UiPath vs. ServiceNow: two market bets on who automates the back office of the future

By The Motley Fool · July 17, 2026.

The article compares two publicly traded companies that more and more people are associating with the "agentic AI" label: UiPath, the veteran of robotic process automation (RPA), and ServiceNow, the enterprise workflow management platform that has forcefully reinvented itself toward autonomous agents. The data the analysis provides is concrete and worth keeping in mind. ServiceNow grows faster and pricier: 22% year-over-year in subscription revenue, $12.64 billion in remaining performance obligations (RPO), nearly 9,000 global customers —85% of the Fortune 500— and 630 accounts paying more than $5 million a year. UiPath, smaller ($1.9 billion in annual recurring revenue, a $6.2 billion market cap versus ServiceNow's $107 billion), grows more slowly in subscriptions (12%) but has just achieved its first quarter of profit under GAAP accounting standards in a first fiscal quarter, with a net margin above 5% versus the double-digit one ServiceNow has long enjoyed.

This is, at bottom, a textbook value-versus-growth investment analysis applied to agentic AI, and as such it does its job: it separates the narrative from the data. Grand View Research, cited in the article, projects a 46.2% compound annual growth rate for the enterprise agentic AI market through 2030, a figure that should be treated as what it is —a consultancy projection, not a certainty— but that does portray well where corporate capital is heading: IT budgets that once went to software licenses are now allocated to platforms that promise to execute tasks, not just display dashboards.

Our reading is that this stock-market duel is, unintentionally, a thermometer of something we have been documenting for months at Zendoric about AI's impact on administrative employment: UiPath and ServiceNow are not only competing against each other, they are competing to replace the same kind of work —back-office processes, support tickets, approval flows, repetitive office tasks— that in our sector analysis we identified as the most exposed in the short term. That a classic robotic automation platform (UiPath) and a corporate workflow management one (ServiceNow) converge on the same terrain of "agents that do the work" confirms that the boundary between RPA and generative AI is dissolving: the technical label matters less now than the commercial promise, executing administrative tasks with fewer people.

This is not a neutral phenomenon. The faster these platforms mature and the more Fortune 500 customers adopt them at the scale ServiceNow's data suggests, the faster the restructuring of administrative workforces we have been anticipating will be: office work does not disappear overnight, but the number of people needed to sustain it does shrink, while demand grows for profiles who know how to design, audit and govern those agents. In the short term this is real friction —it must be said plainly, as part of our editorial line—: fewer hires in administrative and support functions, more pressure on those who fail to retrain toward the judgment and oversight of these systems. In the long term, however, this is exactly the kind of automation that underpins the abundance thesis: if these platforms deliver on their promise, they free human capital from mechanical tasks —processing tickets, cross-checking forms, routing approvals— toward higher-value work, and that is the kind of aggregate efficiency that, sustained over time, lowers the cost of services and frees up resources for the rest of the economy.

That said, it is worth not losing sight of the fact that this is an investment article, not an assessment of technical capability: there is no independent benchmark here of how well the agents of either platform actually work, only financial metrics of growth and margins. As we have kept insisting, measuring real capability matters more than the "agentic AI" marketing both companies use to justify stock-market multiples; investors would do well to demand, beyond RPO and margins, evidence that the deployed agents genuinely automate processes reliably, and do not merely justify a new product label on workflow software that already existed before generative AI was in fashion.