Beyond the Org Chart: How AI Could Change Companies
Frontwise examines how AI-native organizations may shift from fixed job structures to task-driven teams. In this three-minute video, The Ari Lens distills Zeng Ming’s conversation with Zhang Xiaojun into two ideas—task-driven organization and Org-as-Model—and asks what they mean for investment professionals, research teams and operating leaders.
Key takeaways
- Start with the task, then assemble the right human and AI capabilities around it.
- An Org-as-Model learns from shared outcomes so experience can travel across teams.
- In a more fluid organization, human value shifts toward agency, visible contribution and sound judgment in context.
Edited transcript
Edited transcript of the published English narration. Proper names, terminology, capitalization, punctuation and paragraphing have been corrected; the argument has not been rewritten. Quotations attributed in the video should be checked against the original interview before being reused independently.
In the AI era, the job itself may stop being the basic unit of work. People remain, the work remains, but organization could change completely.
Zeng Ming explored this possibility in an interview with Zhang Xiaojun. Two ideas help explain it: task-driven organization and Org-as-Model.
Picture a restaurant. Everyone sticks to their station. AI chops faster, yet plates still pile up at the serving window. Task-driven organization starts with the order. Then it brings together the right people and AI. The task comes first; capabilities follow.
After the meal, feedback helps people and AI revise the recipe and their teamwork. Next time, everyone can use that experience. We call this Org-as-Model, or OaM. Think of a living recipe book: the whole team updates it, and the organization itself learns.
Task-driven organization explains how we team up. Org-as-Model explains how we learn together. Together, they sketch an AI-native organization.
Jobs, departments and hierarchies could all shift. As Zeng Ming puts it, in the future, whoever has the strongest capability gets called on. Tasks are posted. Capable people step forward and deliver. Instead of one fixed box, a person can contribute across a network of teams.
How do we adapt?
First, high agency. Take initiative. Find problems. Learn and experiment. Keep developing your capabilities instead of waiting for instructions.
Second, make contributions visible. Show what you took on, what you delivered and what changed. That makes capability easier to find.
Third, context, not control. Share the map, destination and constraints—not just directions. People and AI can then make better judgments.
When capabilities and experience flow across teams, company boundaries may loosen. Could organizations become networks that constantly regroup and learn?
Zeng Ming warns: “Before a great wave, no one is safe. Those who feel safest are most at risk.” Today’s position cannot guarantee tomorrow’s security. Keep learning. Put ideas into practice. Turn experience into shared capability.
As organizations keep changing, are we ready to relearn how to work together?