As artificial intelligence (AI) usage at work increases, C-suite leaders are placing a new expectation on workers: to be AI-fluent. Research from University of Phoenix conducted among 150 C-suite leaders in North America found 73% of C-suite leaders see AI literacy as a baseline skill and 69% expect to embed AI literacy into performance management by the end of 2026.

What Is AI Fluency, and Why Is This an Expectation for Employment?

Procrastinating about AI is clearly not an option, whether for companies, current employees or job candidates. A growing number of companies are demanding AI fluency, defined as the ability to understand, evaluate and collaborate productively with AI tools at work.

According to the Anthropic AI Fluency Index, the most common expression of AI fluency is augmentation, using AI as a thought partner rather than simply delegating work to it. Anthropic identifies two behaviors that distinguish AI-fluent employees in how they work with AI:

1. Setting the terms of collaboration. AI-fluent employees actively guide the interaction by asking questions such as, “Walk me through the reasoning behind this output,” rather than just accepting the first response.

2. Iterating and refining the output. They ask follow-up questions, challenge assumptions and continually improve the AI-generated work. This becomes especially important when AI produces highly polished outputs, such as an Excel spreadsheet or PowerPoint presentation, that may appear authoritative even when the underlying analysis needs further scrutiny.

This is why AI fluency is increasingly becoming a competitive advantage. It represents more than proficiency with AI tools. Rather, it is a new approach to problem-solving, where employees learn how to collaborate with AI while applying their own judgment, critical thinking and expertise.

But the one critical question remains: What does it mean to be AI fluent?

Defining and Achieving AI Fluency

1. Define AI fluency, don’t just mandate it.

It’s all well and good to mandate that all employees, current and future, are AI-fluent, but what that constitutes and how to make it happen are this era’s challenges.

One company that walks the walk is Zapier, the tech company that automates workflows. In May 2026, Zapier created an AI fluency rubric and integrated this into their talent practices for key job families:

• Unacceptable: has knowledge and uses AI but does not leverage AI in workflow
• Capable: embeds AI into the infrastructure of how work gets done showing business impact
• Adoptive: is a builder of AI to elevate how work gets done in their job
• Transformative: leverages AI to rebuild a function around AI-first delivery

The Zapier rubric defines AI fluency, but it does not create AI-fluent employees. Its real value lies in providing a common language and setting clear expectations throughout the employee experience; from recruitment and onboarding to learning and development, and performance management.

The goal of the rubric is not just to increase AI adoption. It is to make AI part of everyday work and create thoughtful AI usage with accountability for measurable business outcomes.

2. Understand AI value and use case.

As AI usage increases, so does the cost to companies, despite the declining cost of AI tokens. With consumers and enterprises adopting AI agents, token consumption, the units of data processed by generative AI models, is expected to multiply 24 times, to 120 quadrillion tokens per month between 2026 and 2030, reports Goldman Sachs. This cost is escalating as companies are shifting from leveraging prompt-based chatbots to using autonomous AI agents, which consume vastly more tokens. Some companies, including Meta, Disney, and Visa, are launching AI-adoption leaderboards to monitor AI adoption and recognize employees for productive AI usage.

As AI costs escalate, employees will need to move beyond just knowing how to use AI in their roles. They must develop the judgment to know when and whether AI should be used at all and which model would be most cost effective.

David Ashman, chief technical officer of Traliant, believes AI fluency also includes understanding the financial implications of using AI at work. Employees need to know not only how to use AI safely and responsibly, but also how to choose the right tool for the task — whether that means selecting Claude versus ChatGPT, for example — then determine which model within each tool is appropriate for the problem at hand.

The goal is not to employ AI for everything but to develop the judgment to use AI when it adds value, with the right tool and model, and for the right problem.

3. Understand the resistance to AI adoption.

In a working research paper, Harvard Business School professors Das Narayandas and Shunyuan Zhang found that new technologies gain traction not just when they improve performance, but when businesses make clear that the tools support — rather than threaten — their identity, expertise and influence in their jobs.

The professors argue that workers believe generative AI to be particularly disruptive to their sense of identity because of its ability to mimic higher-order human functions, including data analysis, decision-making and writing. When employees are asked to consider AI a teammate, they can perceive their identity with work being challenged, and for them the tradeoff doesn’t add up.

As part of addressing this fear of what an AI future will look like, Dropbox Chief People Officer Melanie Rosenwasser led her department through an assessment of what could be automated and what can remain part of the human resources mandate. The key, says Rosenwasser, is to collaborate with employees to determine what tasks get delegated to AI, helping employees redesign workflows to see how AI can augment rather than replace their work.

4. Encourage leaders to role-model AI fluency.

Top management should stop issuing AI mandates unless they themselves are role models. Such leadership behavior is critical for translating AI investments into business impact. Leaders understand that being a constant learner of AI now applies to them as well as their workers.

For instance, Alex Laurs, chief learning officer of EY Americas, modeled this when he created a strategy and innovation AI agent to assist his team in developing a new learning and development tool.

Similarly, Manuel Diez, CEO of Grupo Diesco, a manufacturing and packaging conglomerate, realized that if AI were going to transform the business, as he believed, it couldn’t be delegated. So, instead of adding an AI hire, he told his executive team, “I just hired that person. That person is me.”

Leaders also need to challenge their heads of learning and development to embed AI into training programs and use AI to train workers in human skills, such as critical thinking, not just AI foundations.

AI Capability Makes a Job Attractive

Finally, if AI fluency becomes an expectation for all jobs, not just technology ones, AI-fluent employees will include access to AI as a criterion for their current or prospective job.

Just as flexible work has become the norm and increasingly job candidates are willing to accept a pay cut of up to 25% for a job that offers flexibility, AI-adept job candidates may require a prospective employer to provide them access to AI and invest in their AI fluency, or work for less at a company that makes and fulfills that commitment.

While today we see CEOs mandating AI fluency, tomorrow’s job candidates may begin demanding it in their employer.