Communications sits closer to public perception than any other function in an enterprise, which is why I think comms leaders are often among the first to sense when something is changing.

The digital environment has made that feeling increasingly difficult to diagnose. 

Communications teams are working across more channels, platforms and partners, while the relationship between the work they produce and the outcomes they're trying to create has become harder to follow. This isn’t due to a shortage of data. Most parts of the function can produce a report showing what they did and how it performed. 

The problem starts when leadership tries to understand what all of that activity means together and what communications is actually producing for the business. That can feel like a strategy or performance problem, but I think it's a data infrastructure problem. 

The economics of communications have changed

Most enterprise communications functions don't operate within one connected environment. They operate across platforms, agencies, vendors, internal teams and systems, each responsible for a different part of the work.

There was a time when that made a lot of sense. Communications was expensive to produce and specialized expertise was difficult to build internally. If you needed media or advertising expertise, you hired the appropriate agency. An organization's scale, authority and budget could compensate for much of the fragmentation that model created, because attention was easier to manage in the age of traditional media.

But digital keeps adding new places for communications to happen. Search and generative engines expand where narratives can develop and persist. Social platforms and forums create environments for influence and risk even where organizations have no formal presence. Each expansion creates another channel to understand, another source of data and often another platform, partner or specialized capability to manage it.

At the same time, AI is making communications outputs significantly cheaper to produce. That's beginning to change the economics of a model built around production. When distribution was concentrated and production was expensive, scale was an advantage. As production gets cheaper and distribution becomes more fragmented, the advantage shifts toward precision: understanding what to produce, where it matters and what impact it's actually having.

Continuing to add execution without addressing the fragmentation underneath it compounds the cost on both sides. Organizations spend more to execute across an expanding mix of platforms, partners and teams, then spend more trying to connect the data and context fragmented across them. 

Fragmentation weakens the function's intelligence

The cost of fragmentation isn't just operational. It limits what the function can understand. Each platform, partner and team sees the work through its own data and objectives, but the questions comms leaders increasingly need to answer cross those boundaries.

What caused the change? Did activity in one channel influence another? Is a shift in sentiment showing up in search behavior? Did any of it change an outcome the business cares about?

Answering those questions requires more than reporting. It requires the ability to connect signals, context and outcomes across the function. Without that connection, more data doesn't necessarily create more intelligence..

AI is exposing the data infrastructure gap

As it stands today, people fill these fragmentation gaps. They remember how metrics were defined, why something changed, which signals are related and where the context lives. 

This becomes a much more noticeable limitation as organizations look to AI to cut costs and improve efficiency. We're asking AI to do many of the things people have historically done between these systems: connect information, identify relationships, recognize changes and help us understand what's happening across increasingly complicated communications environments. 

But AI can only work with the relationships and context available in the data it receives. The same fragmentation people have been compensating for becomes the fragmentation AI inherits.

That's what makes AI such an important test of the infrastructure underneath communications. It exposes how much of the function's understanding actually exists in its data and how much still depends on people, partners and systems connecting it after the fact.

Without the data infrastructure to preserve those relationships over time, AI doesn't solve the fragmentation, it becomes another technology trying to make sense of it.

Stability requires infrastructure

This brings us back to why the strategy can start to feel shaky in the first place. As the communications environment has expanded, the function's ability to understand itself as a whole hasn't expanded with it.

What’s missing is continuity. The organization needs to be able to carry definitions, relationships, history and context forward as platforms, partners and projects change, so each new initiative can build on what it already knows instead of rebuilding that understanding.

That continuity requires infrastructure: an enterprise-owned foundation that connects the data and intelligence created across platforms, partners and teams. With that foundation, new platforms and partners can add to the organization's understanding instead of creating another silo. AI can work from accumulated organizational context instead of trying to reconstruct it, and comms leaders can better understand what's working, why it's working, how the pieces influence one another and whether the function is producing outcomes the business actually cares about.

The strategy isn't necessarily what's shaky. It may just be what the strategy is standing on.