“AI doesn’t adopt itself, and the finance leaders pulling ahead aren’t just buying tools, they’re building the trust, training, and workflows that make those tools actually work.”
-Paul Ursich
The data on AI at work no longer tells a story of curiosity and early adoption. It tells the story of a workforce splitting into two distinct camps, with the gap between them widening. For finance leaders managing teams, evaluating technology investments, and considering their organizations’ efficiency, the new Gallup findings are worth reading carefully.
What the Gallup Numbers Actually Show
Gallup’s February 2026 workforce survey, conducted with 23,717 employed U.S. adults, found that roughly 3 in 10 employees are now frequent AI users at work, meaning they use it daily or several times per week. Another 2 in 10 are infrequent users. That leaves approximately half the U.S. workforce using AI at work once a year or not at all.
Among workers at organizations that have adopted AI tools, about two-thirds report that AI has had an “extremely” or “somewhat” positive impact on their individual productivity and efficiency. The benefit is not evenly distributed, however. Workers in managerial, healthcare, and technology roles are more likely to report productivity gains than those in service jobs. About 7 in 10 leaders using AI at least a few times a year say it has made them more efficient, compared with just over half of individual contributors who say the same.
The productivity signal is clear for those using the tools. The adoption signal is considerably murkier.
Why Workers Aren’t Using It
Among employees who have AI tools available at their organization but choose not to use them, 46% say they simply prefer to work the way they currently do. About 4 in 10 cite ethical concerns, data privacy worries, or skepticism about AI’s ability to help with their specific work. Another quarter say they’ve tried AI and didn’t find it useful. About 2 in 10 say they don’t feel prepared to use it effectively.
These are not irrational responses. The Gallup data reflects real friction points that any organization deploying AI tools will encounter. Preference for existing workflows is arguably the strongest barrier, because it doesn’t require a negative experience to sustain. It simply requires inertia.
Data privacy concerns are particularly relevant in finance and accounting environments, where the sensitivity of financial records, client data, and tax information creates legitimate caution around which AI tools can be trusted with which information. This isn’t obstruction. It’s due diligence.
The Job Security Question Is Getting Louder
The poll found that 18% of U.S. workers now consider it “very” or “somewhat” likely that their current job will be eliminated within the next five years due to new technology, automation, or AI. That is up from 15% in 2025. Workers at companies that have already adopted AI express even greater concern: 23% consider job elimination at least “somewhat” likely.
Specifically for finance and accounting functions, this anxiety is neither unfounded nor the whole story. Routine transaction processing, reconciliation, data entry, and report generation are all areas where AI is already automating tasks that used to require significant human effort. That is real and accelerating.
What’s also real: the demand for financial judgment, client advisory, strategic interpretation of data, and oversight of automated systems is not going away. It is, in many cases, growing. The Gallup data show that workers in management roles who use AI report greater productivity gains than individual contributors, suggesting the technology is augmenting decision-making more effectively than it is replacing it.
What This Means for Finance Leaders
The Gallup survey points to a practical challenge that sits squarely on the desk of any CFO or finance director managing a team: AI adoption is not self-executing. Tools do not produce results because they exist. They produce results when people trust them, know how to use them, and have workflows designed to put them to work.
Organizations that have successfully implemented AI in finance functions have typically done a few things well: they identified specific, high-volume, low-judgment tasks as the starting point for automation; they addressed data privacy and security concerns with explicit policies before rollout; and they invested in helping their teams develop the prompting and oversight skills that make AI outputs useful rather than unreliable.
The 20% of non-users who say they don’t feel prepared is a solvable problem. The 46% who simply prefer to keep working the way they do requires a different kind of conversation, one about what the technology actually changes for their work and what it does not.
Wiss Technology Solutions and Outsourced Accounting
Wiss works with mid-market companies on both sides of this challenge. Through our Wiss Labs innovation division, we help finance teams evaluate where AI automation creates real efficiency gains in their specific accounting environment, and where human judgment remains the critical input. For companies that prefer to outsource the AI-powered accounting function entirely, our outsourced accounting model combines experienced U.S.-based accountants with Basis AI’s automation platform, typically reducing monthly close cycles and accounting costs significantly.
If your organization is working through what AI adoption looks like for your finance function, contact the Wiss technology and outsourced accounting team.
AI Disclosure: This article was produced with AI writing assistance and reviewed by the Wiss editorial team. Original reporting by Matt O’Brien and Linley Sanders, Associated Press, published April 13, 2026. Survey data from Gallup’s quarterly workforce panel, conducted February 4–19, 2026, with 23,717 employed U.S. adults.
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