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Over the past few years, the widespread trial of AI tools by enterprises is about to come to an end. According to a survey by TechCrunch on 24 venture capital firms focused on enterprise investment, most investors predict that: **2026 will be a pivotal turning point for enterprise AI investment, shifting from "broad-spectrum" to "focusing on winners."**
Although overall AI budgets for enterprises are expected to grow, this growth will be highly concentrated. Andrew Ferguson, Vice President of Databricks Ventures, noted that companies are moving from the "experimental phase" of testing multiple tools for the same use case, to cutting overlapping solutions and focusing resources on AI projects that have been proven effective. "2026 will be the year of integration and screening," he said.
This trend is creating a clear "two-tier" division. Rob Biederman, Managing Partner at Asymmetric Capital Partners, predicts that future enterprise AI spending will be highly concentrated on a few suppliers that significantly improve business efficiency, "Only a few winners will receive most of the budget, while other suppliers may face stagnant or even declining revenue."
At the same time, investments in AI security and governance will increase significantly. Scott Beechuk, Partner at Norwest Venture Partners, emphasized: "Real investment is now shifting towards infrastructure that ensures AI reliability, explainability, and compliance. Only when risks are controllable can companies move from pilot projects to large-scale deployment."
Harsha Kapre, Director at Snowflake Ventures, added that enterprise AI investment in 2026 will focus on three directions: **strengthening data foundations, optimizing model post-processing capabilities, and integrating fragmented tools.** CIOs are actively curbing the uncontrolled expansion of SaaS tools, moving toward unified, ROI-measurable intelligent systems.
This strategic shift will profoundly impact the survival landscape of AI startups. Companies with **vertical-specific proprietary data and hard-to-replicate solutions** are more likely to break through; while general-purpose startups whose product functions overlap with giants like AWS and Salesforce may fall into the "pilot trap"—projects difficult to convert and funding difficult to sustain.
Multiple investors agree that the **true moat lies in unique data assets and deep industry coupling capabilities**, not just the stacking of technologies. If current predictions hold true, 2026 may be a year of enterprise AI budget expansion, but it could also become a watershed moment for many startups to be weeded out.