AI@OT: AI-Supported Org Design

AI, of course, can be used to improve the performance of individual single-skilled specialists, and this is what we see as of 2025, but there is a large landscape. Let me share.

AI-Supported Org Design ("AI OD" for short) will become a hugely impactful topic affecting all of us and all the organizations in which we work.

AI OD is the strategic practice of applying AI (agents and other upcoming innovations) to continuously inform, accelerate, and personalize how an organization is structured, how it evolves, and how its people learn. 

It’s not about replacing managers or employees (!) —it’s about empowering us to design adaptable, resilient orgs with fewer structural overhauls and more intelligence baked into the day-to-day.

☝️The core organizing principle: AI Makes Versatile Teams Viable.

☝️ The core operating principle: With Multi-Learning as the Engine.

How do these principles differ from applying AI to get individual performance gains? Let me unpack this slightly.

Organizing Principle: AI Makes Versatile Teams Viable

See Alexey's talk on versatility as an engine for staying relevant in the age of AI:

Operating Principle: With Multi-Learning as the Engine

If we unpack this principle, then we can see that this is all about applying AI to support an organization's development direction and accelerate its evolution to gain high performance and other competitive advantages.

That is a strategic AI application.

Some guides and practices to focus on here:

👌 #1: Multi-Learning enables Adaptivity: Especially in Adaptive Topologies (see Org Topologies Primer for details), learning—not just delivery—is the primary currency. AI enables this by making the unknown known and the unlearnable learnable, with ease.

Sample scenarios:

• A backend developer shadows a customer support session and begins contributing to onboarding scripts.

• A frontline support agent learns enough Python to automate common diagnostic steps, reducing ticket escalations and easing the load on developers.

• An end-to-end team includes privacy review and data protection steps in their regular sprint workflow—not because they were forced to, but because someone on the team got curious, shadowed legal, and brought that knowledge back in.

• A delivery manager starts using AI tools like Cursor or GitHub Copilot—not to replace anyone, but to better understand how her teams are using it, what it’s good at, and the current limits.

  
  

👌 #2: Matching of Work-to-Skill in Real-Time: Rather than static org charts or disruptive reshuffles, AI supports the continuous alignment of skills to work based on live data and evolving interests, and when multi-learning is too expensive, AIs will suggest micro-reteaming without major upheaval as a temporary, quick-fix solution.

We distinguish three skill-to-work matching patterns:

• static matching: pre-analyze and pre-plan work

• dynamic reteaming: reallocate people when needed

• multi-learning: give people the mandate to learn

When multi-learning is a part of work, managers do not need to waste time and energy preplanning or reteaming. People can follow the work and learn what is necessary to achieve the expected outcomes.

👌 #3: Learning Becomes the Flow: AI agents will suggest relevant prior work and recommend 5-minute learning prompts when patterns emerge.

AI agent says to a team:

• “Team X solved this two sprints ago.”

• “Want to see how testing was solved in a similar sprint?”

Strategic AI in Org Design

AI-powered org design harnesses AI to make teams inherently versatile and learning deeply integrated—driving adaptable, resilient organizations without the need for constant structural upheaval.

This requires practicing a new mindset of seeing AI not as a tool to output more, but a strategic lever that enables humans with easier multi-learning and higher outcomes.