AI Workflows That Combat Work Slop: For Technical Leaders

Sadie St. Lawrence is the Founder & CEO of the Human Machine Collaboration Institute (HMCI) and author of Becoming an AI Orchestrator (Pearson). She served on the White House Council on Equitable Data and AI Training, founded Women in Data (a global community of 80,000+ members across 150 countries), and has been recognized as a DataIQ Top 100 Data & AI Influencer, a Sacramento Business Journal 40 Under 40 honoree, and an NVIDIA Inception startup founder.

In this session, Sadie breaks down what it really takes to move from using AI tools to orchestrating AI teammates. 2025 was the marketing year for agentic AI. 2026 is the operational year, and most organizations are discovering the hard way that output volume isn't the same as value. Documents look polished, decks are sharp, code runs clean, and yet strategic alignment quietly disappears underneath the gloss. The bottleneck has shifted from model choice to context.

This talk is built for technical leaders looking to scale AI adoption across their organizations. Attendees will leave with a clear diagnostic for the "work slop" problem and a practical framework they can apply to their own AI workflows and teams the next day.

What to expect:

• The shift from shipping models to shipping systems that act
• Context engineering as the load-bearing discipline of agentic AI
• How automation bias and fluency bias produce "work slop"
• The four-point checkpoint framework (Context, Strategy, Copy, Design)
• A field-tested view from inside HMCI's own transition to agentic tooling