One hand raised. Most considered themselves users of AI, but not builders. No personal agents, no custom assistants or reusable workflows. 

That distinction matters. Assistance creates a one-time productivity gain. Building turns that gain into a reusable tool or workflow that can scale. While most leaders track how many employees use AI, the more revealing question is how many are building with it. Call it the builder activation gap: the distance between the many people who could build with AI and the few who do.

Across executive education sessions and applied AI courses for working professionals, the pattern I see is the same. Nearly anyone who can describe what they want in plain English can now build a working assistant, app or automation without writing a line of code. Yet few are building anything useful. 

A couple of years ago, Caroline Davis, Chief of Staff at Capital Factory, saw herself as an AI user rather than a builder. When I asked her applied AI course who had ever built a working tool with the technology, her hand stayed down.

Today, many recurring parts of her job run through tools she built. Chief among them is an agent called Sunny, named after her daughter. It connects to Davis’s email, calendar, Airtable CRM, and Google Sheets and draws on roughly a dozen documented workflows that prepare briefs, track fundraising, onboard new investors, and more. Data pulls that once took hours now take 10 to 15 minutes. Several automations run on a schedule, completing work before she asks for it. The workflows are versioned, reused, and improved rather than disappearing after a single interaction, and Sunny regularly coordinates with other agents. She has even carried the same approach outside her day job, using AI to rebuild a photography business she had operated a decade earlier, including its website.

The path began with something smaller. In the applied AI course, she used natural language to build her first working AI assistant. Davis describes what followed in terms of confidence rather than technical mastery or coding know-how. That experience, she says, “built up my acumen as a whole and gave me the confidence to test out stronger AI models.” She stopped seeing AI as a reactive conversational helper and started seeing it as something she could leverage to run recurring work. The first build led to the next, and eventually to the library behind Sunny. Along the way, her self-perception shifted.

She still resists the language of expertise. “I don’t think I’m a power user by any means,” she told me, “but most of my day is run through Claude at this point.” 

Her trajectory is still rare. Roughly half of U.S. employees now use AI on the job at least occasionally, but only 15% are daily users, says Gallup. Another recent study in HBR, based on an analysis of 1.4 million AI interactions among more than 2,500 KPMG employees, found only about 5% of employees qualified as sophisticated users, the ones doing iterative, higher impact work beyond casual prompting. For most, using AI means assistance with the one-off task in front of them, like drafting emails or summarizing documents. Useful, but disposable. 

So where are all the builders? 

Some barriers to building are structural. Governance, access, time, and incentives all matter. But once those basics are in place, identity can be the hidden bottleneck. The problem is that enterprise work has long trained people into a division of labor. A few specialists build systems, everyone else operates inside them. That made sense when building took engineering and coding expertise. For many problems, it doesn’t anymore. What hasn’t changed is identity. Most employees simply see themselves as consumers of technology, not creators. And identity is stubborn. Herminia Ibarra’s work on reinvention shows that people rarely think their way into a new self-image. They act their way into it, and identity catches up. 

Plenty of building still belongs to specialists, including complex systems, security-sensitive applications, and anything going to customers without supervision. But a huge share of everyday work problems live somewhere safer, where the person building the tool is the same one who can tell whether it works. What stops many is the perception that building is “not my lane.” 

While AI has made building far more accessible, it hasn’t yet made most people believe it’s for them. Every organization is sitting on people who are where Davis was two years ago. The question is whether leaders leave them there.

Three practices help leaders activate a builder identity across their workforce. 

1. Make the First Build Unavoidable. Training has its place, but the usual kind rarely moves identity. A required build can. In my sessions, every participant has to build a tool or agent that solves a real problem, then demo it to the room. Once it runs, posture changes. People who arrived identifying as AI users start describing what they built. SharkNinja ran a similar play at scale, pausing normal work for a four-day, company-wide AI hackathon involving roughly 4,000 employees. Leaders assigned about 20 major initiatives, while employees added roughly 400 projects of their own. The mindset, its CEO says, shifted from waiting on IT to “I have a problem. I can fix the problem.” Force the first build, and a builder identity begins to follow.

2. Make Builders Visible. When the only visible builders are engineers, most default to seeing themselves as users. When a peer or leader creates real solutions that streamline work, the reference point changes. Airtable’s Howie Liu builds in the open, deliberately sharing what he builds so the company sees its CEO shipping. Instead of circulating a document about a new capability, he built the landing page in Replit and shared the link; he even passes along his prompts so colleagues can follow the method. Liu pushes his teams toward “prototypes over decks”: working demos that can be tried, not words in a PRD. When leaders build publicly and nontechnical peers demo useful solutions, the definition of who builds begins to expand. Making builders visible creates new ones.

3. Measure the Builds. Most companies track proxies like active users, tokens burned, or employees trained. Those numbers tell you who’s using AI. They say little about who’s creating deeper value with it. The better questions are what got built, whether anyone else adopted it, and whether it materially changed a workflow. At BBVA, employees have built more than 20,000 custom GPTs; the bank reports that roughly 4,000 are now in frequent use. Watching those numbers surfaces the builders you already have, while underscoring that building isn’t siloed or a side hobby. It’s counted, normalized and expected outside IT. Job titles stop deciding who builds.  

Organizations searching for more value from AI won’t find it in adoption numbers alone. They’ll need to narrow the builder activation gap, turning more employees who see themselves as users into builders. So go back to the opening question. Ask your people what they’ve actually built and see whether the room goes quiet. The work is making sure it doesn’t. 

The opinions expressed in Fortune.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs of Fortune.

Will Drover is Professor of Entrepreneurship & Innovation and Department Chair at the Neeley School of Business, Texas Christian University, where he serves as Founding Director of Neeley AI Forward, a school-wide initiative, and as the Dean’s Advisor on AI. His work on AI adoption has appeared in MIT Sloan Management Review with coverage in the Wall Street Journal and Los Angeles Times. Drover teaches graduate courses on applied AI, runs executive education programs on AI strategy and leadership, and in practice holds ownership stakes in early-stage AI and robotics ventures.