On September 8, 2026, U.S. software stocks stayed under pressure, with traditional SaaS leaders Salesforce, Intuit, and ServiceNow posting notable declines. The market's core worry points straight at the capability leap of new-generation models like GPT-6 Astra — their powerful agent execution and cross-system orchestration are directly eating away at a large share of the functional modules that traditional SaaS products charge for separately.
Behind this round of valuation swings lies far more than one new product substituting for an old one. The easiest judgment for the market to make is that AI will produce cheaper, more efficient software and thus replace traditional SaaS — but that view stays on the surface of product competition. The industry change actually worth watching runs much deeper than feature substitution or price competition: in the future, users may no longer care which piece of software they use, and may no longer need to operate software interfaces and functions themselves; they will simply state a goal to an agent and get the result. Once the premise that "a person must personally operate software" is broken, the product logic, business model, and corporate way of working that the SaaS industry has built over the past two decades will all be restructured systematically.
I. Two Decades of SaaS Logic: Turning Workflows into Software Interfaces
The SaaS industry's rapid growth over the past twenty years essentially rode the wave of comprehensive enterprise digitization; its core logic was to migrate offline workflows completely online and package them into standardized software interfaces. CRM systems moved sales processes into digital systems; ERP systems covered the full chain of finance, procurement, and supply chain; HR SaaS brought recruiting, performance, and payroll online; collaboration suites carry general scenarios like communication, projects, and documents.
In this process, software delivered three layers of core value. First, the standardization of workflows: work that once depended on individual experience, paper files, spreadsheets, and email was redefined as fixed process nodes inside a system, sharply lowering management cost and error rates. Second, the functionalization of professional capability: work requiring professional skills — calculation, query, approval, report generation, data analysis — was turned into visual buttons, menus, and functional modules that ordinary people can operate after training. Third, the locking-in of the user role: after a company buys a system, it must invest heavily in training employees to log in, enter data, query, and submit according to operating procedures; people were defined as "software users," and the software interface became the only entry point through which humans invoke digital capability.
The industry thus formed a stable human–machine relationship that lasted for decades: people understand the task and issue operating commands, software executes the corresponding function, and people complete work by operating software. The experience, efficiency, and completeness of the software interface therefore became the core competitiveness of SaaS products. And the maturation of agent technology is now shaking the status of that entry point from the very bottom.
II. Agents Reshape Interaction: From People Operating Software to Agents Invoking Capability
When AI first entered the SaaS domain, it did not change the industry's underlying logic. Copilot-style tools that help draft copy, summarize meetings, generate reports, and write formulas were essentially just a smarter function button added to existing software; people remained the ones doing the operating, needing to actively invoke functions, check results, and complete follow-up steps — still a model of people driving software.
The arrival of agents brings a genuinely paradigm-level change. Unlike assistive tools, an agent can autonomously understand a goal stated in natural language, decompose it into steps, invoke tools and data across systems, execute workflows automatically, and finally deliver a complete result. The relationship between people and software is thus fundamentally reversed: in the past, people actively looked for software, used functions, and assembled results; in the future, people only need to define the goal, and the agent independently calls CRM, ERP, email, calendars, databases, and other systems in the background to complete all the intermediate operations. Take customer analysis as an example: a sales manager used to open CRM to export data, open a spreadsheet to analyze, draft follow-up suggestions, send them by email, and then schedule a meeting; in the future, one instruction to the agent is enough — all system calls, data processing, content generation, and scheduling happen automatically in the background.
This also directly changes the competitive logic of the software industry. In the past, the core competitiveness of SaaS products concentrated on the user-facing experience layer — UI design, menu logic, page interaction; in the future, the center of value shifts to API capability, data permissions, fit with business rules, workflow reliability, and execution accuracy. Software is no longer a product facing people directly, but gradually becomes a capability module invoked by agents. Agents will not necessarily eliminate software, but they are eliminating the notion that "a person must personally operate software" — that is the underlying paradigm shift in human–computer interaction.
III. Business Model Restructuring: The Unraveling of the Seat Economy and the Revaluation of Value
Changes in product logic inevitably transmit to the foundation of the business model. The most classic traditional SaaS model is per-seat pricing — billing with "the person using the software" as the core unit, calculated as Seat × User × Month. One hundred salespeople mean one hundred CRM accounts; one thousand employees mean one thousand office-suite accounts. People and accounts correspond one-to-one, and that correspondence is the basis of the entire pricing system.
The arrival of the agent era directly shakes this foundation. When work that used to take ten people can be done by two employees plus twenty agents, and when employees no longer need to open software themselves because all system calls are completed by agents through APIs in the background, the logic of "one user, one account" loses its rational basis. Correspondingly, the SaaS business model will be rebuilt along three directions. First, the unit of pricing: from charging per head to charging by actual usage — API call volume, compute volume, number of task executions, and token consumption become the new billing basis. Second, the logic of value: companies are no longer willing to pay for "owning software" but increasingly prefer to pay for outcomes, demanding that providers prove concrete business value such as efficiency gains, cost reduction, and shorter cycles. Third, the reconstruction of core moats: when interfaces and standardized functions can all be rebuilt by AI, the truly irreplaceable assets become proprietary data, industry know-how, deep business rules, customer networks, and transaction relationships.
It is fair to say that AI's deepest impact on the SaaS industry was never substitution at the level of features; it is that the entire business model built on the seat economy is now facing a foundational shake.
IV. Organizational Evolution: From Swapping Software to Redesigning the Work Itself
Changes in products and business models eventually land on how work gets done inside enterprises. What many companies today call AI transformation still sits inside the old digitization logic: the existing ERP, CRM, OA, and BI systems stay unchanged, with Copilots, knowledge bases, agents, and AI assistants layered on top. It looks like more and more AI applications, but the basic structure of work has not fundamentally changed — people are still information movers between systems, copying data from one piece of software to another, transferring information, organizing materials, submitting approvals, and aggregating results. Adding AI features only makes the moving more efficient; it does not change the logic of the work itself.
Genuine agent-based transformation has to start from one most fundamental question: what result is this work ultimately supposed to produce? Working backwards from there, redraw the boundary between human and machine: which things must be judged by a person, which tasks can be handed entirely to agents, which systems agents need to call and how much permission they need, which nodes must have human confirmation, and which results require a person to bear final responsibility. With that, the evolution path of enterprise AI becomes clear: stage one is software digitization, people operating systems to complete work; stage two is AI assistance, AI helping people operate systems faster; stage three is agentization, AI autonomously calling systems to complete tasks; stage four is organizational restructuring, with people refocusing on goals, judgment, creation, and responsibility.
So what enterprise AI truly needs to redesign was never just the software stack — it is the definition, process, and division of the work itself.
Software Won't Disappear, but the Role of "Software User" Is Disappearing
There is no need to jump to the extreme conclusion that "SaaS is dead." ERP will not disappear; CRM will not disappear; databases, payments, supply chain, and risk-control systems even less so — they remain the core infrastructure of enterprise digitization. But more and more software will retreat from the front stage to the back, no longer facing ordinary employees directly, and instead becoming capability modules that agents can call. What users face in the future may no longer be a row of app icons, a row of function buttons, and layer upon layer of menus, but an agent that understands goals, orchestrates resources, and delivers results.
The industry's change ultimately lands on three layers of value reconstruction. The value of software moves from providing features to providing capability that intelligence can invoke; the value of the enterprise moves from deploying more systems to redesigning work and business processes; the value of the person moves from operating tools, moving information, and executing processes to defining goals, making judgments, creating new possibilities, and bearing final responsibility.
For decades we have been learning how to use software; what will truly matter in the future may be learning how to let intelligence use software for us, while we put our attention back on the work that is genuinely human.
From executor to creator: first fulfill the human, then fulfill the AI.
