The problem isn't a lack of data, it's that it's in 15 different places.
If you work in marketing operations, you'll no doubt recognise the scenario: you need a simple report on campaign performance, and you end up exporting data from HubSpot, cross-referencing it with a Google Ads sheet, another from Analytics, and an Excel file that someone in the sales team updates every Friday. Three hours later, you have a report that, by the time you present it, is already out of date. This article will help you understand how Unify your marketing stack with AI Realistically, without empty promises or projects that take months and never get off the ground.
It's not a problem of a lack of tools. It's quite the opposite: too many tools that don't talk to each other. Each one does its job well in isolation, but when you need a complete view of a customer or a campaign, you have to manually glue together systems that were never designed to connect.
How many tools does a marketing team actually handle
Martech studies place the average number of applications used by a marketing team between 15 and 20: CRM, marketing automation, advertising platforms, web analytics, social listening tools, billing systems, satisfaction surveys… The list grows every year, almost always without a plan behind it. A new tool is added to solve a specific problem, and no one stops to think about how it will coexist with the others.
The outcome is predictable: each platform accumulates its own version of the truth. HubSpot says a lead came from an email campaign. Google Analytics attributes the conversion to organic search. The sales team swears it was a cold call. No one is lying, but no one has the complete picture either.
The real cost of data silos
- Decisions based on intuition, not evidence: When connecting data is so expensive, you end up making decisions with the partial information you have at hand, not with the information that really matters.
- Lost hours on manual labour Entire marketing operations teams spend a significant portion of their week exporting, cleaning, and cross-referencing data instead of analysing it.
- Broken attribution Without end-to-end visibility of the customer journey, it's almost impossible to know which channels generate actual business and which just generate activity.
- Inconsistent customer experiences: If the marketing team and the sales team don't share the same up-to-date information, the customer notices, and not in a good way.
And does the AI come in here automatically? Not necessarily
Before we talk about artificial intelligence, something that is sometimes overlooked needs to be said: no algorithm fixes poorly structured data. If your CRM has unnormalised fields, uncleansed duplicates, and different definitions of «qualified lead» depending on the department, no AI tool is going to magically compensate for that. The first step is to get things in order.
That said, once the database is reasonably healthy, that's where AI offers something that manual work cannot match: the ability to orchestrate real-time data flows between systems, detect anomalies or inconsistencies between platforms, and generate reports that combine different sources without anyone having to copy and paste Excel cells.
Integration platforms: the bridge between your tools
Integration platforms (technically known as iPaaS) act as an intermediary layer between your applications. Instead of each tool trying to connect directly with the others – which creates a tangle of fragile integrations – all data flows through a single central point that normalises and distributes it where it's needed.
On this layer, AI can be applied for very specific tasks: enriching records with missing information, classifying leads according to their behaviour by cross-referencing data from various sources, or alerting when it detects a pattern that deviates from the usual, such as a sharp drop in the conversion rate of a specific channel.
How to approach it in practice
If this sounds like a huge and unmanageable project, the good news is that you don't have to start there. A sensible approach usually follows this order:
- Audit of the current stack: What tools do you actually use, which ones are connected, and which ones operate as silos?.
- Prioritisation by impact: Not all integrations are worth the same. Start with the one that connects your CRM with your analytics or advertising platform, as it's often the one that causes the most headaches.
- Definition of a single source of truth: decide which system «rules» each data type, to avoid two platforms competing for the same information.
- Progressive automation Start with simple and well-defined flows before layering in more sophisticated AI.
A common example in IT services companies
A medium-sized technology consulting company often uses HubSpot as its CRM, runs active campaigns on LinkedIn Ads and Google Ads, and has a sales team that records information in their own documents because «it's faster that way.» The typical result: leads coming in from advertising aren't correctly logged in the CRM, marketing reports don't align with sales figures, and monthly meetings are spent more on discussing which data is correct rather than deciding what to do with it.
When the stack is integrated —connecting advertising platforms directly with the CRM and automating lead status updates— the change isn't just about convenience. Time is recovered for the marketing operations team, real attribution for each channel is improved, and advertising investment decisions begin to be based on consistent data rather than estimations.
An idea to take with you
Before buying another tool or embarking on an ambitious artificial intelligence project, check if the ones you already have are talking to each other. Often, the problem isn't a lack of technology, but connections that were never made. Unifying data flow between your platforms is almost always the step that generates the most ROI before complicating anything further.
