Enterprises are expected to significantly increase their spending on artificial intelligence in 2026, but the growth will come with a major shift: fewer vendors will receive a larger share of the budgets. This is the consensus among venture capitalists who believe the era of broad experimentation with multiple AI tools is drawing to a close.

For the past few years, many organizations have been piloting and testing a wide range of AI solutions to determine the most effective adoption strategies. However, a recent survey of 24 enterprise-focused venture capital firms indicates that companies are now moving beyond trial phases and toward more deliberate, results-driven investments.

According to Andrew Ferguson, Vice President at Databricks Ventures, 2026 will mark a turning point as enterprises begin consolidating their AI investments and identifying clear winners. He noted that organizations are currently testing multiple tools for the same use cases, particularly in crowded areas such as go-to-market solutions, where differentiation is often difficult to assess even during proof-of-concept stages. As tangible results from AI deployments become clearer, Ferguson expects enterprises to reduce experimentation budgets, eliminate overlapping tools, and reinvest those savings into technologies that have demonstrated real value.

This view is echoed by Rob Biederman, Managing Partner at Asymmetric Capital Partners, who predicts not only tighter spending within individual companies but also a narrowing of the broader enterprise AI market. He believes budgets will rise for a small group of AI products that consistently deliver measurable outcomes, while spending on less effective tools will decline sharply. The result, Biederman said, will be a bifurcated market in which a handful of vendors capture a disproportionate share of enterprise AI budgets, leaving many others facing stagnant or shrinking revenues.

Beyond consolidation, investors also expect enterprises to direct more funds toward making AI safer and more reliable. Scott Beechuk, a Partner at Norwest Venture Partners, said organizations increasingly recognize that the true long-term investment lies in governance, safeguards, and oversight layers that ensure AI systems can be trusted. As these risk-mitigation capabilities mature, enterprises are likely to transition from pilot projects to full-scale deployments, driving higher spending.
Harsha Kapre, a Director at Snowflake Ventures, outlined three key areas where AI spending is expected to grow in 2026: strengthening data foundations, optimizing models through post-training processes, and consolidating fragmented tools. He added that chief investment officers are actively reducing software-as-a-service sprawl in favor of unified, intelligent systems that lower integration costs and deliver clearer returns on investment. AI-enabled solutions, Kapre said, stand to benefit most from this shift.

The move away from widespread experimentation toward focused investment is likely to have significant implications for startups. While companies offering hard-to-replicate solutions—such as vertical-specific products or platforms built on proprietary data—may continue to grow, others could struggle. Startups whose offerings closely resemble those of major enterprise providers like AWS or Salesforce may find pilot programs and funding opportunities drying up.

Investors are already factoring this into their evaluations. Many say that the most defensible AI startups are those with strong moats, particularly proprietary data or products that cannot easily be replicated by large technology firms or major language model providers.

If these predictions hold, 2026 could emerge as a paradoxical year for the AI ecosystem: enterprise budgets for AI will rise overall, but a large number of startups may find themselves competing for a smaller slice of an increasingly concentrated market.

Source: Techcrunch