Artificial intelligence is moving beyond productivity tools and becoming part of the operational architecture of financial institutions.
In Balance Sheet Management (BSM) and Asset and Liability Management (ALM), this transition is beginning to reshape how analysis, reporting, and decision support are produced.
The transition follows a consistent three-phase pattern:
Phase 1 involves individual experimentation with generic AI tools; productivity gains are real but bounded.
Phase 2 introduces domain-aware agents connected to institutional data and systems, shifting AI from a productivity tool to an operational capability.
Phase 3 delivers autonomous workflows and coordinated agent teams that execute entire processes without human initiation.
This whitepaper examines how advances in AI capabilities translate into new operational models for finance functions, detailing their progression and explaining how they apply specifically to ALM and treasury functions, including a concrete look at Mirai AI as a live Phase 2 implementation.
Drawing on recent measurements of AI capability growth and real-world developments in AI coding, agent systems, and workflow automation, this publication explains why these technologies are beginning to influence Balance Sheet Management more directly.
How AI capabilities are evolving across financial institutions
The three phases of organizational AI adoption
Why software development offers an early signal
What this means for ALM and treasury functions
The architecture behind the next generation of financial analytics
How Mirai AI supports AI-enabled financial analytics for Balance Sheet Management