Generative AI proved to be a significant success with consumers in 2023, with sales exceeding one billion dollars in record time. The enterprise revenue opportunity in 2024 is expected to be even more critical. However, last year, most enterprise engagement with Generative AI was limited to only a few obvious use cases and shipping “GPT-wrapper” products as new SKUs, leading some to doubt its scalability in the enterprise, as well as the potential for profitability and the number of use cases to consider.
Over the past few months, dozens of Fortune 500 and top enterprise leaders completed surveys with 70 more to gain insights into how they use, buy, and budget for Generative AI. The responses to these surveys revealed that leaders are increasing their budgets by almost three times, expanding the number of use cases deployed on smaller open-source models, and transitioning more workloads from early experimentation into production, even though they still have reservations about deploying Generative AI.
This creates a massive opportunity for founders. As a result, the AI startups that
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.
However, building and selling any product for the enterprise requires a deep understanding of customers’ budgets, concerns, and roadmaps. To help founders understand how enterprise leaders are making decisions about deploying Generative AI, the survey outlined 16 top-of-mind considerations about resourcing, models, and use cases below.
Here are some of the key findings from the survey responses:
1. Budgets for Generative AI are growing dramatically, with the average spend across foundation model APIs, self-hosting, and fine-tuning models being $7M across the dozens of companies we spoke to. Nearly every enterprise saw promising early results of Generative AI experiments and planned to increase their spending from 2x to 5x in 2024 to support deploying more workloads to production.
2. Many leaders are reallocating their AI investments to recurring software budget lines. Fewer than a quarter they were reported that Generative AI spending will come from innovation budgets this year. Some leaders are deploying their Generative AI budget against headcount savings, particularly in customer service, citing significant cost savings.
3. Measuring ROI is still an art and a science. Enterprise leaders are currently mostly measuring ROI by increased productivity generated by AI. While relying on NPS and customer satisfaction as good proxy metrics, they are also looking for more tangible ways to measure returns, such as revenue generation, savings, efficiency, and accuracy gains, depending on their use case.
4. Implementing and scaling Generative AI requires the right technical talent, which currently isn’t in-house for many enterprises. It takes highly specialised talent to implement, maintain, and scale the requisite computing infrastructure. Implementation alone accounted for one of the most significant areas of AI spending in 2023 and was, in some cases, the largest. Foundation model providers offered and are still providing professional services, typically related to custom model development, to help enterprises get up and running on their models.
The use of Generative AI has been successful with consumers, with sales exceeding one billion dollars. Business revenue opportunities for Generative AI are expected to be even more critical in 2024. Most enterprise engagement with Generative AI was limited to only a few obvious use cases, leading some to doubt its scalability in the enterprise. Nonetheless, enterprise leaders are increasing their budgets for Generative AI by almost three times and expanding the number of use cases deployed on smaller open-source models. They are transitioning more workloads from early experimentation into production, even though they still have reservations about deploying Generative AI. This creates a massive opportunity for AI startups that build for enterprises’ AI-centric strategic initiatives and move from a services-heavy approach to building scalable products. Budgets for Generative AI are growing dramatically, and many leaders are reallocating their AI investments to recurring software budget lines. Measuring ROI is still an art and a science. Implementing and scaling Generative AI requires the right technical talent, which currently isn’t in-house for many enterprises.
Source: Andreessen Horowitz
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