After Software Ate the World, AI Is Eating SaaS: The Repricing of Enterprise Software

For more than a decade, enterprise software has been monetized through a deceptively simple mechanism: the seat. Organizations purchased licenses based on headcount, and software vendors grew by expanding adoption across teams, departments, and entire enterprises. Investors rewarded predictable recurring revenue and the compounding logic of per-user pricing.

Recent market movements suggest this foundation is being questioned at a structural level. A new wave of agentic AI tools is not merely augmenting productivity—it is reshaping the economic assumptions underpinning enterprise software, knowledge platforms, and data businesses.

The Market Signal

In January 2026, the iShares Expanded Tech-Software ETF (NASDAQ: IGV) experienced its steepest monthly decline since 2008, accompanied by sharp selloffs in high-margin information services. Then, on February 4th, 2026, it fell 12% in a single day, further adding to concerns about how AI is impacting SaaS pricing foundations, not just research services. Gartner fell nearly 20% in a single session, while legal and data-centric companies saw unusually sharp declines.

The thematic signal was unmistakable: markets are pricing a world where enterprises may no longer need thousands of software seats if a smaller number of AI agents can execute the same workflows. This shift carries profound implications not only for technology companies, but for global enterprises navigating productivity, governance, and competitiveness in an AI-driven economy.

From Users to Agents: A Fundamental Shift

Traditional SaaS assumes value is created through human interaction: that employees log in, navigate workflows, and using the software access they produce outcomes. The unit of monetization is anchored to the number of users.

Agentic AI challenges this directly. Instead of requiring a user to operate a tool, an AI agent can interpret documents, retrieve information, generate drafts, validate outputs, submit transactions, and escalate exceptions for human review. Value is delivered through execution rather than interaction. The enterprise may still rely on the same systems of record, but far fewer humans need direct access. This represents a redefinition of what “software usage” means.

Why Markets Reacted

The SaaS era was built on a well-understood growth pattern: land a team, expand across users, extend into adjacent departments, upsell higher tiers, and renew annually with increasing contract value. Agentic AI disrupts the central lever of this flywheel i.e. the expanding seats.

If an organization can achieve the same operational outcomes with fewer users and more automation, the growth trajectory of many vendors fundamentally changes. This explains why investors reacted swiftly to tools positioned not as “assistants” but as “coworkers.” The market response suggests investors are evaluating not only which AI tools will prevail, but which established software models may compress under this new logic.

The Second Wave: Data Platforms Face Repricing

Once software interfaces appeared vulnerable, markets began asking a harder question: if AI agents can retrieve, synthesize, and reason across information sources, why should curated data continue to command premium margins?

This concern spread to established data and publishing businesses, including S&P Global and Moody’s. The sharpest impact appeared in legal data and publishing, Thomson Reuters experienced a record one-day drop of approximately 16%, while Wolters Kluwer declined roughly 13%.

The reaction reflects growing recognition that AI is not only automating workflows, it is transforming how knowledge is consumed. Even if underlying data remains valuable, the traditional distribution model of human-facing subscriptions may weaken if AI becomes the primary interface between professionals and information.

A Global Context

This transition must be understood within a broader global context. Many advanced economies face structural productivity challenges and demographic constraints. Aging populations, labor shortages, and increasing administrative complexity have created pressure on enterprises and governments alike. Agentic AI arrives precisely when the world is actively seeking new productivity engines.

For enterprises, AI agents promise faster execution, reduced administrative burden, improved compliance automation, and scalable support without proportional headcount growth. For emerging economies, the implications are nuanced—AI agents could accelerate competitiveness while potentially altering the nature of service-sector employment.

What This Means for Enterprise Software

A decade ago, Marc Andreessen famously declared that “Software is eating the world” Today, we are witnessing a new phase: AI is eating the SaaS pricing foundations.

For CEOs, the critical insight is not that software is collapsing, it is that the value equation is fundamentally shifting from interfaces to execution, from user counts to workflow throughput, from software usage to automated outcomes, and from human attention to machine orchestration.

In the coming years, vendors will increasingly compete not on the number of users they attract, but on how effectively they enable secure, governed automation at scale. The winners will be those who become the execution substrate for AI agents—systems that combine reliable data, workflow logic, governance, and auditability. The vulnerable firms will be those whose pricing power depends heavily on human logins and per-seat adoption rather than measurable outcomes.

What SaaS CEOs Should Do This Half

The agentic AI shift is accelerating. The following actions represent pragmatic, board-level moves that can be initiated within a quarter:

1. Redesign AI strategy around Workflows, not Tools. Identify workflows where speed, cost, and compliance matter most: contract review, invoice reconciliation, onboarding, reporting, and evaluation which can be reliably executed by agents with human oversight.

2. Build an Agent Governance Layer before Scaling. Define governance standards for auditability, traceability, permission boundaries, and escalation rules. This foundation will make adoption safe, scalable, and defensible.

3. Renegotiate SaaS Contracts with a Seat-compression Lens. Map software to workflows, identify where agents reduce the need for direct user access, and prepare renegotiation strategies based on usage, throughput, or outcome-based pricing.

4. Treat data as a strategic asset, not a subscription. Reassess which subscriptions are mission-critical, which become redundant once agents can synthesize across sources, and whether internal data quality is sufficient for agent execution.

5. Upgrade talent strategy from operators to supervisors. Begin reskilling around AI supervision, exception handling, workflow design, and compliance oversight. The most valuable employees will be those who can manage agent-driven workflows with accountability.

6. Elevate system reliability and observability standards. In traditional SaaS, bugs were reported by humans who could contextualize issues and work around problems. In an agentic environment, AI systems encounter and report issues at machine speed and scale. A minor bug that might have affected a handful of users can cascade across entire operations within minutes. SaaS providers must mature their logging infrastructure, pre-emptive issue detection, and hot-fix capabilities. SaaS business owners should prioritize enhanced observability, real-time anomaly detection, automated rollback mechanisms, and dramatically faster incident response cycles.

7. Prepare the board for a new definition of digital transformation. Agentic AI expands digital transformation into the automation of knowledge work at scale. Boards should be briefed not only on opportunity, but on vendor risk, changing competitive dynamics, compliance exposure, and workforce implications. Critically, agentic AI will uncover system issues, data quality problems, and edge cases far faster than human users ever could, potentially triggering SLA breaches, compliance violations, and operational failures at scale. This makes robustness, resilience, and comprehensive safeguarding mechanisms essential boardroom priorities, not merely IT concerns.

A Transition, Not a Collapse

The market’s repricing signals that software’s monetization logic is evolving. The per-seat era was built for a world where value depended on human usage. The agentic era is being built for a world where value depends on automated execution.

For technology leaders, the opportunity is significant, but so is the urgency. Those who move early will not merely adopt AI faster, they will redesign how their organizations produce outcomes, control risk, and compete in a world where work increasingly happens through agents rather than interfaces.

In that world, the most important question will not be how many users are logging in, but how effectively the enterprise is executing.

Amandeep Midha, Senior Engineer at Hybrid Greentech Energy -Storage Intelligence ApS, Denmark