AI fluency is quickly becoming an expectation at work, but if you ask ten people what “AI fluent” actually means, you’ll probably get ten different answers.

To understand how this ambiguity is impacting the labor market, we teamed up with YouGov to survey both hiring decision-makers and job seekers. The data points to a clear and growing disconnect where demand is skyrocketing, but baseline definitions haven’t caught up: 

  • AI fluency is the new digital literacy: 74% of employers say using AI effectively is a fundamental skill. But job seekers haven’t caught up, with only 34% considering it important to their current roles.
  • Definitions are missing internally: Only 27% of employers have a documented, consistent definition of AI fluency across roles.
  • Candidates are left guessing: Nearly one-third of job seekers say they see AI requirements in job listings without any explanation of what’s actually expected.

When expectations are fuzzy, everyone loses. Companies end up evaluating candidates inconsistently across teams, and great applicants walk away because they aren’t sure if they have the “right” kind of AI experience.

Closing this gap starts with clarity. For employers, that means replacing vague requests for “AI expertise” with the specific tasks.  At the same time, job seekers shouldn’t let ambiguous listings discourage them from showcasing the practical digital skills they already bring to the table.

Better signals lead to better matches, and even small improvements in transparency can connect the right talent to the right roles.

How we approach AI usage at Indeed

That same principle guides how we support our own employees at Indeed. Rather than applying AI broadly with a one-size-fits-all mandate, we focus on a role-specific approach. We start with the actual work a team does and the specific problem they’re trying to solve, then determine where AI can genuinely add value.

For a Sales team, that might mean prepping for client conversations or finding account insights faster. For Marketing, it might mean analyzing campaign performance or generating initial creative drafts. Because the work is different, the tools, training, and metrics for success must be different, too.

Our approach comes down to three key questions:

  1. Are we measuring a meaningful outcome?
  2. Does the tool fit naturally into how they actually work?
  3. Have we given people the time and support to build the skills to use these tools well?

By focusing on how teams use these tools rather than just tracking usage metrics, we can see if AI is truly improving outcomes.

Training has to be just as specific. It’s not so much about the tools themselves, but rather how they’re being used. Recently, every engineer at Indeed completed hands-on training to learn how to integrate our AI coding assistants into their daily workflows. We also encourage peer-to-peer learning so colleagues can share practical examples of what’s working and why. This approach helps us identify key success drivers, such as agentic development skills, address competency gaps, and establish clearer performance benchmarks.

All this helps us build and iterate faster so we can better serve job seekers and employers.

AI and human judgment go hand in hand

The survey offers a useful reminder of what employers value. While 68% cited using generative AI for tasks such as writing, research and summarization as a component of AI fluency, 58% cited reviewing AI output for accuracy, quality or bias. Strong AI fluency isn’t just knowing how to produce an answer, but whether that answer is any good.

Preparing the workforce for the future starts with how we hire and support our people. At Indeed, we’re helping our teams build these skills through practical, role-based training and shared learning. Bringing that same level of clarity to job descriptions will help candidates recognize the value of the skills they already have, understand where they can grow, and step confidently into the next opportunity. 

FAQ

What is the methodology of this survey? 

This YouGov survey was commissioned by Indeed, conducted July 23–August 13, 2026. It was a global study across the U.S., U.K., Canada and France. The findings above represent the results from the US. The survey targeted 1,007 active and passive job seekers across all experience levels, industries and generations and 500 employers including hiring decision-makers spanning managers, directors, senior management, and C-suite executives at mid-market and enterprise organizations (500+ employees).

Who did you survey for this report? 

We surveyed 1,007 active and passive job seekers and 500 employers in the U.S.

What is AI Fluency? 

The strongest definition emerging from the research is the ability to work effectively, critically and confidently with AI: selecting and using the right tools, judging what they produce and knowing when, and when not, to use AI. That definition earned agreement from 78% of employers and 69% of job seekers.

The capabilities employers cite most often are:

  • Using generative AI for writing, research, brainstorming and summarization: 68%
  • Writing effective prompts or instructions: 60%
  • Reviewing AI output for accuracy, quality or bias: 58%
  • Automating repetitive workflows or processes: 56%

The findings show that technical familiarity alone is not enough. Critical thinking, verification and judgment are central to using AI responsibly and effectively.