Agentic Marketing Department: Full-Scale AI Implementation Is Not A Joke

Think transforming an entire marketing department into Agentic AI automations that generatively produce and present content is years away, if ever? You are sadly being mislead, possibly intentionally. I'm here to identify all the logical reasons why it is so far from far fetched that it has been happening for over a year now.

The hard truth is, that's what they want you to think. As I write this, 9 January 2025, managers are barely comfortable putting the need for experience using AI on job descriptions. Even in engineering and graphic design. But if you're someone who does either, you know that AI is already fully part of your process.

Can you convince yourself that they would hire someone without extensive experience with AI? Only if it is due to their own ignorance, in which case you might want to avoid that job offer.Can you convince yourself that they would hire someone without extensive experience with AI? Only if it is due to their own ignorance, in which case you might want to avoid that job offer.

Put plainly: the means and methods of production, distribution, and consumption have already rapidly changed, more than once in over two years. Imagine you're a hiring manager. Frankly, it is better that you think it is impossible to create an automated marketing department worth its chops, because it could buy them a year, maybe more, before everyone realizes that companies are only hiring a tenth of their prior staff. It only makes sense to play with that timeline if you can, because eventually the surplus labor, outside of the small amount that is required, will end up creating more businesses. Now, we're not going to go down the rabbit hole working through why that is a logically sound statement, you're just going to have to trust that it is human nature to expand, to explore, to create, to trade, to collect, socialize, communicate, travel... you get the picture. There is no removing that aspect of humanity. I challenge you to come up with one truly plausible reason. Because you know that you're not lazy, you're disinterested. You're not addicted to your job, you're addicted to being productive. We've framed these things in a negative light for so long that it is sometimes hard to see the core truth that is who and what it means to be human. AI art looks to "realistic"? We'll start craving art we can tell is human made. We'll create spaces to experience the production of art. Years of "photoshop trickery" is making people only think about things from one side.

Anyway, I could go on and on but the point here is to break down exactly how automation of a marketing department can be done, as well as what the small but significant details are that make it work.

Two years ago I set out to create a brand. The plan was to build out one media and medium channel, then automate it. Optimize it. Then manage it while moving to build out another media and medium channel to be automated, and so on.

Since then I've done it with multiple brands, falling into mediums I never expected to be able to fully automate. As always in marketing, the game however will forever be changing, but now it will be a new game. It will be a game of creating what it is that humans are seeking out to consume *because they see humanity in it*. Thus the success of NotebookLM's podcasts. The obsession with "realistic" generative pictures of humans, as if anyone ever kept the picture of the models in the picture frame they bought at the department store. It's a means to an end; a desire to see it done. Then, much like humans have always done, we'll be on to something new. This has always been the marketing game. We've just swapped out the tools.

I've you've read that missive and want more, proceed to review the strategic AI implementation, from content creation to automated newsletters and social media, revealing a blueprint for scaling operations while maintaining strategic control and creative quality. The kicker is, you've already been falling for it for over a year, possibly years at this point. Time to get in on the game because things are about to get really crazy out there. Crazy in the best way.

Let's start with something most consultants won't tell you about implementing AI in your marketing department: you don't need to start with expensive tools or a team of engineers. That's often exactly the wrong approach. What you need is a systematic way to learn and grow with the technology.

Starting Smart: The Power of Accessible Tools

Two years ago, we built a complete AI-powered marketing department from the ground up. Not with complex custom code or expensive enterprise solutions, but by starting with accessible tools that let our entire team understand and participate in the automation process. Today, I'm going to show you exactly how we did it, and more importantly, how you can do it too.

We started with Make.com - not because it was the only option, but because it's user-friendly, cost-effective, and powerful enough to handle serious automation scenarios. When teams are first exploring AI implementation, they often make the mistake of jumping straight to custom development or expensive enterprise solutions. This usually leads to two problems: limited team understanding and reduced ability to iterate quickly.

Zero-cost scaling using accessible tools can achieve:

Key Learning: Start Simple, Think Big

The most common mistake organizations make when implementing AI? Starting too complex. By beginning with visual, no-code tools like Make.com, your team doesn't just learn automation - they learn how to think about automation. Every workflow becomes visible, every decision point clear. This transparency is crucial because it means:

Just like training a new employee, you need to give AI systems clear instructions and feedback. Make.com's visual interface lets you see exactly where and how these interactions happen, making it much easier to refine and improve your processes.

Building the Foundation: Content Creation at Scale

Strategic Insight: The Power of Multiple AI Reviews

Implementation Note: Building Learning Systems

This foundation became crucial as we scaled up to more complex content creation tasks. By starting with these basic principles and letting the system learn and evolve, we created a robust platform that could handle increasingly sophisticated content requirements.

Key Learning: The Human Touch in Automation

This foundation set us up for more ambitious implementations, which we'll explore in the next sections. The key was building a system that could learn and grow, while maintaining the human elements that make marketing truly effective.

Learning Through Implementation: The Art History Project

When theory meets practice, that's where the real insights happen. Our art history project wasn't just about creating content - it became a masterclass in scaling AI-driven content production while maintaining quality.

Database structure and content organization for art movement articles.

Key Learning: Database Design Matters

1. Database Structure

• Created distinct sections for each content component
• Built relationships between content types
• Established clear hierarchies for information
• Set up automatic cross-referencing

A well-structured database is crucial for AI, enabling it not just to store information, but to understand relationships and context. Modular content allows AI to focus on specific tasks, while clear data relationships enhance coherence. Structured data supports scalable content production, and well-organized prompts can be continuously refined, optimizing AI performance and output quality.

2. Content Creation Process

We developed a multi-stage process where:

• First AI created historical context
• Second AI developed artistic analysis
• Third AI generated cultural impact sections
• Final AI integrated everything into cohesive articles

Strategic Insight: The Power of Sequencing

3. Quality Control Implementation

We built automated quality checks that verified:

• Historical accuracy
• Content completeness
• Writing style consistency
• Cross-reference validity

The Evolution of AI Communication

This is where things get really interesting. We discovered that AI systems could do more than just follow instructions - they could help improve the instructions themselves.

Key Learning: AI's Natural Language Understanding

Here's what we discovered about working with AI language models, starting with the logic: Uh, they have gigantic vocabularies. So large the tiniest differences in words we'd never think would change anything, could make all the difference in the world in a prompt. Seriously, if the AI kept generating a paragraph instead of one sentence, we'd just ask the AI to alter the prompt so that it didn't write more than one sentence. The change it made was virtually never "write one sentence" — it would be just word choice and maybe even shortening the actual prompt itself. Things that humans just wouldn't think would have a better effect than saying "don't write a paragraph. That's like telling an LLM not to use emojis. Chances are it'll start using them like crazy. It is a stream of consciousness. It cannot forget things at request. This seemingly simple difference in cognition has profound results. So yeah, let the AI write its own directions and just provide feedback. Game. Changer.

1. Language Nuance

• AI can understand subtle distinctions in tone
• It can identify patterns in successful content
• It can suggest improvements to its own prompts
• It often sees nuances humans might miss

2. Feedback Implementation

• AI analyzes successful content
• Suggests prompt improvements
• Tests new approaches
• Reports on effectiveness

Implementation Note: Create Structured Communication Channels

We used email-based review requests that were automatedand have built in feedback loops. Taking advantage of AI embeddings, the modern tech-brain's database, we fed literally everything into the AI. This created performanced tracking systems that only required asking for a data analysis and had the aedded benefit of being able to compare almost any metric, content post, purchase, even things like our finances and tax season. It eventualy created for us learning repositories. Once there was enogh data to have significant results in comparing...anything. It was then we learned the magical best practices.

1. Let AI analyze its own output
2. Implemented suggested improvements
3. Tracked performance changes
4. Refined the process continuously

Key Learning: The Power of Iteration

What made this approach so effective wasn't getting everything perfect from the start - it was creating systems that could learn and improve. Each iteration brought:

• Better understanding of what works
• More refined prompts
• Improved output quality
• Greater efficiency

Database-Driven Intelligence

Strategic Insight: Building Institutional Knowledge

Notion became our secret weapon for managing complex automations. But it wasn't just about storage - it was about building a system that could learn and grow:

Implementation Note: The Role of Structured Data

We discovered that proper data structure was crucial for:

• Maintaining consistency across outputs
• Enabling system learning
• Facilitating quick iterations
• Supporting complex automations

Each database became not just a repository, but a living system that:

• Informed future content creation
• Guided process improvements
• Tracked successful patterns
• Identified areas for optimization

This foundation of structured, intelligent data management became crucial as we scaled up our operations and added more sophisticated capabilities to our AI marketing department.

Key Takeaways

Our journey in building an AI-powered marketing department demonstrates that successful AI implementation isn't about having the most sophisticated tools or largest budget - it's about creating intelligent systems that can learn and evolve. By starting with accessible tools, focusing on team understanding, and building structured feedback loops, we created a scalable system that transformed our marketing operations while maintaining strategic control and creative quality.