Microsoft Research has published the fifth annual edition of its New Future of Work report, a synthesis of research on how work is changing. The publication draws on large-scale data analyses, field and lab studies, and theoretical work from inside and outside Microsoft. According to the report summary, generative AI has moved from automating discrete tasks to participating in how people create, decide, collaborate, and learn.
The report frames the distribution of AI’s benefits as “not predetermined” — shaped by individual choices, team norms, organizational systems, and research, rather than by the technology itself. In the report’s words: “The future of work is not something that will simply happen to us. We are actively constructing it.”
Adoption patterns and who benefits
Generative AI is entering workplaces faster than most earlier technologies, but usage varies substantially. A German survey cited in the report found 38 percent of employed respondents using AI at work. Men report using AI at work more often than women, though the report acknowledges it is not yet clear whether that gap is driven by occupational distributions, comfort with new tools, or other factors.
High-income countries still lead overall usage, but the fastest growth is in low- and middle-income regions. The report identifies a specific language access problem: when local languages are poorly served by AI models, people switch to English to get reliable results. Without investment in multilingual model development, the report states, AI risks reinforcing existing divides.
The report finds adoption decisions inside organizations are driven more by culture than strategy. Employees resist tools they perceive as designed to replace them — described as “a common concern among workers” — and many useful applications emerge from employees discovering what helps and sharing it with colleagues rather than from top-down initiatives.
An analysis of millions of Anthropic Claude conversations cited in the report found 37 percent of usage tied to software and mathematical occupations. A separate study of Microsoft Copilot conversations found high applicability to information workers across sales, media, tech, and administrative roles.
Productivity, “workslop,” and labor market effects
Enterprise users of AI report saving 40–60 minutes per day, according to the report. But in one U.S. survey, 40 percent of employees said they had received “workslop” — AI-generated content that appears polished but is not accurate or useful — in the past month.
The report’s labor market data shows no clear aggregate effects on unemployment, hours worked, or job openings. But AI appears to be reducing opportunities for younger workers. Empirical evidence cited in the report shows employment for workers aged 22–25 in highly AI-exposed jobs declined by 16 percent relative to similar but less-exposed roles. Hiring into junior positions slows after firms adopt AI. The report flags a concern: automating entry-level roles may undermine how expertise is built over time, since those roles are where workers historically develop skills.
Roles that mention AI skills in job postings are nearly twice as likely to also emphasize analytical thinking, resilience, and digital literacy, according to the report. Demand for tasks more easily automated — routine data work, translation — continues to fall.
Human-AI collaboration and where it breaks down
The report describes a structural problem with current AI systems: they skip the conversational grounding that humans use — clarifications, acknowledgements, follow-up questions — generating responses that assume understanding rather than build it.
Systems like CollabLLM, which prompt AI to ask clarifying questions across multiple turns, are cited as showing improved task performance. The report identifies trust as another variable: AI that does not understand a person’s objectives can lead to worse outcomes than using no AI at all, and the report says people frequently overestimate AI capabilities in ways that distort their judgment about when to rely on it.
The report also notes a role shift: software developers who once wrote code from start to finish increasingly review and refine AI-generated suggestions. Writers and designers act more as curators and editors. These shifts demand new skills — crafting prompts, vetting outputs, maintaining quality oversight — and the report notes that current chat-based interfaces are often too limited for these evolving workflows.
AI systems are designed to work for individuals, not teams, and when people use AI as a team they often underperform relative to an individual using AI, the report finds. It describes a growing research effort on AI for team and group interaction as a distinct problem not yet adequately addressed.