Schema Happens: How to Keep Your AI Output From Breaking Everything
Enforce structured, machine-readable outputs from LLMs, without sacrificing flexibility or retraining existing systems.
Elvie wasn’t new to managing operational complexity. As fictionnal director of plant operations at MotoFacture, a fictional mid-sized electric bike parts manufacturer, she had a reputation for squeezing inefficiencies out of every process (whether it was reworking floor layouts, coordinating supplier handoffs, or optimizing repair windows on the factory floor). She believed in smart systems, measurable outcomes, and giving her teams tools that didn’t just automate work, but also elevated it.
So when she greenlit an AI-powered assistant to help her maintenance technicians summarize machine health reports, it wasn’t a leap of faith; it was a calculated bet. MotoFacture’s high-precision milling machines and robotic welding arms produced millions of dollars in components each quarter. A single unplanned maintenance delay could throw off inventory forecasts and jeopardize contracts. Elvie’s goal was simple: give her techs a faster way to read through raw sensor logs, spot early signs of mechanical stress, and schedule preventative maintenance before something went wrong.
The AI system, powered by a large language model (LLM), seemed like a perfect fit. It could read messy logs, interpret temperature trends, and explain vibration anomalies in natural language summaries. Her technicians (some of whom weren’t fluent in statistical software) found the conversational tone helpful. The model could even highlight likely failure points, and suggest next steps. For a few weeks, the experiment looked like a win.
Then the customer complaints started rolling in.
Look Beneath the Surface for What Broke Down
One of MotoFacture’s key accounts, SpeedGroove Retailers, had a high-volume order delayed by nearly three days. A spindle motor on the main assembly line had shown abnormal torque fluctuations for over a week, but the repair wasn’t scheduled. Elvie’s AI assistant had picked it up, but the summary it generated never got processed by the automated scheduling system. Why? Because the LLM’s output didn’t follow the required JavaScript Object Notation (JSON) format.
In technical terms, the system expected a structured input with exact field names, like "machine_id", "alert_level", and "recommended_action". Instead, the LLM wrote a grammatically correct sentence like, “Looks like motor unit 4412 might need a tune-up soon—spinning faster than expected!” A human could understand it; the scheduling software couldn’t.
That breakdown exposed a deeper flaw: the AI was too informal to be functional. Its summaries drifted from structure just enough to break downstream automation. And worse, there was no alarm bell. No red flag told the team, “This output is unusable.” It was just quietly skipped.
Elvie found herself back in triage mode, manually reviewing reports and rescheduling maintenance tasks after the fact. The very solution that was supposed to unlock efficiency had become a bottleneck.
Understand What’s Really at Stake
Had Elvie not dug deeper, she might’ve chalked it up to growing pains… AI’s not perfect, let’s give it more time. But she saw the broader implication: if this wasn’t addressed, the consequences wouldn’t just be operational; they’d also be strategic.
Internally, her team was losing faith in the system. Tech teams were spending more time double-checking the AI’s work than doing their own. Supervisors were second-guessing outputs they couldn’t audit. And her team’s morale was slipping; nothing kills enthusiasm faster than a “helpful” tool that creates more work than it saves.
Externally, MotoFacture’s reliability was on the line. SpeedGroove’s delay triggered penalty fees and forced the client to scramble to cover their own gaps. Another missed delivery could be the excuse they needed to trial a competitor. Elvie knew trust like that isn’t rebuilt with an apology; it requires systemic accountability.
The broader concern was existential: as MotoFacture scaled operations and adopted more automation, would its AI integrations be robust enough to keep up? Or would fragile, freeform outputs quietly sabotage workflows and customer relationships alike?
In Elvie’s mind, the answer was clear: if you can’t trust the format, you can’t trust the AI. And if you can’t trust the AI, it doesn’t belong in a critical path. Something had to change—and fast.
Curious about what happened next? Learn how Elvie applied a recently published AI research (from Amazon), reframed the problem (then solved for scale), and achieved meaningful business outcomes.