For the last couple of years, most conversations about AI in marketing have revolved around content generation: writing emails, creating images, summarizing meetings or producing social posts.
That is now changing.
AI is beginning to move deeper into the marketing technology stack and into the day-to-day work of Marketing Operations. A recent practitioner session on Adobe Marketo Engage demonstrated how AI is already being used for activities such as program validation, lead imports, predictive content, email production, webinar repurposing and campaign QA.
From RightWave’s perspective, this is the more important AI story. AI is moving from being a tool marketers use occasionally to becoming part of how Marketing Operations actually gets work done.
From repetitive execution to higher-value work
Some of the most interesting Marketo AI capabilities are not the flashy ones.
Program validation and lead imports, for example, are routine Marketing Ops activities that can consume a surprising amount of time. They involve checking configurations, reviewing campaign logic, mapping fields correctly and making sure nothing breaks downstream.
Marketo’s AI Assistant can begin taking on parts of this work. The sensible approach is to start small: give the AI a clearly defined task, see how it performs and then gradually provide more context through checklists, campaign briefs or operating rules.
That is likely to be the most practical path to AI adoption for many Marketing Ops teams. Instead of trying to automate everything, identify a few repetitive workflows where AI can reduce manual effort without introducing unnecessary risk.
Email production is getting faster
The newer Marketo Email Designer shows how quickly AI can compress traditional campaign production cycles.
Marketers can use generative AI to create email layouts, write copy, generate subject lines and preheaders, and even create images directly within the platform. This reduces the need to constantly move between Marketo, external AI tools, designers and other teams.
The real benefit is not simply faster copywriting. It is fewer handoffs.
One presenter shared an example where bringing email production in-house using these capabilities reportedly saved more than $18,000 in consulting costs and reduced a process expected to take several months to roughly three weeks. That may not be representative of every organization, but it illustrates how much time can be hidden inside campaign production and coordination.
Personalization is becoming more individual
Marketo’s predictive content capabilities also point toward a more advanced model of personalization.
Traditional dynamic content usually works by deciding what a predefined segment should see. Predictive content can use an individual’s engagement and behaviour to determine which approved content is most relevant to that person.
This moves marketing closer to a next-best-content approach.
But it also highlights something Marketing Ops teams already know: AI cannot fix poor foundations. Content still needs to be categorized properly, assets need to be approved and data needs to be trustworthy. The better the operational foundation, the more useful the AI becomes.
Webinars can become ongoing content engines
Generative AI is also being introduced into Marketo’s Interactive Webinars capabilities.
Teams can use it to summarize sessions, identify chapters, highlight key moments and break longer webinars into more usable content. Instead of treating a webinar as a one-time event, Marketing Ops teams can turn it into a repeatable source of nurture content, campaign follow-ups, clips, summaries and sales enablement material.
Again, the value is less about AI creating another piece of content and more about making repurposing a scalable workflow.
Start with the problem, not the AI feature
Perhaps the most useful takeaway from the session was the recommendation to prioritize AI based on business problems rather than available features.
Marketing Ops teams should ask where they are losing the most time, where bottlenecks repeatedly occur and which activities are highly repetitive. They can then evaluate whether AI is practical, safe and valuable for those workflows.
High-frequency, rules-driven and relatively low-risk activities are usually better starting points than complex strategic decisions.
This also makes it much easier to demonstrate business value because teams can measure hours saved, costs avoided, faster campaign turnaround or additional capacity created.
Governance still matters
The session repeatedly reinforced the need to keep humans in the loop.
As AI begins taking actions inside marketing platforms, permissions, roles, brand guidelines, prompt libraries and review processes become increasingly important. Marketing Ops leaders will need clear answers to questions such as who can use AI, what AI is allowed to execute, what still requires approval and how AI-driven actions are audited.
AI governance will increasingly sit alongside campaign governance, data governance and Martech governance as a core Marketing Operations responsibility.
The RightWave perspective
For organizations already using platforms such as Marketo, the first AI opportunity may not require buying another tool. There could already be substantial AI capability inside the technology stack they own.
A practical starting point is to identify two or three repetitive workflows, understand how much time or cost they consume, test whether existing AI capabilities can help and establish appropriate guardrails before scaling further.
The teams that get the most value from AI are unlikely to be the ones experimenting with the largest number of tools. They will be the ones that integrate AI carefully into the right workflows while maintaining the data quality, governance and human judgement needed to make those workflows dependable.
That is where we believe Marketing Operations is heading: AI not sitting outside the Martech stack, but increasingly becoming part of how the stack operates.
Reference – https://youtu.be/AuQGex_JNl4?si=WZPnb3-i9DJ5Z7YI
