After several years of piloting AI products across departments, enterprises are starting to move from experimentation to commitment. The next phase won’t look like “buy a little bit of everything.” Instead, many companies are expected to expand AI budgets while dramatically narrowing the number of vendors they rely on.
That shift has major implications for software procurement, startup survival, and where enterprise AI value will actually be captured. As organizations look for measurable returns, cleaner integrations, and lower risk, investors expect a wave of consolidation that favors a smaller set of tools that can prove impact at scale.
Enterprises are moving past the pilot era
In the early days of enterprise AI adoption, the dominant strategy was to test broadly. Companies ran proof-of-concept projects with multiple tools for the same job—sometimes across competing platforms—because the market was moving fast and differentiation could be hard to spot until solutions were deployed in real workflows.
That broad experimentation created a crowded vendor landscape: startups raced into hot “buying centers” like go-to-market, customer support, analytics, and developer productivity, while incumbents added AI features to existing suites. For enterprise buyers, that meant constant evaluations, overlapping capabilities, and a growing “tool stack” that became expensive to maintain.
According to a TechCrunch report based on a survey of 24 enterprise-focused venture capitalists, an overwhelming majority predicted enterprises will increase AI budgets in 2026—but those dollars won’t be spread evenly. Instead, investors expect spending to become more concentrated, with enterprises putting more money into fewer contracts.
Andrew Ferguson, a vice president at Databricks Ventures, said 2026 will likely be the year enterprises consolidate and “pick winners.” He described a market where enterprises often test multiple tools for a single use case amid an “explosion of startups,” making differentiation difficult even during proof-of-concepts. In his view, as companies see clear proof points from AI, they’ll reduce experimentation budgets, rationalize overlapping tools, and redirect savings to AI technologies that have delivered.
Why consolidation could accelerate in 2026
Enterprise procurement tends to reward stability, simplicity, and accountability—especially when technology touches sensitive data and core processes. The more AI shifts from isolated pilots into production deployments, the more pressure there is to standardize.
Several forces can drive this consolidation dynamic:
- Cost control and “sprawl” reduction: Organizations want to reduce overlapping subscriptions and integration work across similar tools.
- Integration and operations burden: Every additional vendor can introduce new security reviews, legal terms, data pipelines, admin consoles, and support processes.
- Demand for measurable ROI: As budgets rise, finance leaders and operators often require clearer outcomes, not just innovation theater.
- Platform gravity: Large providers with existing enterprise footholds can bundle AI features into broader contracts, making it harder for point solutions to stand alone.
Rob Biederman, a managing partner at Asymmetric Capital Partners, expects not only individual enterprises to focus their spending, but the overall enterprise market to narrow AI spend toward a relatively small group of vendors. He forecast a “bifurcation” in which a narrow set of AI products that clearly deliver results see budgets increase, while spending falls sharply for everything else. In that scenario, a small number of vendors could capture a disproportionate share of enterprise AI budgets, leaving many others with flat or shrinking revenue.
Focused investments: where VCs think budget growth will land
While the general expectation is “more spend, fewer vendors,” investors also pointed to specific categories that could attract incremental dollars as AI moves into scaled deployments.
1) Safeguards, oversight, and enterprise-grade reliability
Scott Beechuk, a partner at Norwest Venture Partners, argued that enterprises are increasingly recognizing that the real investment is in the layers that make AI dependable for enterprise use. In his view, as safeguards and oversight capabilities mature and reduce risk, organizations will gain confidence to shift from pilots to scaled deployments—driving budget increases.
This reflects a common enterprise reality: the model is only one component of the system. To operate AI in production, organizations typically need governance controls, monitoring, auditability, policy enforcement, and workflows that reduce the chance of harmful or non-compliant outputs. As AI becomes embedded in customer-facing and regulated processes, these “guardrails” can move from nice-to-have to mandatory.
2) Stronger data foundations
Harsha Kapre, a director at Snowflake Ventures, predicted enterprises will spend on AI across three areas in 2026: strengthening data foundations, model post-training optimization, and consolidation of tools.
Data foundations matter because enterprise AI value often depends on how well an organization can prepare, govern, and utilize its internal information. Even the most capable AI systems can underperform if enterprise data is fragmented, poorly labeled, inaccessible, or difficult to use safely. Investment here can include cleaning and structuring data, improving accessibility across teams, and building the pipelines that connect business systems to AI applications.
3) Model post-training optimization
Kapre also pointed to model post-training optimization as a spending area. As enterprises push beyond generic use cases, they often need models better aligned to their domain, terminology, and workflows. Post-training work can help tailor systems so they perform more consistently in specific business contexts, which can be essential for reliability and user adoption.
As a practical matter, enterprises often care less about headline model benchmarks and more about whether an AI system produces repeatable, accurate outcomes within their environment—under their constraints and compliance requirements.
4) Consolidation into unified, intelligent systems
Consolidation itself is also expected to attract budget. Kapre said that 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. He expects AI-enabled solutions to be among the biggest beneficiaries of that shift.
This suggests enterprises may prioritize fewer, broader platforms that can cover multiple workflows over a long list of narrow tools—even if those point solutions are excellent at a specific task. In procurement terms, a vendor that can reduce the number of moving parts may win on operational simplicity, pricing leverage, and perceived risk reduction.
What this means for AI startups
A move from experimentation to concentration can be good news for the winners—and painful for everyone else. If enterprises buy fewer tools, the bar rises for startups that want a spot in the stack.
The TechCrunch report notes that it’s not yet clear exactly how this shift will play out, but it could mirror a reckoning that SaaS startups experienced a few years ago. In many SaaS categories, markets began with a wave of point solutions and eventually matured into consolidation, bundling, and platform dominance.
In AI, similar forces could emerge quickly because:
- Incumbents can add AI features fast across massive installed bases, squeezing standalone vendors.
- Enterprises are impatient for value and may standardize on providers that can show results and support long-term deployments.
- Security and compliance expectations are higher when AI touches sensitive content, communications, or decision-making.
Some startups may remain well-positioned. The report suggests companies with hard-to-replicate products—such as vertical solutions or offerings built on proprietary data—may still grow. By contrast, startups whose products resemble what large enterprise suppliers like AWS or Salesforce can offer may find pilot projects and funding harder to sustain as customers consolidate.
“Moats” matter more when budgets consolidate
In a vendor-consolidation environment, differentiation becomes a survival requirement. The report indicates that, when asked how they determine whether an AI startup has a moat, multiple investors pointed to proprietary data and products that can’t easily be replicated by a tech giant or a large language model company as the most defensible.
That emphasis highlights a key market dynamic: as foundational AI capabilities become more widely available, the advantage may shift to companies that control unique assets, deliver specialized outcomes, or embed deeply into regulated or domain-specific workflows. In other words, the harder it is to copy the product (or bundle it into a broader suite), the more leverage a startup may have in enterprise negotiations.
2026: bigger budgets, but not a bigger slice for everyone
On the surface, “enterprises will spend more on AI” sounds like a rising tide. But if spending concentrates around a smaller set of vendors, many startups may not benefit—even if total budgets increase. The report frames 2026 as potentially the year when enterprise AI spending rises while a significant portion of the AI startup ecosystem fails to capture additional share.
For buyers, consolidation could bring simpler stacks and clearer accountability. For vendors, it sets up a high-stakes competition to become one of the few approved, scaled providers inside large organizations.
Conclusion
Enterprise AI is entering a new phase: less experimentation, more standardization, and sharper scrutiny on value and risk. Investors expect 2026 budgets to rise, but the market could narrow to fewer vendors—rewarding platforms and startups with clear proof points, defensible moats, and enterprise-grade safeguards.
<<>>
Related Articles
- Best AI-Powered Dictation Apps of 2025: Top Voice-to-Text Tools for Faster Writing
- Climate Tech in 2026: Investors See Data Centers, Power Demand, and Grid Resilience Driving the Next Wave
- European Deep Tech University Spinouts Hit New Milestones in 2025 as Funding Momentum Builds
Based on reporting originally published by TechCrunch. See the sources section below.
