VCs predict enterprises will spend more on AI in 2026 — through fewer vendors
The Shift in Enterprise AI Spending: A New Era of Concentration
As the world of artificial intelligence (AI) continues to evolve, investors are predicting a significant shift in the way enterprises approach AI spending. According to a recent survey of 24 enterprise-focused venture capitalists, the majority believe that enterprises will increase their budgets for AI in 2026, but not for everything. Instead, they expect a concentration of spending on a narrow set of AI products that have proven to deliver results.
A Move Away from Experimentation
Andrew Ferguson, a vice president at Databricks Ventures, predicts that 2026 will be the year that enterprises start consolidating their investments and picking winners. "Today, enterprises are testing multiple tools for a single-use case, and there's an explosion of startups focused on certain buying centers like go-to-market, where it's extremely hard to discern differentiation even during proof of concepts," Ferguson said. "As enterprises see real proof points from AI, they'll cut out some of the experimentation budget, rationalize overlapping tools, and deploy that savings into the AI technologies that have delivered."
This shift away from experimentation is not unique to AI. Many industries have seen a similar trend, where companies focus on proven technologies and consolidate their spending. However, the implications for AI startups are significant. With a concentration of spending on a narrow set of AI products, startups that fail to deliver results may find themselves struggling to secure funding and partnerships.
A Bifurcation in the Market
Rob Biederman, a managing partner at Asymmetric Capital Partners, agrees that the market will bifurcate, with a small number of vendors capturing a disproportionate share of enterprise AI budgets. "Budgets will increase for a narrow set of AI products that clearly deliver results, and will decline sharply for everything else," Biederman said. "We expect a bifurcation where a small number of vendors capture a disproportionate share of enterprise AI budgets while many others see revenue flatten or contract."
This bifurcation has significant implications for AI startups. Those that operate hard-to-replicate products, such as vertical solutions or those built on proprietary data, may still be able to grow. However, startups with products similar to those offered by large enterprise suppliers, like AWS or Salesforce, may start to see pilot projects and funding dry up.
Investor Insights
When asked how they know that an AI startup has a moat, multiple VCs said companies with proprietary data and products that can't easily be replicated by a tech giant or large language model company are the most defensible. This is a key takeaway for AI startups looking to secure funding and partnerships. By focusing on proprietary data and products, startups can differentiate themselves from the competition and build a sustainable business model.
A New Era of AI Spending
The shift in enterprise AI spending is a significant development for the industry. As investors predict a concentration of spending on a narrow set of AI products, startups must adapt to this new reality. By focusing on proprietary data and products, startups can build a sustainable business model and secure funding and partnerships. However, those that fail to deliver results may find themselves struggling to survive in a crowded market.
Implications for Startups
The implications for AI startups are significant. With a concentration of spending on a narrow set of AI products, startups must demonstrate a clear value proposition and deliver results. This requires a focus on proprietary data and products, as well as a deep understanding of the customer needs and pain points. By adapting to this new reality, startups can build a sustainable business model and secure funding and partnerships.
Conclusion
The shift in enterprise AI spending is a significant development for the industry. As investors predict a concentration of spending on a narrow set of AI products, startups must adapt to this new reality. By focusing on proprietary data and products, startups can build a sustainable business model and secure funding and partnerships. However, those that fail to deliver results may find themselves struggling to survive in a crowded market. As the industry continues to evolve, one thing is clear: the future of AI spending will be shaped by the ability of startups to deliver results and build a sustainable business model.
