(Note: this is the second post in a series on Agentic Marketing, to read the first post please click here.)
—–
One of the easiest mistakes to make in the current AI cycle is to assume that the model is the main event.
It is not.
The visible part of AI is easy to get excited about. The assistant. The recommendations. The generated content. The summaries. The next-best-action prompts. The workspace that seems to know what is happening and what should happen next. Those are the parts people see. Those are the parts vendors demo. Those are the parts executives react to.
But the part that determines whether any of it is actually useful is much less visible.
What is the system reasoning over?
That is the question I keep coming back to.
Because in marketing and sales, AI is only as strong as the intelligence layer underneath it. If that layer is fragmented, stale, incomplete, or inconsistent, the outputs will reflect that. The system may still sound confident. It may still produce something polished. But that is not the same thing as being commercially right.
From where I sit, that intelligence layer has two major parts.
The first is enterprise data.
The second is commercial truth.
And I think a lot of organizations are underestimating how much both matter.
Enterprise data is what helps AI see the opportunity
When most people think about AI in go-to-market contexts, they usually start with customer data. That makes sense. Customer and account data are a big part of the picture.
But in practice, the useful enterprise data layer is much broader than that.
It includes customer and account data, of course, but also product ownership, installed base, transaction history, subscription status, engagement signals, service history, usage indicators and telemetry, financial context, digital behavior, territory alignment, seller activity, partner activity, and often supply or operational signals that shape what is realistic and relevant.
This is the data that helps the system answer practical questions like:
Which accounts look ready for expansion?
Which customers are showing signs of risk?
Which product combinations suggest whitespace?
Which contacts are active, and which are just present in the database?
Which engagement signals matter, and which are noise?
Where is there real potential demand versus superficial activity?
That is what makes enterprise data strategically important in the agentic era. It is not just there for reporting after the fact. It is there to help the system determine where attention should go and what action makes sense.
The problem is that most organizations do not actually have this data in a state that supports strong reasoning.
They have pieces of it. Often a lot of pieces. But those pieces do not always connect cleanly.
Customer identity may not be resolved across systems.
Account hierarchies may be inconsistent.
Duplicates may still be everywhere.
Key fields may be sparsely populated.
Important commercial signals may be trapped in systems that do not easily connect to revenue workflows.
Different teams may define the same concepts in different ways.
The data may technically exist, but not in a form that is timely, governed, or useful enough to support action.
That is where the hidden work sits.
A lot of the promise around AI assumes that the system can reason over a coherent view of the business. But that coherence does not happen automatically. It takes real effort to unify data, improve coverage, manage access, standardize definitions, maintain integrations, and establish enough trust that teams are willing to let the outputs shape real decisions.
This is why the enterprise data conversation matters so much. It is not just about building a better dashboard or a cleaner warehouse. It is about creating an environment where AI can identify meaningful opportunities rather than simply reacting to isolated signals.
But enterprise data is only half the story
This is the part I think gets less attention than it deserves.
Even if the enterprise data layer is strong, AI still needs to know what the company actually sells, how those offerings fit together, how they should be described, where they apply, and what guidance should shape customer-facing actions.
In other words, it needs commercial truth.
I use that term very intentionally.
Because I do not mean “content” in the loose sense. I do not mean a pile of launch assets, random decks, old FAQ docs, or whatever happens to be sitting in a shared folder. I mean the curated commercial knowledge that helps the business speak accurately and act intelligently.
That includes things like:
What products and services are commercially available?
What they do or how they work?
What unique benefits or value they create?
How they are packaged?
How they should be positioned?
Which personas and use cases they are relevant for?
How they fit into broader solution stories?
Which industries they apply to?
What customer outcomes they support?
What public customer stories can be referenced?
How pricing works at a directional level?
What the common objections are?
What questions sellers, marketers, and customers regularly ask?
What the approved answers are?
Where the boundaries or limitations are?
That body of knowledge has always mattered. But in the past, humans could compensate for a lot of inconsistency.
A good product marketer could fill in the blanks.
A seasoned seller could navigate ambiguity.
A marketing operations leader could chase down the right inputs.
A product manager could correct a weak narrative in real time.
AI does not work that way.
If the truth is scattered, stale, contradictory, or trapped in formats that are hard to retrieve and use, the system will produce weaker answers, weaker recommendations, and weaker execution.
That is why I increasingly think of product marketing, product management, product design, and related roles as stewards of a shared intelligence layer, not just owners of messaging or launch materials.
They are helping define the commercial truth that AI will draw from inside marketing, sales, customer success, and other revenue workflows.
That is a bigger job than many teams have historically been staffed or organized to do. But I think it is one of the most important shifts happening right now.
Commercial truth has to be treated like a managed asset
This is where the conversation usually gets more practical.
If commercial truth is now part of the intelligence layer, it cannot be handled casually.
It cannot live in ten different places with no clear owner. It cannot be updated only at launch and then ignored. It cannot rely on a few experienced people to know what is still accurate. And it cannot be written only for humans when the goal is for systems to retrieve and apply it inside workflows.
It needs structure.
It needs ownership.
It needs maintenance.
It needs enough consistency that the system can retrieve the right answer, in the right context, and use it safely.
That usually means the knowledge needs to be curated in a more deliberate way. It needs tagging, organization, versioning, clear sourcing, and regular review. It needs to distinguish between internal guidance and externally usable claims. It needs to separate durable truth from temporary messaging. And it needs to be refreshed often enough that the business is not feeding AI a stale version of itself.
This is not glamorous work, but it is foundational work.
And I do not think enough organizations have fully internalized that yet.
There is still a tendency to treat the knowledge layer as a side issue, as though AI will somehow figure it out. In practice, AI is much better at using truth than inventing it. If the underlying truth is weak, scattered, or poorly maintained, the system does not become strategic. It becomes unpredictable.
This is why the intelligence layer has to be shared
Another thing I think matters here: neither half of the intelligence layer can operate as a silo.
Enterprise data cannot be owned in a way that disconnects it from the workflows of marketing, sales, and customer success. And commercial truth cannot be maintained as a static archive that never makes its way into the systems those teams actually use.
Both have to become shared resources.
The enterprise data side is often led by teams aligned to the CIO or Chief Data Officer. That makes sense. They are usually best positioned to manage identity, integration, quality, access, governance, and data architecture at scale.
The commercial truth side is often led by product marketing, product management, product design, and other subject matter owners. That also makes sense. They are closest to the development of what the company sells and how it should be represented.
But if those two worlds stay disconnected, the outputs will stay limited.
What makes agentic work interesting is when the system can reason across both: the live enterprise signals that suggest an opportunity, and the curated truth that determines what should be said or done about it.
That is when the outputs start becoming more useful.
Not just “this account is active.”
But “this account shows meaningful expansion potential, based on what it owns, how it is engaging, and what approved solution story is relevant here.”
Not just “generate an email.”
But “assemble a message using approved positioning, relevant proof points, and context grounded in what this customer already has and is likely to care about.”
That is a very different level of usefulness.
Better outputs start with better inputs
If there is one point I would want leaders to take from this, it is this:
Before asking how autonomous you want AI to be, ask what intelligence you are prepared to give it.
Do you have connected, governed enterprise data that helps the system identify meaningful opportunity?
Do you have curated, maintained product truth that helps the system make commercially accurate recommendations?
Do those two layers connect in a way that supports real workflows?
Or are you still asking AI to reason over fragmented data and scattered documents and hoping the outputs will somehow rise above the foundation?
Because they usually will not.
This is one of the reasons I think the next phase of this conversation has to get more practical. The future here is not going to be determined only by model quality or interface design. It is also going to be determined by whether organizations treat intelligence as a real operating asset.
That means enterprise data teams matter.
That means commercial truth owners matter.
That means maintenance matters.
And that means the companies that get the most value from AI will not just be the ones with the flashiest tools. They will be the ones that do the deeper work of creating a trustworthy intelligence layer underneath them.
That is what makes the rest possible.
In the next post, I want to stay with that theme and look specifically at marketing operations and brand teams, because I think they are often misunderstood in this conversation. In my view, they do not become less important in the agentic era. They become more important in a different way.