Enterprise AI spending to consolidate in 2026

A survey of 24 enterprise-focused venture capitalists (VCs) indicates that while enterprise artificial intelligence budgets are predicted to increase in 2026, the period of broad experimentation is ending. Instead, enterprises are predicted to consolidate their investments and spend more funds on fewer contracts. This shift marks a transition from testing multiple tools to scaling proven technologies.

Investors from firms like Databricks Ventures, Norwest Venture Partners, and Snowflake Ventures explain that enterprise buyers are prioritizing return on investment (ROI), safeguards, and reducing software-as-a-service (SaaS) sprawl. Andrew Ferguson, a vice president at Databricks Ventures, noted that enterprises currently test multiple tools for a single-use case, making it difficult to discern differentiation during proof of concepts. He predicted that as enterprises see real proof points, they will cut experimentation budgets, rationalize overlapping tools, and deploy those savings into AI technologies that have delivered. Similarly, Scott Beechuk, a partner at Norwest Venture Partners, explained that enterprises now recognize that the real investment lies in the safeguards and oversight layers that make AI dependable, allowing organizations to shift from pilots to scaled deployments.

Harsha Kapre, a director at Snowflake Ventures, predicted that Chief Information Officers (CIOs) are actively reducing SaaS sprawl. According to Kapre, “[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]. AI-enabled solutions are likely going to see the biggest benefit from this shift.”

This consolidation is expected to create a sharp division in the market. Rob Biederman, managing partner at Asymmetric Capital Partners, predicts that the enterprise landscape will narrow its overall AI spending to only a handful of vendors. Under this prediction, budgets will increase for a narrow set of AI products that deliver results, while declining sharply for others, leading to a bifurcation where a small number of vendors capture a disproportionate share of enterprise AI budgets.

Consequently, startups with products similar to those offered by large enterprise suppliers, such as AWS or Salesforce, may see pilot projects and funding dry up. While startups operating with proprietary data are expected to maintain defensibility, those lacking clear differentiation face a challenging environment as enterprises move away from experimental budgets.

Why it matters

The era of broad enterprise AI experimentation is closing as organizations prioritize proven return on investment and vendor consolidation. This shift creates a high-stakes environment for startups, where those lacking proprietary data or products that are easily replicated by large tech incumbents may see funding dry up.