A few months ago, I connected with every single member of our People department to ask them where AI was actually landing in their work. What I kept hearing surprised me: forty-four percent of them named time, not capability or access, as their number one barrier to going deeper with AI. These are people who work at a company built on automation, and they were already at capacity to learn something that would ultimately give them time back. That’s the trap. And it disproportionately hits the people already carrying the heaviest loads.
The pattern extends well beyond any single company. A survey of 1,100 enterprise AI users found that employees spend an average of 4.5 hours per week revising, correcting, or redoing AI-generated work. Workers without employer-provided training were six times more likely to say AI made them less productive. Many barely use AI at all—or use it so ineffectively they never see real gains.
That’s not just a productivity problem. It’s an access problem—one that determines who gets the opportunity to thrive in the AI era and who gets quietly sidelined. And the way we close it has less to do with better training programs and more to do with who we design AI access for in the first place.
When AI adoption becomes performance
As organizations push AI adoption, many employees protect themselves from being seen as behind by performing AI fluency rather than developing it. They generate outputs they can’t fully evaluate and spend more time managing the appearance of proficiency than doing meaningful work.
This self-preservation tactic isn’t disengagement. It’s what happens when expectations shift faster than support. The environment doesn’t make room for people to be beginners.
The cost of this goes beyond wasted time. When AI fluency becomes performative, it erodes trust between teammates and leadership, and between people and the tools themselves. Organizations end up with AI adoption metrics that look healthy on a dashboard. Meanwhile, the actual experience of using AI feels confusing and high-stakes for the people doing the work.
The access architecture matters
When I think about why only some organizations close this gap, it often comes down to a design question: who was the AI built for?
Most enterprise AI implementations still center a technical user. They require configuration, code, or a minimum comfort level with complex interfaces. That’s fine for engineering teams. But it quietly excludes everyone else—like the recruiter rewriting the same job description forty times a week or the finance analyst manually categorizing expense reports. These are people who feel the pain of repetitive work most acutely. And they’re often the ones best positioned to solve it.
This is where no-code AI orchestration platforms change the access equation entirely. Everyone—technical and non-technical alike—can build automated workflows, connecting AI to the tools they already use without writing a line of code. They no longer need to wait for IT to prioritize their use case. Instead, they’re empowered to fix the friction they feel.
That shift moves AI from something that happens to people to something people do with their own agency. The barrier to participation, then, drops from technical skill to curiosity.
Six ways to close the AI opportunity gap
Recognizing the problem is the first step. But closing the gap requires intentional, structural choices about how AI gets introduced, supported, and measured across an organization. Here are six practical ways to start.
1. Audit for confidence
Most organizations measure AI readiness by tracking tool access and training completion. That tells you about AI infrastructure. It tells you almost nothing about whether people feel safe enough to experiment and build real AI fluency over time.
One way to measure if your teams feel psychologically safe is by running listening sessions that surface the emotional landscape of AI adoption. An employee with access but no confidence to experiment will never create value with the tool. Competence data tells you who clicked a button, while confidence data tells you who’s actually learning.
2. Embed AI learning into the workflow
Frontline employees rarely have bandwidth for optional AI training, and standalone workshops tend to fade fast. The most effective approach I’ve seen does two things in parallel: train managers to use AI daily, and weave AI fluency into existing team rituals, like standups and planning sessions.
When a manager uses AI to prepare for a one-on-one, their direct reports notice and start asking questions. Reinforcing that curiosity through team rituals rather than a separate curriculum is what makes it sticky.
3. Measure AI adoption by inclusion, not just usage
Tracking the number of employees who use AI daily looks healthy on a dashboard, but it tells you almost nothing about who’s actually benefiting. Instead, break that number down the same way you’d track representation across your organization: by role, level, and demographic. If AI adoption clusters in engineering and lags in operations or customer-facing teams, for example, that signals an opportunity gap.
Make this audit a regular practice, so you can course-correct access gaps early—before they calcify into permanent divides.
4. Make it safe to be a beginner
This is where performative AI fluency either gets reinforced or broken: when people feel like they should already know how to use AI, they stop asking questions and start protecting themselves.
Someone with authority to openly normalize the learning curve. This gives people permission to experiment, ask basic questions, and share what didn’t work alongside what did. Teams that build this safety tend to iterate faster because people surface real feedback instead of polishing outputs to avoid scrutiny. Over time, that openness compounds into genuine AI fluency, which is something no training program can manufacture on its own.
5. Let the people closest to the work define the use cases
An AI strategy designed at the top often lands on teams that had no voice in defining the problem. The most effective AI implementations start by asking practitioners: where’s the friction? The highest-energy insight is usually small and unglamorous—like a spreadsheet someone has been manually updating for years that no one thought to question. From there, give those people the tools and support to build the solution themselves.
It’s a bottom-up invitation, not a top-down mandate.
6. Build AI governance into how teams actually work
Without shared standards for AI outputs, confident users move fast, and the ones who most need clarity (new AI users and non-technical teams) quietly second-guess themselves.
AI governance closes this gap by making the rules of the road visible. It defines when human review is required, what “good enough” looks like, and how to escalate edge cases. That shift turns governance from a gatekeeping function into an access function.
Start with your highest-volume AI use cases. Keep the criteria specific and accessible enough that a new hire can apply them on day one.
The real work ahead
AI and automation should free people up for creativity, strategy, and connection. They shouldn’t create a two-tier workforce where the AI-fluent hold all the leverage.
The AI opportunity gap won’t close on its own. It closes when leaders decide it matters and when organizations design AI access for the broadest possible set of people. When that happens, something remarkable follows: the recruiter builds their own workflow, the operations coordinator automates the report no one had the bandwidth to fix, and the first-time manager approaches unfamiliar work with confidence. The potential already exists across every role and level. The work now is giving everyone a real shot at unlocking it.
Zapier is an AI orchestration platform that connects more than 9,000 apps to help companies automate workflows and improve productivity. Since 2012, millions of users have trusted Zapier to automate everything from lead routing and data synchronization to customer conversations, all without writing code. By turning complex integrations into simple, point-and-click workflows, Zapier empowers teams of all sizes to focus on strategic work. From startups to Fortune 500 companies, organizations worldwide trust Zapier to streamline operations, reduce errors, and accelerate growth through intelligent automation.
