AI and Complex Change

Complex change recognizes that change is non-linear, emergent, and deeply interconnected with the system in which it occurs. This is even more important as we adapt to the complexity of generative AI.

An abstract image of swirls and lo

Across the Artificiality community, organizations are grappling with a new organizational dynamic characterized by complex change. Change management itself is changing due to the need to adapt to generative AI. Rather than being a linear, sequential process, with each step building upon the previous one to drive successful change, change is getting much more loopy to adjust to the emergence from our new human-machine systems.

In this context, we need a new approach to change management—one that recognizes the inherent complexity of the systems we are part of and the role we play in shaping their evolution.

Treating organizations as if they were machines, with predictable parts and linear cause-and-effect relationships, has worked for a long time. But in reality, organizations are complex adaptive systems, with countless interrelated elements that are constantly evolving and influencing one another. By attempting to control change from the top down, leaders fail to account for the emergent properties of these systems, and risk creating unintended consequences that can derail plans.

Complex change recognizes that change is non-linear, emergent, and deeply interconnected with the system in which it occurs. This is even more so in the age of artificial intelligence and machine learning. These technologies are fundamentally altering the way we work and interact with one another. Predictive models that once seemed reliable are made obsolete as the systems they are meant to describe evolve in unexpected ways.

And generative AI is creating entirely new categories of content and behavior, blurring the lines between human and machine agency. And as these systems become more autonomous and interconnected, the potential for unintended consequences grows exponentially.

Most change programs contain an implicit assumption: that we can predict and control the future from an external vantage point. Complexity science tells us that this is not how the world works. Even if change advocates follow all the right steps such as building awareness, fostering desire, educating people, and rewarding and reinforcing required skills and behavior, it just isn't enough because human systems do not march towards towards a predetermined end.

Consider the shift towards embracing more data-driven approaches. We excel at guiding others through this transformation and making data-driven decision-making a key part of their strategy. We're actually pretty awesome at it. Yet even when people have access to high quality data we still see a group struggle with the analytics process and with making timely and "good enough" decisions.


Great! You’ve successfully signed up.

Welcome back! You've successfully signed in.

You've successfully subscribed to Artificiality.

Success! Check your email for magic link to sign-in.

Success! Your billing info has been updated.

Your billing was not updated.