Tools are available to everyone. Subscription is company-wide. Training sessions have been conducted. And yet, in offices from Wall Street to Silicon Valley, a dark divide is opening up between workers who have artificial intelligence woven into the fabric of their daily work and colleagues who have barely touched it.
The space is not small. According to a New report By analyzing the usage patterns of its more than one million business users, workers from OpenAI 95th percentile of AI adoption At the same companies, the median employee is sending six times more messages to ChatGPT. For specific tasks, the disparity is even more dramatic: Frontier workers send as many messages about coding as their typical peers, and among data analysts, the heaviest users engage a data analysis tool 16 times more often than the median.
This is not a story about accessibility. This is a story about a new form of stability in the workplace that emerges in real time. It’s who gets ahead, who falls behind, and what it means to be a skilled worker in the age of artificial intelligence.
Everyone has the same tools, but not everyone is using them
Perhaps my most surprising find Open Eye Report How does low access explain? Chat GPT Enterprise It is now deployed in more than 7 million workplaces globally, a ninefold increase from a year ago. The tools are the same for everyone. The abilities are the same. And yet usage varies by orders of magnitude.
Among monthly active users – those who have logged in at least once in the last 30 days – 19 percent have never tried a data analysis feature. Fourteen percent never used reasoning skills. Twelve percent never used search. These are not obscure features buried in the subliminals. They are the primary functionality that opens up as transformative for the work of knowledge.
The pattern reverses between daily users. Only 3 percent Most of the people who use chatput every day have never tried data analysis. Only 1 percent gave up reasoning or searching. The implication is clear: the divide is not between those who have access and those who don’t, but between those who have made AI a daily habit and those for whom it remains an occasional novelty.
Employees with more experience are saving dramatically more time
Open Eye Report suggests that AI productivity capabilities are not evenly distributed among all users but are concentrated among those who use the technology most intensively. Workers who engage in nearly seven separate task types—data analysis, coding, image generation, translation, writing, and others—report five times more time savings than those who use only four. Employees who save more than 10 hours per week use eight times more AI credit than those who report no time savings.
This creates a mixed dynamic. Workers who experiment discover more uses. More use leads to greater gains in productivity. Greater productivity potentially leads to better performance reviews, more interesting assignments, and faster development—which in turn creates more opportunities and motivation to deepen the use of AI.
Eighty-five percent of workers surveyed reported being able to complete tasks they previously could not perform, including programming support, spreadsheet automation, and technical troubleshooting. For workers who have embraced these skills, their role boundaries are expanding. For those who aren’t, boundaries can be contracted by comparison.
The corporate AI paradox: $40 billion spent, 95 percent seeing no return
The individual usage differences documented by OpenAI mirror a broader pattern identified by a separate study from MIT’s Project Nanda. Despite $30 billion to $40 billion invested in generative AI initiatives, only 5 percent of organizations are seeing the changes. Researchers call this "Genai distribution" – A gap separates the few organizations that succeed in transforming processes with adaptive AI systems from the majority that get stuck in pilots.
The MIT report was received Limited disruption Across industries: Only two of the nine major sectors – technology and media – demonstrate material business transformation through the use of generative AI. Large firms lead in pilot volume but lag behind in successful deployment.
The pattern is consistent across both studies. Organizations and individuals are buying technology. They are launching a pilot. They are attending training sessions. But somewhere between adoption and conversion, most are stuck.
While official AI projects stall, the shadow economy thrives
An MIT study revealed a striking disconnect: While only 40 percent of companies have purchased an official LLM, employees at more than 90 percent of companies regularly use personal AI tools for work. Almost every respondent reported using the LLM in some form as part of their regular workflow.
"This ‘shadow AI’ often provides a better ROI than formal measures and shows what actually works to eliminate distributions," MIT’s Project Nanda found.
The shadow economy provides an indication of what is happening at the individual level within organizations. Employees who take the initiative — who sign up for personal subscriptions, who experiment on their own time, who figure out how to integrate AI into their workflows without waiting for approval — are moving ahead of peers who wait for official guidance that may never come.
These shadow systems, largely unsanctioned, often deliver better performance and faster adoption than corporate tools. Workers’ sentiments reveal a preference for flexible, responsive tools.
The biggest gaps show up in the technical work that required specialists
The biggest relative differences between frontier and median workers appear in coding, writing, and analysis — particularly task categories where AI capabilities have advanced rapidly. Frontier workers aren’t just doing the same thing faster. They appear to be doing completely different things, expanding into technical domains that were previously inaccessible to them.
between one Chat GPT Enterprise For users outside of engineering, IT, and research, messages about coding have increased 36 percent over the past six months. Someone in marketing or HR who learns to script and automate workflows is going to be a markedly different employee than a peer who hasn’t — even if they hold the same title and started with the same skill set.
Academic research on AI and productivity paints a complex picture. A number of studies presented in the OpenAI report show that AI has a "Equivalent effectfor , for , for , ." Helping disproportionately lower-performing workers close the gap with their higher-performing peers. But the equivalent effect may only apply to the population of workers who actually use AI regularly. A significant proportion of workers are not in this group at all. They remain light users or non-users, even drawn by their more adventurous peers.
The companies are also divided, and this month the gap is widening
The division is not only between individual workers. It exists across organizations.
Frontier firms—those at the 95th percentile of adoption intensity—generate nearly twice as many messages per employee as the median enterprise. For messages going through custom GPTS, purpose-built tools that automate specific workflows widen the gap by up to seven times.
These numbers suggest a fundamentally different operating model. In median companies, AI can be a productivity tool that individual workers use at their discretion. At frontier firms, AI is embedded in the core infrastructure: standardized workflows, consistent custom tools, systematic integration with internal data systems.
Open Eye Report notes that one in four enterprises still haven’t enabled the connectors that give AI access to company data. This is a fundamental step that dramatically increases the utility of the technology. An MIT study found that companies that bought AI tools from specialized vendors succeeded. 67 percent At the time, internal construction had only a one-in-three success rate. For many organizations, the AI ​​era has technically arrived but has not yet practically begun.
Technology is no longer the problem – organizations are
For executives, data presents a daunting challenge. Technology is no longer a barrier. Openei notes that it releases a new feature or capability every three days. Models are moving faster than most organizations can absorb. The disruption has shifted from what AI can do to whether or not organizations are structured to take advantage of it.
"The dividing line is not intelligence," MIT authors write. Enterprise AI issues have to do with memory, adaptability and learning ability. The problems are less about rules or model performance, and more about tools that fail to learn or adapt.
According to leading firms Open Eye Reportconsistently invest in executive sponsorship, data preparation, workflow standardization, and deliberate change management. They create cultures where custom AI tools are created, shared and improved across teams. They track and evaluate performance. They make AI adoption a strategic priority rather than an individual choice.
The rest is leaving it to chance – hoping that workers will discover the tools on their own, experiment on their own time, and somehow spread best practices without infrastructure or incentives. A six-fold difference suggests that this approach isn’t working.
The window to catch up is closing faster than most companies realize
By closing enterprise deals over the next 18 months, there is a shrinking window for vendors and adopters to cross the divide. MIT report Doesn’t last forever. But organizations that find their way soon will be the ones defining the next era of business.
Both reports have caveats. OpenAI data comes from a company with a clear interest in promoting AI adoption. Productivity data is already self-reported by paying users of this product. The MIT study, while independent, relies on interviews and surveys rather than direct measurements. The long-term effects of this technology on employment, wages and workplace dynamics are uncertain.
But the underlying finding—that access alone does not drive adoption, and that adoption varies greatly even among organizations that have made the same tools available to all—is consistent with how previous technologies have varied through the economy. Spreadsheets, email, and the Internet all created similar distributions before eventually becoming universal. The question is how long the current gap persists, who benefits during the transition, and what happens to workers who find themselves on the wrong side of it.
For now, the division is tight. Ninety percent of users said they prefer humans "mission critical work," While AI is "The war is won for easy work." Workers who are pulling ahead are not doing so because their peers have access. They’re moving because they’ve decided to use everyone who’s already there – and keep using it until they figure out what it can do.
6x Gap is not about technology. It’s about behavior. And behavior, unlike software, can’t be deployed with a company-wide rollout.