Editorial
The Gist
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Smarter audience targeting. AI agents can dynamically build and update customer segments in real time, which allows better personalization and campaign precision.
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Faster content production. Content agents create and personalize assets across channels, which frees up marketers and allows real-time adjustments based on performance.
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Adaptive customer journeys. Journey agents adjust campaigns on the fly. They react to behavior and engagement signals to guide users toward better outcomes.
In Part 2 of this three-part series on agentic AI in martech, we talked about internal “operational” and external “customer engagement” use cases. We also considered both internal and external “categories” of AI agents. Internal agents are part of a martech ecosystem but aren’t customer facing, and external agents engage with the organization’s end customer.
And in Part 1 of this three-part series, we looked at the progression of technology in martech that has led us to the point we are at today.
Let's start here:
Internal vs. External Agents in AI-Driven CX
This table outlines how internal and external agentic AI roles typically divide across customer experience functions. Internal agents focus on creation and orchestration; external agents engage directly with customers.
| Internal Agents | External Agents |
|---|---|
| Audience creation | Information and discovery |
| Journey creation | Service and support |
| Model creation | Conversational commerce |
| Content creation | Lead qualification |
| Decision determination | Orchestration and decision delivery/nudging |
So where do you get started? What are some of the quick win use cases where you can measure value and demonstrate ROI? Here are just a handful of use cases that will set you on your path to agentic success.
Building Smarter Audiences
Creating and maintaining a library of up-to-date customer audiences (also known as segments) based on real-time data can be a challenge. Source systems change, integrations break, and siloed decisions and changes occur. And as a result, maintaining dynamic, scalable and responsive audiences that drive better personalization, targeting and decisioning is often a massive undertaking.
But what if you had an audience agent that monitored customer behavior changes and picked up on customer signals and preferences? What if it could dynamically create, update or archive and delete audiences based on organizational rules, goals or insights? What if it synced audiences in your library to downstream martech solutions for activation and engagement purposes? Just imagine the improved personalization, suppression, optimization and overall adaptive marketing that could be performed.
Creating Content at Scale
Marketers are responsible for creating content almost daily. What if that burden was eased by an agent that autonomously created, personalized and updated content across all channels? This agent would be pivotal in allowing real-time personalization, accelerating campaign production and continually updating content based on engagement performance. Amazing!
Content agents should integrate with CMSs, DAMs, WCMs, CRMs and decision engines. They’re responsible for making sure content is brand compliant, localized and adaptable to changing audiences and journey stages. A content agent allows contextual content automation and turns what was once a dated library of static marketing into an insight-driven adaptable set of assets.
Making Journeys More Adaptive
Adjusting customer journeys mid-flight was never considered a possibility until just a few years ago, specifically when AI and reinforcement learning hit the scene. We are now at the point where a journey agent can autonomously create, test, update and optimize customer journeys across channels. A journey agent will be able to monitor intent, conversion signals and customer engagement (or lack thereof) and then adjust the path, campaign workflow or conversions/outcomes accordingly.
Journey agents will interface with CDPs, data layers, content agents, execution platforms and other AI and analytics tools to conduct the customer experience. This is of extreme value for organizations that have hundreds if not thousands of journeys in production.
Powering Decision Intelligence
One of my favorite agent types is the decisioning agent. It connects to the organization-wide enterprise or AI decisioning layer and acts as the intelligent brain of your martech stack. It decides which offer or interaction to send, when to send it and through which channel.
These AI agents, regardless of where they sit in the organization, should be well governed. After all, we don’t want a decisioning agent saying or doing something it shouldn’t. Decisioning agents will be key for use cases surrounding cross-sell/up-sell, journey optimization, offer management, personalized content delivery and suppression/compliance.
Related Article: How Agentic AI Broke the Rules of Martech Decisioning
Supporting Customers in Real Time
These customer-facing AI assistants take an active role in engaging users through conversational interfaces. Most known are website chatbots, but these can be used in messaging apps, in-app support or even via voice assistants at point of sales locations like quick service restaurants (QSR). They are designed to provide real-time assistance, education and nudges along the buyer journey. Most are still low-level and suggest actions rather than complete them.
But we’re quickly approaching the point where brands will trust these AI agents to perform tasks in an autonomous manner. This is a type of support where quick wins from agents can occur if guardrails are maintained.
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Hopefully this series has given you a taste of what you might see inside your martech environment in just a few short months. Orchestrating and infusing AI agents into stacks should come with an understanding of the abilities of these agents to act in an autonomous and intelligent manner. It should also come with an understanding that governance, data quality and customer experience consistency will be required to set them up for true success.
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