What happens when companies move so fast to adopt AI that they forget everything they already learned about building technology safely?

In this special edition of Signal & Noise Live at AI Con, Signal & Noise Executive Voice contributor Lucas Longacre, Head of Product at Inlightened, sits down with Ken Johnston, co-founder of the AI Governance Operations Foundation (AI GovOps), for a candid conversation about what it really takes to deploy and scale AI inside an organization.

Ken argues that the rush to embrace AI has created a massive case of enterprise FOMO. Companies feel enormous pressure to demonstrate that they're "doing AI," but in the process, many are abandoning fundamentals that took decades of software engineering to establish: observability, testing, CI/CD, security, rollback capabilities, cost controls, and disciplined product development.

Lucas and Ken dig into the rise of what Ken calls "demo theater" — where an impressive AI prototype can be created in hours and appear 90% finished, even though it may represent less than 10% of the work required to turn it into a secure, scalable production system.

They also explore:

• Why enterprises need to bring DevOps, DevSecOps and FinOps discipline into AI

• How AI can dramatically increase the "blast radius" of software failures

• Why observability may be one of the most important — and overlooked — components of enterprise AI

• The difference between an impressive AI demo and a production-ready product

• Why companies should build AI projects around learning loops, not just deployment

• How natural-language interfaces could finally replace dashboards with direct answers to business questions

• Why AI-generated code is creating entirely new challenges for software development and code review

• The opportunities — and dangers — created by vibe coding and the democratization of software development

• How Lucas is using AI inside product development while controlling security, token usage and access to enterprise data

• Why human experience, judgment and taste remain so important when working with increasingly capable AI systems

It's a fascinating, funny and highly practical conversation about moving fast with AI — without forgetting everything we learned before AI arrived.