Why most AI products fail: Lessons from 50+ AI deployments at OpenAI, Google & Amazon

When you start small, it forces you to think about what is the problem that I'm going to solve. In all this advancements of the AI, one easy, slippery slope is to keep thinking about complexities of the solution and forget the problem that you're trying to solve.

Every time you hand over decision-making capabilities to agentic systems, you're kind of relinquishing some amount of control on your end.

To replace any critical workflow or to build something that can give you significant ROI, it easily takes four to six months of work, even if you have the best data layer and infrastructure layer.

Persistence is extremely valuable. Successful companies right now building in any new area, they are going through the pain of learning this, implementing this and understanding what works and what doesn't work. Pain is the new moat.

Building is really cheap today. Design is more expensive, really thinking about your product, what you're going to build. Is it going to really solve a pain point? Is what is way more valuable today?

Most of the times, if you're obsessed with the problem itself and you understand your workflows very well, you will know how to improve your agents over time instead of just slapping an agent and assuming that it'll work from day one.

80% of so called AI engineers, AIPMs spend their time actually understanding their workflows very well. They're not building the fanciest and the most cool models or workflows around it. They're actually in the weeds understanding their customer's behavior and data.