Agentic AI in marketing is the future. But it’s also the now. We’ll show you agentic AI examples, business best practices, and some real-world applications of AI agents and how we use them for our clients at In Marketing We Trust.
Before we dig in, here’s a question. Do you have an AI Policy in place? If not, you need one. And you can steal ours.
What is an AI Agent?
Before we talk about agentic AI in marketing, we need to first clarify what an AI agent really is to ensure we’re on the same page.
It says it in the name—an AI agent has agency. It’s not just an LLM. It takes action, learns from feedback, and adapts over time. While an LLM generates text based on a prompt, an AI agent operates within a system of triggers, memory, models, and tools to complete tasks autonomously.
For example, we have these systems working at In Marketing We Trust. An AI agent might be triggered by a request, process the input using an LLM, reference stored context or past interactions (memory), execute one or more tools (e.g., querying data, publishing content, adjusting bids), and then output the required action, all without human intervention. This structured workflow makes AI agents far more powerful than standalone LLMs, especially in automating complex, ongoing processes.
Agentic AI in Marketing Examples
At In Marketing We Trust we use a combination of tools for different purposes.
We offer bespoke LLM engineered solutions for clients. Therefore, we use a number of different LLMs for different requirements based on their strengths (e.g., ChatGPT, Claude, Gemini, Grok).
For video, image, and audio marketing we use multiple AI tools, including Descript, Midjourney, 11labs. And for learning, we use Google Notebook.
Agentic AI in Travel Marketing
We often get the best results from agentic AI in marketing when working with our travel clients.
Agentic AI Case Study
AI-Powered Content Refresh for a Global Travel Brand
Challenge
A global travel brand had tens of thousands of outdated content pages. The content was not refreshed regularly due to:
- Scale limitations – updating manually was too slow.
- Inconsistent quality – some pages were more outdated than others.
- Impact on search performance – older content was less relevant, leading to declining organic traffic.
Solution
We built a custom AI-driven system to refresh content at scale while maintaining brand tone and factual accuracy. The system:
- Generated updated content using a fine-tuned LLM and bespoke datasets, reducing the need for manual rewriting.
- Maintained brand consistency by enforcing editorial guidelines in AI outputs.
- Reduced human oversight, with checks focused on verification rather than full rewrites.
- Increased update frequency, allowing thousands of pages to be refreshed much faster than before.
Results
- Significant improvement in organic search performance across multiple countries.
- Content updates at scale, improving accuracy and relevance.
- Higher efficiency, reducing the need for manual content updates.
- Open up performance in new geographic markets.
This approach, refined over three years, has outperformed any off-the-shelf AI solutions by delivering higher accuracy and better brand alignment at scale.
AI Agents for Business
For businesses that are looking to integrate AI agents into their operations successfully, there are a number of important strategies.
Develop a Robust AI Policy
Establish a clear AI usage policy that outlines how AI is deployed, how data is handled, and where human oversight is required. This policy should be shared with clients to ensure transparency. Steal ours here.
Balance Innovation and Risk Management
Your AI policy should not stifle AI-driven efficiencies but must mitigate risks related to accuracy, compliance, and reputational impact. Regularly update it as AI capabilities evolve.
Use Bespoke Data Solutions
Implement Retrieval-Augmented Generation (RAG) and custom datasets to ensure AI outputs are tailored, accurate, and brand-aligned in a secure way.
Maintain a Human-in-the-Loop Approach
At this point, AI should assist, not replace. Human oversight is essential for quality control, brand consistency, and risk mitigation.
Ensure Data Privacy & Compliance
AI workflows must align with data protection regulations and minimise risk for both the agency and its clients.
Be Transparent and Share the Wins
Tell your clients and customers how AI is used and how it benefits both parties. If AI increases efficiency, adjust pricing models accordingly to maintain trust. AI should create shared value—if only the business benefits, relationships will suffer.
Need Help?
We have delivered AI solutions for some of the biggest websites in the world, our approach is proven, scalable, and built for real business impact.
Unlike agencies experimenting with off-the-shelf models, we’ve developed proprietary methods and tooling to help brands deploy highly customised, brand-aligned, and accurate LLMs at scale in multiple languages.
Our data and engineering team ensures AI is not just theoretical but fully implemented and delivering measurable results. With deep experience working in compliance-heavy, ROI-driven environments, we build AI solutions that don’t just work in a lab, they work in the real world and can demonstrate an ROI that justifies continued investment in our AI marketing programs.
