Enterprises are poised to significantly increase their investments in artificial intelligence in 2026, but a new survey reveals that spending won’t be spread evenly. According to a recent TechCrunch report, venture capitalists overwhelmingly predict a shift from broad experimentation with AI tools to focused investment in a smaller number of proven solutions. This consolidation is expected to reshape the landscape for AI vendors and startups alike.
The survey, which included 24 enterprise-focused venture capitalists, indicates that while overall AI budgets will likely grow, companies are tiring of testing numerous, often overlapping, technologies. Instead, they plan to concentrate funds on a select few AI platforms and applications demonstrating clear return on investment. This trend suggests a maturation of the enterprise AI market, moving beyond pilot programs towards large-scale deployment.
The Coming Consolidation of Artificial Intelligence Spending
For the past few years, organizations have been exploring the potential benefits of AI across various departments – from marketing and sales to customer service and operations. However, this period of experimentation is nearing its end, according to industry observers. Andrew Ferguson, a vice president at Databricks Ventures, believes 2026 will be a pivotal year where enterprises begin strategically “picking winners” and streamlining their AI technology stacks.
Ferguson noted that enterprises are currently evaluating multiple tools for the same use cases, particularly in areas like go-to-market strategies. He stated that the proliferation of startups in these niches makes it difficult to differentiate between offerings even during proof-of-concept phases. As companies definitively observe the value of AI, they’ll reduce experimental spending and reallocate resources to the most effective technologies.
Rob Biederman, managing partner at Asymmetric Capital Partners, anticipates an even more pronounced narrowing of focus. He predicts not just individual companies, but the entire enterprise sector, will limit AI spending to a handful of dominant vendors. “Budgets will increase for a narrow set of AI products that clearly deliver results and will decline sharply for everything else,” Biederman said.
Focus on AI Safety and Foundation
A key driver behind this budgetary shift is the growing emphasis on responsible AI implementation. Scott Beechuk, a partner at Norwest Venture Partners, highlights that enterprises are realizing the substantial investment required to ensure AI systems are secure, reliable, and aligned with organizational values. “Enterprises now recognize that the real investment lies in the safeguards and oversight layers that make AI dependable,” Beechuk explained.
This focus extends to strengthening the underlying data infrastructure necessary to support AI initiatives. Harsha Kapre, a director at Snowflake Ventures, identified three specific areas where enterprises will concentrate AI spending in 2026: bolstering data foundations, optimizing AI models post-training, and consolidating existing tools. Kapre added that chief investment officers are actively trying to reduce software-as-a-service sprawl and move toward integrated, intelligent systems to lower costs and demonstrably improve return on investment.
The rise of the data fabric and data mesh architectures, enabling more accessible and reliable data, are related secondary trends supporting these investments. These approaches aim to create a unified data layer that fuels AI and machine learning applications.
Implications for AI Startups
The impending consolidation presents both opportunities and challenges for AI startups. Some experts draw parallels to the software-as-a-service (SaaS) market correction a few years ago, where well-differentiated companies thrived while others struggled. Those startups with uniquely defensible products – such as vertical-specific solutions or applications built on proprietary data – are expected to fare well.
However, startups offering solutions readily replicated by tech giants like Amazon Web Services (AWS) or Salesforce may face increasing headwinds in securing funding and landing enterprise deals. Investors are already scrutinizing the “moats” of potential investments, prioritizing companies with proprietary datasets and technologies difficult for larger players to duplicate. This focus suggests the competitive advantage will increasingly lie in specializing and providing unique value.
The growth of large language models (LLMs) also adds complexity. Startups competing directly with LLM capabilities may face an uphill battle, as established companies continue to expand and refine these powerful tools. Instead, many may need to position themselves as offering complementary services or specialized applications leveraging existing LLMs.
If these investor predictions hold true, 2026 could see enterprise AI budgets increase overall, but with a disproportionate share of that growth captured by a select few vendors. The coming months will be crucial for AI startups to demonstrate clear differentiation and build defensible positions in the market. The ability to deliver measurable results and navigate the evolving landscape of data governance and AI ethics will be key determinants of success. Looking ahead, monitoring enterprise vendor selection processes and tracking funding patterns in the AI space will provide valuable insights into the unfolding consolidation.
