Per-Seat Licensing Meets Its AI Limits
Per-seat licensing has underpinned SaaS pricing models for roughly two decades, on the assumption that more people using a tool meant more value created. That logic breaks down in an era of agentic AI. Autonomous software agents now draft briefs, triage backlogs, and generate updates without human intervention. In practice, this means a highly automated platform may require fewer human users to log in at all. The paradox is clear: the better the AI, the fewer seats a customer needs to buy, creating a structural revenue trap for vendors that depend on headcount-based contracts. Investor anxiety around this mismatch has already surfaced in volatile software valuations, even as overall enterprise software and AI spending continues to grow. The tension is nudging vendors toward pricing mechanisms that track actual work done, not how many humans are assigned a login.
Inside monday.com’s Shift to Consumption-Based Pricing
monday.com’s latest results highlight both the pressure on legacy SaaS pricing and a possible way forward. The vendor reported year-over-year revenue growth of 24%, with enterprise customers spending USD 500,000 (approx. RM2,300,000) or more growing 74% annually. Alongside those numbers, it launched an AI Work Platform and, crucially, a new seats-plus-credits structure that ties part of its revenue to AI consumption instead of pure headcount. This hybrid model mirrors a broader pattern: many SaaS providers are layering usage-based billing for AI features on top of existing per-seat licensing rather than abandoning it entirely. The approach allows monday.com to monetize intensive AI usage without penalizing customers that reduce human operators. It also signals to the market that future growth may come less from selling more seats and more from powering higher volumes of automated work.
Why Consumption-Based Pricing Is Attractive—and Risky—for IT
Consumption-based pricing promises to align software costs with actual usage, tackling a chronic complaint about per-seat SaaS pricing models: paying for dormant accounts and underused features. For IT leaders, usage-based billing can tighten the link between spend and measurable outcomes, especially in AI-rich tools where agents perform discrete, countable tasks. However, the same dynamism that creates fairness also introduces budgeting headaches. When usage can spike with a new initiative or automation rollout, monthly bills become harder to predict. Many vendors are still building the telemetry, billing infrastructure, and sales playbooks needed to support scalable consumption models, which complicates forecasting for customers. Enterprise procurement teams, long accustomed to budgeting by headcount, now face the challenge of shifting internal thinking from “how many users” to “how much work is likely to flow through this platform over time.”
How AI in Project Management Is Reshaping Value—and Pricing
monday.com’s move lands in a project management market where AI is rapidly absorbing coordination and reporting work. Asana’s AI Studio lets teams build custom agents that orchestrate tasks and approvals across systems, while Adobe Workfront goes further by treating AI as an assignable project resource with tasks and deadlines like any human contributor. Microsoft’s Copilot, tightly integrated with Planner and Project, raises similar questions about how much value enterprises truly capture relative to license costs. Across these platforms, AI handles status summaries, workload balancing, and risk flagging—activities that used to justify multiple full-time users inside each tool. As human logins become less central to the value story, per-seat licensing looks increasingly misaligned. Vendors that cannot decouple revenue from headcount risk seeing their most successful AI features cannibalize traditional seat-based income.
What IT Leaders Should Do Before the Next Renewal Cycle
monday.com’s early-stage transition offers a playbook for IT and procurement leaders preparing for consumption-based pricing. First, model likely consumption before signing: estimate how many AI tasks, workflows, or credits your use cases will require, and pressure-test best- and worst-case scenarios. Coming to the table with data improves your ability to negotiate credit caps, usage safeguards, and price protections. Second, reframe ROI internally away from seat counts. Metrics such as time saved, automation rates, and throughput gains are better suited to usage-driven models than simple user numbers. Third, use the transition period as leverage. Vendors still refining their billing systems and compensation structures are more receptive to multi-year commitments that provide predictable usage data. The teams that modernize forecasting and negotiation strategies now will be better positioned as consumption-based models spread across the SaaS landscape.