10th Jun 2026
AI in marketing automation: why data still matters most
By Len Van Hoogenhuijze
Marketing automation (MA) vendors are promising that AI will revolutionise how marketers attract, nurture and convert audiences. But will it genuinely move the needle for marketing and sales teams? Not really, in our opinion – at least not in the big platforms.
This doesn’t mean that AI doesn’t matter; it does. In fact, it’s essential to vendors’ ability to keep up with the expectations of buyers. And already, nearly every major platform offers an ever-expanding suite of AI-powered capabilities, promising smarter campaigns and better customer experiences.
AI-powered marketing automation enhancements
There are great examples of this across the marketing automation landscape:
- Adobe Marketo Engage offers predictive audiences, dynamic personalisation and even generative AI for email and image creation via Adobe Firefly
- Oracle Eloqua combines generative AI for subject lines and content with fatigue analysis and send-time optimisation
- HubSpot embeds AI into workflows for content generation, predictive lead scoring and real-time personalisation across email and web assets
- Salesforce Marketing Cloud Account Engagement leverages AI-driven insights for lead scoring, campaign optimisation and predictive recommendations.
And these are to name but a few.
All this new functionality is undeniably transformative, adding value through better targeting, faster content creation and convenient automation enhancements. Many marketers experience genuine efficiency and productivity gains from these tools. But despite all this, I don’t believe AI, at least in its current form within these platforms, will fundamentally transform marketing automation. And that’s because the real limitation isn’t the AI.
The core marketing automation bottleneck: weak data foundations
The real bottleneck isn’t the algorithms; it’s the underlying data infrastructure and reporting engines of these platforms. For years, MA tools have struggled with fragmented data models, limited cross-channel visibility and rigid reporting frameworks.
And that’s because marketing automation platforms were never designed to be sophisticated data orchestration hubs. Their architecture grew over time to support more channels, more integrations and more features, but not necessarily to support complex, largescale, multi-source data processing.
Without robust data processing, even the most advanced AI models are constrained. They can only be as good as the data they ingest. When they sit on top of incomplete, delayed, or poorly structured datasets, their potential is inherently capped.
As a result:
- Data remains fragmented across CRM, web analytics, paid media, sales systems and enrichment tools
- Cross channel visibility is limited, making it hard to understand a prospect’s entire journey
Data models are rigid, forcing marketers to work within predefined objects and schemas that rarely match real-world business needs - Historical data depth is shallow, inhibiting strong trend analysis or robust AI training.
This is why even the most impressive AI features in MA tools tend to focus on surface-level enhancements such as optimising subject lines, generating content snippets, segmenting audiences based on simple patterns or nudging send times. These capabilities improve efficiency and engagement, but they are not strategic gamechangers.
To truly revolutionise marketing automation, platforms need to support real-time ingestion, transformation and unification of complex data from multiple systems. They also need to deliver full journey analytics with granular attribution. And critically, they need flexible, powerful processing engines built into the core, not added as afterthoughts.
Reporting: the persistent marketing automation weak link
Similarly, reporting remains an ongoing weak link. Most platforms offer dashboards that look impressive. They show KPIs, conversion funnels and engagement metrics. They present a wide variety of data in a wide variety of engaging, user-friendly formats. But once you try to dig deeper into multi-step journeys, pipeline contribution, cohort analysis or multi-channel attribution, you quickly hit the platform’s limits.
In a nutshell, reporting often fails to deliver actionable insights at the depth modern marketers need. AI-driven recommendations built on shallow or siloed data will inherently not move the needle in a meaningful way.
Recommendations based on incomplete insights rarely lead to significant optimisation. They may help a campaign perform slightly better, but they won’t help a marketing team uncover hidden opportunities, diagnose structural funnel issues, or make data driven investments with confidence.
To move the needle, marketing teams need tools that can:
- Analyse data at scale
- Deliver real-time or near–real-time insights
- Support flexible and custom reporting frameworks
- Combine data from marketing, sales, support, product, and revenue systems.
MA platforms weren’t built for this and simply layering AI on top of weak reporting capabilities doesn’t fix that.
Where real marketing automation transformation will happen
If the future of marketing automation isn’t inside the MA platforms themselves, where will it emerge?
The real innovation is happening in data orchestration and advanced analytics tools.
Platforms like Openprise excel at ingesting, cleaning, normalising and distributing data at scale. They were designed from the ground up to unify data from disparate sources and make it analysis ready.
Similarly, business intelligence platforms like Domo and Tableau now offer strong marketing-revenue integrations and can visualise complex cross channel journeys far more effectively than MA tools.
When you combine these capabilities with advanced AI, whether built into the BI platform or layered through custom models, you finally unlock the potential that MA platforms promised:
- True multi-touch attribution
- Predictive revenue modelling
- Behavioural analysis across channels and devices
- Real-time journey orchestration
- Automated decision-making across the entire funnel
These are the kinds of capabilities that actually transform performance, not incremental improvements to subject lines or segmentation logic.
Marketing automation tools still play a critical role: they execute campaigns, manage scoring and routing and serve as operational engines. But the strategic power will increasingly sit in the data ecosystem around them.
Conclusion: AI is essential, but as part of a cohesive marketing automation ecosystem
Leveraging AI is absolutely essential to keep up in modern marketing. It helps teams work faster, target more effectively and personalise experiences at scale. But within large marketing automation platforms, current AI features are incremental rather than revolutionary.
For true transformation, organisations must invest in robust data orchestration and analytics infrastructure. Only when the data foundation is strong can AI deliver meaningful, high impact change in how marketing is planned, executed and measured.
Marketing automation won’t be replaced by AI – but it will be redefined by the systems around it.
Next Steps
Contact us today for a bespoke AI Readiness Assessment that will clarify the AI opportunities for your business.
FAQs
Yes, AI absolutely adds value to marketing automation tools, especially when it comes to improving efficiency, accelerating content creation and providing more responsive and personalised campaigns. These capabilities are meaningful for day-to-day execution: marketers can generate first drafts of emails in seconds, optimise subject lines automatically, and build segments that would’ve taken hours to create manually. However, while these improvements streamline operations, they are mostly incremental rather than transformative.
Marketing automation platforms weren’t originally designed as robust data processing engines; they were built primarily for campaign execution, email delivery, segmentation and lead scoring. Over time, vendors have bolted on additional features, but the underlying architecture in most systems still reflects their early, channel-centric roots. This means they often struggle when handling large, diverse or multisource datasets.
Investing time in an MA platform’s AI features can still deliver great value, as long as your expectations are set appropriately. These features excel at improving speed, reducing manual workloads and enhancing tactical outputs such as subject lines, send time optimisation and personalisation. They’re particularly helpful for busy teams that need to scale campaign production without increasing headcount. However, they won’t resolve deeper issues around measurement, attribution, data quality or forecasting. time optimisation and personalisation.
Marketing automation platforms perform best when supported by purpose-built data orchestration and analytics tools. Data orchestration platforms like Openprise excel at ingesting, cleansing, standardising and distributing data from multiple systems, ensuring marketing teams have a unified, reliable view of each prospect or customer. built data orchestration and analytics tools.
When this high-quality data flows into business intelligence tools such as Domo, teams can finally visualise complex journeys, run advanced attribution models and analyse behaviour across channels and devices in ways MA platforms alone can’t support.
It’s possible that MA platforms will evolve to support more sophisticated data processing and analytics in the future, but doing so would require major structural changes rather than the incremental enhancements vendors are currently prioritising. Most platforms still rely on legacy architectures that make real-time data unification or complex modelling difficult to implement without a fundamental rebuild.
At the same time, vendors face competitive pressure to focus on visible, marketable features like generative AI assistants, rather than deep engineering upgrades that are expensive and less immediately commercial. As a result, while we may see ongoing AI enhancements, it’s unlikely that MA platforms will transform into full scale data orchestration or analytics engines anytime soon. The more probable future is one where MA tools specialise in execution, while surrounding technologies deliver on the strategic intelligence a modern organisation needs.
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