TITLE: VCs Predict Enterprise AI Spending Rise in 2026 on Fewer Vendors

Enterprises Set to Boost AI Budgets in 2026, Targeting Fewer Vendors

A survey of 24 venture capitalists specializing in enterprise technology forecasts a significant uptick in corporate AI spending next year, but with a strategic pivot toward consolidation. After years of experimenting with diverse AI tools, enterprises are expected to allocate larger budgets to a narrower set of proven vendors, according to the investors.

The predictions mark the end of an era of broad AI pilots, where companies tested multiple solutions for similar use cases. Instead, 2026 will see enterprises rationalizing overlapping technologies and channeling funds into those demonstrating tangible value.

Andrew Ferguson, vice president at Databricks Ventures, described the shift as enterprises "picking winners." He noted that current testing involves numerous tools for single functions, particularly in areas like go-to-market strategies, where differentiation is often unclear even in proof-of-concept stages. "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," Ferguson said.

Industry-Wide Consolidation Anticipated

Rob Biederman, managing partner at Asymmetric Capital Partners, anticipates not just individual company consolidation but a broader industry trend. He predicts enterprises will narrow AI expenditures to a handful of dominant vendors, leading to a bifurcation in the market.

"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 small number of vendors to capture a disproportionate share of enterprise AI budgets while many others see revenue flatten or contract."

This concentration could reshape the competitive landscape, favoring established players and select startups with unique advantages. Investors emphasized that defensibility comes from proprietary data or hard-to-replicate vertical solutions, which shield companies from competition by tech giants or large language model providers.

Focus on Safety, Optimization and Unified Systems

Scott Beechuk, a partner at Norwest Venture Partners, highlighted the growing emphasis on AI safeguards. Enterprises, he said, will prioritize investments in oversight layers that ensure reliability and reduce risks, paving the way for scaled deployments.

"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, director at Snowflake Ventures, outlined three key spending areas: bolstering data foundations, optimizing models post-training, and consolidating tools to curb software-as-a-service sprawl. Chief investment officers, he noted, are seeking unified intelligent systems that minimize integration costs and provide measurable returns.

"[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."

Implications for AI Startups

The move toward focused investments poses challenges and opportunities for AI startups. Similar to the reckoning faced by software-as-a-service companies in recent years, AI firms offering commoditized products—those easily replicated by giants like Amazon Web Services or Salesforce—may encounter drying pilots and funding.

Conversely, startups with moats, such as those leveraging proprietary datasets or niche vertical applications, are positioned for growth. Multiple venture capitalists identified these characteristics as critical for long-term viability.

If the predictions hold, 2026 could witness expanding enterprise AI budgets overall, yet many startups might not capture a larger share. The year may thus become a litmus test for the sector's maturity, weeding out undifferentiated players while rewarding those with genuine differentiation.

The survey reflects sentiment among investors deeply engaged in enterprise AI, underscoring a maturing market where efficiency and results drive decisions over novelty.