VCs Predict Strong Enterprise AI Adoption Next Year — Again

It's been three years since OpenAI released ChatGPT, triggering a surge in AI innovation and attention. Since then, optimists have consistently claimed that AI would become a crucial part of the enterprise software industry, leading to a boom in enterprise AI startups backed by massive investments.

However, enterprises are still struggling to see the benefits of adopting these new AI tools. An MIT survey in August revealed that 95% of enterprises weren't seeing a meaningful return on their AI investments.

So, when will businesses begin to see tangible benefits from using and integrating AI? TechCrunch surveyed 24 enterprise-focused VCs, who overwhelmingly believe that 2026 will be the year enterprises start to adopt AI meaningfully, derive value from it, and increase their AI budgets.

But enterprise VCs have been predicting this for three years now. Will 2026 be the different year?

Let's hear from some of the experts:

What Enterprise-Related Trends Do You Expect to Take Off in 2026?

Kirby Winfield, founding general partner at Ascend:
Enterprises are realizing that LLMs are not a silver bullet for most problems. Just because Starbucks can use Claude to develop its CRM software doesn't mean it should. We'll focus on custom models, fine-tuning, evaluation, observability, orchestration, and data sovereignty."

Molly Alter, partner at Northzone:
"A subset of enterprise AI companies will shift from product businesses to AI consulting. These companies may start with specific products, such as AI customer support or AI coding agents, but as they scale, they will transition to broader AI implementation services."

Marcie Vu, partner at Greycroft:
"We're excited about the opportunity in voice AI. Voice is a more natural, efficient, and expressive way for people to communicate with machines, and we are eager to see how builders reimagine products with voice as the primary interaction mode."

Alexa von Tobel, founder and managing partner at Inspired Capital:
"2026 will be the year AI reshapes the physical world — particularly in infrastructure, manufacturing, and climate monitoring. We're moving from a reactive to a predictive world, where physical systems sense problems before they escalate."

Lonne Jaffe, managing director at Insight Partners:
"We're watching how frontier labs approach the application layer. There's a shift from just training models to delivering turnkey applications directly into production, especially in finance, healthcare, and education."

Tom Henriksson, general partner at OpenOcean:
"Quantum momentum is building fast. Trust in its advantages is increasing, and while we won't see major breakthroughs in software just yet, hardware performance is rapidly improving."

Areas Enterprise VCs Are Looking to Invest In

Emily Zhao, principal at Salesforce Ventures:
"We are targeting two frontiers: AI entering the physical world and the next evolution of model research."

Michael Stewart, managing partner at M12:
"We're looking at future data center technology — cooling, compute, memory, and networking within and between sites, as these will advance the efficiency of data centers."

Jonathan Lehr, co-founder and general partner at Work-Bench:
"Vertical enterprise software, particularly in regulated industries and complex operational environments like retail and supply chain, where proprietary workflows and data create defensibility."

Aaron Jacobson, partner at NEA:
"We're focusing on software and hardware that can drive breakthroughs in performance per watt, especially for GPUs and AI chips in the AI ecosystem."

What Do You Look For in AI Startups?

Rob Biederman, managing partner at Asymmetric Capital Partners:
"A moat in AI is about economics and integration, not just the model. We look for companies deeply embedded in enterprise workflows, with proprietary data and defensibility through switching costs or outcomes that are difficult to replicate."

Jake Flomenberg, partner at Wing Venture Capital:
"I'm skeptical of moats based solely on model performance. The question I ask is, if OpenAI or Anthropic releases a model tomorrow that's 10x better, does the company still have a reason to exist?"

Molly Alter, partner at Northzone:
"It's easier to build a moat in a vertical category rather than a horizontal one. The best moats are data moats or workflow moats, where customers' interactions continuously improve the product."

Harsha Kapre, director at Snowflake Ventures:
"The strongest moat comes from transforming enterprise data into actionable insights without creating new data silos, enabling better decisions and experiences."

Will Enterprises Gain Value From AI in 2026?

Kirby Winfield, founding general partner at Ascend:
Enterprises are realizing that random experimentation with dozens of solutions creates chaos. They will focus on fewer solutions with more thoughtful engagement."

Antonia Dean, partner at Black Operator Ventures:
"Many enterprises will use AI as a cover to cut back spending in other areas or trim workforces. AI will become a scapegoat for executives."

Scott Beechuk, partner at Norwest Venture Partners:
"2026 will be the year when AI applications start delivering real value, especially as AI systems become more reliable in daily workflows."

Marell Evans, founder and managing partner at Exceptional Capital:
"AI will still be improving incrementally, but it will begin to showcase pain-point solutions for enterprises across various industries."

Jennifer Li, general partner at Andreessen Horowitz:
"Enterprises are already gaining value from AI this year, and the benefits will multiply next year."

Will Enterprises Increase Their AI Budgets in 2026?

Rajeev Dham, managing director at Sapphire:
"Enterprises will shift labor spend toward AI technologies, generating ROI that effectively pays for itself three to five times over."

Rob Biederman, managing partner at Asymmetric Capital Partners:
"Budgets will increase for AI products that deliver clear results, but there will be a bifurcation where only a small number of vendors capture a disproportionate share of AI budgets."

Gordon Ritter, founder and general partner at Emergence Capital:
"Enterprises will increase AI budgets where AI expands institutional advantages and reduce spend on tools that only automate workflows without capturing proprietary intelligence."

Andrew Ferguson, vice president at Databricks Ventures:
"Enterprises will rationalize AI spending and cut out experimentation budgets as AI technologies deliver clear proof points."

What Does It Take to Raise a Series A as an Enterprise-Focused AI Startup in 2026?

Jake Flomenberg, partner at Wing Venture Capital:
"Successful AI startups need a compelling 'why now' narrative tied to new opportunities created by GenAI, and they need concrete proof of enterprise adoption, usually with $1–2 million in ARR."

Lonne Jaffe, managing director at Insight Partners:
"Focus on expanding the total addressable market (TAM) as AI drives down costs. You should show how the market expands as AI becomes more affordable."

Jonathan Lehr, co-founder and general partner at Work-Bench:
"Be able to clearly demonstrate how your product saves time, reduces costs, or increases output in real-world, day-to-day operations."

Michael Stewart, managing partner at M12:
"Conversions from pilots to long-term contracts will be key, as investors expect solid traction in the market before committing to Series A funding."

Role of AI Agents in Enterprises by the End of 2026

Nnamdi Okike, managing partner and co-founder at 645 Ventures:
"AI agents will still be in their early adoption phase, with technical and compliance hurdles to overcome for enterprises to benefit from them."

Rajeev Dham, managing director at Sapphire:
"We'll see a universal agent emerge that breaks down silos and enables more unified, contextual conversations across roles like sales, customer support, and product discovery."

What Companies in Your Portfolio Are Seeing the Strongest Growth?

Jake Flomenberg, partner at Wing Venture Capital:
"Companies addressing security gaps created by GenAI adoption and those focusing on Answer Engine Optimization (AEO) are growing fastest."

Andrew Ferguson, vice president at Databricks Ventures:
"Companies that start with focused use cases, nail them, and expand are seeing the most growth."

Jennifer Li, general partner at Andreessen Horowitz:
"Companies that help enterprises put AI into production, such as in data extraction, developer productivity, and voice and audio for apps, are doing well."