McKinsey just published a twelve-point AI transformation manifesto. It's worth reading.

The twelve themes are sound. Build enduring capabilities, not one-off solutions. Focus on economic leverage points. Make senior business leaders accountable for the tech agenda. Treat data as a performance asset. Design for adoption before you scale. These are the right principles, and most organizations are still failing at them.

But the frame that runs through the document "rewired companies" obscures the more specific question most commerce organizations are facing right now. The question is not whether they've rewired for AI in the general sense. It's whether they've built the specific infrastructure layer that determines whether they're visible and selectable in agent-mediated commerce.

Theme 8 is where McKinsey gets closest: "make data easy to consume and enrich it for advantage." The observation is correct. The implication for commerce brands is more specific than the document makes it. Clean, structured, machine-readable product data isn't just a general AI readiness requirement. It's the specific input that determines whether an agent querying your catalog can find, evaluate, and select your products at all. The brands that have treated product data as a business-owned performance asset for the last two years are the ones showing up in agent recommendations today.

Theme 10 "no trust, no right to deploy AI" names something important that most commerce AI discussions underweight. The agentic trust problem isn't just internal governance. It's consumer-facing. A consumer who configures an agent to shop on their behalf is making a trust decision about the agent, the platform, and the brands the agent will interact with. The authorization surface where that trust is established or lost is the most consequential UX problem in commerce right now, and almost nobody is designing for it.

The twelve themes are a useful diagnostic for organizational AI readiness. For commerce specifically, themes 8 and 10 are where the most urgent work is concentrated, and both require more specificity than a general transformation framework can provide.

Worth reading in full. McKinsey Quarterly, April 2026.

https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/the-ai-transformation-manifesto