Intro: Generative AI, intelligent agents, and frontier models are transforming enterprise software from static systems of record and engagement into living systems of work that require new governance, sourcing, and operating models.

Frontier models and AI enable systems of work to improve as models advance, making it essential to embed permanent engineering roles near the business to guide and refine learning systems.

Systems of work are built on evolving ontologies with continuous data ingestion and learning agents, endowing software with adaptability and ongoing evolution.

Systems of record remain the stable foundation for data integrity and reliable transactions, while systems of engagement are dynamic interfaces built around predefined interactions.

Traditional development methods and static operating models won’t suffice; enterprises must design frameworks that connect to rapidly changing tools and capabilities.

The shift away from standardization-driven scaling requires flexible methodologies that accommodate evolving AI capabilities and work processes.

Pricing, support, and vendor relationships must evolve as systems of work continuously learn, with greater emphasis on ontologies and governance.

Global talent and sourcing should adapt by embedding engineers near the business for systems of work, while core development can stay offshore for stable components.

Strategically, most enterprises will operate along a spectrum from systems of record to systems of work, and early adoption will drive productivity and innovation while static models fall behind.