree.

The human skills employers rank highest#

Every time AI gets better at routine work, the skills that remain human get relatively more valuable. Employer research reflects that clearly.

NACE's Job Outlook research on what employers seek on resumes found that nearly 90% of employers look for evidence of problem-solving skills, and nearly 80% look for teamwork. At least 70% look for written communication, initiative, a strong work ethic, and technical skills. More than two-thirds look for verbal communication, flexibility and adaptability, and analytical skills. NACE's 2026 spring update put teamwork at 85%.

LinkedIn's 2025 list tells a similar story from a different angle. After AI literacy, the next three fastest-growing US skills were conflict mitigation, adaptability, and process optimization, with innovative thinking, public speaking, and stakeholder management also in the top 10.

Adaptability#

Adaptability means learning new tools, processes, and priorities quickly without losing output. Show it with a specific ramp-up: the new system you learned in two weeks, the reorganization you worked through, the role you covered on short notice.

Conflict mitigation and stakeholder management#

These skills keep projects moving when people disagree. They matter in management, customer service, project work, and any role that sits between teams. Your proof is a situation that could have escalated, what you did, and the result.

Communication#

Communication rose 13 points in GMAC's survey. The version employers want is concise and audience-aware: a one-page recommendation, a clear status update, a presentation that leads to a decision. The guide to communication skills for a resume has examples by role.

Problem-solving#

Problem-solving is the single most-sought attribute in NACE's research. It's also one of the hardest to prove with a label. Show the problem, your diagnosis, the action, and the measured outcome. See problem-solving skills examples for interview-ready framing.

For a broader look at how these fit with technical abilities, read hard skills vs. soft skills and the guide to soft skills for your resume.

Business skills on the rise#

Several of LinkedIn's fastest-growing skills point to the same employer priority: doing more with less and growing revenue efficiently. That's useful if you work in operations, finance, sales, or management, because these skills sit close to the numbers leaders watch.

If you already do some of this work, the fastest win is to describe it in the language employers use. "Fixed the invoicing process" becomes "Optimized invoice approval workflow, cutting cycle time from 12 days to 5."

In-demand skills by career stage#

The same skill list plays out differently depending on where you are. A new graduate and a director both need AI literacy, but they need to prove it in different ways and at different depths.

Students and new graduates#

Your challenge is that AI-exposed fields are posting fewer entry-level roles, as the Indeed data showed. Employers who do hire juniors want evidence that you can produce work quickly. Focus on:

• One technical skill at a working level, such as SQL, spreadsheet modeling, or a design tool, proven with a finished project.
• AI literacy shown through output, such as a research brief or analysis you produced faster and checked carefully.
• Teamwork and problem-solving stories from internships, group projects, or part-time jobs, since NACE data shows these top employer screens for graduates.

The college student resume guide shows where to put projects and coursework so they read as experience.

Mid-career professionals#

You likely have the domain skills. The gap is usually that your resume describes duties from five years ago in language from five years ago. Your priorities:

• Add one growth skill on top of your role, such as data analysis for a marketer or automation for an operations specialist.
• Translate existing work into current terms, such as process optimization, stakeholder management, and risk assessment, where those are accurate.
• Quantify recent wins so your resume shows you're current, not coasting.

Managers and senior leaders#

At senior levels, employers assume technical competence and screen hard on judgment. Indeed found the senior share of postings in AI-exposed occupations rising sharply, so competition at this level is real. Emphasize:

• Leading change, especially adopting AI or new systems across a team, with adoption and outcome numbers.
• Budget and resource management, with the size of what you controlled and what it produced.
• Conflict mitigation and stakeholder management at the cross-functional or executive level.

Career changers#

You're competing against people with direct experience, so your skills have to carry more weight. Lead with the in-demand skills you already have that transfer, then show one new skill proven with a project in the target field. The guide to transferable skills shows how to map what you have to what employers in a new field want.

Skills losing ground#

Demand is a two-way street. BLS projects office and administrative support occupations to shrink 4.0% from 2025 to 2035, a loss of about 752,100 jobs, the largest decline of any major group. Sales and related occupations are projected to shrink 1.4%. Indeed's September 2026 snapshot also found tech postings still depressed, even as overall postings sat about 3% above the pre-pandemic baseline, per Indeed Hiring Lab.

That doesn't mean these fields are disappearing. It means the routine parts of them are being automated or consolidated. Tasks most at risk tend to share a few traits:

• Repetitive and rule-based, such as manual data entry, basic scheduling, and form processing.
• Easy to check, so automation errors are caught quickly and cheaply.
• Low in judgment, with few decisions that depend on context or relationships.
• Isolated, done by one person without coordinating across teams.

How to find the skills a specific employer wants#

Market-wide lists tell you where to invest. The job post in front of you tells you what to put on your resume this week. Here's a process that takes about 30 minutes per target role.

1. Collect 8 to 10 postings for the same title in your target industry and city or remote market.
2. Paste each into a keyword tool such as the job description keyword finder to pull out the skills and tools each one emphasizes.
3. Tally how often each skill appears. Skills in more than half the posts are core; skills in a quarter to a half are differentiators.
4. Sort into three groups: skills you can prove now, skills you have but haven't documented, and genuine gaps.
5. Close the documentation gap first. Rewrite bullets so your existing skills use the employer's wording.
6. Pick one genuine gap to build over the next 60 days, ideally one that also appears on the market-wide lists above.
7. Check your match for each application with the resume keyword scanner before you submit.

In this hypothetical, SQL, visualization, stakeholder communication, and A/B testing are core. AI tools and Python are differentiators worth building. For a deeper walkthrough, see how to find keywords in a job description and the broader guide to resume keywords.

How to signal in-demand skills on your resume and in interviews#

Recruiters see the same skill words on hundreds of resumes. What separates candidates is the evidence behind the words. The table below compares weak and strong ways to signal the most in-demand skills.

Before-and-after rewrites#

These rewrites turn skill labels into proof. Each "after" names the skill in action, the method, and a result someone could verify.

If your bullets need restructuring, the resume skills generator builds a role-specific skills list to start from, and the guide to the skills section of a resume explains how to organize it.

On LinkedIn#

LinkedIn's rankings are built partly from what members add to their profiles and how often those members get hired. Add your in-demand skills to your profile's skills section, attach them to the specific roles where you used them, and mention the strongest one in your headline. The LinkedIn profile optimization guide walks through each section.

In interviews#

Interviewers test in-demand skills with behavioral questions: "Tell me about a time you had to learn something quickly," or "Describe a conflict with a stakeholder." Prepare one STAR story for each skill you claim on your resume. The guide to the STAR method shows how to structure them in under two minutes each.

A quick skills-signal checklist#

• Each top skill from the job post appears in your skills section using the same wording
• Your two most important skills each appear in at least one bullet with a measured result
• AI skills are described as outcomes, not as tool names alone
• Your LinkedIn skills match the ones on your resume
• You have a STAR story ready for every soft skill you claim

A 60-day plan to build one in-demand skill#

Reading about skills doesn't change your resume. A short, finished project does. This plan assumes 5 to 8 hours a week and a skill that's adjacent to work you already do.

Days 1–10: Define the target#

Pick one skill from your job post tally that also ranks high on the market-wide lists. Write a one-sentence goal that names the skill, the work product, and the result you'll measure. For example: "Use SQL and a dashboard tool to build a weekly sales report that replaces the manual spreadsheet."

Days 11–40: Learn by building#

Use one structured resource, not several. Spend most of your time applying it to a real problem from your job or a realistic public dataset. Every time you get stuck, write down what you learned. Those notes become interview material later.

Days 41–55: Finish and measure#

Ship the work product, even if it's imperfect. Then measure the difference: hours saved, errors avoided, decisions made, people who use it. A finished project with one real number beats an ambitious project that's 80% done.

Days 56–60: Make it visible#

Add the skill and the result to your resume and LinkedIn. Tell your manager what you built. Prepare a STAR story about it. If the skill was AI-related, document the review step you used to check the output, because employers increasingly ask how you verify AI work.

Your next steps#

You don't need every skill on these lists. You need the two or three that matter most for your next role, proven clearly enough that a recruiter can't miss them.

Start with these moves this week:

• Pick your target role and run the 30-minute job post analysis above.
• Choose one growth skill that appears both in your target posts and in the market-wide data, such as AI literacy or data analysis.
• Rewrite three bullets so they prove skills you already have using the employer's language.
• Prepare one STAR story for each human skill you list.
• Revisit your list every six months. Skills demand is moving fast, and LinkedIn, GMAC, BLS, and Indeed all publish fresh data at least yearly.

The specific skills at the top of the rankings will keep shifting. The pattern beneath them is steady: employers pay for people who combine judgment and human skills with the ability to use data and AI well, and who can prove it.