{"slug": "why-enterprise-ai-roi-goes-beyond-efficiency", "title": "Why enterprise AI ROI goes beyond efficiency", "summary": "Only 7% of organizations succeed across all three markers of AI success — proving value, transforming workflows, and aligning the organization around AI — according to the Wharton School's fourth annual AI adoption report, produced with GBK Collective from a survey of 1,416 US enterprise leaders and employees. The report found 35% of departments that track at least one growth measure report strongly positive returns versus just 13% of those tracking efficiency alone, while 58% of managers with a newly designated Chief AI Officer report strongly positive ROI. About 40% of respondents judge ROI by hours saved and only 27% track net-new revenue, with 62% using AI daily and 91% using it multiple times a week.", "body_md": "Though enterprises are eager to deploy AI, returns are still eluding many of them.\n\nThe Wharton School of the University of Pennsylvania has published its [fourth annual report on the state of AI](https://ai.wharton.upenn.edu/wp-content/uploads/2026/10/Wharton-GBK-AI-Adoption-Report_Exec-Summary_FINAL.pdf) since the launch of ChatGPT, with the findings becoming one of the most closely watched bellwethers in the industry. Now, the report turns to the question everyone is asking: who is winning with AI, and what is their secret?\n\nThe survey, created in collaboration with market research and analytics firm GBK Collective, surveyed 1,416 US enterprise leaders and entry to mid-level employees, finding that there is a widening gap between organizations that simply implement AI and those who are seeing more meaningful transformations. The three markers of AI success it identified are proving value, transforming workflows, and aligning the organization around AI. However, only 7% of organizations surveyed succeed across all three metrics.\n\n\"Companies are now moving into the harder phase: integrating AI into how work gets done, learning new skills, changing processes, reorganizing departments. It’s like changing the engine while the car is running,\" Brian Smith, executive vice president of the AI, cloud and enterprise technology practice at GBK, told The Deep View. \"That is hard work, and a lot less exciting than the early experimenting.\"\n\nThe report calls this year the fourth \"wave\" of AI, where the tech has been embedded in the core of enterprise operations, with 62% using the tech daily, 91% using it multiple times a week, and 73% reporting significantly changed workflows as a result.\n\nYet, the report identifies major flaws in adoption, including how companies are actually measuring ROI.\n\n- A large portion of respondents, around 40%, reported judging return on investment by hours saved. Net-new revenue was the least utilized metric for success, with 27% reporting that they track this.\n- However, the departments tracking returns by more than time saved are doing better comparatively: 35% of departments that track at least one growth measure report strongly positive returns, against just 13% of those tracking efficiency alone.\n- Only 22% of managers look to AI to grow the business, whether through new revenue, new products, or better customer experience. Meanwhile, more than half are focused on improving productivity or cutting costs.\n\nOne way companies can implement more meaningful deployments of AI include having a designated Chief AI Officer position: 58% of managers with a newly designated CAIO report strongly positive ROI. Additionally, as enterprises lean into [AI efficiency over tokenmaxxing](https://www.thedeepview.com/articles/why-ai-s-tokenmaxxing-obsession-ran-out-of-steam), in tracking AI spend correlates with higher reported return.\n\n## Our Deeper *View*\n\nPerhaps the most meaningful part of this report is seeing how the trajectory has changed over the past years. It allocates the first two \"waves\" of AI as the years 2023 to 2024 as exploration, marked by trial, error and experimentation by early adopters. Then, by wave three, which was 2025, regular usage of AI became more deeply integrated. Now, in wave four, as the report states that \"adoption is no longer the question.\" Understanding this business mindset and why so many enterprises are willing to invest so much money so quickly also helps understand why the AI labs have redirected their attention to working professionals, creating two forces that are largely driving the AI market.", "url": "https://wpnews.pro/news/why-enterprise-ai-roi-goes-beyond-efficiency", "canonical_source": "https://www.thedeepview.com/articles/why-enterprise-ai-roi-goes-beyond-efficiency", "published_at": "2026-10-16 19:53:37+00:00", "updated_at": "2026-10-16 20:53:44.254228+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-research", "ai-products"], "entities": ["Wharton School of the University of Pennsylvania", "GBK Collective", "Brian Smith", "The Deep View", "ChatGPT"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/why-enterprise-ai-roi-goes-beyond-efficiency", "markdown": "https://wpnews.pro/news/why-enterprise-ai-roi-goes-beyond-efficiency.md", "text": "https://wpnews.pro/news/why-enterprise-ai-roi-goes-beyond-efficiency.txt", "jsonld": "https://wpnews.pro/news/why-enterprise-ai-roi-goes-beyond-efficiency.jsonld"}}