Matt Biilmann didn’t set out to kill SaaS. The CEO of Netlify, a web development platform used by millions of developers, simply noticed that his team had started building their own tools. Not as a side project. As a practical response to a new reality. When employees realized that AI could generate a fully functional survey application in minutes, the company’s $50-per-month subscription to a dedicated survey tool started looking like an expensive habit. So they canceled it. Then they did the same thing with their quoting software. Then a few internal reporting tools.
Biilmann’s experience is a preview of what’s about to hit every software budget in America. Gartner predicts that by 2026, 40% of enterprise applications will include task-specific AI agents. By 2030, 35% of point-product SaaS tools will be replaced by AI agents or absorbed into larger agent ecosystems. The shift from AI tools (things that help you do work faster) to AI agents (things that do the work for you) is the biggest structural change to the software industry since the cloud migration of the 2010s.
- Deloitte’s 2025 Tech Value survey found that 57% of organizations allocated 21-50% of their digital transformation budgets to AI automation, with agentic AI investment expected to reach 75% of companies by end of 2026.
- Gartner predicts that by 2026, 40% of enterprise applications will include task-specific AI agents, and by 2030, 35% of point-product SaaS tools will be replaced or absorbed by AI agent ecosystems.
- Netlify CEO Matt Biilmann has publicly stated his team used AI to build internal replacements for SaaS survey and quoting tools, demonstrating that dedicated software loses value when AI can generate functional alternatives in minutes.
- 83% of AI-native SaaS companies already offer usage-based pricing as of early 2026, signaling a shift away from traditional seat-based licensing toward outcome-driven models.
- Deloitte projects that by 2030, at least 40% of enterprise SaaS spending will shift from subscriptions to usage-based, agent-based, or outcome-based pricing models.
What Are AI Agents and Why Should Founders Care?
An AI agent is software that doesn’t just respond to prompts but takes autonomous action across tools, workflows, and systems to complete multi-step tasks without continuous human direction. Unlike a chatbot that answers questions, an AI agent can research a prospect, draft a personalized email, schedule the send, log the activity in your CRM, and flag the response for follow-up when it arrives.
The distinction between AI tools and AI agents is the difference between a calculator and an accountant. The calculator helps you do the math faster. The accountant handles the entire process: pulling the numbers, running the analysis, filing the reports, and flagging the anomalies. In 2026, agents are crossing the threshold from experimental to operational for businesses of every size.
Founders should pay attention because this shift threatens the business model of much of the software they currently pay for. Gartner predicts that by 2026, 40% of enterprise applications will include task-specific AI agents. By 2030, the firm projects that 35% of standalone SaaS tools will be replaced or absorbed within larger agent ecosystems. That’s not a gradual evolution. That’s the ground moving under the feet of every startup that depends on software subscriptions.
How Is This Different From the AI Hype of 2023?
The 2023 wave of AI tools was mostly about individual productivity: write faster, summarize better, generate images. Useful, but limited. You still needed a human to orchestrate the work. The 2026 wave of agentic AI is about replacing the orchestration itself.
Here’s a concrete example. Netlify CEO Matt Biilmann publicly stated that his employees used AI to build internal replacements for commercial SaaS survey and quoting tools. When an AI agent can generate a fully functional survey application in minutes, the value proposition of a dedicated survey subscription at $50 per month weakens dramatically. You’re paying for software that an agent can recreate on demand.
This pattern is repeating across categories. Deloitte’s 2025 Tech Value survey found that 57% of organizations are already putting 21-50% of their annual digital transformation budgets into AI automation, with 20% investing more than half. These aren’t pilot programs. These are operational budget shifts that directly compete with existing SaaS spending.
Which Software Categories Face the Biggest Disruption?
Not all SaaS is equally vulnerable. The categories most at risk share three characteristics: they handle structured, repeatable tasks; they charge per seat rather than per outcome; and their core functionality can be replicated through combinations of foundation models and APIs.
| SaaS Category | Disruption Risk | Why | Agent Alternative |
|---|---|---|---|
| Survey/Form Builders | High | Structured output, simple logic | AI generates custom forms on demand |
| Basic CRM | High | Data entry and tracking are automatable | Agents log interactions and surface insights automatically |
| Email Marketing | Medium-High | Segmentation, writing, scheduling are all AI-native tasks | Agents draft, personalize, and send based on triggers |
| Project Management | Medium | Task tracking is simple; collaboration features less replicable | Agents assign, track, and update tasks across tools |
| Enterprise Security | Low | Compliance requirements, audit trails, and liability create moats | Agents augment but can’t replace regulatory infrastructure |
The pattern is clear: the more your SaaS tool does structured, repeatable work that a human currently orchestrates, the more vulnerable it is to agent replacement. Complex platforms with deep integrations, regulatory requirements, and network effects (think Salesforce at the enterprise level, not a basic CRM for a 5-person startup) are more insulated.
What the Major SaaS Players Are Actually Doing About It
Every major enterprise software company is racing to build agentic features into their existing platforms, betting that the best defense against agent disruption is becoming the agent platform yourself.
Salesforce launched Agentforce, a suite of AI agents that operate within the Salesforce ecosystem to handle sales development, customer service, and data analysis. SAP introduced Joule Agents for enterprise resource planning workflows. ServiceNow’s Now Assist AI Agents automate IT service management. Workday built Illuminate Agents for human resources processes. Google Cloud released an Agent Development Kit for custom agent creation. Oracle launched AI Agent Studio. Adobe built an Experience Platform Agent Orchestrator.
The common strategy is “embed or be replaced.” These companies are betting that if AI agents live inside their platforms, customers have less reason to replace the platform entirely. It’s a defensive play that could work for enterprise customers locked into multi-year contracts and deep integrations. For startups and small businesses with lighter tech stacks, the calculus is different. Switching costs are lower, and the appeal of agents that work across tools (rather than within one vendor’s walled garden) is stronger.
How Is Software Pricing Going to Change?
The seat-based pricing model that defined SaaS for two decades is breaking down. When an AI agent can do the work of three seats, charging per seat penalizes customers who adopt the technology you’re supposedly enabling. Deloitte predicts that by 2030, at least 40% of enterprise SaaS spending will shift toward usage-based, agent-based, or outcome-based pricing.
Already, 83% of AI-native SaaS companies offer usage-based pricing, per early 2026 data. Three pricing models are emerging to replace the traditional subscription:
Usage-based pricing charges per agent action, API call, or computing resource consumed. This is the most common transition model because it’s familiar (cloud infrastructure has used it for years) and measurable. The downside for vendors is revenue unpredictability. The upside for customers is paying only for what they use.
Outcome-based pricing ties payment to measurable business results: resolved support tickets, qualified leads generated, revenue influenced. This is the most radical shift because it aligns vendor incentives directly with customer value. If the agent doesn’t produce results, the vendor doesn’t get paid. A few early-stage companies are experimenting with this model, though defining and measuring “outcomes” contractually remains complex.
Hybrid models combine a base subscription (access to the platform and core features) with usage or outcome tiers on top. This gives vendors some revenue predictability while offering customers cost flexibility. Expect most large SaaS companies to land here by 2027 as they transition away from pure seat-based models.
What Should Founders Do Right Now?
If you’re running a startup or small business, the AI agent transition creates both risk and opportunity. Here’s how to position for it.
Audit your SaaS stack for agent-replaceable tools. List every tool you pay for monthly. For each one, ask: “Could an AI agent handle 80% of what I use this for?” If yes, that subscription is a candidate for replacement within 12 months. Focus first on the tools where you’re paying per seat but only one or two people actually use the software regularly.
Experiment with agent platforms before committing. Platforms like the current generation of AI tools for solopreneurs are rapidly adding agentic capabilities. Start with a single workflow: lead qualification, customer onboarding emails, or weekly reporting. Measure the time saved and quality difference before expanding.
If you’re building a SaaS product, build the agent layer now. The opportunity to build AI-first is closing. Every month you wait, the major platforms get closer to offering native agent features that match what standalone tools provide. Your competitive advantage needs to come from proprietary data, unique workflows, or domain expertise that agents can’t easily replicate.
Rethink your pricing model. If you charge per seat and your customers start using AI agents to reduce their team size, your revenue shrinks as their efficiency improves. That’s a misalignment that will cost you customers. Start modeling what usage-based or outcome-based pricing would look like for your product. The transition is uncomfortable, but it’s better to lead it than to be forced into it by customer churn.
The companies that adapt to the agentic era won’t just survive the disruption. They’ll use it to build deeper moats, stronger customer relationships, and more defensible businesses. The companies that ignore it will slowly discover that their customers have found AI agents willing to do the same work for a fraction of the cost.
Frequently Asked Questions
What is an AI agent?
An AI agent is software that takes autonomous action across tools, workflows, and systems to complete multi-step tasks without continuous human direction. Unlike chatbots that respond to prompts, agents can execute sequences of actions like researching, drafting, scheduling, and logging independently.
Will AI agents replace SaaS tools?
Partially. Gartner predicts that by 2030, 35% of standalone SaaS tools will be replaced or absorbed by AI agent ecosystems. Tools handling structured, repeatable tasks (surveys, basic CRMs, email marketing) face the highest disruption risk, while complex platforms with regulatory moats are more insulated.
How much are companies spending on AI agents in 2026?
Deloitte’s 2025 survey found that 57% of organizations allocate 21-50% of their digital transformation budgets to AI automation, with 20% investing more than half. Agentic AI investment specifically is expected to reach 75% of companies by end of 2026.
How is SaaS pricing changing because of AI agents?
Seat-based pricing is giving way to usage-based, agent-based, and outcome-based models. Already, 83% of AI-native SaaS companies offer usage-based pricing. Deloitte projects that by 2030, at least 40% of enterprise SaaS spending will shift to these newer models.
What SaaS tools are most at risk from AI agents?
Tools handling structured, repeatable tasks with per-seat pricing face the highest risk. Survey builders, basic CRMs, and email marketing platforms are most vulnerable. Enterprise security, compliance tools, and platforms with deep network effects are more insulated from disruption.
