Frameworks, core principles and top case studies for SaaS pricing, learnt and refined over 28+ years of SaaS-monetization experience.
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SaaS disrupted software licenses. It largely left the services industry intact. A SaaS CRM helped salespeople work more efficiently. It didn’t replace them. A SaaS analytics platform gave insights to consultants. It didn’t perform the consulting.
Agentic AI changes this equation because agents don’t provide information or tools. They perform work. When the agent performs the work, it competes with the humans and agencies who previously performed that work, not with another piece of software.
Imagine a boardroom at a large bank. On the table: a project to modernize 400 pieces of legacy software, budgeted at over $600 million with large teams of human coders. McKinsey documented what happened next. Human workers moved to supervisory roles overseeing groups of AI agents that retroactively documented legacy applications, wrote new code, reviewed each other’s code, and integrated features. The staffing model changed from hundreds of coders to a smaller team managing agent fleets. Same project. Fraction of the headcount. Healthcare RCM (Revenue Cycle Management) is seeing agentic AI systems that autonomously handle prior authorizations, claim scrubbing, and denials management. The US health system spends an estimated $140 billion annually on these operations. McKinsey projects AI could significantly reduce cost-to-collect. If realized at the scale McKinsey models, that represents tens of billions in potential savings from a single category.
“Agentic Consulting Firms” have now emerged. Companies positioning themselves as consultancies whose consultants happen to be AI agents. They’re selling against Accenture’s headcount, not Salesforce’s licenses.
The pattern is consistent: agentic AI turns services businesses into software businesses. A human consulting engagement running at 30% gross margins becomes an agent-delivered outcome running at 60-72%. A staffing firm’s placement becomes an agent subscription. An agency’s monthly retainer becomes an automated workflow.
This is a critical point for reading the agentic P&L correctly. The comparison isn’t just agentic vs. SaaS. It’s also agentic vs. Services. 72% gross margins look very different when the alternative isn’t 78% (SaaS) but 25-35% (the services and staffing businesses that agentic AI is disrupting).
There are entire categories of economic activity that SaaS could never meaningfully address because they required human judgment, multi-step execution, or real-time adaptation.
Claims processing and adjudication: SaaS provided workflow management. Agentic AI performs the actual adjudication.
Tax preparation and filing: SaaS provided data entry interfaces. Agentic AI prepares and files the taxes.
Legal document review and contract analysis: SaaS provided search and highlight tools. Agentic AI reads, analyzes, and drafts.
Recruiting and candidate screening: SaaS provided applicant tracking. Agentic AI conducts screens, evaluates fit, and schedules interviews.
Financial reconciliation and audit prep: SaaS provided ledger management. Agentic AI reconciles, identifies discrepancies, and prepares documentation.
In each case, the TAM for an agentic solution includes the software budget for the workflow tool and the labor budget for the humans who performed the work. That’s typically a 5-20x TAM expansion over the SaaS version of the same category.
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