Artificial intelligence is rapidly becoming a fixture in large enterprises. It\u2019s being funded, deployed and tested across nearly every function that keeps a business running. On the surface, the AI story looks unified: greater investment, more use cases and growing confidence in what the technology can deliver. Look closer, though, and a different picture emerges.<\/p>\n
Organizations aren\u2019t moving along a single path. They\u2019re building entirely different use cases for AI, shaped less by a global strategy than by the discrete pressures they face every day. Some are embedding it deep into core operations. Others are using it to relieve overextended teams. Still others are focused on the customer experience. The result isn\u2019t one AI transformation but several unfolding at once.<\/p>\n
These differences illustrate where AI creates value, where it falls short and how quickly it can move from experimentation to genuine impact. They also reveal a more important truth: The biggest obstacles to AI are structural.<\/p>\n
These are just some of the insights explored in the latest installment of the PYMNTS Intelligence<\/strong><\/b> exclusive series \u201cThe Enterprise AI Benchmark Report<\/strong><\/b><\/a>.\u201d The report draws on a March 2026 survey of 60 verified senior technology executives at U.S. enterprises with at least $1 billion in annual revenue, equally divided across financial services and insurance, healthcare, and media and advertising. The survey tracked adoption across 75 specific AI-supported tasks spanning eight business functions, allowing for mapping not just of whether companies are using AI, but also where and how deeply.<\/p>\n [branded_divider]<\/p>\n Every enterprise sector has jumped into AI, but embracing something and scaling it are very different things. Financial services and insurance firms have reached high adoption (i.e., at least half of companies in this sector are actively using AI for a given task) for 27 of the 75 tasks included in the survey. Healthcare manages just 10. Media and advertising land in between at 16.<\/p>\n In other words, financial services and insurance firms have scaled AI across nearly three times as many tasks as healthcare firms. That gap reveals telling structural differences. The financial services sector has deeply embedded AI into revenue recognition, credit scoring and sales forecasting. Healthcare, by contrast, has concentrated its AI investments in a handful of workforce and operational areas, leaving most tasks unautomated. Media firms show breadth in audience-facing functions but haven\u2019t matched financial services\u2019 penetration elsewhere.<\/p>\n All three sectors report some AI use across every function surveyed. The divergence lies in whether AI is a tool that a few teams experiment with or something most of the organization depends on. Financial services firms cross that threshold often, while the others are still in the early stages.<\/p>\n What makes this finding particularly striking is that it isn\u2019t about technological access or organizational enthusiasm. It\u2019s about the internal stuff: messy data in financial services, siloed systems in healthcare and, in media, a lack of basics such as clear governance and leadership buy-in. Fix those, and AI can potentially scale. Leave them unaddressed, and it can stagnate as a support tool.<\/p>\n\n
The Adoption Spread<\/h2>\n
Financial services and insurance firms are going all in on AI.<\/h3>\n