After years of piloting and testing various artificial intelligence (AI) tools, enterprises are poised to significantly increase their AI budgets by 2026. However, this growth won't be spread evenly. Venture capitalists (VCs) predict a decisive shift from broad experimentation to concentrated investments in fewer, high-performing vendors.
A recent TechCrunch survey of 24 enterprise-focused VCs revealed an overwhelming consensus: while AI spending will rise, it will be highly focused. Most investors anticipate that enterprises will allocate more funds to fewer contracts, signaling an end to the widespread experimentation phase.
Andrew Ferguson, a vice president at Databricks Ventures, believes 2026 will mark the year enterprises begin consolidating their AI 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."
Rob Biederman, a managing partner at Asymmetric Capital Partners, echoed this sentiment, predicting that not only will individual enterprise spending become more concentrated, but the broader industry will also narrow its overall AI spending to just a handful of vendors.
"Budgets will increase for a narrow set of AI products that clearly deliver results and will decline sharply for everything else," Biederman stated. "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."
Focused Investments
Scott Beechuk, a partner at Norwest Venture Partners, highlighted that enterprises will increasingly invest in tools that ensure AI safety and reliability.
"Enterprises now recognize that the real investment lies in the safeguards and oversight layers that make AI dependable," Beechuk explained. "As these capabilities mature and reduce risk, organizations will feel confident shifting from pilots to scaled deployments, and budgets will increase."
Harsha Kapre, a director at Snowflake Ventures, identified three distinct areas where enterprises will focus their AI spending in 2026: strengthening data foundations, optimizing models post-training, and consolidating tools.
"[Chief investment officers] are actively reducing [software-as-a-service] sprawl and moving toward unified, intelligent systems that lower integration costs and deliver measurable [return on investment]," Kapre noted. "AI-enabled solutions are likely going to see the biggest benefit from this shift."
This shift away from widespread experimentation and towards concentration will undoubtedly impact AI startups. While the full extent remains unclear, it's possible that AI startups will face a similar reckoning point that SaaS startups encountered a few years ago.
Startups offering hard-to-replicate products, such as vertical solutions or those built on proprietary data, are likely to continue growing. Conversely, startups with products similar to those offered by large enterprise suppliers like AWS or Salesforce may see pilot projects and funding diminish.
Investors are keenly aware of this dynamic. When asked about what constitutes a "moat" for an AI startup, multiple VCs emphasized that companies with proprietary data and products that cannot be easily replicated by tech giants or large language model providers are the most defensible.
If these investor predictions hold true, 2026 will indeed be a year of increased enterprise AI budgets, but one where many AI startups may find themselves competing for a smaller, more concentrated slice of the pie.
