Based on the latest market analysis, Soochow Securities Company Limited. has released a research report indicating that the software industry in the AI era is experiencing structural divergence rather than a sector-wide revaluation. The report highlights four key investment directions for investors to monitor closely: platform-oriented software that possesses core enterprise data, permission systems, and workflow entry points; vertical software scenarios where ROI is more quantifiable and willingness to pay for AI is stronger; security, observability, and development governance solutions that benefit from the scaling of AI applications; and comprehensive platforms with ecosystem entry points, cloud infrastructure, and AI distribution capabilities.

According to Soochow Securities Company Limited., AI's impact on the software industry is not a simple disruption but rather a shift in value from the front-end interface to the enterprise AI execution infrastructure. Previous market concerns suggested that AI agents would weaken the traditional SaaS front-end, seat-based pricing, and high-profit margin assumptions. These concerns hold some validity for lightweight tool-based SaaS products with lower data barriers and shallower process integration. However, the brokerage firm believes that the real implementation of enterprise-grade AI depends not only on model capabilities but also on robust data governance, permission management, process orchestration, system integration, security compliance, and result traceability. When AI agents enter real production environments, their ability to reliably access data, understand business rules, adhere to permission boundaries, execute process actions, and leave auditable records will become critical in determining whether AI can scale effectively. Consequently, the value assessment of software companies in the AI era is shifting from "how frequently users open applications" to "whether the system occupies a critical position in the enterprise task execution chain." Platform-based software equipped with core data, process permissions, governance auditing, and system connectivity capabilities is likely to be revalued from traditional application software to enterprise AI infrastructure.

AI commercialization is moving from proof of concept to proof of metrics. During the July-September 2026 period, leading software companies began disclosing more traceable AI commercialization metrics, demonstrating that AI can generate new revenue streams, workloads, and customer demand. Salesforce is validating AI revenue and task execution volumes through metrics such as Agentforce ARR and Agentic Work Units, exploring multiple parallel pricing models. This indicates that enterprise customers are already paying for agent capabilities, and AI task execution volumes are becoming new quantifiable indicators. Snowflake is confirming the growth in data workloads driven by expanded AI applications, as more enterprise AI usage leads to increased data migration, governance, sharing, model invocation, and processing requirements. ServiceNow, meanwhile, is achieving better value transmission through a hybrid pricing model that combines base licenses with AI usage, validating its workflow platform's ability to support the closed loop of agent execution. Soochow Securities Company Limited. suggests that future valuation frameworks for software companies should include not only revenue growth, net revenue retention rates, and profit margins but also AI call volumes, task execution volumes, automation process penetration rates, agent dependency levels, pricing architecture migration progress, and whether AI features can generate measurable, chargeable, and auditable workloads.

AI is reshaping the software value chain, with the MCP protocol layer and a four-tier vertical structure driving valuation divergence. Soochow Securities Company Limited. recommends dividing the AI-era software industry into four tiers: the data control layer, workflow execution layer, vertical scenario layer, and cloud & DevOps layer, with MCP serving as the horizontal protocol that connects all segments. The data control layer addresses "what agents know," encompassing both comprehensive analytical and operational paths, and benefits from the growth in data call and model workloads associated with enterprise AI implementation. The workflow execution layer addresses "what agents can do," focusing on embedding AI into enterprise processes such as CRM, enabling agents to move from information generation to task execution. The vertical scenario layer addresses "how much business value agents create," with high-ROI environments such as healthcare, legal, finance, and industrial sectors benefiting from quantifiable outcomes like time savings, efficiency gains, and error reduction. While short-term outcome-based pricing may be constrained by compliance requirements, there is long-term potential for subscription models to evolve into task-based, usage-based, or outcome-based pricing. Finally, the cloud & DevOps layer addresses "how agents run stably, securely, and cost-effectively," as the increasing number of AI applications and longer invocation chains will drive sustained demand for observability, security operations, and access control.

Key risks to consider include slower-than-expected AI commercialization progress, delays in enterprise agent implementation, and weaker-than-anticipated macroeconomic conditions and enterprise IT spending.