AI Bubbles: Augmenting Cynefin with AI for Enhanced Decision-Making

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Considering that AI is increasingly being used in various fields, it is imperative to consider how AI can extend existing frameworks to help leaders around the world make better decisions. In this article, we will look at how the Cynefin Framework, developed by Dave Snowden, can be augmented with AI to address the new accelerated reality.

It is already clear that many organizations have been struggling to make better decisions in the face of accelerated change and high uncertainty. In these new conditions, most companies are panicking or laying off employees. The AI bubbles offer an alternative for them to better sense complexity and make more balanced decisions.

This article introduces the concept of “AI Bubbles,” a novel approach that extends the Cynefin framework by harnessing the power of artificial intelligence to facilitate decision-making processes in environments characterized by emerging complexity.

The AI Bubbles Approach

This approach suggests that AI can be used to create localized “bubbles” within the Complex domain of the Cynefin framework. These bubbles represent areas where AI can provide enhanced understanding, pattern recognition, and decision support based on a new value creation model, effectively creating pockets of enhanced sense-making within the larger complex system — note here that complexity itself is not reduced, but rather the ability for leaders to navigate and sense the environment. These are the 4 critical aspects of the AI Bubbles:

1. Holistic AI-powered Sense-Making

In Enterprise Agility, and sense-making there is a comprehensive approach that considers different aspects of the organization. We divide it into 3 areas.

a. Science of Accelerated Change to understand how individuals sense the environment and mobilize people (Behavioral Science, Neuroscience of Change, and Strategic Mobility)

b. A new financial model called the Trivalue Company model to support a more balanced decision-making during accelerated change (bifocal approach).

c. A new way to prepare the company for the future called Enterprise Agility Future thinking.

AI can be used in high-uncertainty and complex environments to analyze large amounts of data, identify patterns, and balance it considering areas a, b, and c to create a better balance.

It also provides insights that are not immediately apparent to human decision-makers. By using machine learning algorithms and natural language processing techniques, leaders can help employees to grasp complex situations more efficiently and accurately using AI trained with the science of accelerated change, a new value creation model, and forecasting principles (Future Thinking).

This can also be important in places where individuals don’t know what they don’t know. Especially in environments where leaders believe they can sense the environment during accelerated times but lack the necessary knowledge or skills or where leaders exhibit low levels of intellectual humility or possess psychopathic traits. In these scenarios, the ability to make sound decisions and provide effective guidance when following the Cynefin framework recommendations might be severely compromised. This is especially critical, as such circumstances can lead to misinterpretation, poor judgment, and potentially harmful outcomes for the organization or group.

Artificial Intelligence (AI) systems can also help mitigate these challenges by using the Trivalue Company Model. This balances value creation across three key dimensions: client value, company value, and workforce wellbeing value. By considering these aspects holistically, AI can optimize decision-making to ensure that the needs of all stakeholders are met during complex times. In addition, AI helps to navigate situations where individuals have no idea how to act (Aporia).

2. Enhanced Navigation of Complexity

Within the Complex domain of Cynefin, AI creates localized bubbles where the navigation of complexity is enhanced. These bubbles are created by applying AI algorithms to specific subsets of the complex system, focusing on particular aspects or dimensions of the problem using mixture of experts architecture (MOE). By providing enhanced sense-making and pattern recognition within these localized bubbles, leaders can better understand the situation and identify potential interventions more effectively. It is crucial to keep in mind that complexity itself is not reduced, but rather, the ability to navigate and sense it is enhanced.

3. Real-time AI Evolution through feedback

AI evolves based on the new situations to sense and on the amount of chaos or complexity. For this, it uses real-time learning feedback loops within the AI Bubbles. As the complexity evolves and new data becomes available, these AI algorithms can continuously update their models and recommendations. This adaptive nature allows leaders to respond to changing circumstances in easy ways. One approach to enable targeted updates is, again, the use a mixture of experts architecture. Here, the AI consists of several specialized agents or “experts”. Each of them focuses on a specific area or subtask. When new data or feedback arrives, it can be forwarded to the appropriate expert(s) to optimize the exact parts of the system that relate to that input.

For example, if the signals coming from the environment indicate that the AI needs to improve its understanding of a particular industry or type of signal, this data can be used to retrain the industry-specific expert without having to update the entire AI. The gating mechanism in the expert blend learns which expert to consult for each input. This modular architecture enables efficient and targeted updates based on incoming data streams. It makes the AI system more adaptable, reduce energy consumption and costs and potential disruptions from relearning the entire model. The mix of experts enables flexible development of AI capabilities in response to feedback from the real world and changing circumstances.

4. Scalability and Global Reach

One of the key challenges with any framework is its scalability. While Cynefin provides valuable insights for sense-making and decision-making in complex environments, it relies on human expertise and facilitation. As you can imagine, this can limit its applicability in large-scale organizations or global contexts where rapid sense-making and decision-making are required across multiple locations and teams.

The AI Bubbles approach addresses this scalability challenge by leveraging the power of AI and advanced technologies that have been covered before. AI algorithms can process large amounts of data from various sources, including internal systems, external databases, social media, and IoT devices. This enables the AI Bubbles to capture and analyze information from a wide range of contexts, feelings, and geographies, providing a more comprehensive and global perspective on complex situations.

As you can see, the AI Bubbles approach proposes an augmentation of the Cynefin framework by leveraging the capabilities of artificial intelligence and new technologies to support decision-making in complex situations. By creating localized bubbles of enhanced sense-making and pattern recognition within a complex domain, as well as considering the science of accelerated change, a new value creation model (TriValue Company Model), and a future-thinking approach, AI can provide valuable insights and balanced adaptive decision support. This AI Bubbles approach does not aim to replace the Cynefin framework but rather to complement it by integrating AI and the latest technologies mentioned here as a valuable tool for navigating complexity.

© AI Bubbles, Erich R. Bühler and Enterprise Agility University

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