Enterprise automation enters its agent-first phase
Enterprise automation is the use of AI-powered agents and software systems to take over repetitive, rules-based, and data-heavy business tasks at scale so employees can focus on higher-value work, strategic decisions, and customer relationships instead of administrative busywork. Five recent AI startup funding rounds, totalling more than USD 72 million (approx. RM331.2 million), show this shift moving from experiment to execution. Rather than backing new model builders, investors are funding business automation startups that package existing AI into focused agents for specific problems: small business administration, lab operations, customer communication, industrial reliability, and enterprise-wide AI adoption. These AI agents are not generic chatbots; they plug directly into tools, data, and workflows to act on behalf of users. The pattern points to AI’s next phase: operational automation, where value comes from embedded, task-specific systems that run quietly in the background.
Horizontal AI agents: SMB admin and customer communication
Two of the largest rounds target horizontal automation problems that cut across industries: administrative work and customer communication. Lassie raised USD 35 million (approx. RM161 million) in Series A funding to build autonomous AI systems that take over administrative tasks for small businesses, with an early focus on healthcare practices where insurance reimbursement and payment reconciliation consume staff time. According to Lassie, its AI agents already operate in more than 700 businesses across 49 states and deliver over 250,000 hours of labour each year. On the customer side, fonio.ai secured USD 17 million (approx. RM78.2 million) to automate phone-heavy customer interactions for more than 7,500 businesses, processing over two million calls each month. Together, these AI startup funding rounds show investors backing AI agents Series A bets that remove routine phone and paperwork work from humans at scale.
Vertical automation: life sciences labs and industrial reliability
Beyond horizontal use cases, investors are backing AI agents deeply embedded in technical domains. Scispot’s USD 8 million (approx. RM36.8 million) Series A supports an AI-native digital operating layer for modern laboratories, connecting instruments, samples, workflows, and approvals across more than 100 labs. Its model-agnostic context layer aims to turn fragmented lab tools into a foundation for self-driving labs, where coordination and reporting are automated while scientists retain oversight. In heavy industry, Rotomate raised €2.1 million to act as a reliability assistant for equipment fleets. The platform continuously analyses sensor data, maintenance history, and operational context to recommend actions, moving beyond simple alerts. Both startups reflect a vertical enterprise automation trend: rather than generic AI, they build domain-specific systems that understand instruments, machines, and regulatory requirements, making AI agents useful in environments where errors carry high cost and risk.
Mendo and the rise of AI adoption as a product
While some startups automate work directly, Mendo focuses on making AI adoption itself repeatable. Its €12 million Series A backs a platform that helps enterprises identify practical use cases, deploy generative and agentic AI, and guide employees through workflow changes. The company positions agentic AI as an orchestration layer across operations, which shifts the problem from building tools to reshaping processes and habits. Mendo’s bet is that enterprises need more than pilots; they need structured ways to scale AI agents across teams without leaving workers behind. This turn toward enterprise automation consulting-as-software is important: if adoption stalls, even powerful AI agents sit unused. By packaging best practices, training, and governance into a product, Mendo shows a new category emerging in AI startup funding—startups that automate the rollout of automation itself.
What the $72M+ wave reveals about AI’s next phase
Taken together, these five funding rounds signal a clear investor thesis: AI’s next phase lies in automation-first business models built on practical agents, not raw infrastructure. Lassie and fonio.ai tackle horizontal workloads for SMBs; Scispot and Rotomate focus on vertical, domain-specific reliability and compliance; Mendo handles enterprise-wide AI adoption. Instead of chasing new foundation models, these business automation startups wrap existing AI in opinionated workflows, data integrations, and measurable outcomes such as saved labour hours, reduced downtime, or faster experiments. The spread across small business admin, life sciences, customer service, industrial operations, and enterprise change management suggests a broad, durable demand for AI agents that own end-to-end tasks. As more organisations look beyond pilots, this pattern hints at a market where value accrues to those who make AI an invisible, dependable part of everyday work.
