As AI takes over parts of many jobs, the question shifts from “will my job exist?” to “what will my job consist of?” Employer surveys, including the World Economic Forum’s Future of Jobs Report 2025, list technological literacy and AI skills among the fastest-growing in importance — alongside distinctly human capabilities such as analytical thinking, resilience, creativity and leadership. Here are the skills that matter most and practical ways to build them.

1. AI fluency

Not programming necessarily, but knowing how to use AI tools well in your field:

  • choosing the right tool for a task;
  • writing clear instructions and providing context;
  • recognizing typical failure modes such as invented facts;
  • verifying outputs efficiently.

How to build it: pick two recurring tasks in your job and use AI for them every week for a month. Keep notes on what works. Our prompting cheat sheet is a good starting point.

2. Judgment and verification

When producing a first draft becomes cheap, the value moves to deciding what is correct, relevant and good enough. Professionals who can quickly check facts, spot weak reasoning and take responsibility for final decisions become more valuable.

How to build it: practice reviewing AI outputs against primary sources; learn the basics of statistics and source evaluation.

3. Deep domain expertise

AI is most useful in the hands of people who know a field well enough to ask the right questions and notice subtle errors. Studies of AI assistance show large gains for less experienced workers on routine tasks, while experts remain essential for novel and high-stakes problems.

How to build it: go deeper in your specialty rather than broader; document your knowledge, which also makes it easier to use AI effectively.

4. Communication and collaboration

Explaining complex ideas, persuading, negotiating and building trust remain human strengths. So does coordinating people — and, increasingly, coordinating people and AI systems.

How to build it: present your work more often, write clear summaries for different audiences, and take on cross-functional projects.

5. Problem framing

Defining the right problem is often harder than solving it. AI can generate many answers; humans must decide which questions matter and what success means.

How to build it: before starting any task, write a one-paragraph problem statement with goals, constraints and success criteria.

6. Learning agility

Tools change every few months. The ability to learn new tools and methods quickly — and to unlearn outdated habits — may be the most durable skill of all.

How to build it: set aside regular time for learning, follow a curated source of updates such as our Daily Digest, and experiment with new tools in low-stakes settings.

Knowing what data can be shared with AI tools, how to respect copyright and privacy, and when AI use must be disclosed protects both you and your employer.

A personal plan

Choose one skill from this list each quarter, set a concrete goal (for example “automate my weekly report with AI and cut the time in half”), and review progress. Small, steady improvements compound.

Start this month

Choose one AI tool relevant to your work, use it daily on a real task, and note what improves and what doesn’t.

What to watch next

  • Employer surveys on AI skills demand in your industry.
  • Certifications and short courses in AI fluency from universities and companies.
  • Changes in entry-level roles as AI handles routine tasks.
  • Workplace policies defining acceptable AI use.

Key takeaways

  • Employer surveys rank AI and technology skills among the fastest-growing in importance.
  • AI fluency means choosing tools, instructing them well and verifying results.
  • Judgment, domain expertise and communication grow in value as drafting becomes cheap.
  • Problem framing and learning agility help you adapt as tools change.
  • Build one skill per quarter with concrete, measurable goals.

Frequently asked questions

Do I need to learn programming?

Not for most roles. Understanding what AI can and cannot do and using tools effectively matters more. Programming remains valuable for technical careers.

Are soft skills really more important now?

They are increasingly valued because they are harder to automate and because AI shifts work toward coordination, judgment and communication.

How can I show AI skills to employers?

Describe concrete results: tasks you improved with AI, time saved, quality gains, and how you ensured accuracy and compliance.