When Your AI is Quietly Failing
Inside: The checklist to rescue your AI projects that are silently failing. Find out the systematic process to make these failures visible and measurable - so you can improve these systems continously
Few months ago I meet a VP of Engineering of a SaaS Startup building document processign solutions. He said, “We shipped our AI six months ago, my team spends most of the time fire-fighting issues - I am not sure we know what’s wrong with it.”
They had no measurement infrastructure. No test cases. No way to trace decisions. They’d celebrated the launch, moved the team to the next project, and now they are spending most time resolving issues - patching, and fixing.
He’s not alone. I have similar discussions with many companies who shipped thier AI features under pressur - from boards, from investors, from competition.
I keep seeing this pattern
Three times in the last quarter, I’ve worked with organizations dealing with the same crisis:
Scenario 1: This company deployed document processing AI to 10 enterprise customers. Six months in, their largest customer threatened to cancel. The AI was extracting wrong data from contracts. The team had no way to see why.
Scenario 2: A fintech launched an insurance documentation assistant. Their customers complained it was “getting slower” and “less accurate.” Thier team couldn’t verify either claim - they’d never established baselines.
Scenario 3: A retail bank deployed a chatbot handling internal claim disputes. Support tickets about “wrong AI answers” were climbing. Nobody could trace which policy the AI used or why it made specific decisions.
Here’s the common thread I noticed?
All three had deployed without building evaluation infrastructure.
They’d built the AI, but not the system to know if the AI was working.
What Actually Happens in These Meetings
I sit in a conference room with engineering, product, and business leaders. I ask:
“What’s your accuracy?”
“X%”
“What’s it costing per query?”
“Infrastructure costs are $X, but we don’t track per-query.”
“Do you have test cases?”
“We tested it before launch...”
“Can you show me traces of what went wrong?”
Silence.
This is what I call Evaluation Debt.
You deployed a system without building the measurement infrastructure to operate it. And now you’re paying interest - in firefighting, guessing, and eroding stakeholder trust.
The Recovery Framework I Use
Here’s what most advice gets wrong: it assumes you’re starting from scratch. But you’re not. Many AI POCs mde it to production - just that they were not production-ready. They need a recovery framework, not a startup guide.
I’ve developed a four-phase Recovery Pathway that rescues these systems without rebuilding from scratch:
Define what success should have been
Build measurement infrastructure retroactively
Diagnose with data, not guesses
Fix and validate systematically
That SaaS company? Itook them from 73% accuracy and customer threats to 96% accuracy and $1.4 million in annual savings.
Not by switching models.
By implementing evaluation infrastructure and working systematically.
Why This Matters Now
According to recent IDC research, enterprises are collectively spending $154 billion on AI initiatives in 2024. But McKinsey data shows that only 11% of organizations have achieved significant financial returns from their AI investments.
The gap between investment and return isn’t a capability problem.
It’s a measurement problem.
You can’t fix what you can’t measure.
And you can’t defend an investment you can’t prove is working.
Watch the Recovery Pathway Framework Video
I just recorded a complete walkthrough of the Recovery Pathway framework. -including a real case study where we rescued a failing AI system.
👉 I will be bringing more practical content like this - please subscribe to my channel if you want to stay updated. I share clips and short formats so you can learn something new everyday.
You’ll see:
The exact diagnostic process that reveals where failures are happening
How to implement tracing retroactively without rebuilding
The week-by-week action plan to go from crisis to recovery
Real numbers: $4.20 per document down to $1.80, 47 complaints per month down to 3
This isn’t theory. This is the actual process I use when organizations call me to rescue production AI systems.
If you’re dealing with an AI system that shipped but isn’t delivering the value you promised - this framework will show you the way out.
And if you know someone firefighting a struggling AI deployment, share this with them. Recovery is possible. But it requires working backward with discipline.
👉 BONUS: I’ve created a Recovery Pathway checklist with the workshop agenda, tracing implementation guide, and diagnostic framework.
Get in touch if you have questions.
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