Summary
Enterprise artificial intelligence is moving past the pilot stage, where marketing, finance, HR, and customer service teams each run their own large language model applications, retrieval-augmented generation tools, and copilots. That fragmented approach delivered early wins, but it is now creating a distinct governance problem: shadow AI, duplicated infrastructure spend, inconsistent intelligence across business units, and compliance blind spots that leave executives without a clear view of how AI is actually being used.
The shift underway is from AI as a collection of disconnected applications to AI as a managed enterprise capability, built around a shared landing zone architecture. This means centralising identity, data connectors, monitoring, and orchestration so that new AI workloads inherit governance controls rather than rebuilding them from scratch. As agentic AI systems move from generating recommendations to autonomously executing transactions and workflows, the case for automated guardrails -- policy-as-code -- becomes urgent rather than optional.
A risk-based triage model classifies AI workloads by criticality, paired with a portfolio rationalisation framework for deciding which applications to tolerate, invest in, migrate, or eliminate, and a maturity model spanning ad-hoc experimentation through optimised autonomy.
- Covers identity federation, data sovereignty, and audit trail requirements for AI governance
- Addresses model routing, vendor consolidation, and engineering productivity implications
What separates enterprises that scale AI safely from those still managing a sprawl of disconnected pilots?
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