Despite a surge of investment and optimism, many enterprises are still struggling to realize a return on their investments in artificial intelligence (AI). A recent MIT survey revealed that 95% of companies aren’t seeing meaningful benefits from AI adoption. However, a survey of 24 enterprise-focused venture capitalists suggests a turning point may be on the horizon, with 2026 widely predicted as the year enterprises will begin to meaningfully adopt AI and increase related budgets.
For the past three years, venture capital firms have consistently predicted this shift, raising the question of whether 2026 will truly be different. The consensus among investors points to a maturing of the AI landscape, a greater focus on practical applications, and a move away from simply experimenting with new technologies.
The Path to Enterprise AI Adoption: Why 2026?
Kirby Winfield, founding general partner at Ascend, emphasizes that enterprises are realizing Large Language Models (LLMs) aren’t a universal solution. He notes that successful implementation requires custom models, rigorous evaluation, and a focus on data sovereignty. Molly Alter, partner at Northzone, predicts a shift for some AI companies from product-focused businesses to AI consulting, leveraging customer workflows to build additional use cases.
Several investors highlighted the importance of AI in specific sectors. Marcie Vu, partner at Greycroft, is particularly excited about the potential of voice AI, citing its natural and efficient communication capabilities. Alexa von Tobel, founder and managing partner of Inspired Capital, believes 2026 will see AI reshape the physical world, particularly in infrastructure, manufacturing, and climate monitoring, enabling predictive maintenance and proactive problem-solving.
Frontier Labs and the Application Layer
Lonne Jaffe, managing director at Insight Partners, is observing how leading AI labs are approaching application development. Contrary to expectations of simply providing models, these labs may increasingly ship turnkey applications directly into production across sectors like finance, law, healthcare, and education. Tom Henriksson, general partner at OpenOcean, anticipates growing momentum in quantum computing, though major software breakthroughs are still some time away due to hardware limitations.
Investment Focus: Key Areas for Growth
Looking ahead, investors are prioritizing specific areas within the broader AI ecosystem. Emily Zhao, principal at Salesforce Ventures, is targeting the intersection of AI and the physical world, as well as continued advancements in model research. Michael Stewart, managing partner at M12, is focusing on future datacenter technology, including cooling, compute, memory, and networking, to support the demands of AI workloads.
Jonathan Lehr, co-founder and general partner at Work-Bench, is interested in vertical enterprise software with defensible workflows and data, particularly in regulated industries. Aaron Jacobson, partner at NEA, emphasizes the need for software and hardware that improves performance per watt, addressing the energy demands of AI systems. These areas represent opportunities for innovation and potential returns on investment.
Building a Sustainable Moat in AI
A key concern for investors is identifying AI startups with a sustainable competitive advantage. Rob Biederman, managing partner at Asymmetric Capital Partners, believes a “moat” lies in deep integration into enterprise workflows, access to proprietary data, and defensibility through switching costs. Jake Flomenberg, partner at Wing Venture Capital, is skeptical of moats based solely on model performance, emphasizing the need for a compelling value proposition that extends beyond cutting-edge technology.
Molly Alter, of Northzone, suggests that moats are easier to build in vertical categories, leveraging data and workflow understanding. Harsha Kapre, director at Snowflake Ventures, highlights the importance of transforming existing data into actionable insights and automation, integrating directly with a customer’s governed data.
Will Enterprises Finally See Value in 2026?
The prevailing sentiment is cautiously optimistic. Kirby Winfield believes enterprises will shift towards fewer, more thoughtfully implemented AI solutions. Antonia Dean, partner at Black Operator Ventures, cautions that AI may be used as a justification for budget cuts in other areas. Scott Beechuk, partner at Norwest Venture Partners, anticipates 2026 will be a critical year for determining whether the infrastructure investments in AI will translate into tangible value.
Marell Evans, founder and managing partner at Exceptional Capital, expects incremental progress, with AI continuing to improve and address specific pain points. Jennifer Li, general partner at Andreessen Horowitz, argues that enterprises are already gaining value from AI tools, and this will accelerate in the coming year. The success of AI implementation will depend on addressing challenges related to integration, reliability, and oversight.
Budget Increases and Shifting Priorities
Rajeev Dham, managing director at Sapphire, predicts that enterprises will shift portions of their labor spend towards AI technologies. Rob Biederman anticipates budget increases for AI products that deliver clear results, while spending on less effective solutions will decline. Gordon Ritter, founder and general partner at Emergence Capital, believes budgets will increase where AI expands institutional advantages.
Andrew Ferguson, vice president at Databricks Ventures, expects CIOs to push back on vendor sprawl, consolidating tools and focusing on technologies that have demonstrated value. Ryan Isono, managing director at Maverick Ventures, anticipates a transition from pilot programs to budgeted line items, as enterprises recognize the benefits of AI at scale.
Raising a Series A in the Evolving AI Landscape
Securing Series A funding for an enterprise-focused AI startup in 2026 will require demonstrable traction and a compelling narrative. Jake Flomenberg emphasizes the need for at least $1 million to $2 million in annual recurring revenue and a clear understanding of the market opportunity. Lonne Jaffe stresses the importance of targeting markets with high elasticity of demand.
Jonathan Lehr highlights the need for customer validation and a product that solves real-world problems. Michael Stewart emphasizes the importance of a strong team and a winning marketing message. Marell Evans prioritizes execution, traction, and the ability to attract top talent.
The Future of AI Agents and Growth Opportunities
The role of AI agents is expected to expand significantly. Nnamdi Okike, managing partner and co-founder at 645 Ventures, anticipates initial adoption challenges related to technical and compliance hurdles. Rajeev Dham predicts the emergence of a universal agent with shared context and memory. Antonia Dean emphasizes the need for a balance of autonomy and oversight.
Investors are seeing the strongest growth in companies that address specific workflow gaps created by AI adoption, particularly in areas like cybersecurity and marketing. Retention is highest where software becomes foundational infrastructure, deeply embedded in customer operations. As the AI landscape matures, the focus will shift towards practical applications, demonstrable value, and sustainable competitive advantages.
Looking ahead, the next 12-18 months will be crucial for determining whether 2026 truly marks a turning point for enterprise AI. Key indicators to watch include the development of standardized AI agents, the emergence of clear ROI metrics, and the ability of startups to build defensible moats in a rapidly evolving market. The success of AI adoption will ultimately depend on bridging the gap between technological potential and practical business outcomes.
