From experiments to production: what teams get wrong

Experiments don’t fail — transitions do

Most teams don’t struggle with experimenting with AI. Prototypes are easy to build and often look promising.

The real challenge appears when teams try to move those experiments into production. What worked in isolation starts breaking under real constraints.

Production exposes hidden assumptions

In production, assumptions become liabilities. Data isn’t always clean. Inputs aren’t always predictable. Edge cases appear more often than expected.

AI systems need to be designed with these realities in mind. Otherwise, small gaps turn into recurring issues.

Reliability is a design choice

Reliable systems aren’t the result of better models alone. They’re the result of thoughtful system design.

Clear scopes, defined responsibilities, and explicit failure handling matter more than raw intelligence. Teams that prioritize these early avoid painful rewrites later.

Visibility changes behavior

When teams can see what an AI system is doing and why, they engage with it differently.

Logs, summaries, and clear signals turn automation into something understandable. Visibility reduces fear and makes improvement easier.

Scaling requires restraint

One common mistake is trying to automate too much, too quickly.

The teams that succeed are selective. They automate where value is clear and leave space for human judgment elsewhere. This restraint creates systems that scale calmly.

Building for the long term

At Sprig, we focus on helping teams make the transition from experimentation to dependable execution.

Production-ready AI isn’t about doing more. It’s about doing the right things, consistently.