# AI-Augmented Multi-Team PBRs

> How AI inside Multi-Team Product Backlog Refinement lets teams learn from customer feedback, regulations and competitors, not from over-prepared experts.

- Author: Alexey Krivitsky
- Published: 2025-05-16 (updated 2025-08-01)
- Type: article
- Categories: Elevating Katas, AI
- Canonical: https://www.orgtopologies.com/post/ai-augmented-multi-team-pbrs
- Audio: https://www.orgtopologies.com/audio/post/ai-augmented-multi-team-pbrs/male.mp3 · https://www.orgtopologies.com/audio/post/ai-augmented-multi-team-pbrs/female.mp3

> LeSS—an org design system for large-scale product development—describes Multi-Team Product Backlog Refinement as one of the key enablers of organization-wide adaptability.

Multi-team PBRs are where teams learn from customers, stakeholders, and each other. This is where all the involved teams dive into the problem space to come up with innovative and effective product experiments.

## Forward-Looking Orgs with AI-Augmentation

Today, **AI augmentation** offers a powerful and complementary capability: **accelerating deep, multi-directional learning** during PBRs without compromising the core LeSS design principle of simplicity.

We can think of many AI applications that can streamline the learning process, especially in product backlog refinement events.

Using my recent post on [**Never Read Alone**](https://www.linkedin.com/posts/alexeykrivitsky_multilearning-activity-7315670527354761218-ZzXU?utm_source=share&utm_medium=member_desktop&rcm=ACoAAACU2WIBRxv-fzfhRRzp_TnWRfGcaiV3tk0), I explained how AI can enable teams to **interrogate vast datasets**, including user feedback, legal regulations, and competitor analysis, with **just-in-time learning via AI-powered dialogs** over any given corpus of knowledge. This amazing innovation must also be used during refinement.

And not before or after—a critical aspect.

A typical dysfunction of a PBR event is **overpreparation by product managers** and other expert roles who act as knowledge providers, turning the teams into students. Though it is essential for teams to learn, minimizing the associated process waste of knowledge transfer would be beneficial.

Namely, **applying AI** ***right in*** **the PBRs** to learn and structure knowledge, by the teams directly, can dramatically reduce the need for an expert or a special role to prepare for these meetings.

We predict that this will become increasingly common now, in the age of AI.

AIs (even before the hypothetical emergence of AGI) are already more **knowledgeable than any average subject-matter expert** and can easily **cross-pollinate between domains**. Teams that find ways to learn from AIs will no longer need to rely on and wait for part-time human experts. Thus, streamlining the learning process makes it way *lean*er than imaginable.

The main criticism of LeSS-inspired org design is the high cognitive load in teams, potentially caused by the constant need to learn and switch context. AI, when applied systemically and methodically, can reduce the burden of **absorbing complexity** by providing information in a clear structure, in smaller chunks, just in time, with runnable examples, etc.

These are a few examples I'm bringing above are very different from how most people currently see and use AI.

Somehow, the whole narrative revolves around using AI agents as free labor. Although this is probably where many innovations will be made due to obvious economic reasons, AI is not limited to doing things.

> AI is not (just) a doer, it is a great teacher.