It has been three years since OpenAI released ChatGPT, kicking off a surge in innovation and attention on AI. Since then, optimists have regularly claimed that AI will become a critical part of the enterprise software industry, leading to a mushrooming of enterprise AI startups backed by immense investment. However, enterprises are still struggling to see the benefit of adopting these new AI tools. An MIT survey in August found that 95% of enterprises were not getting a meaningful return on their investments in AI.
So when will businesses start seeing real benefits from using and integrating AI? TechCrunch surveyed 24 enterprise-focused venture capitalists, and they overwhelmingly think 2026 will be the year when enterprises start to meaningfully adopt AI, see value from it, and increase their budgets for the technology. Enterprise VCs have been saying that for three years now. Will 2026 actually be different? Here is what they have to say.
What enterprise-related trends do you expect to take off in 2026?
Kirby Winfield, founding general partner at Ascend, said enterprises are realizing that large language models are not a silver bullet for most problems. The focus will shift to custom models, fine-tuning, evaluation, observability, orchestration, and data sovereignty.
Molly Alter, partner at Northzone, predicted a subset of enterprise AI companies will shift from product businesses to AI consulting. These companies may start with a specific product but, once they have enough customer workflows running on their platform, they can replicate the forward-deployed engineer model to build additional use cases. In other words, many specialized AI product companies will become generalist AI implementers.
Marcie Vu, partner at Greycroft, expressed excitement about the opportunity in voice AI. Voice is a more natural, efficient, and expressive way for people to communicate. Builders will reimagine products, experiences, and interfaces with voice as the primary mode of interaction with intelligence.
Alexa von Tobel, founder and managing partner at Inspired Capital, said 2026 will be the year AI reshapes the physical world, especially in infrastructure, manufacturing, and climate monitoring. We are moving from a reactive world to a predictive one, where physical systems can sense problems before they become failures.
Lonne Jaffe, managing director at Insight Partners, is watching how frontier AI labs approach the application layer. We may see these labs shipping more turnkey applications directly into production in domains like finance, law, healthcare, and education than people expect.
Tom Henriksson, general partner at OpenOcean, said that for quantum in 2026, the word is momentum. Trust in quantum advantage is building fast, but do not expect major software breakthroughs yet, as more hardware performance is needed.
Which areas are you looking to invest in?
Emily Zhao, principal at Salesforce Ventures, is targeting two frontiers: AI entering the physical world and the next evolution of model research.
Michael Stewart, managing partner at M12, is focused on future datacenter technology, including everything within the data center walls: cooling, compute, memory, and networking.
Jonathan Lehr, co-founder and general partner at Work-Bench, is interested in vertical enterprise software where proprietary workflows and data create defensibility, particularly in regulated industries, supply chain, retail, and other complex operational environments.
Aaron Jacobson, partner at NEA, is looking for software and hardware that can drive breakthroughs in performance per watt to address the limit of humanity’s ability to generate enough energy for power-hungry GPUs.
When it comes to AI startups, how do you determine that a company has a moat?
Rob Biederman, managing partner at Asymmetric Capital Partners, said a moat in AI is less about the model and more about economics and integration. He looks for companies deeply embedded in enterprise workflows, with access to proprietary data, and defensibility through switching costs or cost advantages.
Jake Flomenberg, partner at Wing Venture Capital, is skeptical of moats built purely on model performance. He asks if a company would still have a reason to exist if a frontier lab launched a much better model tomorrow.
Molly Alter, partner at Northzone, said it is easier to build a moat in a vertical category. The best moats are data moats, where each incremental customer makes the product better, or workflow moats based on deep industry understanding.
Harsha Kapre, director at Snowflake Ventures, said the strongest moat comes from how effectively a startup transforms an enterprise’s existing data into better decisions and workflows, blending technical expertise with deep industry knowledge.
Will 2026 be the year when enterprises start to gain value from AI investments?
Kirby Winfield of Ascend said enterprises will focus on fewer solutions with more thoughtful engagement, moving away from random experiments.
Antonia Dean, partner at Black Operator Ventures, noted that AI may become a scapegoat for executives looking to cover for past mistakes, even as they claim to increase investments.
Scott Beechuk, partner at Norwest Venture Partners, said 2026 is when we begin to see if the application layer can turn infrastructure investment into real value as specialized models mature.
Marell Evans, founder and managing partner at Exceptional Capital, believes value will be seen, but incrementally, as AI continues to improve and solve industry pain points.
Jennifer Li, general partner at Andreessen Horowitz, argued enterprises are already gaining value this year, and it will multiply across organizations next year.
Do you think enterprises will increase their AI budgets in 2026?
Rajeev Dham, managing director at Sapphire, believes they will, though it is nuanced. Organizations may shift labor spend toward AI or generate such strong ROI that the investment pays for itself.
Rob Biederman of Asymmetric Capital Partners said budgets will increase for a narrow set of AI products that clearly deliver results and decline sharply for everything else, leading to a bifurcation in the market.
Gordon Ritter, founder and general partner at Emergence Capital, said yes, but spend will concentrate where AI expands on institutional advantages, pulling back from tools that just automate workflows without capturing proprietary intelligence.
Andrew Ferguson, vice president at Databricks Ventures, said 2026 will be the year CIOs push back on AI vendor sprawl. Enterprises will rationalize overlapping tools and deploy savings into technologies that have delivered proof points.
Ryan Isono, managing director at Maverick Ventures, said in aggregate, yes, with a shift from experimental budgets to budgeted line items, aided by enterprises realizing the difficulty of building in-house solutions.
What does it take to raise a Series A as an enterprise-focused AI startup in 2026?
Jake Flomenberg of Wing Venture Capital said the best companies combine a compelling “why now” narrative with concrete proof of enterprise adoption. Revenue around one to two million dollars is a baseline, but customers must view the product as mission-critical.
Lonne Jaffe of Insight Partners said founders should aim to show they are building in a space where the total addressable market expands rather than evaporates as AI drives down costs.
Jonathan Lehr of Work-Bench said customers must be using the product in real operations and be willing to talk about impact. Companies must clearly show how the product saves time, reduces cost, or increases output.
Michael Stewart of M12 said investor focus has shifted from doubting pilot revenue to evaluating customer interest and willingness to engage. Conversions after pilot use are becoming the leading part of the story.
Marell Evans of Exceptional Capital pointed to execution, traction, and user delight. Key signals include long-term contractual agreements and the ability to attract top-tier talent.
What role will AI agents play at enterprises by the end of 2026?
Nnamdi Okike, managing partner and co-founder at 645 Ventures, believes agents will still be in their initial adoption phase, with many technical and compliance hurdles to overcome.
Rajeev Dham of Sapphire predicted one universal agent will emerge, converging siloed roles into a single agent with shared context and memory, breaking down organizational silos.
Antonia Dean of Black Operator Ventures said the winners will be organizations that figure out the right balance of autonomy and oversight, enabling sophisticated collaboration between humans and agents.
Aaron Jacobson of NEA predicted the majority of knowledge workers will have at least one agentic co-worker they know by name.
Eric Bahn, co-founder and general partner at Hustle Fund, speculated that AI agents could be a bigger part of the workforce than humans, as proliferating them is essentially free at the margin.
What kinds of companies in your portfolio are seeing the strongest growth?
Jake Flomenberg of Wing Venture Capital said the fastest-growing companies identified a workflow or security gap created by generative AI adoption. Examples include cybersecurity tools for data security and agent governance, and new marketing areas like Answer Engine Optimization.
Andrew Ferguson of Databricks Ventures is seeing growth in companies that land with a focused use case, nail it, become sticky, and then expand from that initial wedge.
Jennifer Li of Andreessen Horowitz cited companies that help enterprises put AI into production, in areas like data extraction, developer productivity, infrastructure for generative media, and voice AI.
What kinds of companies are seeing the strongest retention?
Jake Flomenberg of Wing Venture Capital said strong retention comes from being mission-critical, accumulating proprietary context, and solving problems that grow with AI adoption.
Tom Henriksson of OpenOcean said the highest retention is in serious enterprise software providers enhanced with AI, which go deep into a customer’s organization and build up proprietary data that makes them hard to replace.
Michael Stewart of M12 pointed to startups in data tooling and vertical AI apps that use forward-deployed teams to assist in customer satisfaction and product improvement.
Jonathan Lehr of Work-Bench said retention is highest where software becomes foundational infrastructure, such as authorization systems or orchestration layers for end-to-end workflows in sectors like healthcare and government.