What Six Years of Operations Taught Me About AI Systems
The path from Excel macros to applied AI systems is not a straight line. Looking back, every phase was preparation for the same systems problem.
The Minnesota.AI story usually starts in 2020. The real story starts earlier, around 2015, in manual CRM work where repetitive patterns were obvious.
That led to Excel macros, then Python, then API-driven workflows across systems and platforms.
The Years Nobody Talks About
Before eCommerce, a lot of work was automation for digital marketing agencies: analytics, ads, SEO, reporting, and workflow follow-up.
The constant thread was ETL work: extracting data, transforming it into usable form, and loading it where decisions could be made.
That is where one core lesson became non-negotiable: downstream quality depends on upstream data integrity.
Automated Intelligence Before the LLM Wave
In early 2020, Minnesota.AI was formed in a period where logic-heavy automation dominated: explicit rules, conditionals, and custom edge-case handling.
Build and research costs were higher then. The barrier to sophisticated systems was mostly budget and implementation time.
What changed is execution speed. Modern AI tooling compresses the build cycle, but only when system design is clear before implementation starts.
What Operations Actually Teaches You
Systems do not usually fail in clean demos. They fail at the edges: bad inputs, silent dependency changes, undocumented exceptions, or ownership gaps.
Years of debugging broken pipelines and noisy data builds pattern recognition for where systems actually break in production.
That is why applied AI systems need instrumentation, edge-case handling, and maintenance plans from day one.
The Thread That Connects It
Excel macros, Python automation, ETL pipelines, pre-LLM logic systems, eCommerce operations, and applied AI systems are the same core problem with different tools.
The job is consistent: turn messy real-world inputs into reliable outcomes that keep working without a specific person in the room.
Applied AI systems are not a pivot from operations work. They are the continuation of it.
David Valencia is a full stack developer and systems thinker focused on applied AI systems and LLM discoverability. He works with organizations that want AI to produce outcomes, not just outputs. Minnesota.AI
Need Operator-Level System Thinking?
If your AI initiative needs real production constraints and outcome-first architecture, we can scope it before build begins.
Book a Discovery Call