AI is changing how marketing teams work with data. We’re moving away from dashboards and toward smart, AI-powered tools that don’t just give you answers. They remember what you asked, understand what you meant, and help you dig deeper.
That’s where Model Context Protocols come in. Or MCPs, for short.
You might not have heard of them yet. But they’re about to be everywhere. MCPs help AI tools keep track of conversations and context, so each step builds on the last. And that’s especially important when you’re using AI for marketing data analysis.
Marketers rarely ask one question. You explore. You follow up. You compare campaigns, channels, and regions. MCPs let your AI follow along and keep the logic intact from one step to the next.
But even the best MCP can’t work with messy inputs. If your data is inconsistent or unclear, AI won’t know what to do with it. And that’s a problem in marketing, where data tends to come from a lot of different places.
So while it’s exciting to think about where AI is going, we’ve got to get our data right first.
What are MCPs and why should you care?
For AI to be helpful, it needs to remember what you’ve already said. It needs to track what you’re looking at and what filters are in place.
MCPs are what make that possible. They manage the memory of the conversation. They help the AI hold context so each new step feels connected and useful.
In the context of marketing, that means remembering which campaigns, products, or regions you’re focusing on. It means knowing you already applied a filter for Q1 or already excluded paid social.
Without MCPs, your AI resets every time. With them, it keeps up with you.
Why the conversation around MCPs is heating up
MCPs are becoming a big deal because the way we use AI is changing fast.
We don’t just want one-off answers anymore. We want back-and-forth conversations. We want AI to help us think through problems, not just respond. That kind of experience only works if the system knows what’s already happened.
We’re also seeing more complex systems that use multiple tools and agents working together. For them to collaborate, they need a shared understanding of what’s going on. MCPs help them stay in sync.
At the same time, language models are getting bigger and more powerful. They can process massive amounts of text. But giving them all the data at once doesn’t work. It just creates noise. MCPs help filter and focus the context so the model stays sharp.
And we’re seeing AI used for bigger decisions now, like forecasting, budget planning, or campaign strategy. When it matters that the answer is right, MCPs give the system the continuity it needs to stay accurate.
That’s what makes MCPs so relevant for marketing teams using AI to analyse data and make decisions quickly and confidently.
Why are MCPs such a hot topic right now?
- Multi-turn conversations are expected: People expect AI to follow along as they explore. MCPs make that possible.
- AI agents need to work together. More tools are using multiple agents to solve tasks. MCPs help them share what they know.
- Model capacity is growing fast. Big models like GPT-4-turbo can read a lot. MCPs help manage that information in a useful way.
- AI is working with real business data. As AI is used in marketing, finance, and operations, accuracy matters more. MCPs help make that possible.
The data quality problem, amplified
Here’s where things get messy.
Most organizations don’t have clean data. That’s not a knock, it’s just reality. There are mismatched column names, missing values, inconsistent labels, and outdated tables. Maybe your “Region” column is sometimes called "Geo" or "Sales Area" depending on the spreadsheet.
In the dashboard world, that causes visual bugs or broken filters. Annoying, but manageable, (especially with platforms like Adverity).
In the AI world, especially one powered by MCPs, bad data doesn’t just cause a glitch. It causes a misunderstanding that persists.
Because MCPs maintain memory, if the model misunderstands a term or mislabels a metric early on, it will carry that mistake forward. It’s like giving your intern the wrong definition on day one and then letting them brief the board a week later.
This is the shift we need to internalize: in an AI-first interface, data quality is no longer a backend issue. It’s part of the user experience.
Good MCPs rely on good data. Period.
When your data is well structured, when your fields are cleanly named, your schemas consistent, and your key business metrics clearly defined, then AI is able to operate like a trusted partner. It can build a coherent internal model of what your data represents. It knows what matters and how different pieces relate. That lets it not only answer your current question, but build the connective logic that turns isolated responses into a thread of understanding.
That means it can recall important details without being told twice. It can follow your train of thought across multiple steps. It can spot when something doesn’t add up. And most importantly, it does all of this without getting lost or confused in the weeds.
But if the data is inconsistent, so is the AI. And the more context you feed it, the more brittle it becomes.
What happens when you get it right
Let’s look at a simple example.
You upload a spreadsheet and say, "Show me sales by region in Q1."
- The AI scans your schema.
- It notices columns like "Region", "Quarter", "Revenue".
- It gives you a clean summary.
Then you say, "Break that down by product line."
- The AI remembers what “that” means (Q1 sales by region).
- It knows which filters you already applied.
- It uses the "Product Line" column and builds on the previous answer.
Then: "Compare to the previous quarter."
- It recalls the time series logic.
- Applies the right offset.
- Delivers a new, contextual insight.
No re-explaining. No starting over. Just fluid, intelligent dialogue.
That’s what happens when clean data meets well-designed MCPs.
What happens when you get it wrong
Now flip that scenario on its head.
You upload that same spreadsheet and ask, "Show me sales by region in Q1."
- But the region column is inconsistently labeled across sheets.
- The model guesses wrong, or skips regions entirely.
You follow up with, "Break that down by product line."
- The model isn't sure which filters were applied.
- It starts over, or worse, assumes something incorrect and builds on it.
Then: "Compare to the previous quarter."
- The model can’t find a consistent date format.
- It mixes up quarters or fails to match the data sets.
Instead of delivering insight, the AI delivers noise. Or worse, confidently wrong insights.
And here’s the kicker: because MCPs preserve context, those mistakes aren’t one-time slips. They stick around. Each new turn of the conversation compounds the error, eroding trust and making the experience harder to untangle. Your users go from feeling empowered to feeling misled, and your AI goes from assistant to liability.
Final thoughts: What you can do today
If you’re a technical leader thinking about how to prepare your marketing team for AI-powered data analysis, my advice is to start with the basics.
And that means data hygiene. It sounds unglamorous, but it's the foundation of everything that follows. Standardizing column names, cleaning up schemas, and aligning metric definitions may not make headlines, but they directly determine whether your AI is helpful or confusing. And there are tools to automate this process for you, one of which is, of course, Adverity.
Here’s the bottom line: AI isn’t just about fast answers anymore. It’s about meaningful, memory-driven dialogue. And that dialogue is only as good as the data it’s built on.
As AI becomes more conversational, the interface isn’t just the prompt box. It’s the context model behind the scenes.
And the quality of that context? It depends almost entirely on the quality of your data.
So yes, invest in AI. But also invest in the thing that makes AI smart: clean, clear, consistent data. Because in this new world, your data doesn’t just speak. It remembers.
