AI has quickly gone from pilot projects to everyday practice. In a recent Gallup poll, almost half of American workers said they now use AI at least sometimes, about double the proportion in the previous wave of its survey. It remains a smaller share on a daily basis, about 10 percent, but the trend line clearly points upwards as tools get easier to use and are trusted within companies.
Adoption spreads across offices and industries
The Gallup data shows a sharp divergence by industry. Technology and information systems tops the list with 76% of staff saying they use it, followed by finance (58%) and professional services (57%). Retail (33%), healthcare (37%) and manufacturing (38%) have the lowest adoption, with regulated workflows, device capabilities and safety-critical environments limiting deployment.
Knowledge workers are leading the charge, but awareness is uneven. Almost a quarter of workers say they are unsure whether their employers use AI. Non-managers are less likely to feel unsure than their managers, while executives experience the most awareness. That communications gap is significant: when people don’t see sanctioned use cases, they fall back on shadow tools or avoid AI altogether.
The best jobs for AI right now across key professions
Software engineers are among the most frequent users, relying on coding assistants to produce boilerplate, write unit tests and recommend fixes. Gallup points out that workers who are using coding and analytics tools tend to use AI at more than double the rate of other categories — a sign that technical roles, in particular, are operationalizing AI, not just experimenting with it.
AI helps data scientists and analysts explore datasets, translate between programming languages, and write small SQL or Python code snippets. In finance, both equity and credit analysts lean on AI to automatically summarize earnings calls and pull key metrics from filings, as well as write the first draft of investment notes — work that makes use of language and numbers where generative methods can deliver while still having a human looking over their readings.
Professional services — consultants, accountants and legal teams — use AI for research synthesis, memo drafting and slide creation. Other marketing and communications-related positions require the same type of writing and editing tools to brainstorm campaigns, communicate with segments, and maintain a consistent brand, at pace. And increasingly, customer support agents are working side-by-side with AI copilots within CRM systems to suggest responses and surface knowledge articles; research from MIT and Stanford found that such an arrangement boosted productivity by double digits, with the highest lift for less experienced agents.
And HR and recruiting teams make up the early majority, using AI to screen resumes for skills, write job descriptions and prepare interview guides. The common denominator: roles that can take a long time synthesizing information or writing first drafts have the fastest adoption.
What workers really do with AI in their daily jobs
Top uses among survey respondents, according to Gallup, are pragmatic:
- Consolidating information (42%)
- Generating ideas (41%)
- Learning new topics (36%)
- Automating basic tasks (34%)
Chatbots are the most prevalent, deployed by 61% of AI implementers. Writing and editing assistants come in at 36%, and coding copilots at 14%. Coding tools are less commonly used across the board, but among those who use them, they use them more extensively — a dynamic also at play with data science and analytics platforms. That intensity suggests where durable productivity gains are building up first.
The enterprise race in AI and what’s coming next
Vendors are working hard to pivot to the enterprise. OpenAI has signaled a greater focus on business-grade features, as Microsoft Copilot and Anthropic’s Claude compete to embed into office suites, developer tools and contact centers. “Buyers are becoming more savvy in how they evaluate vendors and look for governance, privacy, auditability and whether it integrates with their data stack,” she added. “Not just model quality.”
Look for that jobs leaderboard to keep changing as guardrails mature in regulated fields and as line-of-business platforms add native AI. The World Economic Forum’s continual study of job task change and data from LinkedIn about workforce skills both indicate an exponential increase in AI-related skills among knowledge workers, with correlative growth in the importance of complementary skills: data literacy, prompt design, and critical assessment.
The playbook for workers is pragmatic: identify repetitive tasks, pilot AI on low-risk drafts, measure the time saved and iterate. Clarity is a currency for leaders — publish acceptable-use policies, fund training anchored in real workflows and measure results beyond novelty. One thing is clear from the Gallup numbers: AI at work is no longer an experiment. It’s a skill that is reinventing how top jobs are done, and the companies that systematize it will surge ahead.
