Fears that agentic artificial intelligence could dismantle the traditional software industry are prompting a strategic response from incumbents, with Microsoft advancing a framework that would fold AI agents into the same commercial logic that has underpinned enterprise software for decades.
The argument, articulated by Microsoft Executive Vice President Rajesh Jha, is that AI systems will not eliminate software demand but redefine who, or what, counts as a user. Speaking at a recent conference, Jha described a near-term scenario in which AI agents operate inside corporate environments as digital workers, each with its own identity, credentials, and access rights.
“All of those embodied agents are seat opportunities,” he said, invoking the industry’s core pricing unit: the per-user license.
Under Microsoft’s model, an organization deploying AI at scale could see its licensing footprint expand rather than contract. Ten employees supervising multiple agents would not reduce demand for software seats; it could increase demand for them. Each agent, performing discrete tasks across systems, would require authorized access, audit trails, and integration into enterprise identity frameworks.
This framing is designed to counter a competing narrative gaining traction among analysts and technologists. In that view, large language models and autonomous agents act as intermediaries that sit above traditional software stacks, executing tasks without requiring users to engage directly with multiple applications. If that model holds, the value of many SaaS products could be compressed, with AI interfaces becoming the primary layer of interaction.
Microsoft’s counter-position is rooted in infrastructure realities. Enterprise systems are governed by strict controls around identity, permissions, and compliance. Even highly autonomous agents must authenticate, access data through approved channels, and leave verifiable records of activity. These requirements create a structural argument for preserving licensing frameworks, even as the nature of “users” evolves.
There is also a financial imperative. The global software industry, dominated by subscription-based SaaS models, depends heavily on predictable, per-seat revenue streams. A shift toward fewer human users interacting with software would, under traditional assumptions, threaten that model. Microsoft is attempting to extend its revenue base into the automation layer itself by redefining agents as licensable entities.
However, this approach introduces new tensions. Nenad Milicevic of AlixPartners argues that agentic AI may push enterprises in the opposite direction, toward minimizing the number of active “users” altogether. As automation scales, a smaller group of human supervisors could manage increasingly complex workflows, reducing the need for widespread software access.
In such a scenario, the logic of per-seat licensing begins to strain. If a single AI agent can perform the work of several employees, charging per “seat” may not align with perceived value. Vendors could respond by increasing prices for machine-based operators or introducing new tiers of licensing, but that carries competitive risk. Enterprises may favor providers that adopt usage-based or outcome-based pricing models better suited to automated environments.
The debate points to a deeper question about how value is measured in an AI-driven enterprise. Traditional software pricing assumes a linear relationship between users and output. Agentic systems break that link. One agent can operate continuously, scale tasks dynamically, and interface with multiple systems simultaneously. This creates ambiguity around what constitutes fair pricing: access, activity, or results.
There is also a dimension tied to platform control. If AI agents become the primary interface through which work is executed, the layer that orchestrates those agents could capture disproportionate value. Microsoft’s broader AI strategy, including its investments in enterprise copilots and cloud infrastructure, suggests it is positioning itself not just as a software vendor but as the operating environment for these agents. Maintaining licensing control within that stack would reinforce its ecosystem advantage.
However, the emergence of more open and interoperable AI systems could challenge that dominance. If enterprises can deploy agents that move seamlessly across different software environments, the switching costs that have historically protected large vendors may weaken. That would shift bargaining power toward customers, particularly large enterprises capable of negotiating bespoke licensing arrangements.
Operational considerations further complicate the picture. Treating AI agents as employees requires organizations to rethink identity management, cybersecurity, and governance frameworks. Each agent would need defined permissions, monitoring protocols, and accountability structures. These requirements could reinforce the role of established enterprise software providers, which already offer integrated solutions for managing users and access.
For now, the agentic AI economy remains in an early, largely experimental phase. Most deployments are limited in scope, and the economics of large-scale automation are still being tested. The contrasting perspectives from Jha and Milicevic reflect an industry attempting to map out its future before the underlying dynamics fully materialize.
What is emerging, however, is a clear divide, where incumbents like Microsoft are working to adapt existing revenue models to a world of machine-driven work, effectively extending the concept of a “user” to include AI. Critics believe that the same technology could erode those models, forcing a transition toward more flexible and potentially less lucrative pricing structures.
