Trust in the Machine: Leading People Through AI Fear and Change
Proposed session for SQLBits 2026TL; DR
As AI transforms how we code, collaborate, and make decisions, the greatest challenge isn’t technical—it’s human. In this session, Dr. Davis McAlister, a leadership and AI ethics expert featured on Ticker News, explores how tech leaders can navigate fear, skepticism, and misinformation surrounding AI. You’ll learn how to communicate with empathy, build credibility, and guide your teams and users through change with transparency and trust. Attendees will leave with practical frameworks for bridging the gap between human concerns and technological progress.
Session Details
As AI transforms how we code, collaborate, and make decisions, the greatest challenge isn’t technical—it’s human. In this session, Dr. Davis McAlister, a leadership and AI ethics expert featured on Ticker News, explores how tech leaders can navigate fear, skepticism, and misinformation surrounding AI. You’ll learn how to communicate with empathy, build credibility, and guide your teams and users through change with transparency and trust. Attendees will leave with practical frameworks for bridging the gap between human concerns and technological progress.
3 things you'll get out of this session
1. How to Understand AI Without the Jargon
Cut through the tech-speak and learn what AI really is—through simple, real-world analogies that make machine learning and automation easy to grasp.
2. Why Hesitation is Normal—But Risky
Unpack the common fears around AI adoption and why doing nothing could quietly cost your organization its competitive edge.
3. How Real Companies Are Winning with AI
See how everyday businesses are using small, smart AI steps to save time, improve performance, and boost team morale.
4. How to Build a Human-AI Partnership
Discover how AI can enhance—not replace—leadership by accelerating decision-making and freeing up time for strategic thinking.
Speakers
Davis McAlister's other proposed sessions for 2026
When AI Goes Bad: What Every Tech Leader Should Learn from Failure - 2026