An AI model without a feedback loop isn’t innovation—it’s entropy in slow motion. An AI model without a feedback loop isn’t innovation—it’s entropy in slow motion. An AI model without a feedback loop isn’t innovation—it’s entropy in slow motion. “It worked in the demo.” “It worked in the demo.” That sentence haunted me for months. The first AI initiative I led was supposed to reduce 40% of our service desk tickets. We trained a model, built a slick UI, and hit our go-live deadline with a sense of premature pride. Two weeks in, support teams had abandoned it. Users were confused. Confidence tanked. What happened next? The AI agent was quietly decommissioned. The AI agent was quietly decommissioned. No lessons documented. No lessons documented. No retrospectives. No retrospectives. Just one more experiment lost to the corporate AI graveyard. Just one more experiment lost to the corporate AI graveyard. That was project one. Project two? Same result. Project three? A bit better — but still not in production. It took me three failed launches to see it clearly: The problem wasn’t the model. The problem was the mindset. The problem wasn’t the model. The problem was the mindset. The problem wasn’t the model. The problem was the mindset. The Hidden Trap in Enterprise AI The Hidden Trap in Enterprise AI Most AI projects die in one of three ways: Death by isolation – where the tech team builds in a vacuum, far from users.
Death by overpromise – where business stakeholders expect a magic black box.
Death by drift – where no one maintains the model post-deployment. Death by isolation – where the tech team builds in a vacuum, far from users. Death by isolation Death by overpromise – where business stakeholders expect a magic black box. Death by overpromise Death by drift – where no one maintains the model post-deployment. Death by drift We thought having clean data, a flashy interface, and a strong business case was enough. But we were missing one crucial thing: AI isn’t a prototype. It’s a product. And products require ecosystems, not just engines. AI isn’t a prototype. It’s a product. And products require ecosystems, not just engines. The Turning Point he Turning Point My fourth project had all the usual signs of impending failure. We were building an internal AI assistant for a healthcare enterprise—designed to help doctors summarize patient records and retrieve compliance policies. The model was technically sound. The interface was clean. The sandbox tests passed. But this time, I did one thing differently: I brought in a product manager. I brought in a product manager. I brought in a product manager. Not a project manager. Not a data scientist. A real product thinker who challenged everything: A real product thinker who challenged everything: What’s the real user need?
What happens if the model is 80% right?
Who owns this product six months from now? What’s the real user need? What’s the real user need? What happens if the model is 80% right? What happens if the model is 80% right? Who owns this product six months from now? Who owns this product six months from now? Building Like It’s Meant to Live Building Like It’s Meant to Live Once we switched gears, the project started to breathe. We made critical changes: Once we switched gears, the project started to breathe. Experience-Led Design We stopped optimizing for the “cool demo” and instead shadowed real users—doctors, nurses, administrators. One insight changed everything: Doctors weren’t struggling to_find_ information. They were struggling to trust it. trust So we redesigned the interface to highlight source citations next to every response. Confidence went up. Adoption spiked. source citations Expertise-Driven Curation Early on, we’d let junior analysts tag data. Now, we involved compliance officers and clinicians. Our accuracy jumped from 68% to 91%—because context matters more than compute. Trustworthiness at the Core We added: Content provenance tracking
Disclaimers for AI-suggested content
A feedback loop where users could rate responses Content provenance tracking Disclaimers for AI-suggested content A feedback loop where users could rate responses We even made it okay for the model to say: “I don’t know.” That honesty made people trust it more. “I don’t know.” What Happened Next What Happened Next Six months later, the AI assistant wasn’t just live - it was part of the workflow. Six months later, the AI assistant wasn’t just live - it was part of the workflow. 36% of record reviews were now fully automated
21% reduction in compliance errors
Over 80% positive user satisfaction
It had become an evolving product, not a one-time deployment. 36% of record reviews were now fully automated 21% reduction in compliance errors Over 80% positive user satisfaction It had become an evolving product, not a one-time deployment. My Personal Playbook for AI Projects That Work My Personal Playbook for AI Projects That Work If I could go back and advise my past self, I’d say this: AI is not a sprint. It’s a subscription.
Users matter more than models. Design for trust, not just accuracy.
You need a feedback loop. If the system isn’t learning post-launch, you’ve built a fossil.
Start small, scale wisely. Win trust with one reliable task. Then expand. AI is not a sprint. It’s a subscription. AI is not a sprint. Users matter more than models. Design for trust, not just accuracy. Users matter more than models. You need a feedback loop. If the system isn’t learning post-launch, you’ve built a fossil. You need a feedback loop. Start small, scale wisely. Win trust with one reliable task. Then expand. Start small, scale wisely. Final Thought Final Thought AI isn’t a moonshot anymore. It’s a muscle. And like any muscle, it grows when trained consistently, not sporadically. AI isn’t a moonshot anymore. It’s a muscle. And like any muscle, it grows when trained consistently, not sporadically. AI isn’t a moonshot anymore. It’s a muscle. And like any muscle, it grows when trained consistently, not sporadically. So if you’re tired of watching AI prototypes crash and burn, stop treating them like one-off experiments. Build like it’s meant to live. That’s the one thing I changed - and it changed everything. Build like it’s meant to live.

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