Enterprises are expected to increase spending on artificial intelligence in 2026, but investors say the additional capital will be directed toward a smaller number of vendors. After several years of pilots and experimentation, companies are beginning to consolidate tools and prioritize solutions that deliver measurable results.
Enterprise-focused investors surveyed by TechCrunch said many organizations are testing multiple AI products for the same use cases, creating overlapping capabilities and rising costs. As proof points emerge, enterprises are expected to reduce experimentation budgets, eliminate redundant contracts, and reallocate savings to technologies that have shown operational impact.
Investors predict this shift will benefit a narrow set of AI vendors while pressuring others. Products tied to proprietary data, vertical-specific use cases, or difficult-to-replicate capabilities are seen as more defensible. By contrast, startups competing directly with large platform providers may see pilot activity and revenue growth slow.
Spending is also expected to increase around AI governance, data foundations, and post-training model optimization. Investors say enterprises are placing greater emphasis on safeguards, oversight, and integration as they move from limited trials to scaled deployments, reshaping the competitive landscape for AI startups.
The shift toward concentrated AI spending reflects broader global patterns in capital allocation. In India, startup funding slowed in 2025 as investors became more selective about AI bets, while in the U.S., Bank of America CEO Brian Moynihan has said AI investment is increasingly contributing to economic growth with limited systemic risk. Together, these trends suggest enterprises are moving from experimentation to disciplined deployment, favoring proven AI technologies over speculative pilots.
