#4:There are no AI-native enterprises

· Source: Turing Post · Field: Business & Management — Corporate Strategy & Leadership, Operations & Process Management, Consulting & Professional Services · Depth: Intermediate, quick

Summary

The article, part of "The Org Age of AI" series, argues that no truly AI-native enterprises exist yet, primarily due to the deep-seated internal "physics" of large organizations. These companies operate as complex ecosystems with internal economies driven by budget allocations, headcount units, and cost centers, where information hoarding and functional tribalism are rational behaviors. This internal structure, optimized for a steady state, inherently resists change, making genuine AI transformation difficult. The author distinguishes between "AI in the business" (upgrading products and services) and "AI on the business" (rebuilding organizational machinery like decision-making and budget flow). While most current AI efforts focus on the "in the business" aspect, true AI-nativeness requires transforming the underlying organizational systems, which often operate on principles of illegibility. The article also introduces the concept of an "agent version of Cold Start" to map the real, informal organizational structure.

Key takeaway

For executives leading AI initiatives, recognize that true AI-native transformation extends beyond product upgrades to fundamentally altering your organization's internal operating machinery. Your efforts must address the deep-seated "physics" of budget allocation, information flow, and informal power structures, not just technology adoption. Prioritize understanding and then transforming these underlying systems to achieve sustainable, enterprise-wide AI integration.

Key insights

Enterprises resist AI-native transformation due to deeply entrenched internal organizational "physics" and economies.

Principles

Method

An "agent version of Cold Start" algorithm, inspired by Andrew Bosworth's method, can map the real, informal organizational structure by asking three questions repeatedly.

In practice

Topics

Best for: Executive, Consultant, Director of AI/ML, VP of Engineering/Data

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Editorial summary, takeaway, and curation by AIssential. Original article published by Turing Post.