Harvard Business Review: AI adoption shifts

Harvard Business Review on June 1 published new research showing generative AI use widening across both work and personal settings, with technical and workflow uses gaining ground alongside earlier consumer-style experimentation. The article, “How People Are Really Using AI in 2026,” was written by Marc Zao-Sanders and describes the latest installment of his annual “AI in the Wild” study. HBR said the 2026 edition analyzed 12,637 AI use cases drawn from a larger database of nearly 50,000 records collected between March 2025 and February 2026. ### What did HBR actually publish on June 1? Marc Zao-Sanders said the June 1 article was the third edition of an annual study tracking how people use generative AI in practice. HBR said the research used a “social listening” approach, adding LinkedIn, TikTok and YouTube to earlier source pools that already included Reddit, Quora and articles. The article said the study was designed to answer a practical question — how people are actually using the technology now — rather than to measure corporate spending or model performance. (hbr.org) HBR also said trends from 2025 to 2026 should be read as shifts in emphasis, not as abrupt breaks with prior patterns. ### Where does the “hype to habit” idea come from? HBR said generative AI has been in public use for about three-and-a-half years and that adoption is now spreading across “an ever-widening range of uses.” The article described continued momentum in “vibe coding,” wider use of agentic workflows, and growing platform scale, citing 900 million regular ChatGPT users and more than 750 million Gemini users. (hbr.org) That framing supports the idea that AI use is becoming more routine, but the article did not present the shift in the language of a formal enterprise survey. Instead, Zao-Sanders wrote that the breadth and depth of use cases had grown and that both emotional and technical applications remain in the mix as the overall user base expands. ### Which work uses are getting the most attention? (hbr.org) HBR’s June 1 article said technical uses remain prominent even after a year in which emotional use cases had ranked highly. The piece specifically cited “vibe coding” and agentic workflows as signs of more embedded, task-level use. A separate February 3 HBR article by Amanda Pratt and Melissa Valentine described the same pattern in workplace terms. (hbr.org) They wrote that the payoff from generative AI comes when employees define valuable problems inside workflows, test tools against those problems, and integrate successful practices into day-to-day work. They identified those steps — problem definition, tool selection, experimentation and workflow integration — as central to stronger adoption. ### What does HBR say companies are struggling with as use expands? A February 26 HBR article by Jazz Croft, Sumer Vaid, Lily Cheng and Ashley Whillans said research based on interviews and focus groups with 35 executives at global enterprises found leaders wrestling with “continuous disruption,” disputed definitions of value and mixed emotional responses as they try to scale AI. (hbr.org) A May 25 HBR article by Liz Fosslien and Mollie West Duffy said AI has accelerated the speed of work to the point that managers are becoming a bottleneck. The article cited Atlassian research saying 89% of leaders agreed AI had accelerated work speed, while 87% of knowledge workers said teams lacked enough time or capacity to coordinate. ### So what is the cleanest way to read this HBR story? (hbr.org) The June 1 HBR article presents a usage study, not a quarterly market report. Its most concrete evidence is the size and timing of the dataset — 12,637 use cases identified from nearly 50,000 records collected over the prior year — and its clearest workplace examples are coding and workflow-related uses rather than one-off novelty prompts. (hbr.org) The next reference point is the article itself on HBR, where Zao-Sanders’s June 1 piece sits alongside other 2026 HBR reporting on adoption barriers, management strain and enterprise-scale rollout. (hbr.org)