Enterprise AI is about to hit its consolidation phase.
After years of pilots and experiments, a TechCrunch survey of 24 enterprise-focused VCs found an overwhelming majority expect companies to increase AI budgets in 2026 — but spread that money across fewer vendors and tools.
The message from investors: the experimentation party is ending. Now comes the pruning.
From AI experiments to “pick the winners”
Andrew Ferguson, vice president at Databricks Ventures, thinks 2026 is when enterprise buyers finally start choosing long‑term AI partners instead of running endless proof‑of‑concepts.
“Today, enterprises are testing multiple tools for a single‑use case, and there’s an explosion of startups focused on certain buying centers like go‑to‑market, where it’s extremely hard to discern differentiation even during proof of concepts,” he told TechCrunch.
As those pilots start to show real results, Ferguson expects the CFO’s red pen to come out: “As enterprises see real proof points from AI, they’ll cut out some of the experimentation budget, rationalize overlapping tools and deploy that savings into the AI technologies that have delivered.”
Translation: if you are one of three vendors doing roughly the same thing inside a big company, 2026 is the year that turns into one contract, not three.
Rob Biederman, managing partner at Asymmetric Capital Partners, thinks that dynamic will play out at the market level, not just inside individual IT budgets.
“Budgets will increase for a narrow set of AI products that clearly deliver results and will decline sharply for everything else,” he said. “We expect a bifurcation where a small number of vendors capture a disproportionate share of enterprise AI budgets while many others see revenue flatten or contract.”
In other words: power laws are coming for enterprise AI.
Where the AI money will actually go
More spend does not mean more experimentation. The investors TechCrunch spoke to were surprisingly aligned on the specific areas where budgets are likely to grow.
1. Safety, governance and oversight
Scott Beechuk, partner at Norwest Venture Partners, expects spending to rise first around the layers that make AI safe to use in regulated, risk‑averse environments.
“Enterprises now recognize that the real investment lies in the safeguards and oversight layers that make AI dependable,” he said. “As these capabilities mature and reduce risk, organizations will feel confident shifting from pilots to scaled deployments, and budgets will increase.”
Think: model monitoring, policy and access controls, auditability, content filtering and compliance tooling sitting on top of models that enterprises already use.
2. Data foundations and post‑training optimization
Harsha Kapre, director at Snowflake Ventures, breaks 2026 AI spend into three buckets:
- Strengthening data foundations
- Model post‑training optimization
- Consolidation of tools
For Kapre, the data story comes first. If your data is a mess, your AI strategy is a mess.
“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 said. “AI‑enabled solutions are likely going to see the biggest benefit from this shift.”
The post‑training optimization piece is where a lot of differentiated value will sit: adapting base models to specific domains, workflows and proprietary datasets so they actually move the needle on enterprise KPIs.
3. Fewer, more integrated platforms
Several of the VCs TechCrunch surveyed expect 2026 procurement strategies to look very different from the “try everything” mindset of 2023–2024.
Instead of stitching together a dozen point solutions, CIOs will look for a smaller number of platforms that can:
- Plug into existing data infrastructure
- Cover multiple adjacent use cases
- Offer native governance and observability
- Prove hard ROI in production, not just in demos
That is good news for incumbent vendors that can keep shipping AI features into products enterprises already pay for. It is much tougher for the wave of narrow tools targeting a single function inside the org chart.
What this means for AI startups
The shift from experimentation to consolidation will not hit every startup the same way.
TechCrunch’s respondents repeatedly came back to one question: does this company have a real moat, or could a cloud provider or large language model platform simply build the same thing?
The consensus: the most defensible startups will be those that either:
- Operate in a deep vertical with hard‑to‑replicate workflows, or
- Are built on proprietary data that hyperscalers and generic LLM vendors cannot easily access
If you are an AI startup whose product overlaps heavily with what AWS, Salesforce or another major enterprise supplier already offers, the 2026 landscape could get rough. Pilot projects and experimental budgets will be the first line items to go when CIOs start “rationalizing overlapping tools”.
The comparison several investors made is to what happened in software‑as‑a‑service a few years ago: once CFOs realized they were paying for three tools to do what one could handle, they started ripping out the extras. AI looks set to follow the same script — only faster.
Bigger AI budgets, smaller slice of the pie
If the investor predictions hold, 2026 could deliver a paradox:
- Enterprise AI budgets go up
- AI makes it into more production workflows
- But many AI startups do not see a corresponding revenue bump
Instead, those extra dollars will accumulate with a relatively small set of vendors that can show clear, repeatable impact on revenue, cost or risk.
For enterprise buyers, that is a healthy correction from the hype phase. For founders, it is a wake‑up call: in 2026, “we use AI” will not be enough. You will need a moat, proof points in production, and a clear answer to the question every CIO will be asking — why should this be one of the few AI vendors we keep?
