How to scale marketing performance with agentic AI

The buzz around AI in marketing has finally settled into something more useful. We’ve moved past the initial excitement over generative images and into a more grounded, practical phase — one focused on transformation and leveraging data more effectively. And right now, a new chapter is opening up: agentic AI.

Unlike traditional AI, which works in a straightforward prompt-response loop, agentic AI acts more like a smart collaborator. It reasons through problems, utilizes the tools at its disposal and completes multi-step tasks to meet specific goals — often without requiring much assistance from you. No matter how complex your marketing setup is, these agentic systems tend to drive two things: better performance and greater efficiency.

Start with what’s easiest: driving efficiency

For most global brands, the quickest wins with agentic AI come from efficiency-focused use cases. These don’t change the nature of the work, but they significantly reduce the time it takes to complete it. Think about automating the manual stuff — building slides, auditing datasets, pulling reports — so teams can spend more time on strategy.

Dig deeper: How agentic AI is changing the future of marketing

This is where many brands should begin. Efficiency wins are fast to deploy, easy to measure and a great proof of concept for expanding AI use. Here are a few examples:

• AI-enabled competitor offer mapping: Instead of manually tracking the competition every week, agentic tools can scan platforms like Meta or YouTube to collect and organize competitor creatives. One global car brand used this approach to benchmark real-time campaign activity across key channels.
• Conversational analytics chatbots: These enable non-technical teams to ask questions about complex datasets using everyday language. No waiting on a data pull — just quick, helpful answers.
• AI-driven product feed audits: These agents can scan thousands of SKUs to spot missing attributes or taxonomy issues. Clean feeds ensure your shopping ads appear correctly and perform more effectively.

Turning efficiency into effectiveness

While efficiency saves you time, effectiveness improves the quality of what you produce. These use cases enable AI to perform tasks at scale that humans can’t — such as predicting market shifts, enriching data and delivering more innovative outputs that lead to a more substantial ROI.

Here’s how brands evolve from efficiency to effectiveness with agentic tools:

1. Advanced chatbots with forecasting power: What starts as a simple chatbot can grow into a strategic advisor. Add demand forecasting, and suddenly the agent isn’t just reporting ROI — it’s helping you fix it. One consumer health brand achieved this by predicting seasonal cold and flu spikes using trend data, which resulted in a doubling of their website traffic.
2. Agent-based modeling for what-if scenarios: This method simulates the behavior of individual agents — such as consumers and competitors — to see how real-world market conditions might unfold. Want to know what happens if your competitor launches a discount campaign? This tool lets you model it safely before making a move.
3. Real-time product feed optimization: Instead of just auditing feeds, effectiveness agents rewrite product titles and descriptions dynamically based on live search trends. Salomon, the sporting goods brand, tested this and saw a 43% bump in click-through rate and an 83% lift in ecommerce revenue.

How to roll this out

Integrating agentic AI into your marketing ops doesn’t happen all at once. It’s a journey — typically with three phases:

• Phase 1: Plan
  Begin by establishing a solid foundation for your data. That means clean, structured, well-labeled datasets — both structured (like CRM) and unstructured (like brand guidelines). Garbage in, garbage out.
• Phase 2: Implement
  This is where embedded AI begins to take over repetitive tasks. You’ll also want to build AI literacy across teams so that the tools don’t just exist — they get used effectively.
• Phase 3: Deploy
  Here’s where it gets exciting. Agentic use cases, such as predictive budget planning or competitor modeling, go live, and brands shift from reacting to shaping outcomes.

No matter how intelligent your AI agent is, it’s only as good as the data it runs on. For agentic solutions to be effective, your environment must support them — this includes centralized data, solid governance and system compatibility.

Dig deeper: Why agentic AI is the next big shift in CX strategy

Agentic AI isn’t just another tool in the stack — it’s the connective tissue that enables different platforms to communicate and take action. By starting with efficiency to build trust and momentum, then pushing toward effectiveness to drive impact, marketers can use agentic AI to meet today’s complexity with clarity and control.

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