Co_
Subscribe for Weekly Insights
AI visualization
Back to Insights
<<Older Newer>>
Systems Jan 6, 2026 7 min read

From Pilot to Production: Scaling AI

DZ
Dietrich Zeledon
Founder, Co_

87% of AI projects never make it past the pilot stage. The technology works. The demo impresses. Then it sits on a shelf. The problem isn't AI—it's the gap between "interesting experiment" and "running in production."

Data systems
Section 01

Why Pilots Fail to Scale

The pilot-to-production gap exists because pilots optimize for the wrong things:

  • 01.Pilots prove possibility: "Can AI do this?" But production requires reliability, edge cases, and failure modes.
  • 02.Pilots use clean data: Production data is messy, incomplete, and constantly changing.
  • 03.Pilots ignore integration: Real value requires connecting to existing systems, workflows, and people.
  • 04.Pilots skip change management: People have to actually use the thing. That's harder than building it.
System Insight_

"A pilot that works 95% of the time is a success. A production system that fails 5% of the time is a disaster. That 5% is where all the hard work lives."

// The gap between impressive demo and reliable system is larger than most teams realize.

Section 02

The Production-First Mindset

Successful AI implementations think about production from day one:

>Start with the Workflow

Don't ask "what can AI do?" Ask "what decision needs to be made, by whom, with what information, and when?" Then design backwards.

>Build for Bad Data

Your production data will be worse than your training data. Design systems that degrade gracefully, flag uncertainty, and fail safely.

>Human in the Loop

Don't automate decisions—augment them. Let AI recommend, let humans decide. Build trust gradually before expanding autonomy.

>Measure What Matters

Model accuracy is a vanity metric. Business impact is the real measure. Track decisions made, time saved, revenue generated.

Section 03

The Scaling Checklist

Before moving from pilot to production, verify these elements are in place:

  • 01.Data pipeline: Automated, monitored, with clear ownership and quality checks.
  • 02.Monitoring: Real-time performance tracking with alerts for drift and degradation.
  • 03.Fallback system: What happens when AI fails? Manual process, rule-based backup, or graceful degradation.
  • 04.User training: People who will use the system understand it, trust it, and know its limitations.
  • 05.Feedback loop: Mechanism to capture corrections and continuously improve the model.

Missing any of these? You're not ready for production. And that's okay—it's better to know now.

Ready?

Let's get your AI from pilot to production.