Signals

Better context beats a better model — which means AI risk governance needs to shift from the model layer to the retrieval layer. That's where defensibility lives now.

LLMs break traditional observability — and that creates a compliance gap most governance teams haven't addressed yet. If you can't trace the full AI pipeline, you can't audit it.

The trajectory is encouraging — the most capable models performed best. But 20 percent is not a foundation for compliance frameworks.

Before MCP, every AI application needed custom connectors for each data source. Without foundation governance, that success creates three risks: proprietary lock-in, protocol fragmentation, or de facto control by a single company. AAIF prevents all three

The WEF and Capgemini framework tackles how to deploy AI agents that act independently without creating liability exposure you can't defend. When autonomous agents execute without human approval, your organization owns the outcome directly.

When an agent makes a bad decision—books the wrong vendor, approves an improper expense, shares sensitive information—who owns the outcome?

Based on Claude's estimates, these tasks would take on average about 90 minutes to complete without AI assistance, and Claude speeds up individual tasks by about 80%

The question isn't whether to accommodate agent-mediated commerce. It's whether your infrastructure can support it.