McKinsey's latest from their Technology and QuantumBlack teams puts a number on what many of us have been saying: nearly two-thirds of enterprises worldwide have experimented with AI agents. Fewer than 10 percent have scaled them to deliver tangible value.
The reason is not model capability. It is data. Eight in ten companies cite data limitations as the primary roadblock to scaling agentic AI.
The article prescribes the right medicine for CIOs. Modernize your data architecture layer by layer rather than rebuilding from scratch. Build a semantic layer that codifies business meaning into machine-readable form through ontologies and knowledge graphs. Shift from periodic data cleanup to continuous, real-time quality management. Evolve your operating model so humans supervise and orchestrate agent-driven workflows rather than execute what agents now handle.
All of that is correct. And all of it describes the internal infrastructure problem.
Here is the part missing from the conversation.
The same gaps that break internal agents break external ones too
The data architecture gaps that prevent a company from scaling its internal agent workflows are the same gaps that make the brand invisible to external agents acting on behalf of consumers. Product data sitting in silos across channels does not just produce inconsistent internal recommendations. It produces a brand that an AI shopping agent cannot evaluate accurately, cannot compare against competitors, and cannot transact with reliably.
McKinsey illustrates this with an omnichannel retail example. Product data and purchase histories sat in silos, so context broke as customers moved across channels, producing inconsistent recommendations and service experiences. Their prescription is an agent-ready architecture that connects systems and data to support the entire customer commerce journey.
That is the right architecture. But it solves the problem from the inside out. The same data fragmentation that breaks the internal customer journey also breaks the external one, the journey where a consumer's AI agent is evaluating your brand against every competitor in the category before the consumer ever visits your site. The agent does not average your conflicting signals across channels. It routes around you entirely.
The semantic layer is the commerce layer
The most technically significant section of the McKinsey piece describes the semantic layer: the structure that sits between raw data and AI applications, codifying what things are, how they relate, and what rules govern them. Without this shared semantic foundation, they argue, agents act on incomplete or conflicting interpretations of the same data.
This is the exact problem I see in every commerce vertical I work in. A hotel described as "family-friendly" in marketing copy is invisible to an agent evaluating family suitability. An agent needs connecting room availability, kids menu status, step-free access, crib availability, each as structured, queryable fields. A financial product described as having "competitive rates" is equally invisible. An agent needs a specific APR, a minimum balance, documented eligibility criteria, a fee schedule.
The semantic layer McKinsey describes for internal enterprise data is the same layer that makes a brand's products legible to external agents. The enterprise data architecture community and the commerce readiness community are converging on the same requirement from different directions. One is asking how to make agents work inside the organization. The other is asking how to make the brand legible to agents working outside it.
The board-level argument hiding in these numbers
The scale gap statistic, fewer than 10 percent of enterprises have moved beyond experimentation, reframes the competitive conversation for any brand considering agentic commerce investment.
The question most boards are asking is whether competitors are ahead of them. The honest answer, based on this data, is that almost nobody is ahead of anybody. The field is wide open. More than 90 percent of enterprises that have experimented with agents have not figured out the data infrastructure to make them work at scale.
That is not a reason to wait. It is the reason to move now. The brands that close the data infrastructure gap in the next 12 to 18 months are not competing against a mature field. They are building a structural advantage in a market where the vast majority of participants are still stuck at the pilot stage. The compounding effect of being early, in merchant trust scores, in agent selection rates, in the quality of the data that agents learn from, is real and difficult to replicate from behind.
Both conversations need to happen at the same time
I have spent 30+ years in digital product strategy and commerce. The pattern I see now is one I have seen before. When mobile commerce emerged, the brands that treated it as a channel optimization problem, separate from their core commerce architecture, spent years catching up to the brands that treated it as an infrastructure problem from the start.
Agentic commerce is the same inflection, but the stakes are higher because agents are less forgiving than mobile browsers. A mobile site that loaded slowly lost a percentage of conversions. A brand that is invisible to agents loses the evaluation entirely. The consumer never knows the brand was an option.
The CIO conversation McKinsey is driving, restructuring internal data for agentic workflows, is necessary. But it is not sufficient. The CMO conversation, ensuring the brand's products, pricing, and attributes are structured for the external agents making purchase decisions on behalf of consumers, needs to happen in parallel.
Both problems start with the same foundation: structured, complete, semantically clear data. The brands solving both problems simultaneously are the ones building a durable advantage. The brands solving only one are building half an architecture and hoping the other half does not matter.
It will matter. It already does.