
What happened
IDC released a report on 2026年9月23日 analyzing the "SaaSpocalypse" theory, concluding AI agents extend enterprise SaaS data models rather than replace them, and forecasting usage-linked pricing will rise from 13% to 63% within 10 years.
Why it matters
This contradicts the assumption that AI agents spell the end of enterprise software, and points to a shift in how vendors will charge for software.
What to watch
The 63% is a forecast, not current reality, so the outcome hinges on whether vendors actually move away from seat-based pricing. Watch the 34% that IDC's survey identified as the top constraint on AI adoption.
WHO IT HITSEnterprise software procurement teams budgeting for AI agents will likely need to plan for usage-based billing models, while CIOs choosing vendors may find that deep data integration, not standalone AI, becomes the deciding factor.
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The report arrives amid a debate over whether AI agents will make traditional enterprise software obsolete. IDC's analysis argues that AI agents cannot simply replace SaaS because the value of enterprise applications lies not in the user interface but in the underlying data model, business logic, compliance rules, and process connections. The report points to vendors like SAP, Workday, Salesforce, and Oracle, which have already embedded AI into their offerings, as evidence that the industry is moving toward AI-driven applications rather than away from them. IDC's framework, published in 2026年9月, describes a stage where AI agents operate across multiple systems by extracting data and workflows from ERP, accounting, HCM, and CRM. However, the report also notes that APIs and data models that agents cannot call will remain, potentially increasing dependence on vendors. The shift in pricing models, from seat-based to outcome-based, is expected to accelerate, with usage-linked pricing rising from 13% to 63% within 10 years. Market maturity varies widely, with contact centers, HCM, and supply chain seeing faster adoption, while ERP, hiring, and digital commerce lag. The stakes for enterprise software buyers hinge on whether vendors can adapt their pricing and integration strategies to support AI agents effectively, and whether the promised productivity gains materialize.
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