Why change management must become an organizational capability in the AI era

2026-06-10 · Source: Dataconomy · Field: Business & Management — Corporate Strategy & Leadership, Operations & Process Management, Human Resources & Workforce Development · Depth: Advanced, medium

Summary

The article argues that change management must evolve from temporary programs to an embedded organizational capability, especially in the AI era. Drawing on Dmitry Papusha's experience at Sberbank, Playrix, and a Southeast Asian FMCG conglomerate, it highlights the need for distributed ownership and localized decision-making. Papusha developed frameworks for building internal centers of expertise and piloting risky initiatives, emphasizing that transformation approaches must adapt to specific organizational cultures. He contends that AI's rapid, autonomous changes necessitate flat decision chains, data treated as operational infrastructure, and a redefined human role focused on orchestration, not execution. Traditional hierarchical models conflict with autonomous AI agents, requiring companies to clarify decision rights and accountability to manage risk and maintain speed.

Key takeaway

For Directors of AI/ML preparing for autonomous agentic systems, recognize that traditional hierarchical operating models will impede AI's speed and introduce critical risks. You must proactively redesign your organizational structure to feature flat decision chains with explicit rights for AI agents, treat data as a core operational product, and redefine human roles towards outcome orchestration. Failing to clarify accountability for agent decisions upfront will lead to significant operational crises.

Key insights

Change management must evolve from temporary programs to an embedded organizational capability to navigate the rapid, autonomous shifts of the AI era.

Principles

• Change capability must be built into the organization.
• AI necessitates flat decision chains and data as infrastructure.
• Transformation approaches require adaptation, not mechanical copying.

Method

The article describes frameworks for building internal centers of expertise (talent profile, training, mentoring, development plans) and piloting risky initiatives (testing methodology, building middle management autonomy, strengthening internal change muscle).

In practice

• Define clear decision rights for AI agents.
• Treat data as a product with owners and quality standards.

Topics

• Change Management
• AI Transformation
• Organizational Capability
• Autonomous Agents
• Data Governance
• Operating Model Design

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

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