Media buying itself is becoming automated. If you don’t learn to manage AI, it will replace you.
The platforms already handle targeting, bidding, and optimization automatically. Creative testing is being automated. Budget allocation is getting smarter without human input. The manual skills that made media buyers valuable are rapidly becoming obsolete.
Most media buyers respond by perfecting their manual processes – getting better at spreadsheet analysis, manual creative testing, and hands-on campaign management. But they’re competing in a shrinking market against machines that work 24/7.
Elite media buyers are taking a different approach: they’re building AI workflows that only humans can create, then letting those systems handle the execution. They’re not trying to outwork AI – they’re directing it.
Jordan, a verified media buyer in our network, managed over $45M+ in ad spend by treating AI like his business intelligence team, not his copywriting assistant. While others polish copy manually, Jordan built automated systems that collect data, analyze competitors, and provide strategic recommendations without his intervention.
I interviewed Jordan to understand how he’s staying ahead of the automation curve.
Jordan’s AI System
Jordan recognized that his value wasn’t in doing manual work better – it was in creating intelligent systems that AI alone couldn’t build. These systems require human strategy, business context, and creative problem-solving to set up, but then operate autonomously.
AI can execute workflows, but it can’t design strategic workflows that understand business context, competitive positioning, and client goals. That’s where humans remain irreplaceable.
Jordan’s approach: Build the intelligence layer that tells AI what to do, how to do it, and when to escalate to human judgment.
Here’s how he built systems that AI can’t replicate:
Workflow 1: Data Collection
“Being able to scrap all that information and pull it into one house allows the LLM to parse through that information so you can ask questions directly.”
Jordan didn’t just automate data collection – he created a system that understands business context. His AI knows which metrics matter for each client, how to weight different data sources, and when performance changes require strategic pivots. The system provides answers like “CPM increases are driven by iOS audience shifts, recommend testing Android-heavy creative” – analysis that requires business strategy, not just data aggregation.
Workflow 2: Competitive Monitoring
“I had it go grab landing pages from a competitor the other day and put it into a Google sheet so then I can start to look at competitors and what they’re doing.”
Jordan’s system doesn’t just track competitors – it understands competitive strategy. The AI monitors 50+ competitors continuously, identifies pattern changes, and flags strategic shifts that impact client positioning. It knows the difference between tactical creative tests and fundamental strategy pivots because Jordan programmed that strategic understanding into the workflow.
Workflow 3: Creative Strategy Development
“Throw those landing pages into another model to get it to break it down into a markdown file and use that to go create additional assets for the brands I’m working with.”
The system converts competitive intelligence into strategic creative briefs that align with client brand positioning and campaign objectives. This requires understanding brand voice, competitive differentiation, and creative strategy – context that only humans can provide to AI systems.
Jordan’s Tech Stack
Cursor – Creates Python automations without coding knowledge. Jordan builds custom scripts that handle data collection and analysis automatically.
n8n – Connects different platforms with complex workflows. More powerful than Zapier for integrating Slack, Google Sheets, and AI models.
- ChatGPT: General analysis and strategic thinking
- Claude: Processing large amounts of data and technical tasks
- Gemini: Research and information synthesis
ChatGPT Pro Agents – Jordan uses these for autonomous research and data collection that runs without supervision
The Automation Trap (And How to Avoid It)
Many media buyers fall into the trap of automating just to feel productive, rather than focusing on workflows that actually save meaningful time.
The key is identifying your biggest time drains first. Most media buyers spend 2-3 hours daily on:
- Data collection from multiple platforms
- Competitive research and analysis
- Report generation and client communication
These high-impact, repetitive tasks are perfect automation candidates because they save hours, not minutes.
Try This
This week, focus on automating workflows that actually matter:
- Track your time for two days (document what takes the most manual effort)
- Pick your biggest time drain (likely data collection or competitive research)
- Document every manual step in that workflow
- Start with n8n to connect two platforms and automate the data movement
- Test the time savings (measure actual hours saved)
Focus on workflows that save 1-2 hours daily, not tasks that only save 5-10 minutes.
The media buyers building these systems now will have insurmountable advantages within months. Start building yours today.
For more, watch the full Q&A interview with Jourdan here.
