AI is spreading across industries, but most use remains selective. Experts say workers who apply it thoughtfully can help firms improve how work gets done.
For businesses weighing AI adoption, the clearest near-term shift may be in how employees handle tasks, rather than in how many people they employ. A Google study found AI use across many industries and occupations, but said the technology mostly supports workers and automates only a small share of tasks. That distinction matters to owners deciding where to invest time, and to employees deciding which skills to build.
The AI & Economy ATLAS report, released in July, drew on 15 million interactions with Google AI products, including the Gemini app and AI Mode. It found AI in use across all industry sectors and 68% of occupations, but applied to approximately 21% of tasks. The report describes a tool being used selectively for support with ideas, strategy and related work, while full automation remains rare.
That pattern suggests businesses are still working out how AI fits into daily operations. Adoption alone doesn’t show whether a company has changed its staffing, workflows or output. In a separate analysis, DoubleTrack found that many businesses using AI had made no corresponding organizational changes. Some assigned the work to existing staff; a smaller share hired people with formal AI training or brought in consultants.
Employers want practical fluency
Kass Moore, director of Career Services and Workforce Development at the University of Nevada, Las Vegas, said job descriptions increasingly mention familiarity with AI. Employers aren’t necessarily seeking technical specialists, she said. They want people who can use the tools to help with their work and judge when their use is appropriate.
“Nobody has to be an expert,” Moore said. The value, in her view, comes from using AI strategically without allowing the result to sound or feel detached from human judgment. For a small firm, that points to a practical starting place: identify a task where assistance could help, then make sure a person remains responsible for the work and its quality.
Moore said some students interpret a difficult job market as evidence that work is disappearing because of AI. She acknowledged that finding work is tough, but attributed the conditions she described to broader economic effects rather than AI alone. Her advice was to adapt as workplace expectations change.
Moore’s office offers guidance on using AI in job searches. She advises applicants against having a platform write a cover letter for them. Instead, they can ask for suggestions based on a role’s description, then make their own choices about what to include. AI can also help someone explore possible roles while they remain in control of the search.
A skills gap creates an opening
Andy Boettcher, chief innovation officer at DoubleTrack, a data and AI consultancy based in Eden Prairie, Minnesota, framed the employment question around workers’ ability to use the technology. “AI is not coming for your job; people that know how to use AI to do the job better—those are the people that are coming for jobs,” he said.
DoubleTrack’s summer analysis found that Nevada had one of the widest gaps between demand for AI skills and the available local talent. Nearly 26% of Nevada businesses used AI, while the state had 7.1 AI specialists per 1,000 jobs. Boettcher said local firms would like to find people nearby who can help them put the tools to work, rather than turning to large consultancies elsewhere.
For workers and owners, the gap may make hands-on learning especially useful. Boettcher said people don’t necessarily need the multiyear degree that might once have been expected to enter this area. He encouraged people to try AI platforms through conversation: ask questions, test how they respond and invite them to challenge an idea. The aim is to learn where the tool helps a person think through a problem, then carry that experience into workplace tasks.
That approach also reflects DoubleTrack’s findings about barriers to adoption. Boettcher said the main obstacles are understanding and trust, rather than cost or a shortage of talent alone. If staff don’t know what a system can do or don’t trust its output, buying access won’t automatically change the work. Teams need a reason to use it, a way to assess its results and enough practice to understand its limits.
Productivity gains take time to reach the workplace
Steve Miller, an economics professor and research director at UNLV’s Lee Business School Center for Business and Economic Research, said concerns about technology making jobs obsolete have recurred through earlier waves of change. The gains from new tools can take years to show up in productivity and output, he said, in part because workplace use determines how the technology affects the economy.
Miller pointed to the computerization era, when economists questioned productivity gains for years. AI could spread through businesses faster, he said, but that outcome isn’t assured. Firms still have to work out how to incorporate it into their operations.
Southern Nevada’s hospitality and entertainment businesses are likely to use more AI too, Miller said. What that looks like in hands-on work remains uncertain. He raised the example of changing bed sheets or moving linens from rooms as a question, not a settled use case. The broader point is that adoption will vary by task and workplace.
Miller described productivity as output relative to labor. If AI helps a business produce more with fewer labor hours, the effect on employment depends partly on what happens to the resulting savings. Reinvesting those savings in the economy can create demand for more workers, he said. Whether that happens depends on how firms deploy their gains.
For business owners, the near-term lesson is to connect AI experiments to specific work and judge the results before expanding them. For employees, basic fluency can help them contribute to that process. The technology may change individual tasks before it changes whole roles, and people who can combine AI assistance with sound judgment are positioned to help their teams decide what should change next.
