Tracy Lee
CEO of This Dot Labs, Technologist, Google Developer Expert, Microsoft MVP
Atlanta, Georgia, United States
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Tracy Lee is the CEO at This Dot Labs, where she helps organizations turn ambitious ideas into products and business capabilities. She is a GitHub Star, Google Developer Expert, Microsoft MVP, RxJS Core Team member, and frequent international keynote speaker. She is also a longtime open source leader, community builder, and co-host of the Modern Web Podcast.
Today, Tracy is exploring what becomes possible when AI gives business leaders the power to build. She has redesigned operational workflows using ChatGPT Work, reusable skills, and automation, and making previously impractical projects possible. Her work brings business, product, and engineering together to establish new ways of working and create measurable value with AI.
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I let AI have my job
Every operator carries an invisible system in their head: what to check, who to ask, which details matter, how to recognize risk, and what needs to happen next.
I began making that system explicit. I mapped my recurring work, captured the judgment inside it, and used ChatGPT to build reusable workflows, skills, and automations. The result is an AI operating layer that helps me execute across the business and has removed at least $100,000 in spend.
This session will show the process behind that transformation. I will walk through real examples, explain how each workflow progressed from manual execution to reliable automation, and share the controls that keep the system useful and trustworthy.
Attendees will learn how to uncover the operating logic hidden inside a role and transform it into a scalable system.
How I Automated $100,000 in Operations Spend with ChatGPT
Operational work accumulates quietly. A recurring report, a research request, a status update, or a process that lives inside one person’s head may seem small. Together, these workflows consume meaningful time and money.
I began treating every repeated piece of work as a system that could be automated. Using ChatGPT work, reusable skills, connected tools, and scheduled automations, I built an AI operating system around my role. These workflows now complete recurring work, prepare decisions, create deliverables, and keep important processes moving. So far, they have removed at least $100,000 in operating spend.
In this session, I will "live code" and show what I did. We will examine real workflows before and after automation, how I captured the context and judgment each process required, where I added human review, and how I measured the financial value.
Finding the Business Problems Worth Solving
This session gives developers a practical method for moving from “What can I build?” to “Where can I create the most value?”
The greatest AI opportunities often hide inside ordinary work: repeated decisions, manual handoffs, scattered information, and processes that depend on knowledge held by one person.
In this talk, I "live code" and share a business workflow and show how I approach it as an executive using AI. We will discuss how an engineer can observe the work and find the points where AI can create leverage.
From there, how do we turn those insights into concrete possibilities, including automation, reusable AI skills, decision support, and fully redesigned workflows.
Attendees will learn how to collaborate with business partners, evaluate workflows, prioritize opportunities, and connect technical work to measurable outcomes.
Engineers, Your Role Just Got Bigger
AI is giving every team new capabilities. People can analyze information, create prototypes, automate tasks, and build solutions for themselves. Many feel more powerful than ever.
For engineers, that same shift can feel destabilizing. A leader recently told me that employee NPS was climbing across the company while falling dramatically within engineering. His engineers were asking a difficult question: if producing code becomes faster and more accessible, where do I create value?
The answer is an expanded role.
The industry moved from specialized frontend and backend roles toward full stack engineering. AI is driving the next evolution: the product engineer. Product engineers understand users, business models, workflows, constraints, and desired outcomes. They find the right problems, determine where technology can create leverage, and remain accountable for whether the solution works.
This talk will show developers how to evaluate real workflows alongside business partners, recognize valuable opportunities, and transform technical possibility into measurable impact. Your new stack includes the customer, the workflow, the product, and the business.
AI Made Everyone a Builder. What Are Engineers For Now?
A company leader recently shared a surprising employee NPS pattern with me. Scores were rising across the organization while engineering’s score fell sharply. AI gave people in marketing, operations, sales, and leadership the ability to create things they had never imagined building themselves. Engineers were left questioning how their role would evolve.
This talk explores the next evolution of engineering: the product engineer. Product engineers combine technical ability with customer understanding, workflow analysis, business judgment, and ownership of outcomes. They help teams discover valuable possibilities, determine where AI can create leverage, and turn experiments into capabilities the organization can trust.
Attendees will leave with a practical framework for expanding their impact, collaborating directly with business teams, and measuring their contribution through adoption and business results.
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