AgenticCMO: Weekly Links for November 11th
The Signal: From AI Theater to AI Impact
I've been watching marketers race to implement AI agents for months now, and McKinsey's latest research reveals what many of us suspected: we're collectively struggling to move from impressive demos to actual business results.
The numbers tell a sobering story. While 72% of organizations have adopted AI in at least one business function, only 8% are achieving significant revenue increases from AI agents. That gap isn't just a statistic, it's a wake-up call about how we're approaching this technology.
What struck me most in McKinsey's research isn't the challenges, but the pattern of what's actually working. The organizations seeing real impact aren't deploying agents everywhere, they're being ruthlessly selective about where AI agents can genuinely transform workflows.
AI agents work best when they're handling repetitive, high-volume tasks with clear decision rules. Think customer service routing, lead qualification, or content personalization at scale. They struggle with nuanced judgment calls, complex creative work, or situations requiring deep contextual understanding.
A takeaway: If your AI agent requires constant human intervention or produces work that needs extensive revision, you haven't found the right use case yet. The best agent implementations are the ones where humans barely notice them running, they just see better outcomes.
McKinsey points to three critical success factors that align with what I'm seeing in the field:
Integration matters more than capability. The most sophisticated AI agent is worthless if it doesn't connect smoothly with your existing systems. Marketing leaders need to prioritize agents that work within their current tech stack rather than requiring wholesale replacement.
Data quality is still the killer. Every AI agent is only as good as the data it accesses. If your customer data is fragmented across systems or your content library is poorly tagged, your agents will amplify those problems, not solve them.
Change management isn't optional. The organizations achieving that 8% success rate didn't just deploy technology—they rebuilt workflows around it. They trained teams not just on using agents, but on managing them, measuring them, and knowing when to override them.
What concerns me is the pressure many marketing leaders face to "show AI progress" by the end of the year. This research suggests that rushing into agent deployment without addressing these foundational issues could actually be worse than waiting. Better to pilot one agent thoroughly in a contained use case than to deploy five half-baked ones across your organization.
The opportunity is certainly real. McKinsey's successful companies are seeing 10-20% efficiency gains in specific functions. But they got there through disciplined experimentation, not through AI theater.
