A recent report published by MIT’s NANDA has revealed that over 95% of generative AI pilots at companies are failing. Based on 150 interviews with industry leaders, a survey of 350 employees, and an analysis of 300 public AI deployments, the research reveals a sharp contrast between the rare breakthroughs and the many projects that falter.
Aditya Challapally, the lead author of the report, said “Some large companies’ pilots and younger startups are really excelling with generative AI.” Startups led by 19- or 20-year-olds, for example, “have seen revenues jump from zero to $20 million in a year,” he stated.
Challapally further added, “it’s because they pick one pain point, execute well, and partner smartly with companies who use their tools.” MIT’s research also highlighted deeper issues in how AI is integrated within enterprises. Generic tools like ChatGPT perform well for individual users because of their versatility, but they struggle in corporate settings where they don’t learn from or adapt to existing workflows, Challapally explained.
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Maisa AI, a year-old startup is attempting to fix these issues. The startup built its approach around the premise that enterprise automation requires accountable AI agents, not opaque black boxes. It has received $25 million in funding through a seed round led by European venture capital firm Creandum, which it used to launch Maisa Studio, a model-agnostic self-serve platform that helps users deploy digital workers that can be trained with natural language.
Maisa states its approach is fundamentally different from that of “vibe-coding” platforms like Cursor. “Instead of using AI to build the responses, we use AI to build the process that needs to be executed to get to the response — what we call ‘chain-of-work,” Maisa CEO David Villalón told TechCrunch.
Maisa employs a system called HALP — Human-Augmented LLM Processing. This asks users about their needs while the digital workers outline each step they will follow.
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The startup also developed the Knowledge Processing Unit (KPU), a deterministic system designed to limit hallucinations. While Maisa started out from this technical challenge rather than a use case, it soon found that its bet on trustworthiness and accountability resonated with companies hoping to apply AI to critical tasks.
Maisa hopes that by serving enterprise clients, it can position itself as a more advanced form of robotic process automation (RPA) that unlocks productivity gains without requiring companies to rely on rigid predefined rules or extensive manual programming. The enterprise-first approach means that the startup’s customer base is relatively small. However, Maisa studio has been designed to grow its customer funnel and ease adoption.
Maisa plans to use its funding to grow from 35 to as many as 65 people by the first quarter of 2026 in order to meet demand. The startup is anticipating rapid growth starting from the last quarter of this year. “We are going to show the market that there is a company that is delivering what has been promised, and that it’s working,” Villalón said.