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Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →No—not as a way of delivering software, but potentially as a way of packaging and selling it. AI agents can reduce the need for people to open separate applications, and that puts pressure on products built mainly around dashboards and per-user subscriptions. But agents still need software to access data, enforce permissions, execute transactions, and keep records. The likely change is a shift in where software’s value sits, not the disappearance of software.
What would “the end of SaaS” actually mean?
SaaS—software delivered online and typically paid for through a recurring subscription—is often discussed as if it were one thing. The threat from AI is more specific: agents may weaken the assumptions that people must operate each application, that every employee needs a paid seat, and that a visible interface is where a product’s value lives.
- Applications could become less visible. A person may ask one agent to complete work that currently requires opening several tools. Those underlying applications may still be doing the work.
- Some subscriptions could be replaced. If a customer can reproduce a narrow feature with a model and connectors, a standalone product may be harder to justify.
- Seat pricing could lose its fit. If agents do more of the work, employee headcount may no longer track software usage or value.
- A new software layer could grow. Agents need secure execution, data access, integrations, monitoring, identity, audit trails, approvals, and ways to control costs.
So it helps to separate SaaS as hosted software from SaaS as a person-operated dashboard sold by the seat. The first can persist even if the second changes substantially.
How agents put pressure on existing software
Gartner calls one version of the mechanism “agentic arbitrage”: an agent completes work across multiple systems, reducing the need for a person to operate each system separately. In a customer-support workflow, for example, an agent could identify an account, check its contract and entitlement, search a knowledge base, update a ticket, issue a policy-compliant refund, schedule a follow-up, and record the actions for audit. The customer may experience one interaction; several SaaS products may still be involved behind the scenes.
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That distinction matters. The agent can displace time spent navigating interfaces without replacing the systems that hold authoritative customer, contract, payment, or ticket records. At the same time, vendors whose value is mainly the interface—or a simple feature that an agent can reproduce—may face lower usage, weaker pricing power, or cancellation.
Gartner estimates that up to $234 billion of enterprise application spending, roughly 20% of enterprise SaaS spending, could be exposed to agentic arbitrage by 2030. “Exposed” or “at risk” does not mean that this spending will certainly disappear. Gartner also predicted that 40% of enterprise applications would include task-specific agents by the end of 2026, up from less than 5% in 2025; that is a forecast, not a completed adoption count. Separately, Gartner’s enterprise application software forecast anticipates market growth through 2030. Taken together, these signals point toward spending being rearranged, not proof that software demand is vanishing.
Which kinds of SaaS are most exposed?
The key question is not whether an AI model can copy one feature. It is whether an agent can complete the full customer workflow reliably, including exceptions, approvals, security, and recordkeeping. Deloitte expects simpler processes such as customer service to face disruption earlier than complex ERP or CRM workflows.
| Exposure | Examples | What to examine |
|---|---|---|
| Higher | Basic text transformation, content generation, summarization, simple scheduling, transcription, lightweight workflow automation, routine support triage, and narrow productivity features. | Is the product mostly a thin interface over broadly available model capabilities? Can a general agent reproduce the useful result with a prompt and a connector? |
| Mixed | Marketing automation, sales engagement, recruiting administration, expense management, project coordination, business-intelligence front ends, document processing, IT service management, and low-code workflow tools. | Does the product own a valuable process, data, or decision—or mainly present information and move it between systems? Can an agent handle exceptions and controls as well as the routine path? |
| Often more defensible | Systems of record and mission-critical platforms in areas such as ERP, payroll, healthcare, finance, compliance, infrastructure, and security. | Does the product provide authoritative records, complex permissions, difficult integrations, regulatory controls, transaction reliability, or operational knowledge that is costly to replace? |
These are tendencies, not guarantees. A system of record can lose customers if it has poor integrations or inflexible workflows; a simple product can be resilient if it owns distribution, proprietary data, or a trusted customer relationship. A product may also become less visible to users while remaining essential to the business.
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Why cheaper software development does not make software free
AI-assisted coding can lower the cost of a prototype. A prototype, however, is only one part of a dependable product. A business still has to maintain integrations, protect data, manage permissions, respond to incidents, support customers, meet compliance obligations, and get the product in front of buyers. A custom tool also needs an owner when the original builder leaves or the underlying system changes.
Deloitte notes that cheaper code generation could increase competition from AI-native companies and customers building for themselves, raising the importance of differentiation. The practical distinction is:
- Prototype cost: may fall as AI makes it easier to generate code and assemble basic workflows.
- Production reliability: requires testing, monitoring, fallback paths, and handling unusual cases.
- Security and governance: require controlled access, data policies, auditability, and ownership of decisions.
- Integration and maintenance: remain recurring work as APIs, data, and business processes change.
- Distribution and support: still determine whether customers can adopt and rely on the product.
AI can make software easier to produce without making it equally easy to trust, deploy, govern, distribute, or defend.
What happens to per-seat pricing?
A seat remains valuable when a person needs a workspace, a tailored view, training, approval authority, or control over exceptions. But if an agent performs work that used to require several employees, charging only by the number of human users may no longer reflect either the customer’s consumption or the vendor’s value.
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| Pricing approach | What it charges for | Main question for the buyer |
|---|---|---|
| Per seat | Named or active human users | Does the number of people who need access still track value? |
| Usage-based | Actions, API calls, tokens, compute, or time in operation | Can usage and total cost be forecast and capped? |
| Per agent or workflow | An agent, deployed process, or automated capability | What is included, and how are retries, monitoring, and human review counted? |
| Per task or transaction | A defined unit of work, such as a processed request | What counts as a completed task, and who pays for failures? |
| Outcome-based | A result such as a resolved ticket or qualified lead | Can the result be attributed and independently verified? |
| Hybrid | A base subscription plus usage, task, or outcome charges | Does the base cover enough to keep the bill predictable? |
Deloitte describes usage- and outcome-based pricing as likely areas of growth, not settled standards. AlixPartners has argued that hybrid usage- and outcome-based pricing could capture a majority of enterprise software revenue by 2026; that is a forecast, not an established market result. Outcome pricing also needs precise contractual definitions of the agent, task, process, interaction, and outcome. Without them, parties can disagree about causation, incomplete customer data, failed attempts, edge cases, audit rights, and responsibility for results involving multiple vendors.
For buyers, compare total workflow cost—not the headline price of a seat or an automated task. Include implementation, human review, security, governance, support, and metered usage. For vendors, fewer seats do not automatically mean less revenue: automated work may increase transactions, API calls, compute, storage, and monitoring. Revenue quality and gross margin matter alongside seat counts.
Who could capture the value?
AI is not simply a contest between “AI” and “SaaS.” Several layers can benefit, and the company controlling the interface may not be the company providing the underlying record or transaction.
- Model providers supply reasoning and generation and may become a new route through which employees access business tools. OpenAI describes a strategy of a company-wide intelligence layer and a unified experience across existing systems. That is the company’s strategic position, not independent proof that model providers will capture all software value. (OpenAI’s enterprise AI strategy.)
- Cloud providers and agent platforms can sell inference, storage, identity, compute, runtimes, integration, observability, and marketplace distribution. AWS argues that agents can create new distribution for SaaS; as a cloud provider, it has a commercial interest in that view. (AWS on agentic SaaS.)
- Incumbent SaaS vendors have customer relationships, integrations, data, procurement access, compliance controls, and embedded workflows. Their challenge is to make those strengths usable by agents without depending entirely on the old interface or seat model.
- AI-native application companies can design around a specific outcome and adopt new pricing or architecture without as much legacy product debt. They may still lack distribution and proprietary data, depend on third-party models, face enterprise trust hurdles, or be exposed to platform bundling.
- Service firms and systems integrators may gain work in data cleanup, permissions, workflow redesign, testing, governance, and change management. Gartner identifies services and platforms that support cross-domain agent workflows as potential opportunities.
The strategic test for a software vendor is shifting from “How do we keep users inside our application?” to “Can the right agents discover and safely invoke our capability?” Useful foundations include stable APIs, structured data, clear tool descriptions, permission-aware actions, event-driven architecture, audit logs, transaction guarantees, human escalation paths, and agent monitoring.
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Why computer-using agents change the risk—and do not remove APIs
Not every agent must use a clean API. Microsoft describes Windows 365 for Agents as providing environments where agents can operate in applications, portals, and legacy systems, including cases where APIs are unavailable. This can extend automation to software that was previously difficult to connect.
Using a screen is not the same as having a reliable integration. A changed button or page layout can break a workflow; visual interpretation can fail; broad credentials can expose too much; and a mistaken action may be hard to reverse or audit. APIs remain preferable for high-volume, stable, permissioned transactions because they can provide more explicit controls and observability. Browser or desktop automation is most useful when no suitable API exists and the surrounding environment can contain and monitor the agent.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What still limits agents in production?
A convincing demonstration is not enough to justify handing an agent a consequential business process. Buyers and vendors have to account for the full failure and control surface.
- Reliability and evaluation: A plausible answer is not proof that the correct action was taken. Tests need to cover unusual inputs, exceptions, and recovery—not just a successful standard case. In payroll, finance, healthcare, legal, or infrastructure work, even occasional errors may be unacceptable.
- Security: Prompt injection, malicious documents or webpages, tool misuse, credential theft, data exfiltration, excessive permissions, and cross-tenant leakage are possible risks when agents can act on systems.
- Governance: Organizations need to know which agents exist, who owns them, what they can access, how actions are logged, how spending is controlled, and how to stop or investigate them.
- Cost predictability: Multi-step tasks, retries, long context, tool calls, and computer-use sessions can make inference costs vary. A low unit price does not guarantee a low end-to-end bill.
- Accountability: Responsibility for a harmful action may be split among the model provider, agent platform, application vendor, integrator, and buyer. Contracts and escalation paths need to make that division workable.
- Change management: Moving from a dashboard to an agent changes training, roles, approvals, and operating procedures. It is a process change, not just a new interface.
A practical test for SaaS companies and investors
To judge whether a particular software business is exposed, assess the workflow and economics rather than relying on the “AI-proof” or “SaaS is dead” label. These questions work for a vendor, a buyer, or an investor evaluating a company’s resilience:
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- How much of the product is interface? Can the work be completed through a tool call or API, and does a person need to see the application for ordinary cases?
- How reproducible is the core capability? Could a capable customer recreate it with a model, data, and connectors, or does the product have differentiated process knowledge?
- What data or workflow does the vendor uniquely own? Is the data proprietary, enriched through use, legally usable, and valuable enough to improve decisions?
- How costly is replacement? Consider integrations, migration, regulatory obligations, switching disruption, and whether the software is the authoritative system of record.
- Can the product prove value? If pricing shifts away from seats, can the vendor and customer measure a completed task or outcome and agree on attribution?
- How costly is an error? Low-risk drafting is easier to automate than financial, medical, legal, or operational decisions that require approval and human review.
- Who controls distribution? Does the vendor own the customer relationship, or could a productivity suite, cloud provider, or agent platform bundle a comparable capability?
- Do AI economics work? Compare inference and support costs with revenue per workflow, including failures, retries, human oversight, and the vendor’s ability to pass through costs.
AI can be a threat and a growth engine for the same company. A vendor may lose revenue per seat while gaining automated transactions, higher-value enterprise demand, cross-product usage, or new distribution. The deciding issue is whether that growth is profitable and defensible.
How buyers should evaluate an agent before replacing software
Do not treat an impressive demo as evidence that a system is ready to replace a dependable workflow. Before shifting business-critical work, ask the vendor and your own team:
- Does the agent complete the whole workflow, including exceptions and approvals?
- What does it do when it is uncertain, a tool fails, or the source data conflicts?
- Can you restrict permissions to the minimum needed and audit every consequential action?
- Can you set usage limits and forecast a bill that includes retries and human review?
- Is the full cost lower after integration, security review, governance, support, and change management?
- Can you switch models or vendors without losing records, controls, or the ability to operate the workflow?
Build-versus-buy remains conditional. Building can make sense for a narrow workflow tied to proprietary data when the organization has the engineering and governance capacity to operate it. Buying is often more compelling when compliance is demanding, integrations are numerous, the process is mission-critical, or the buyer needs vendor support and accountability. Agentic AI changes the trade-off; it does not make either choice automatically right.
What the “end of SaaS” story gets wrong
It treats every software category as equally exposed, mistakes a hidden interface for a replaced system, and focuses on model capability rather than the cost and reliability of a completed workflow. It also assumes that fewer seats mean less software consumption, overlooks the possibility of platform bundling, and underestimates customer reluctance to replace a reliable system before security, procurement, auditability, and accountability are settled.
The more useful question is not whether AI can produce a prototype or answer a prompt. It is whether an agent can do valuable work reliably, at an acceptable total cost, with controls the organization trusts—and whether the software vendor still owns a hard-to-replace part of that process.
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