In this episode of the Shift AI Podcast, Cynthia Tee, former CTO of Smartsheet, joins host Boaz Ashkenazy for a wide-ranging conversation on what it really takes to integrate AI at enterprise scale responsibly, securely, and in a way that earns lasting customer trust.
Cynthia shares her unconventional journey from growing up in Manila and working her first job at a library at age 12, to earning a computer science degree from MIT, building her career at Microsoft, running Ada Developers Academy, and ultimately leading engineering at Smartsheet through one of its most consequential chapters, including the company's transition from public to private and the rollout of its first generation of AI-powered features.
The conversation dives deep into how Smartsheet approached AI integration: using generative AI to simplify formula generation and data visualization, being deliberate about what information was and wasn't sent to LLMs, and communicating transparently with enterprise customers who needed to trust the system before they would adopt it. Cynthia explains why trust, governance, and data classification aren't afterthoughts, they're the foundation that makes AI deployment possible at scale.
Boaz and Cynthia explore the emerging role of MCP in connecting LLMs like Claude to applications like Smartsheet, translating user intent into real-world action across complex workflows. They also get into what SaaS executives often underestimate: that shipping AI features is the easy part, and evolving the rest of the organization, pricing, enablement, customer support, and role definitions is where companies get stuck.
The discussion turns to the next generation of workers and the genuine tension young people face between learning a craft and leaning on AI to accelerate it. Cynthia shares a perspective on hustle, curiosity, and what it looks like when someone who's never written a line of code builds an inventory system for vintage clothing because a tool like Claude made it possible.
This episode is essential listening for CTOs, engineering leaders, and product executives who want to understand what responsible AI deployment actually looks like inside a company operating at scale.
