#ResumeWriting : 7 AI Skills To Put On Your Resume In 2026. MUst Read!

1. Generative AI Skills

The basics are still essential. Coursera defines this as using artificial intelligence systems to create new text, images, code, audio, and other content from instructions or data.

Some professionals might be proficient at talking to ChatGPT to help you create written content but not as well-versed in designing images with AI.

And by designing images with AI, I don’t mean asking ChatGPT to create a business flyer for an upcoming conference and then taking whatever ChatGPT generates and using it for your work. I’m talking about giving the AI specific context, constraints, voice guidelines, and detail to fully have your human input throughout the creative direction and process, and then reviewing the output to ensure that what is generated is in line with brand guidelines and is professionally acceptable without looking like an AI mistake.

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2. Model- And Assistant-Specific Skills

This is a new one. Previously, simply adding generative AI to your resume might have earned you a quick pass, but now companies want specifics.

There are differences between LLMs: ChatGPT versus Gemini versus Claude versus Copilot. (I spot the differences here.)

How do you know which model to use, for what, and when? What are the best models for your type of work?

Employers will want to know you have a solid answer, because if you don’t, you’d waste company AI tokens and compute on the wrong models and model strengths instead of producing focused work; it will show in the resulting outputs, too.

3. Agentic AI Skills

At the beginning of last year, I interviewed IBM’s vice president, Justina Nixon-Saintil, at the IBM headquarters in London. She told me that agentic AI would be an emerging skill that would pretty soon become in demand across employers globally. More than a year and a half later, we’re seeing that prediction come true.

AI agents are being embedded across enterprise for multi-step tasks and complex workflows, running quietly in the background while employees focus on other human-demanding tasks. This gives you a strong advantage in productivity per employee, because you can create more value as a professional.

There is a caveat: companies don’t want you to use agentic AI ruthlessly.

You need to understand how to direct the agent in the same way you would train and direct a human teammate. You also need to understand what good looks like and know how to incorporate agentic AI into your decision-making without letting it do all the work for you. Ownership and ethics still sit with you.

That’s why agentic AI skills, both on the technical side (from building an AI agent to knowing how to use existing agents, like Asana’s teammates) and on the human control and ethical side, are very important. That’s also why domain expertise in your field is essential: you can’t give an AI agent the right instructions if you don’t know your field inside out.

4. Prompting And AI Interaction Skills

This refers not only to writing prompts, but to refining and iterating so you’re guiding AI to create the best, most useful, and reliable outputs and solutions.

Do you know how to construct a useful Claude or ChatGPT skill? Can you analyze why your prompt produced a terrible output and what you need to add to reduce ambiguity?

5. LLM Application And Modeling Skills

This is best suited for your resume if you’re in a tech or startup environment and want to pursue a technical career. LLM application and modeling skills refer to knowing how to develop LLMs and LLM-based applications to “understand, generate, and transform language,” as Coursera puts it.

This is a highly in-demand skill, and professionals with it are in the top percentile of high-paying earners in the AI industry, per LinkedIn, including technical staff and AI and machine learning engineers.

6. AI Security

Cybersecurity professionals are experiencing a surge in demand, with AI security incidents and agentic AI implementation heavily contributing to the need for this skill set — not just within consultancies, but internally within organizations as well.

There are far too many recent headlines about AI agents going rogue to make it easy to see why this is a necessary skill.

But beyond cybersecurity, everyday non-technical professionals also need AI security skills.

Every professional should take responsibility and ownership for how they use AI within an organization, protecting against shadow AI misuse, hallucinations, and closely monitoring and following company guidelines and policies around high-risk, high-consequence activities (such as hiring, using AI tools, for example).

7. AI Workflow Integration And Orchestration

Given my project management background, this is actually my favorite AI skill.

And if you’re an organizer, operations manager, leader, or a program or project management professional, this will appeal to you too, because this is where you’re able to:

• Coordinate AI tools, systems, and agents
• Connect them to applications within your organization
• Redesign processes and workflows to remove bottlenecks and make them more efficient

This slashes costs, reduces time, and boosts efficiency in project delivery while maintaining a high bar for quality.

Outside of project and operations management roles, AI workflow design and the skills listed above can also help individual contributors. Instead of taking the long route and adding technical (and cognitive) debt, you can ask yourself:

How can I reimagine the work I’m doing right now while maintaining a high-quality bar? What might it look like if I used the best AI model or tool for each part of this process?

Remember, an AI tool in your workspace apps–in itself–is not a flex. The flex comes from what you do with it, and how you apply your knowledge. That’s precisely what your resume needs to convey.