Enterprise AI Shifts Toward Model-as-a-Service and Agentic Systems

Software as a Service (SaaS) continues to provide ready‑to‑use applications such as Salesforce or Workday, with vendors handling infrastructure, security, and user interfaces. Model as a Service (MaaS) offers programmatic access to cloud‑hosted AI models—large language, speech, or vision engines—through APIs, leaving engineering teams to build custom applications around raw inference capabilities. The two models differ in pricing (predictable per‑seat SaaS fees versus consumption‑based MaaS charges), target users (non‑technical business users versus software engineers), and operational overhead.

A newer development is Agentic AI, where autonomous software agents can read corporate approval hierarchies, make decisions, and execute transactions across finance, HR, and supply‑chain processes. Vendors such as SAP (with its Joule platform showcased at the Sapphire conference) and Oracle (integrating similar capabilities into Fusion) are embedding these agents natively into their cloud suites. While this promises greater automation, the technology ties customers to the vendor’s cloud platform, raising concerns about lock‑in and migration costs.