2026: The Year Enterprise AI Finally Takes Off
It’s been three years since OpenAI released ChatGPT and kicked 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, and so enterprise AI startups mushroomed on the back of immense amounts of investment.
But enterprises are still struggling to see the benefit of adopting these new AI tools. An MIT survey in August found that 95% of enterprises weren’t getting a meaningful return on their investments in AI.
So when will businesses start seeing real benefits from using and integrating AI? JS surveyed 24 enterprise-focused VCs, 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 tech.
Enterprise VCs have been saying that for three years now. Will 2026 actually be different?
Let’s hear what they have to say:
Kirby Winfield, founding general partner, Ascend: Enterprises are realizing that LLMs are not a silver bullet for most problems. Just because Starbucks can use Claude to write their own CRM software doesn’t mean they should. We’ll focus on custom models, fine tuning, evals, observability, orchestration, and data sovereignty.
Molly Alter, partner, Northzone: A subset of enterprise AI companies will shift from product businesses to AI consulting. These companies may start with a specific product, such as AI customer support or AI coding agents. But once they have enough customer workflows running off their platform, they can replicate the forward-deployed engineer model with their own team to build additional use cases for customers. In other words, many specialized AI product companies will become generalist AI implementers.
Marcie Vu, partner, Greycroft: We’re very excited about the opportunity in voice AI. Voice is a far more natural, efficient, and expressive way for people to communicate with each other and with machines. We’ve spent decades typing on computers and staring at screens, but speech is how we engage in the real world. I am eager to see how builders reimagine products, experiences, and interfaces with voice as the primary mode of interaction with intelligence.
Alexa von Tobel, founder and managing partner, Inspired Capital: 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, Insight Partners: We’re watching how frontier labs approach the application layer. A lot of people assumed labs would just train models and hand them off for others to build on, but that doesn’t seem to be how they are thinking about it. We may see frontier labs shipping more turnkey applications directly into production in domains like finance, law, healthcare, and education than people expect.
Tom Henriksson, general partner, OpenOcean: If I had to pick one word for quantum in 2026, it’s momentum. Trust in quantum advantage is building fast, with companies publishing roadmaps to demystify the tech. But don’t expect major software breakthroughs yet; we still need more hardware performance to cross that threshold.
Which areas are you looking to invest in?
Emily Zhao, principal, Salesforce Ventures: We are targeting two distinct frontiers — AI entering the physical world and the next evolution of model research.
Michael Stewart, managing partner, M12: Future datacenter technology. For the last year or so, we’ve been standing up a few new investments that signal our interest in future “token factory” technology, with an eye towards what can really advance how efficiently and cleanly they run. This is going to continue in 2026 and beyond, in categories that include everything within the walls of the data center: cooling, compute, memory, and networking within and between sites.
Jonathan Lehr, co-founder and general partner, Work-Bench: 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, NEA: We are at the limit of humanity’s ability to generate enough energy to feed power-hungry GPUs. As an investor, I’m looking for software and hardware that can drive breakthroughs in performance per watt. This could be better GPU management, more efficient AI chips, next-gen networking approaches like optical, or rethinking thermal load within AI systems and data centers.
When it comes to AI startups, how do you determine that a company has a moat?
Rob Biederman, managing partner, Asymmetric Capital Partners: A moat in AI is less about the model itself and more about economics and integration. We look for companies that are deeply embedded in enterprise workflows, have access to proprietary or continuously improving data, and demonstrate defensibility through switching costs, cost advantages, or outcomes that are difficult to replicate.
Jake Flomenberg, partner, Wing Venture Capital: I’m skeptical of moats built purely on model performance or prompting — those advantages erode in months. The question I ask: If OpenAI or Anthropic launches a model tomorrow and is 10x better, does this company still have a reason to exist?
Molly Alter, partner, Northzone: It’s much easier today to build a moat in a vertical category rather than a horizontal one. The best moats are data moats, where each incremental customer, data point, or interaction makes the product better. These are somewhat easier to build in specialized categories like manufacturing, construction, health, or legal, where data is more consistent across customers. But there are also interesting “workflow moats,” where defensibility comes from understanding how a task or project moves from point A to point B in an industry.
Harsha Kapre, director, Snowflake Ventures: For AI startups, the strongest moat comes from how effectively they transform an enterprise’s existing data into better decisions, workflows, and customer experiences. Enterprises already sit on incredibly rich data; what they lack is the ability to reason over it in a targeted, trustworthy way. Startups that are looking to raise a Series A in 2026 as enterprise-focused AI companies need to demonstrate a strong blend of technical expertise and deep industry knowledge. This combination allows them to develop domain-specific solutions that can directly address a customer’s governed data, without creating new silos. By delivering insights and automation that were previously not possible, these startups can prove their value to potential investors.
According to industry experts, such as Kirby Winfield from Ascend and Antonia Dean from Black Operator Ventures, enterprises are moving away from random experiments with multiple solutions and are focusing on more thoughtful engagement with a select few. This shift towards a more strategic approach to AI investments will likely lead to increased budgets for AI technologies in the coming years.
Scott Beechuk from Norwest Venture Partners believes that 2026 will be the year when enterprises start to see real value from their AI investments. As specialized models mature and oversight improves, AI systems are becoming more reliable in daily workflows, leading to tangible benefits for organizations across various industries.
However, Marell Evans from Exceptional Capital notes that the progress will be incremental, with a lot of iteration still needed to showcase pain-point solutions for enterprises. Solving simulation to reality training is seen as a key opportunity that could unlock new possibilities for industries both established and emerging.
In terms of raising a Series A as an enterprise-focused AI startup in 2026, industry experts like Jake Flomenberg from Wing Venture Capital emphasize the importance of a compelling narrative and concrete proof of enterprise adoption. Companies need to demonstrate that their product is mission-critical to customers and can deliver tangible benefits in terms of saving time, reducing costs, or increasing output.
Overall, the outlook for enterprise-focused AI startups in 2026 is positive, with increased budgets expected from enterprises and a growing recognition of the value that AI technologies can bring to organizations. By combining technical expertise with industry knowledge and demonstrating real-world impact, startups in this space can position themselves for success in the coming years. The success of a startup can be measured in various ways, but one of the best signals is when users are genuinely delighted to use the product. This, coupled with the technical sophistication of the business, can indicate a promising future. However, looking at a north star of real contractual agreements lasting 12+ months can further solidify the potential for growth. Additionally, the ability of a founder to attract top-tier talent to join their startup over competitors or traditional hyper-scalers can set them apart in the industry.
Looking ahead to 2026, the role of AI agents in enterprises is expected to continue evolving. Nnamdi Okike from 645 Ventures highlights the technical and compliance hurdles that need to be overcome for enterprises to fully benefit from AI agents. Rajeev Dham of Sapphire predicts the emergence of a universal agent that combines various roles into a single entity, breaking down organizational silos. Antonia Dean of Black Operator Ventures emphasizes the importance of collaboration between humans and agents on complex tasks, rather than a strict division of labor.
In terms of portfolio growth, companies that identify gaps created by the adoption of AI and execute relentlessly on product-market fit are seeing the most success. Jake Flomenberg of Wing Venture Capital mentions cybersecurity tools addressing data security and agent governance, as well as new areas like Answer Engine Optimization in marketing. Companies that help enterprises put AI into production, such as those focusing on data extraction and structuring, developer productivity, and infrastructure for generative media, are also thriving.
Retention in the enterprise space is crucial for long-term success. Companies that solve problems that intensify as customers deploy more AI, accumulate proprietary context, and grow with AI adoption are seeing the strongest retention rates. Tom Henriksson of OpenOcean highlights the success of serious enterprise software providers enhanced with AI, such as Operations1, which digitizes employee-led production processes end-to-end. Startups serving the enterprise in data tooling and vertical AI apps, with forward-deployed teams assisting in customer satisfaction, are also experiencing high retention rates.
In conclusion, the future of AI agents in enterprises, the growth of companies in various sectors, and the importance of retention in the enterprise space all point towards a dynamic and evolving landscape. Startups that can adapt to changing trends, attract top talent, and provide value to their users are well-positioned for success in the coming years. The world of technology is constantly evolving, with new advancements and innovations being made every day. One such innovation that has captured the attention of tech enthusiasts is the development of artificial intelligence (AI). AI has the potential to revolutionize various industries and change the way we live and work.
AI refers to the simulation of human intelligence processes by machines, particularly computer systems. These intelligent systems are designed to perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation. AI has the ability to analyze large amounts of data quickly and efficiently, enabling businesses to make more informed decisions and improve their operations.
One of the most exciting applications of AI is in the field of healthcare. AI-powered systems can help healthcare providers diagnose diseases, develop personalized treatment plans, and improve patient outcomes. For example, AI algorithms can analyze medical images, such as X-rays and MRIs, to detect abnormalities and assist radiologists in making accurate diagnoses. AI can also be used to predict patient outcomes and identify individuals at risk of developing certain conditions, allowing for early intervention and preventive care.
In the world of finance, AI is being used to detect fraudulent activities, predict market trends, and automate trading strategies. AI-powered chatbots are also being used by financial institutions to provide customer service and support, making it easier for customers to access information and carry out transactions.
AI is also being used in the field of transportation to improve safety and efficiency. Self-driving cars, equipped with AI algorithms, are being developed to navigate roads and highways autonomously, reducing the risk of accidents and traffic congestion. AI-powered systems are also being used in logistics and supply chain management to optimize routes, reduce fuel consumption, and improve delivery times.
In the field of education, AI is being used to personalize learning experiences for students and provide personalized feedback to teachers. AI-powered tutoring systems can adapt to individual learning styles and pace, helping students to grasp difficult concepts and improve their academic performance. AI can also be used to automate administrative tasks, such as grading assignments and managing student records, allowing teachers to focus more on teaching.
Despite its many benefits, the development of AI also raises ethical and societal concerns. Issues such as data privacy, algorithm bias, and job displacement are some of the challenges that need to be addressed as AI continues to advance. It is important for policymakers, industry leaders, and the public to work together to ensure that AI is developed and deployed responsibly and ethically.
In conclusion, AI is a powerful technology that has the potential to transform various industries and improve our quality of life. By harnessing the capabilities of AI, we can unlock new opportunities, solve complex problems, and create a better future for generations to come. As we continue to explore the possibilities of AI, it is important to approach its development with caution and mindfulness, ensuring that it benefits society as a whole.
