Enterprises have been piloting and testing different AI tools for the past few years to figure out what their adoption strategy will look like. Investors think that period of experimentation is coming to an end.
TechCrunch recently surveyed 24 enterprise-focused VCs and an overwhelming majority predicted enterprises will increase their budgets for AI in 2026 — but not for everything. Most investors said this budget increase will be concentrated, and that many enterprises will spend more funds on fewer contracts.
Andrew Ferguson, a vice president at Databricks Ventures, predicted 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.”
Rob Biederman, a managing partner at Asymmetric Capital Partners, agreed. He predicts that enterprise companies will not only concentrate their individual spending, the broader enterprise landscape will narrow its overall AI spending to only a handful of vendors across the entire industry.
“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.”
Focused investments
Scott Beechuk, a partner at Norwest Venture Partners, thinks enterprises will increase their spending on the tools that make AI safe for enterprises to use.
Techcrunch event
San Francisco
|
October 13-15, 2026
“Enterprises now recognize that the real investment lies in the safeguards and oversight layers that make AI dependable,” Beechuk said. “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, predicted enterprises will spend on AI in three distinct areas in 2026: strengthening data foundations, model post-training optimization, and consolidation of 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 said. “AI-enabled solutions are likely going to see the biggest benefit from this shift.”
A shift away from experimentation and towards concentration will affect startups. What’s not clear, is how.
It’s possible that AI startups will reach the same reckoning point that SaaS startups arrived at a few years ago.
The companies operating hard-to-replicate products such as vertical solutions or those built on proprietary data, will likely still be able to grow. 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.
Investors see this possibility too. 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.
If investor predictions are true and enterprises do start to concentrate their AI spend next year, 2026 could be the year enterprise budgets increase but many AI startups don’t see a bigger slice of the pie.
