AI & Automation
Understanding artificial intelligence and automation technologies that are reshaping industries and workflows.
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Customer-facing AI agents need more than data access. They need ontology: shared business meaning across customers, contracts, assets, policies, and actions.
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For the first wave of enterprise agents, building an agent meant writing a loop. That loop is becoming substrate: hosted execution, tool permissions, identity, observability, workflows, skills, state, and governed tools.
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In one week an AI agent broke out of its sandbox and chained real zero-days – and a coalition argued models should stay open. They read as opposites. They are the same argument: containment is dead, governance is its successor.
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New models ship monthly and the price of intelligence is falling ~10x a year – because AI now improves AI. Why leaders should architect for model mobility, not model loyalty.
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Intelligence is the new metered utility – and tokens are its meter. How to see, cap, optimize, and govern AI spend (tokens and agent credits) on the Microsoft stack, from pilot to scale.
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For thirty years we moved data to the intelligence. That just inverted.