An AI model without a feedback loop is not modern – its entropy in slow motion.
“It worked with the demo.”
That sentence has haunted me for months.
The first AI initiative I led was to reduce 40% of our service desk tickets. We trained a model, built a smooth UI, and hit our go-live deadline with a feeling of premature pride. Two weeks in, the support teams left it. Users are confused. The confidence that has been in the scene.
What happened next?
The AI agent is silent -decommission.
No lessons are documented.
There are no retrospectives.
There is only one experiment lost to the corporate AI Graveyard.
That's the project.
Project two? Both results.
Three of the project? It's a bit better – but still not doing.
I take three failed launch to see it clearly:
The problem is not the model. The problem is the mindset.
The hidden trap in Enterprise AI
Most AI projects die in one of three ways:
- Death by separation – where the tech team builds in a vacuum, far from users.
- Death by excessively – where business stakeholders expect a magic black box.
- Death by drifting -When no one maintains a post-deployment model.
We thought that having clean data, a sticky interface, and a strong business case was enough. But we're missing something important:
AI is not a prototype. This is a product. And products require ecosystems, not just machines.
THe turned away
My fourth project has all the usual signs of upcoming frustration. We are building an internal AI assistant for a health business – designed to help doctors summarize patient records and obtain compliance policies.
The model is technically sound.
The interface is clean.
Sandbox tests have passed.
But this time, I did something differently with something:
I brought a product manager.
Not a project manager. Not a data scientist. A truly thought of the product that challenged everything:
- What does a real user need?
- What happens if the model is 80% correct?
- Who owns this product for six months from now?
Building like it is meant to live
As we moved the gears, the project began to breathe. We have made critical changes:
Design led by experience
We stopped at the “cool demo” and instead shadowed the real users – doctors, nurses, administrators. One perspective has changed everything:
Doctors do not have difficulty with_find_ information. They are having a hard time Trust This
So we redesign the interface to i -Highlight Citations Next to each response. Confidence climbs. Spiked by adoption.
Curation driven by expertise
Early on, we let the data of junior analysts analysts. Today, we are involved in compliance officials and clinics. Our accuracy jumps from 68% to 91% – because contexts are more than compute.
Being trustworthy on the core
We added:
- Monitoring Content Content
- Declines for AI-Guggested Content
- A feedback loop where users can relate responses
We even made it okay for the model to say: “I don't know.” That loyalty was what trusted people.
What happened next
Six months later, the AI's assistant was not just live – it was part of the workflow.
- 36% of record reviews is fully automatic
- 21% reduction in compliance errors
- More than 80% positive user satisfaction
- It has become an emerging product, not once expansion.
My personal playbook for AI projects that work
If I can go back and advise my past self, I will say it:
- AI is not a sprint. This is a subscription.
- Users are more than just models. Design for trust, not just accuracy.
- You need a feedback loop. If the system does not learn post-launch, you have built a fossil.
- Start small, scale wisely. Win confidence in a reliable task. Then expand.
Conclusion
Ai is no longer a moonshot. This is a muscle. And like any muscle, it grows when trained continuously, not sporadically.
So if you're tired of watching crashes and burning AI prototyps, stop treating them like one-off experiments. Build as it is meant to survive. That's one thing I changed – and it changed everything.
