Summary: A JSON error message may seem like the driest thing a marketer or developer could encounter. Yet it reveals volumes about messaging, interface design, automation, user experience, and the breakdown that happens when raw logic meets human expectation. Today, we dissect one such message—seemingly irrelevant outside of programming—but full of lessons on clarity, friction, trust, and product success.
What Does the Message Actually Say?
At its surface, the statement reads: "Unfortunately, the provided text does not contain a story or narrative that can be extracted and rewritten. The text appears to be a JSON error response, which typically does not have a story or main narrative. The text simply conveys an error message related to an insufficient account balance. There is no story or narrative to extract and rewrite in this case."
This is not storytelling. It's not meant to evoke emotion. It’s technical, defensive, precise. But underneath, it's a symptom. It points to something missing. Whether in user expectations, product design, or marketing communication—someone expected a story where there was only structure. That gap is your real opportunity.
Where Is the Breakdown Happening?
When an AI, bot, or software returns a message like the one above, it’s often triggered by input it couldn’t frame in the way the user intended. That implies:
- The user expected contextual intelligence—something smarter than literal parsing.
- The system wasn’t trained or prompted correctly to handle or reframe error content.
- The communication between human intent and machine understanding broke down.
Mirroring this idea: So the system only read "JSON error," but the user was looking for a deeper interpretation?
Exactly. This misalignment is common—especially in customer-facing automations. The challenge isn’t just fixing the backend or training the system better. It’s about asking: Why did the user believe there was a story? What story were they hoping to receive?
Information vs. Emotion: Who’s This For?
Let’s be blunt: Systems are good at precision, but they rarely check how their precision lands emotionally. The user—in this case, probably non-technical—entered content, expecting insight.
They didn’t know, or didn’t care, that it was “just a JSON error.” All they saw was: something went wrong and the system refused to help further.
So what happens next? Frustration. Abandonment. Lost trust. And if it’s part of your brand? Lost revenue.
The Power of Frictionless Apologies
A flat message like the one above compounds failure. Not only did the system not deliver a story—it didn’t even attempt to bridge the user's disappointment. Smart UX writers and product marketers know that even an error can reinforce trust. How?
- Apologize for the confusion—even if it’s not a fault.
- Offer the user a next step. “It looks like this was a technical error. Would you like help understanding this message?”
- Let them say “No.” Give control back. Sometimes reducing frustration is as simple as offering a painless exit.
This small shift can mean the difference between “this didn’t work” and “this still helped me.”
Precision Isn’t Enough—Interpretation Wins
Now let’s go back to the original failure: the user pasted in a message, and the AI couldn’t identify a story. But contextually? It is a story. Just not the story the system was programmed to recognize.
There’s a customer who tried something. They hit a wall. The bot told them: "Nothing here." But that user had a question buried in their action—their curiosity was the real input, not just the text. Ignoring that is more than bad interface—it’s bad marketing.
Authority comes from understanding, not just correctness. If your product “technically works” but doesn’t feel useful, you’ll start losing buy-in. That’s how companies die slowly—silent churn, not loud failure. So, how are you building safety nets around mechanical thinking in human workflows?
The Balance of Logic and Empathy
Let’s say your marketing automation or AI support is generating similar error messages. Do they just throw up their hands when unstructured content arrives? Or do they respond like humans trained in negotiation?
Because not everyone wants a perfect outcome—they want to feel heard.
That’s where Chris Voss’s tactics matter. Instead of stopping at “you’re wrong,” say: “It seems like you're trying to get more context from this error. What about this response wasn't helpful?”
Use silence meaningfully. Give the user room to rephrase or pivot. Every “No” they say is a boundary worth respecting and a step deeper into dialogue.
So Why Do These Dry Failures Keep Happening?
Most teams building AI, bots, or automated systems are optimizing for technical correctness. Fair. They rarely collaborate deeply with marketers, UX copywriters, or support teams who understand emotional expectation. This is a structural flaw, not an individual failure.
And it's worse in small companies, where product and marketing rarely sit at the same table. The cost? Invisible. Yet real.
Fixing it doesn’t require a product overhaul. It starts by asking: “What did the user think they were doing?” Then: “How did our system make them feel wrong for trying?”
Rethinking the Purpose of Error Responses
The truth? An error message isn’t just a diagnostic tool. It’s a piece of microcopy. A moment of brand voice. A signal to your customer about whether you’re worth their trust. Its job isn’t just to report failure—it’s to build resilience and keep them engaged.
If your system talks like the original JSON message—factual, cold, dismissive—then customers will quietly disconnect. Replacing these moments with curiosity, empathy, and clarity is a competitive advantage.
So here’s the challenge: What do your systems say when something goes wrong? Do they defend the architecture... or defend the user?
What To Do Differently—Starting Now
Start with three simple tests:
- Audit your error messages. Read every automated reply from the user's point of view. What are they likely feeling?
- Inject strategic empathy. Don't over-apologize or patronize—but acknowledge intent. Use mirroring to reflect what the user might be doing: “It looks like you were expecting this text to contain a narrative…”
- Rebuild message logic. Instead of dead-ends, offer path options: reinvoke differently, talk to support, or try a human summary.
You don’t need thousands of hours or a new product roadmap. You need better context logic, smarter text prompts, and people in the room who build for human expectations, not system inputs alone.
Because the story isn’t in the data—it’s in the moment when someone hoped your system would behave like a partner, not a parser. That’s where trust lives. And that’s what turns customers into believers.
#UXDesign #ErrorMessaging #ProductMarketing #CustomerTrust #EmpathyInAutomation #MarketingClarity #HumanCenteredAI
Featured Image courtesy of Unsplash and Brett Jordan (0JO6jgWShUA)