Don’t Automate the Mess—Rethink the Problem First
Why Smarter System Design Beats Over-Engineering with AI and Automation
Too often, teams rush to automate complex problems without asking a more important question: Should this process even exist in its current form?
That’s the difference between engineering and intelligent engineering.
In this article, we’ll explore a real-world example from an Intelligent Document Processing (IDP) project, compare it to a historic breakthrough from IBM, and reveal why rethinking the problem is often a better path than throwing more AI or automation at it.
Intelligent Document Processing: The Default (and Flawed) Approach
In many IDP pipelines, one of the first major challenges is form classification—determining exactly what type of document has been submitted.
The standard AI-driven suggestion?
- Collect tens of thousands of documents
- Label each one
- Train a machine learning model to classify forms by type
Sounds modern, but it’s inefficient, expensive, and high-maintenance—especially when you have to retrain models every time a form changes.
And it’s only one step in a typical IDP workflow.
Rethinking the Problem: What Do We Already Know?
Here’s the insight we applied:
Instead of identifying the form after it’s received, why not leverage information we already have?
We started logging metadata at the point of submission—like which user, department, or partner submitted each document. Most of the time, this instantly narrowed the possibilities down to:
- One known form
- Or a shortlist of 10–15 possible forms
That’s a 90% reduction in classification complexity. And in many cases, no AI was needed at all—just a few smart database joins.
Throwback: How IBM Solved a Similar Problem in the 1980s
Back in the early PC era, IBM engineers were trying to automate the assembly of personal computers. They documented every step, tool, and motion required to build a machine—and realized it was a nightmare to automate.
Their breakthrough?
Redesign the PC so it was easy to assemble, rather than building a robot to handle the chaos.
They reduced the assembly process to 8 steps or fewer, created parts that only fit one way, and removed unnecessary tools. That single decision led to faster production, lower error rates, and the birth of Design for Assembly (DFA)—a philosophy still used today in manufacturing and product design.
Key Lesson: Don’t Automate a Bad Design
Whether you’re working with:
- AI workflows
- Software architecture
- Business processes
- Manufacturing systems
…you face the same choice:
Automate complexity, or eliminate it?
Smarter teams rethink the process first. They ask:
- “What if we didn’t need this step?”
- “Can we simplify before we optimize?”
- “What data do we already have?”
This mindset applies everywhere—from machine learning to DevOps, from HR workflows to supply chains.
The Real AI: Experience + Common Sense
Ironically, the best solution to a high-tech problem is sometimes a few lines of SQL or a well-labeled metadata field.
In our IDP case, we didn’t need to build a model to classify 1,000+ forms. We just needed to record where the document came from.
This approach was:
- Faster to build
- Cheaper to maintain
- Easier to scale
- More accurate in production
Final Thoughts: Simplicity Is a Superpower
Automation is valuable—but only when built on a foundation of clear thinking and strong design.
If your team is struggling with automation, AI models, or complicated workflows, stop and ask:
What’s the real problem we’re solving?
Are we designing for automation—or cleaning up a mess?
Experience isn’t just solving problems. It’s knowing which problems shouldn’t exist at all.
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