Written by Anjali Yakkundi, VP of Product Marketing at Movable Ink, this post covers four methods for marketers to leverage AI for their marketing: content creation, journey decisioning, content decisioning, and workflow efficiency. To put these methods into practice, marketers must use several branches of AI within Machine Learning and Deep Learning.

It’s Time to Move Beyond Traditional AI Approaches

Gone are the days when AI was only used for recommendation engines, cluster analysis, or propensity models. While those were powerful in their own right, AI’s current iteration has the potential to drive far more impact.

AI’s potential boils down to four key marketing use cases: content creation, journey decisioning, content decisioning, and workflow efficiency. With the power of several AI subsets within Machine Learning and Deep Learning, marketers can bring these use cases to life and truly reshape how their brand engages with their audience.

Why Predictive Analytics Isn’t Enough

AI, broadly speaking, performs tasks that would typically require human intelligence—understanding language, recognizing images, or solving complex business problems. None of these use cases are new, and tasks like image recognition and facial identification have been in play for years.

For marketers specifically, predictive modelling that forecasts customer behavior or auto-segmentation is one of the most established examples. But with this approach, customers are reduced to their lowest common denominator and targeted based on that data.

One of the other main issues with this type of predictive analysis is the fact that it’s based on stale historical data that is largely reactive and analytical. While it uncovers "what" customers might do next, it doesn’t empower marketers to act in real time or create personalized experiences at scale.

Today, marketers can use AI to be far more proactive. It can integrate directly into the marketing workflow and enable brands to create, decide, and optimize in real-time—not just analyze.

The Branches of AI and How They’ve Progressed

But how has AI progressed so rapidly over the last few years? It boils down to specific capabilities under the broader umbrella of AI, which can be quickly broken down into Machine Learning, Deep Learning, and Specialized Deep Learning.

Marketers have likely come across the term Machine Learning before, which is a subset of AI that allows machines to learn from data without explicit programming. Over time and experience, Machine Learning’s output improves. 

Within Machine Learning is another subset called Deep Learning. Inspired by the intricacies of the human brain, this type of learning uses algorithms to recognize patterns in large data sets. Deep Learning is often leveraged for complex tasks like Content Decisioning, a key application that will be covered in the section Four Game-Changing Applications for Marketing AI.

‍Deep Learning has many specific applications, most prominently Generative AI and Agentic AI. Generative AI is especially well-known, its most popular iteration being ChatGPT, and instantly creates new content like text, images, or music based on inputted data using the power of Large Language Models (LLMs). 

Agentic AI uses Deep Learning techniques to make complex decisions, mimicking the human brain. But it doesn’t just make the decision; it autonomously executes decisions on behalf of the human, and it can be refined with pre-defined guardrails. 

Four Game-Changing Applications for Marketing AI

With these AI definitions in mind, it’s time to move to how they can be used for marketers’ gain. There are four key use cases throughout the campaign creation process, and each leverage one or more of the listed AI subsets.

Content Creation

When the general public thinks of modern AI and content creation, Generative AI is one of the first things to come to mind—and for good reason, as its instant production helps brands scale creativity.

While Generative AI alone isn’t enough for a perfect, on-brand product—particularly in longer form content and images/videos—it can still provide major benefits. Let’s dive into a few of the ways Generative AI can be utilized for enhanced content creation: 

• Subject lines that are ever-fresh and ever-relevant are made scalable with AI, a critical task in the age of message-clipping and automatically organized subscriptions. 
• Long-form copy is no longer a marathon task, as marketers can speed up the process by leveraging AI to either generate an outline, take an initial draft of content, or refine their finished product.‍
• Image and video creation likely can’t rely on Generative AI alone yet, given the issues surrounding creating on-brand and data-activated content. But marketers can still enjoy a major head-start with initial drafts to build upon.

Journey Decisioning

As customers evolve, Machine Learning can adapt content right along with them. This automatic approach completely transforms marketers’ journey building:

• Identify next-best steps in the customer journey automatically in real-time, rather than setting up cumbersome journey maps.
• Go beyond simple triggers within common customer journeys—such as abandoned carts or welcome/onboarding series—with personalized follow-up content.
• Understand the best send time to increase customers’ likelihood to engage with marketing messaging.

Content Decisioning

Marketing relies on a set of complicated decisions to deliver the right content to the right customer, and those processes are currently manually done—marketers have to test, learn, and then personalize, requiring a long and cumbersome process. That’s where AI can come in: 

• Always-on testing ensures that messaging is constantly iterated and improved with every send, a step up from the manual methods of A/B testing.
• Personalized content is pulled from the marketer’s library of creatives, and the asset most likely to resonate with the customer is automatically included in the campaign.
• Finesse frequency to further enhance AI-personalized content, greatly increasing customers’ likelihood to engage and convert. This goes beyond frequency capping, and is a complex decisioning engine that balances unsubscribes with engagement and conversion rates.

Workflow Efficiency and Effectiveness 

Getting campaigns to market faster is always top-of-mind for marketers. But it can’t just be a quantity game; marketers need campaigns that truly drive results and engagement. AI is the tool to complete both of those tasks:

• Automate routine tasks that ultimately streamline marketing workflows in an instant.
• Optimize user interfaces that use AI to complete formerly complex tasks in the system. In this case, Agentic AI is a game-changer, as agents can both navigate and execute from a conversational interface.  
• Drive exponential results as Agentic AI can automatically analyze and execute next-best steps, whether that be in personalization, subject lines, send time, or frequency.

Reframe Your AI Techniques

AI is an undeniable asset in the toolbox, and with the right solution, marketers can combine its data-driven power with the human touch. They can retain complete control by always setting the objectives and guardrails for any of these use cases. Necessary overrides are only a click away—because the creative brainpower always rests with the marketer. 

Bring these powerful AI use cases to life by seeing Da Vinci in action, or explore the related resources below.