Inside most companies using AI right now, a divide is forming between two kinds of employees: the ones using AI fluently across their core work, and the ones still experimenting around the edges. New survey data puts a number on how leaders perceive that divide: 5X. And the career consequences are starting to show.
What Does the 2026 Research Actually Show?
In early 2026, enterprise AI company Writer published its AI Adoption in the Enterprise report, surveying 2,400 employees and C-suite leaders worldwide in partnership with independent research firm Workplace Intelligence. The core finding is stark.
One caveat worth naming up front: Writer's employee sample included workers already using generative AI at work, so the findings are most useful for organizations already in the AI adoption cycle, not a clean snapshot of the full workforce.
Employees the report calls AI super-users are those who use AI tools actively and fluently across their core job responsibilities. They save nearly 9 hours every week. Employees who use AI minimally or not at all save closer to 2 hours.
Seven hours. Every week.
That difference compounds across a year into something that looks entirely different on a performance review.
The productivity number is even wider. Eighty-seven percent of business leaders say their AI super-users are at least 5X more productive than employees who have not embraced AI tools.
AI super-users are at least 5X more productive than employees who have not embraced AI tools, according to 87% of business leaders surveyed.
Source: Writer, AI Adoption in the Enterprise, 2026Does That Productivity Difference Show Up in Careers?
The data says yes. Clearly.
In the same study, AI super-users were 3 times more likely to have received both a promotion and a pay raise in the past year.
The output difference is visible. Seven extra hours of productive capacity per week, compounded across months, shows up as better results, faster delivery, and the ability to absorb work that colleagues cannot. That likely helps explain the career split, though the survey shows correlation, not causation.
Likely, the manager who got the promotion did not tell anyone they were an AI power user. They just produced.
What Leadership Is Planning Next
The data gets more pointed when you look at what companies are preparing for. Ninety-two percent of C-suite executives told Writer they are actively building a class of AI-proficient employees. The other finding from that same survey: 60% of companies plan to lay off employees who cannot or will not adopt AI tools.
That number generated strong reactions when Writer published it in April. Some read it as a threat. Some dismissed it as vendor-survey overreach.
When you pair it with the productivity data, the concern becomes easier to understand. Companies are starting to see the divide between AI-fluent employees and slower adopters. Many leaders are preparing to rethink how they evaluate performance, and 60% say they plan layoffs for employees who cannot or will not adopt AI.
The productivity divide between AI super-users and everyone else is also a career divide. Seven extra hours a week, compounded across months, shows up in pay, in promotions, and in 60% of companies, in retention decisions.
Why Are 79% of Companies Still Struggling?
Even with AI super-users delivering real gains, 79% of organizations say they face challenges in AI adoption. That is a double-digit increase from 2025. Only 29% see significant ROI from their generative AI investments.
The disconnect is structural.
Individual productivity wins do not automatically scale into organizational results. A team of five people where two are AI super-users does not produce 5X organizational output. It produces two people carrying more than their share, and three people who are uncertain what they are supposed to be doing with these tools.
Grant Thornton's 2026 AI Impact Survey of 950 senior executives found that leaders are split on what drives AI ROI, with CFOs and COOs most likely to point to strategy. The same research shows operations leaders are far less confident that AI strategy and workforce readiness are fully in place.
Companies are deploying tools. Most have not built the training structure, the proficiency framework, or the governance layer that turns individual wins into shared ones.
The super-users are pulling ahead. The rest of the organization has not caught up.
What This Means for Indiana Businesses
Indiana's mid-market companies in manufacturing, logistics, healthcare services, and professional services are inside this same dynamic. The divide is not a big-tech story.
The pattern shows up across industries. A company gave employees access to AI tools with no training, no framework, and no way to measure who is actually proficient. The super-users figured it out on their own. Everyone else is still experimenting with basic prompting or not using the tools at all.
The 7 Levels of AI Proficiency exists precisely because that pattern is measurable. Super-users operate at Level 3 (AI Fluent) and above. They have moved past occasional prompting into consistent, integrated use of AI across their actual job responsibilities. Level 4 (AI Architect) and above are building systems and context layers that multiply their output further. Employees at Level 1 (AI Aware) and Level 2 (AI Capable) are the ones the productivity data describes as laggards.
The question for an Indiana CEO or HR leader is not whether this divide exists on their team. It does. The question is whether they know where each person stands and whether there is a framework in place to move them up.
That is what The 7 Levels of AI Proficiency assessment measures. It places you across seven stages in about ten minutes and identifies a specific next step.
Related reading: Level 3: AI Fluent in The 7 Levels of AI Proficiency.
Sources
- Writer. “Enterprise AI Adoption in 2026: Why 79% Face Challenges Despite High Investment.” April 2026. writer.com/blog/enterprise-ai-adoption-2026
- Business Wire. “WRITER Survey Finds 60% of Companies Plan to Lay Off Employees Who Won’t Adopt AI.” April 2026. businesswire.com
- Grant Thornton. “Without C-suite alignment, AI performance sputters.” 2026. grantthornton.com/insights/articles/advisory/2026/without-c-suite-alignment-ai-performance-sputters
- Challenger, Gray & Christmas. “April Job Cuts Rise 38% from March; YTD Cuts Down 50%.” May 2026. challengergray.com
Frequently Asked Questions
What is an AI super-user?
An AI super-user is an employee who uses AI tools actively and fluently across their core job responsibilities. The 2026 Writer Enterprise AI Adoption Report, which surveyed 2,400 employees and C-suite leaders, defines super-users as employees saving close to 9 hours per week through AI use, compared to 2 hours for minimal users. According to 87% of business leaders surveyed, super-users are at least 5X more productive, and the same study found they were 3X more likely to have received a promotion or pay raise in the past year.
How can a company find out which employees are AI super-users vs. laggards?
A structured proficiency assessment is the most reliable method. Self-reporting is unreliable because employees who are laggards often do not know they are. A framework like The 7 Levels of AI Proficiency assessment places employees across seven capability stages and identifies specific skill shortfalls, making the divide visible and actionable rather than a matter of management impression.
What is the connection between the AI super-user divide and The 7 Levels of AI Proficiency?
The 7 Levels of AI Proficiency framework maps directly to what the 2026 data shows. Super-users typically operate at Level 3 (AI Fluent) or above, meaning they use AI consistently for real work, not just experiments. Employees at Level 1 (AI Aware) or Level 2 (AI Capable) are the ones the productivity studies describe as laggards. The framework measures where employees actually are, not where they say they are, and identifies the specific capabilities that move someone from one level to the next.
Find your AI Proficiency level
The free 7 Levels assessment places you across seven stages of AI capability. Under ten minutes. Research-backed scoring.