16 Changes to the Way Enterprises Are Building and Buying Generative AI

Generative AI took the consumer landscape by storm in 2023, reaching over a billion dollars of consumer spend¹ in record time. In 2024, we believe the revenue opportunity will be multiples larger in the enterprise.

Last year, while consumers spent hours chatting with new AI companions or making images and videos with diffusion models, most enterprise engagement with genAI seemed limited to a handful of obvious use cases and shipping “GPT-wrapper” products as new SKUs. Some naysayers doubted that genAI could scale into the enterprise at all. Aren’t we stuck with the same 3 use cases? Can these startups actually make any money? Isn’t this all hype?

Over the past couple months, we’ve spoken with dozens of Fortune 500 and top enterprise leaders,² and surveyed 70 more, to understand how they’re using, buying, and budgeting for generative AI. We were shocked by how significantly the resourcing and attitudes toward genAI had changed over the last 6 months. Though these leaders still have some reservations about deploying generative AI, they’re also nearly tripling their budgets, expanding the number of use cases that are deployed on smaller open-source models, and transitioning more workloads from early experimentation into production.

This is a massive opportunity for founders. We believe that AI startups who 1) build for enterprises’ AI-centric strategic initiatives while anticipating their pain points, and 2) move from a services-heavy approach to building scalable products will capture this new wave of investment and carve out significant market share.

As always, building and selling any product for the enterprise requires a deep understanding of customers’ budgets, concerns, and roadmaps. To clue founders into how enterprise leaders are making decisions about deploying generative AI—and to give AI executives a handle on how other leaders in the space are approaching the same problems they have—we’ve outlined 16 top-of-mind considerations about resourcing, models, and use cases from our recent conversations with those leaders below.

Read the Rest from Andreesen Horowitz.

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