AI could help you find a new career path — but this University of Phoenix expert says there’s one thing it can’t do for you

The Career Optimism Index® shows that 53% of workers say AI advancements are increasing their confidence in building skills, and that among the 58% using or planning to use AI at work, writing assistance is the most common reason (57%), followed by data analysis/research (50%); training and development ranks third 38%, with image generation/content creation not far behind (37%). Meanwhile, 81% say AI helps them identify new ways to apply the skills they already have for future growth.

I think that distinction is important. AI can help someone close a knowledge gap, practice something new or recognize that a skill they already use in one context may translate to another. But I would think about career readiness more broadly in three areas: the technical skills required for your field, durable human skills like critical thinking, judgment and collaboration, and increasingly, the AI fluency to bring those things together.

Yes, but I don’t think AI fluency should be treated as a standalone skill. It’s becoming part of how people practice almost every profession.

Knowing how to use AI in accounting is different from knowing how to use it in healthcare, education or technology. The value comes from understanding the work well enough to know what to ask AI to do, how to evaluate what it gives you and when human judgment needs to take over. That’s why I think AI has to be learned in context. Technical skills and durable human skills remain important; AI fluency is becoming the layer that runs through both of them.

That urgency is showing up in the Index as well: half of workers said they were learning AI independently, while 60% wanted more guidance learning AI tools.

Absolutely. Confidence is valuable, but confidence by itself is not evidence of readiness.

The test I would use is: Can you demonstrate the skill without simply asking AI to do the work for you? Can you explain your reasoning? Can you evaluate whether an AI-generated answer is right? Can you apply the skill to a new problem, receive feedback and improve?

That is why assessment matters. Workers should compare what they believe they can do against real job requirements and look for evidence—completed work, assessments, credentials, feedback from experienced people and demonstrated performance. AI can help you identify possibilities and prepare for them. It shouldn’t be the only judge of whether you are ready.

I would caution against outsourcing the durable human skills to AI. Critical thinking, judgment, teamwork, communication and ethical decision-making develop through practice, feedback and experience with other people.

AI can help you rehearse a difficult conversation or expose you to different ways of thinking about a problem, but that is different from leading a team through a difficult situation, making a consequential decision or applying professional judgment when the answer isn’t obvious.

The same is true for specialized skills where practice, assessment or professional standards matter. AI can be a very useful learning partner, but it doesn’t eliminate the value of experienced faculty, mentors, colleagues or hands-on application. In many cases, those are what turn knowledge into genuine capability.

AI is unusually good at helping people explore quickly. You can describe your experience, ask what adjacent roles might use those skills, identify gaps between where you are and where you want to go, practice an interview or get another explanation of something you don’t understand.

What AI is not particularly good at is knowing you in the full context of your life and making consequential career decisions for you. It doesn’t replace judgment, lived experience, mentorship or accountability. I think its best role is to expand the possibilities you can see and help you prepare—then let human judgment determine what you do with those possibilities.

Yes, and that may be one of the most interesting findings in the research. 73% of workers said using AI at work helped close knowledge gaps and reduce the need for additional education/training, while 79% said it increased their confidence in taking on stretch assignments or responsibilities outside their role.

A worker may start by asking, “Here are the things I do well today. Where else are those skills valuable?” AI can help translate experience into a skills vocabulary, surface adjacent occupations and identify what additional skills may be required.

That can be eye-opening because careers are rarely as linear as we once imagined them. But discovery should be the beginning of the process, not the end. From there, workers should validate those possibilities against real job descriptions, talk to people in the field and determine what education, experience or credentials they would actually need.

Use AI to expand your capabilities, not to substitute for them.

The people who will be best positioned for change are not necessarily those who know the most AI tools. They’ll be the people who understand their field, continue building relevant skills and know how to use AI thoughtfully to do more with what they know.

And I wouldn’t wait for someone else to create that path for you. Half of workers in our Index are already learning AI independently. Start experimenting with it in the context of the work you actually do. Ask where it can make you better, where you need to build additional knowledge and where your human judgment adds the most value.

That combination—continuous learning, AI fluency and strong durable skills—is what I believe gives workers the best foundation for navigating whatever comes next.