AI agents could weaken one of software-as-a-service’s central assumptions: that more users must work through more application interfaces. Gartner estimates that up to $234 billion in enterprise application spending could be exposed to agentic arbitrage by 2030, about 20% of enterprise SaaS spending that year. That is a forecast of spending at risk—not a prediction that the money will disappear or that SaaS is already dead.
How can AI agents challenge SaaS?
Traditional SaaS often puts a person in front of an application: the user opens it, navigates its interface and performs a task. Agentic AI changes that interaction. An agent can carry out work across multiple systems, reducing the need for a person to visit each application separately.
Gartner calls this mechanism “agentic arbitrage.” The exposure estimate concerns software value that could be bypassed when agents deliver outcomes directly. It does not mean the underlying records, services or workflows are no longer needed. A system may become less visible to employees while remaining essential to the work an agent performs.
The potential business-model pressure is clearest where revenue depends on charging for each person who uses an interface. If a smaller number of agents can do work previously spread across many users, the link between user growth and revenue growth may weaken. As Gartner Managing Vice President George Brocklehurst put it, “Agentic systems deliver outcomes directly, bypassing traditional user experience (UX)-heavy applications and making the software invisible. This breaks the link between user growth and revenue growth for many enterprise software vendors.” His comment accompanied Gartner’s July 1, 2026 estimate; it describes a risk mechanism, not a measured outcome across all SaaS products.
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Does interface disruption mean SaaS is going away?
No. A less prominent interface is not the same as an obsolete application. Agents still need reliable data, workflow logic, integrations and access to the systems where organizations keep their records. They also need relevant business context. Gartner’s argument is that software vendors may adapt by embedding execution capabilities and preserving context, rather than losing all value when users stop opening their apps as often.
That distinction matters for buyers: a conversational assistant layered over a product is not necessarily the same as a system that can safely complete a business workflow across products. Gartner expects buyer emphasis to shift toward outcomes, but that does not establish that outcome-based pricing will always be cheaper or that every vendor can deliver it.
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Why is low-code caught between two AI effects?
Low-code platforms can make it faster to assemble applications and processes with visual tools, reusable components and controlled development environments. AI can strengthen that proposition when it is built into the platform. Gartner’s June 2026 analysis says market leaders widened their advantage by embedding agentic AI in their core development environments. Gartner’s 2025 enterprise low-code application platform report abstract also describes AI-assisted tooling, composable architectures and governance as approaches to delivery speed, legacy complexity and integration.
But AI can also make conventional coding more productive. If developers can build and change software faster with AI coding tools, some teams may have less need for low-code. The effect depends on the team’s work, technical capacity and governance needs; the evidence does not show that all customers or vendors benefit equally.
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Forrester’s January 2024 analysis illustrates the competing possibilities. It estimated the combined low-code and digital process automation market at $13.2 billion at the end of 2023 and reported that 87% of enterprise developers in its survey used low-code platforms for at least some development. Those are historical estimates and survey findings, not current market measurements.
| Forrester scenario, published 2024 | What it assumes | Indicative 2028 outcome |
|---|---|---|
| Citizen-development growth continues | Citizen development sustains the growth assumed in the scenario. | Approximately $30 billion for the combined low-code and digital process automation market. |
| AI expands citizen development | AI-fueled citizen development and AI-infused platforms accelerate expansion. | Approximately $50 billion for the combined market. |
| AI makes coding more productive | More productive coding reduces demand for low-code relative to the other scenarios. | Growth could slow toward 11% annually. |
Forrester considered both the approximately $30 billion and $50 billion outcomes plausible, but neither likely. They are alternative scenarios published in 2024, not observed 2028 results or a current forecast update. Their value is in showing why AI’s effect on low-code is not one-directional.
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What should an organization compare before choosing a platform?
Compare the work the system can complete, the authority it needs and the full cost of operating it—not just the quality of its interface or AI demonstration.
| Decision area | Questions to ask |
|---|---|
| Workflow outcome | Does the product complete a meaningful workflow, or mainly add a conversational feature or dashboard? |
| Integration and system access | Can it work across the systems of record the workflow depends on, while respecting each system’s access boundaries? |
| Governance and control | How are identity, permissions, policy enforcement and audit handled when an agent takes action? |
| Context and institutional memory | Can it use and retain relevant customer or organizational context over time? |
| Development model | Does low-code’s speed, composition and governance suit the team, or would AI-assisted conventional coding fit its needs better? |
| Economics | How do per-seat charges compare with outcome- or usage-based pricing after accounting for integration, services, AI use and human oversight? |
These comparisons prevent a misleading like-for-like price check. A lower subscription charge may not represent lower total cost if integration, services or oversight are substantial. Conversely, an outcome-oriented offer is not automatically a bargain: buyers need to understand what result is covered, how usage is counted and what happens when the workflow fails or requires human review. No universal price advantage follows from the shift toward outcomes.
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Why do permissions and auditability matter more when agents act?
When software only displays information, a person usually makes the consequential decision and carries out the action. An agent that changes records or triggers processes needs authority to act, so organizations must know whose identity it uses, which permissions constrain it, how policies are enforced and what actions can be audited.
Gartner’s April 2, 2026 forecast says more than half of enterprises may stop paying for assistive AI such as copilots and smart advisors and favor platforms committed to workflow results by 2028. This is a forecast, not an observed adoption rate. Gartner VP Analyst Alastair Woolcock framed the control issue this way: “In this environment, execution authority is not a product feature. It is an architectural position that spans control over identity, permissions, policy enforcement, system-of-record access, and auditability.” He added that vendors embedding AI within that control plane will shape workflow execution, while vendors treating AI as an enhancement layer risk being abstracted.
For buyers, the practical test is whether the platform can demonstrate those controls for the specific workflow—not merely promise that it uses AI responsibly. An agent should have only the access the task requires, and its actions should be traceable under the organization’s policies.
Where does the competition leave SaaS?
The contest is over where value and control accrue: the user interface, the workflow outcome, the organizational context, or the governed execution layer. SaaS products that retain valuable records, integrations, workflows and controls may remain important even if users interact with them less directly. Low-code may benefit when embedded AI and governed composition help teams build useful workflows, while AI-assisted coding may displace it in other settings.
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