In this episode, I share a hard-earned lesson from years in data analysis, business systems, and AI workflows: If you automate a mess, you just make a mess faster.
We dive deep into why clean, simple workflows should always come before automation or AI integration. From real-world examples like PDF data extraction, CRM integrations, and dashboard pitfalls, Nate explains how the illusion of efficiency can derail businesses and lead to bad decisions.
Learn why simplifying your systems first — not jumping straight to automation — is the real cheat code for sustainable growth.

Key Topics Covered:

• Why working from spreadsheets > PDFs when building AI workflows

• The dangers of automating broken processes

• Black box models vs explainable models

• Clean automation principles (hint: simple wins)

• How bad data kills AI insights

• How to design scalable business systems BEFORE adding AI

• Real-world examples from product development and CRM integrations

• Why simplicity actually scales

Takeaway:
Automation and AI are incredible tools — but they can’t fix broken systems. Start by simplifying your workflows, cleaning your data, and thinking critically before layering on automation.

If you're into automation, AI tools, or building content with smart workflows, this one’s for you.

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