“When a PoC works, leadership assumes that the hard part is done.”
Getting the model to work is not the same thing as building a system your organization can actually operate, and that misunderstanding is probably responsible for a meaningful percentage of failed enterprise AI projects.
In today’s webinar, AI engineer and Udacity mentor Thiago Grabe unpacked the gap between impressive demos and production-ready AI systems, including:
✅ Why most enterprises should start with workflow-first AI instead of multi-agent autonomy
✅ The operational problems that show up after launch: runaway costs, observability gaps, latency, and governance failures
✅ Why organizational alignment is becoming just as important as model quality
This is a practical conversation for teams moving from AI experimentation to real deployment.
Watch the full recording here 📽️ bit.ly/3R4BxNj
English
