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As AI agents handle more analytics and workflows, the perceived value of older, non-agentic platforms decreases. To avoid churn, these legacy vendors may have to offer significant price cuts to customers who are getting a large portion of the value from their own AI layer.
As AI agents become primary software users, SaaS companies like Salesforce are building "headless" versions where the API is the UI. This fundamentally breaks the traditional B2B SaaS business model based on pricing per human user, forcing a shift towards consumption-based, agent-native pricing models.
Enterprises no longer need to buy expensive SaaS products for tasks like customer feedback. They can now spin up custom AI agents internally, making it harder for SaaS companies to acquire new customers and leading to higher-than-modeled churn. This poses a fundamental threat to the SaaS business model.
The biggest threat to incumbent software companies isn't a new feature, but a business model shift. AI enables outcome-based pricing, which massively favors agile newcomers as incumbents struggle to adapt their entire commercial structure away from seat-based subscriptions.
The value in software is shifting from SaaS platforms (like CRMs) to the AI agent layer that automates work on top of them. This will turn established SaaS companies into simple data repositories, or "hooks," diminishing their stickiness and pricing power as agents can easily migrate data.
The fundamental business model of many SaaS companies is based on per-user pricing. AI agents pose an existential threat to this model by enabling smaller teams to achieve the same output as larger ones. As companies wonder why they should pay for 100 seats when 10 people can do the work, the entire economic foundation of the SaaS industry faces a crisis.
As AI agents become the primary "users" of sophisticated software, the traditional per-seat licensing model becomes obsolete. Pricing will inevitably shift to a value-based model, tied to outcomes the AI delivers—such as cycle reduction or performance gains—rather than human operators.
Instead of a full "rip and replace," large companies like Sanofi are keeping systems like Salesforce as a "system of record" but are moving significant workloads (up to 80%) to custom AI agent-driven processes. This subtly undermines the value and pricing power of incumbent SaaS vendors.
The ability for customers to build their own software features using AI agents directly threatens the traditional SaaS upsell model. During negotiations, customers can now credibly threaten to "roll their own" features instead of paying for higher-priced tiers, weakening the vendor's pricing power.
SaaS products like Salesforce won't be easily ripped out. The real danger is that new AI agents will operate across all SaaS tools, becoming the primary user interface and capturing the next wave of value. This relegates existing SaaS platforms to a lower, less valuable infrastructure layer.
As AI agents perform more work and human headcount decreases, the traditional seat-based pricing model becomes obsolete. The value is no longer tied to human users. SaaS companies must transition to consumption-based models that charge for the automated work performed and value generated by AI.