Hard-Earned Lessons from AI's Front Lines, with Melissa Tondi
Show notes
Melissa Tondi is an agentic AI and quality engineering leader with deep experience in process engineering across the software development lifecycle. She has deployed AI at scale inside real organizations, and in this conversation we dig into the traps companies fall into and how leaders can use proven change management to avoid them.
We start with why AI is an operating model change, not a tool rollout, and why buying licenses is the easy part. Melissa shares a candid story about a company-wide adoption metric that measured activity instead of value, and how it sparked fear, shadow AI, and eroded trust.
We then get into what worked instead: letting department leaders own their outcome metrics, one-on-one conversations with respected detractors, and an AI ambassador program built on peer credibility.
From there, we make AI governance concrete, covering approved tools, data classification, human review by risk level, and customer disclosure. We close with agentic engineering, including how a risk-tiered approach to testing and autonomy keeps quality built in rather than inspected at the end.
If you're a leader trying to move your organization from tossing out AI licenses and hoping for the best to generating real results, this one is for you.
🔗 Episode Links:
Melissa Tondi on LinkedIn: https://www.linkedin.com/in/melissa-tondi-186551/?isSelfProfile=false
🔖 Chapters:
00:00 - Intro
00:52 - Introduction to AI and Operating Models
03:37 - Common Traps in AI Rollouts
12:43 - Navigating AI Mistrust and Detractors in Organizations
28:54 - Building an Ambassador Program for AI Engagement
36:44 - Establishing Governance in AI Implementation
42:03 - Understanding Agentic Engineering and Its Impact
54:02 - Balancing Quality and Flexibility in Software Development