Generative AI

Generative AI refers to artificial intelligence systems that create new content — text, images, audio, video, or code — rather than simply analyzing or classifying existing data, the way earlier generations of machine learning primarily did. The category exploded into mainstream awareness with the release of ChatGPT in late 2022 and has since become one of the fastest-adopted technology categories in business history, moving from novelty to genuine enterprise infrastructure in under four years.

Market size estimates for generative AI vary considerably depending on methodology and what's counted as "generative AI" revenue specifically (the underlying models, the applications built on them, or both), with credible 2026 estimates ranging from roughly $90 billion to $160 billion depending on scope, and virtually every forecaster agreeing the category is growing at a compound annual rate well above 25%. What's more consistently measured, and arguably more meaningful, is adoption: an estimated 65% of organizations now use generative AI in at least one business function, roughly double the rate of just ten months earlier, and more than 80% of enterprises are expected to have deployed generative-AI-enabled applications by the end of 2026. Enterprise spending specifically on generative AI reached an estimated $37 billion in 2025, more than tripling the roughly $11.5 billion spent in 2024.

The technology's practical business applications have broadened considerably beyond the chatbot format that first made it famous. AI-powered copilots and virtual assistants embedded directly into existing software (Microsoft 365 Copilot, Google's Gemini in Workspace, and similar tools from countless other software vendors) now automate substantial portions of routine knowledge work — drafting documents, summarizing meetings, writing and reviewing code, and increasingly taking multi-step actions across software systems with only limited human oversight, a category increasingly described as "agentic AI." That shift — from generative AI as a content-creation tool to generative AI as an increasingly autonomous digital worker — is arguably the more consequential development of 2026 relative to raw model capability improvements, since it changes not just what tasks AI can do but how directly it can act on a business's behalf.

The competitive and cost dynamics of generative AI remain in genuine flux. Training and running the largest, most capable models requires enormous, continuously growing capital investment — a major driver of the hundreds of billions of dollars in AI infrastructure spending from Microsoft, Google, Amazon, and Meta — while a parallel trend toward smaller, more efficient models has made "good enough" generative AI capability available at a fraction of frontier-model cost, widening who can practically build AI-powered products rather than narrowing the field to only the best-funded companies. For businesses evaluating generative AI, the core practical question has shifted from "should we adopt this" — largely settled, given adoption rates already above 80% among larger enterprises — to which specific workflows benefit most from automation, how to manage the real risks (factual errors, data privacy, and overreliance on outputs that aren't being adequately checked), and how to measure return on what has become, for many companies, one of their largest new technology budget lines.