Is Everyone Using AI at Work? [2026 Survey]

It depends on who you ask. When AISA asked 98 people starting the AI fluency assessment "How would your colleagues describe your AI usage?", the answers spread across the entire spectrum: 16.3% said colleagues would say "What's AI?", while 25.5% identified as the person everyone asks for help. The middle ground is crowded too, with 27.6% placing themselves at about average. If you feel like everyone around you is using AI at work except you, the data suggests the picture is more mixed than it looks from your desk.

1. What's AI?16.3%16
2. Behind the curve7.1%7
3. About average27.6%27
4. Ahead of most23.5%23
5. The one everyone asks for help25.5%25

98 people answered, 14 August to 20 August 2026. Source: AISA research, aisa.to

Is Everyone Using AI at Work Except Me?

No. The spread of answers shows that people place themselves at every point on the AI-usage spectrum, not clustered at the top. The feeling that everyone else is ahead is common, but 16.3% of people said their colleagues would describe them with "What's AI?" and another 7.1% chose "Behind the curve." Combined, 23.4% see themselves as trailing their peers.

That said, the top end is real. Nearly half of people (49.0%, combining "Ahead of most" at 23.5% and "The one everyone asks for help" at 25.5%) believe colleagues would place them above average. The largest single group, at 27.6%, sits right in the middle with "About average."

This creates a visibility problem. The people who use AI confidently tend to be vocal about it. They share prompts in Slack, demo tools in meetings, and volunteer for pilot programs. The people who feel behind stay quiet. So the workplace signal is skewed: you hear from the enthusiasts and assume everyone is like them.

Microsoft and LinkedIn's Work Trend Index found that 78% of AI users bring their own AI tools to work (May 2024). That figure captures people who are already using AI. It does not mean 78% of all workers use AI. The distinction matters when you are comparing yourself to colleagues.

The "About Average" Middle Is the Biggest Group

More than a quarter of people (27.6%) chose "About average" when asked how colleagues would describe their AI usage. This is the single largest answer. It suggests that many people see AI as something they use but do not stand out for, neither lagging nor leading. If you are in this group, you are in the most common position.

The Gap Between Perception and Reality

Perception of where you stand relative to colleagues is not the same as a measured skill level. Someone who describes themselves as "About average" might have strong prompting skills but weak knowledge of how AI actually works. Someone who says "Ahead of most" might be fast with ChatGPT but skip verification entirely. The only way to know where you actually stand is to measure it, which is what a structured AI fluency assessment does.

How Do People Think Colleagues See Their AI Use?

People's self-placement follows a pattern that leans toward the upper half. The 27.6% who chose "About average" form the centre, but the combined share above average (49.0%) is more than double the combined share below it (23.4%). This is not unusual in self-assessment: people who take the step to evaluate their skills often have some confidence in them.

What matters for teams is the spread itself. In a room of 10 people, these numbers suggest roughly 2 would say they barely know what AI is, 1 would say they are behind, 3 would call themselves average, 2 would say they are ahead, and 2 or 3 would say they are the go-to person. That is a wide range of AI fluency sitting in the same team, often without anyone knowing it.

What This Means for Engineering Managers

If you manage a team, you probably have a rough sense of who uses AI and who does not. But rough senses miss the middle. The 27.6% who see themselves as average are the group most likely to grow quickly with the right support, and most likely to stall without it. They are not resistant to AI. They just have not found the workflow that clicks.

What This Means for Individual Contributors

If you are the person wondering whether everyone else is ahead, look at the numbers. Nearly a quarter of people place themselves below average. You are not alone. The more useful question is not "Am I behind?" but "What specific skill would move me forward?" Our sibling post Am I Behind on AI? [2026 Survey] explores that question with the same dataset.

How Many People Are the One Everyone Asks for Help?

Exactly 25.5% of the 98 people chose "The one everyone asks for help." That is 25 people, making it the second-largest answer after "About average." One in four people starting the AISA assessment sees themselves as their team's AI resource.

This is a significant share. It means that in many teams, there is at least one person who has become the informal AI expert. They answer questions, troubleshoot prompts, and recommend tools. They are doing task decomposition and iterative refinement whether they call it that or not.

The Burden on Go-To People

Being the team's AI person is useful but unsustainable if it stays informal. These individuals spend time helping others instead of doing their own work. Without a structured way to raise the team's baseline, the go-to person becomes a bottleneck. This is one reason engineering leaders are investing in AI upskilling programs that spread capability across the team rather than concentrating it in one person.

Go-To People Still Have Gaps

Self-identifying as the person everyone asks does not mean you have no gaps. The AISA rubric covers 11 criteria across prompting, critical thinking, technical understanding, workflow integration, and safety. Most people who feel confident in AI are strong in one or two of these dimensions and weaker in others. AI safety, for example, is consistently the area where even experienced users have the most room to grow.

How Many Say Colleagues Would Ask "What's AI?"

A total of 16.3% of people, 16 out of 98, chose "What's AI?" as how colleagues would describe their usage. This is not a trivial share. It means that roughly one in six people starting the assessment see themselves as complete beginners in the eyes of their peers.

This group matters for workforce planning. 66% of leaders say they would not hire someone without AI skills, according to Microsoft and LinkedIn's Work Trend Index (May 2024). If a sixth of people see themselves at the "What's AI?" level, there is a clear gap between what leaders expect and where a portion of the workforce currently sits.

"What's AI?" Does Not Mean Incapable

Someone who picks this answer might be highly skilled in their domain but has not yet engaged with AI tools. They are not lacking ability. They are lacking exposure. The path from "What's AI?" to competent AI user is shorter than most people think, especially with structured resources like the AI Fluency Academy guides.

Combined with "Behind the Curve"

Adding the 7.1% who chose "Behind the curve" to the 16.3% who chose "What's AI?" gives 23.4% of people who see themselves as trailing their colleagues. Nearly a quarter. If you are in this group, you are not an outlier. You are part of a substantial segment that has not yet found its footing with AI at work.

What If a Colleague Uses AI for Everything?

If you work next to someone who seems to use AI for everything, you are probably looking at someone in the 25.5% who identify as the go-to person, or the 23.5% who say they are ahead of most. Combined, 49.0% of people place themselves above average. So yes, there is a real chance your most visible colleague is genuinely further along.

But "uses AI for everything" is not the same as "uses AI well." Volume of use does not equal quality of use. Someone who pastes every task into ChatGPT without checking outputs, adjusting prompts, or considering data privacy is not demonstrating AI fluency. They are demonstrating speed without judgment.

What You Can Learn from Them

Instead of feeling behind, observe what your AI-forward colleague actually does. Which tasks do they delegate to AI? How do they phrase requests? Do they verify outputs? The answers reveal patterns you can adopt. The post Am I Using AI Enough? [2026 Data] looks at how people assess their own usage volume.

What They Might Be Missing

High-volume AI users often skip critical thinking steps. They accept outputs at face value, do not cross-reference claims, and rarely audit their prompts for bias. If you are more cautious, that caution has value. The goal is not to match their volume but to build a workflow where AI makes your work better, not just faster.

How Do You Close the Gap with Colleagues?

Start by finding out where you actually stand, not where you think you stand relative to the person in the next seat. Self-placement is useful for motivation but unreliable for planning. A measured score across specific dimensions tells you exactly which skills to build.

Here is a practical breakdown based on where you placed yourself:

Measure, Do Not Guess

The core problem with comparing yourself to colleagues is that you are comparing your inside view to their outside behaviour. You see your own uncertainty. You see their confidence. The comparison is unfair by design. A structured assessment removes the guesswork. AISA's published rubric scores 11 criteria with evidence from your own words, and the free report shows exactly where you are strong and where you have room to grow.

Build a Learning Habit, Not a Panic Response

The worst response to feeling behind is to panic-adopt every AI tool at once. The best response is to pick one skill, practice it deliberately, and measure your progress. Whether that is prompt engineering, output verification, or understanding how models work, focused effort beats scattered anxiety every time.

If you are an engineering manager or team lead, the same principle applies at the team level. Instead of assuming your team is fine because a few people are vocal about AI, measure the full spread. Posts like AI Skills Gap Statistics [2026 AISA Data] and AI Change Management: The People Side [2026] dig into what that looks like in practice.

Start with Your Own Score

Whether you placed yourself at "What's AI?" or "The one everyone asks for help," a measured AI fluency assessment gives you something self-comparison cannot: a specific, evidence-based picture of your skills across every dimension that matters. It takes about 10 minutes, and you get a free report and certificate at the end.

About this research

This finding comes from AISA's own research. AISA asks a short research question of people as they start the AISA AI fluency assessment, one question at a time. The question in the chart above was asked from 14 August to 20 August 2026, worded exactly as shown and with the answers in the order shown, and 98 people answered it. Each person counts once: when someone answered more than once, their latest answer is the one counted.

More findings from this research are collected in AISA research and data.

How to cite this research: AISA (2026). "Is Everyone Using AI at Work? [2026 Survey]". AISA research. https://aisa.to/blog/is-everyone-using-ai-at-work

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Related reading: Am I Behind on AI? [2026 Survey], the sibling analysis of the same research question, focused on whether people feel they are falling behind.

Related reading: Am I Falling Behind on AI? [2026 Data], a deeper look at the fear of falling behind and what the data actually shows.

Related reading: AI Upskilling and Measurement in 2027 [Strategy], how teams are structuring AI skill development with measurement built in.

Frequently Asked Questions

Does Everyone Use AI at Work Now?

No. AISA's research shows that 16.3% of people said colleagues would describe their AI usage as "What's AI?" and another 7.1% chose "Behind the curve." While AI adoption is growing, a significant share of people have not yet integrated it into their work. The perception that everyone uses AI is driven by the visibility of enthusiastic users, not by universal adoption.

Am I Falling Behind If I Do Not Use AI?

Not necessarily, but the pressure is real. Microsoft and LinkedIn's Work Trend Index (May 2024) found that 66% of leaders say they would not hire someone without AI skills. If you are not using AI yet, you are in the company of the 23.4% of people who placed themselves below average in AISA's research. The key is to start building skills deliberately rather than comparing yourself to the most vocal user on your team.

How Do I Get Better at AI Than My Colleagues?

Stop comparing and start measuring. Take a structured AI fluency assessment to find out which of the 11 skill criteria you are strongest and weakest in. Then target your weakest area with focused practice. The people who improve fastest are not the ones who use AI the most; they are the ones who identify specific gaps and close them systematically.