Agentic AI in Marketing: Use Cases from the Field

Agentic AI in Marketing was front and centre at a recent partner event I attended with a leading MarTech vendor in the UK. Much of the discussion focused on where AI is genuinely adding value today, not just in theory, but in day-to-day marketing delivery.

There was healthy debate around risk, governance, and the wider philosophical questions (for those interested, my colleague, Tim, explored that side of the discussion here). But for me, the more compelling thread was practical: how Agentic AI is being applied to real use cases, and where marketers are seeing measurable benefit.

Here’s a summary of what came out of that conversation.

Automating Routine, Elevating Relevance

We all agreed the clearest early value lies in two areas:

• Automation of routine marketing tasks: Brief creation, journey building, channel execution
• Hyper‑personalisation at scale: Not just customising messages, but allowing agents to make real-time decisions on what to say, when, where, and why

The power here isn’t just in speed or efficiency – it’s in freeing teams to focus on strategy, while the agent handles complexity and context in the background.

Some of the Practical Use Cases for Agentic AI in Marketing

1. Journey Orchestration with Live Inputs

Instead of mapping out linear, rules-based journeys, we talked about how agents can design and adjust flows on the fly – based on real-time behaviour, channel preference, product interest, or even inactivity.

The agent acts like a concierge: always learning, adjusting, and nudging the customer toward the next best action – without waiting for a marketer to make that decision manually.

2. Personalised Offer Assignment and Guardrails

This was a strong area of focus. Delegates talked about using AI to assign offers based on real-time eligibility, availability and intent – but only within carefully designed business rules.

This allows for personalisation with control: the agent knows what can be offered, what shouldn’t, and when to act.

3. Dynamic Content and Creative Selection

One of the bigger challenges faced today has been the correct and accurate assignment of relevant content, to the right people, at scale. Rather than serving a fixed piece of content, agents can generate and test variations – selecting the most relevant creative version for a given individual or segment.

This shifts the creative model from batch-and-send to real-time adaptation.

4. Customer Support and Service Agents

There was interest in how AI agents are supporting front-line service too – resolving Tier 1 queries, pre-empting issues based on sentiment signals, and escalating intelligently when human intervention is needed.

It’s not just cost saving – it’s also about maintaining CX quality at scale.

It was at this point we discussed the impact of the recently publicised Lenovo AI Service Agent vulnerability  (https://cybersecuritynews.com/lenovo-ai-chatbot-vulnerability/) – it’s imperative that any AI agent is built with security in mind.

5. Lead Engagement and Sales Handover

We talked at high level about the shared use cases where agents are handling lead engagement post-capture – qualifying interest, sharing personalised resources, and handing off to Sales once thresholds are met.

This closes the gap between marketing and sales more cleanly and with less drop-off.

6. Internal Operations and Briefing Workflows

While less flashy, this was another area people were excited about. Agents can prepare creative briefs, schedule campaigns, conduct QA checks and track compliance – all tasks that eat into valuable human time unnecessarily.

It’s this management of internal operational processes that can create the time, and headspace, that our marketers need to make process and journey improvements that deliver incremental gains for their business.

From Use Case to Implementation

What became clear is that these aren’t speculative ideas – they’re being tested and implemented today. But success comes down to a few core principles:

• Start where there’s structure: Pick processes where you already have rules, guidelines, or constraints in place
• Define levels of autonomy: Decide where the agent acts alone, and where human approval is needed
• Reframe the process: Don’t just bolt AI onto old workflows. Rethink the journey around how an agent could do it differently
• Embed transparency and governance: Ensure all decisions made by the agent are logged, traceable and aligned with your data ethics standards

This was one of the more interesting conversations I’ve had about AI in marketing. It moved past the “what if” and into the “how are we already doing this?”

Agentic AI isn’t about replacing marketers – it’s about supporting them, automating the complexity, and enabling faster, smarter, more relevant interactions with customers.

As the tech matures, the challenge won’t be in finding use cases – it’ll be in prioritising them, and putting the right structures in place to ensure AI works in service of the strategy, not instead of it.