Enactive AI Framework for Enterprise Decision-Making
Key takeaways
- Enactive AI is a decision-centric framework for deploying AI in complex enterprise systems.
- It integrates strategic organizational logic with operational site-level execution.
- The framework emphasizes reliability, feasibility, resilience, and responsibility in AI deployment.
- It shifts AI focus from model capability to system-aware, governable action.
Who benefits
Summary
Enactive AI is a new conceptual framework that integrates AI tools and agents into complex enterprise and industrial systems, prioritizing decision intelligence and system-aware action. It organizes AI deployment around four roles: Organizational World, Site World, Schema Intelligence, and Enactive Decision Cycle, aiming for reliable, governable, and socially valuable AI.
Why it matters
Enactive AI provides a strategic framework for professionals to design, deploy, and govern AI systems in complex organizational settings, ensuring reliability, accountability, and alignment with business objectives.
How to implement this in your domain
- 1Assess current AI initiatives against the Enactive AI framework to identify gaps in decision-centric integration and governance.
- 2Define "Organizational World" and "Site World" models for specific enterprise operations to structure AI deployment.
- 3Develop "Schema Intelligence" mechanisms to effectively link various AI applications and data sources across different operational levels.
- 4Implement an "Enactive Decision Cycle" for continuous feedback, learning, and auditing of AI-driven decisions.
- 5Prioritize AI projects that focus on supporting consequential actions and ensuring responsible governance within complex systems.
Original post by Zuojun Max Shen, Yuan Qu, Pujun Zhang, Anbang Liu, Yunhao Liang
"arXiv:2608.03413v1 Announce Type: new Abstract: As artificial intelligence (AI) continues to evolve and mature, recent AI practices have moved beyond large language models (LLMs) and text or image generation tasks, increasingly integrating tools, agents, and harnesses to solve re…"
View on XOriginally posted by Zuojun Max Shen, Yuan Qu, Pujun Zhang, Anbang Liu, Yunhao Liang on X · view source
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