AI - Rethinking society and organisations, part 3
Moving beyond systems thinking in change
AI generated image
Dr Reg Butterfield © 2024
17-Minutes read and associated video
Our journey thus far
On August 8, 2024, we launched our discussion with the first instalment, “AI - Rethinking Society and Organisations.” In that segment, we explored how transitioning from a "symbolic" to a "connectionist" perspective can unveil a variety of strategies and outcomes for both society and organisations. We argued that traditional change models, typically hierarchical, linear, and driven by top-down directives may function effectively in stable environments with predictable changes. However, in today’s dynamic and fast-paced world, these models often fall short. In contrast, connectionist approaches are decentralised, iterative, and adaptive, making them ideally suited for environments where rapid innovation and responsiveness are paramount, a reality that increasingly shapes the future of both society and work.
Then, on November 10, 2024, we published the second part, focusing on the importance of revisiting an organisational development (OD) perspective on change. We emphasised that we are now in an era where artificial intelligence (AI) plays a critical role in gathering information and supporting decision-making through data analysis and real-time updates. This modern approach transforms OD from a concept many perceive as outdated into a powerful catalyst for change throughout entire organisations, think of it as “OD on steroids.”
The map of our next stage in the journey
In this third instalment of our series, we guide readers in re-evaluating the traditional “commercial” view of systems thinking to enhance their effectiveness, regardless of their role or purpose. By “commercial view,” we refer to the often-simplistic perspective found on websites, blogs, and certain change models that tend to frame systems as merely adaptations of linear change models for managing complexity.
We emphasise that for change to be successful and sustainable, it is essential to delve into the intricacies of how systems operate at a deeper level. Recognising the complexity of the various elements and their interactions, comprising visible, invisible, conscious, and unconscious factors, can pave the way for more fruitful interventions, informed decisions, and positive outcomes.
To illustrate these concepts, we weave together a narrative about building a city. We compare the processes and results of constructing the city through a traditional linear change model with those achieved using the connectionist Futocracy Change approach.
This is not an academic excursion
In our previous newsletters from September 25 and October 2, 2022, we took a close look at decision-making and systems, covering these topics in detail with supporting references. One feature, “Understanding the Complexity of Decision-Making - Part 2,” explored the work of George Herbert Mead and the Complex Responsive Processes (CRP) perspective. If you haven’t read these, they provide valuable and detailed insights into the complexities of these concepts.
This time, we’re introducing the basics of General Systems Theory and tracing how it has evolved over time. We’ll also highlight the work of George Herbert Mead, a key figure in social philosophy known for his contributions to symbolic interactionism, a foundational idea for understanding social dynamics within systems thinking. Alongside a look at CRP, these theories build a case for moving beyond traditional systems thinking and linear cause-effect models of change. Rather than viewing organisations as fixed structures ruled by strict rules, these perspectives encourage us to see them as living systems, shaped by ongoing, dynamic interactions between individuals.
Beyond General Systems Theory
General Systems Theory (GST) was pioneered by thinkers like Ludwig von Bertalanffy and Kenneth Boulding in the 1950s and has since branched into various fields that study complex systems, emergence, and self-organisation. This includes areas like Game Theory, Collective Behaviour, Network Theory, Nonlinear Dynamics, Pattern Formation, Evolution, and Adaptation, all of which look at how systems function and evolve under multiple, interacting influences.
When we talk about complex systems, we’re usually looking at any system with more than three interacting variables, a definition broad enough to include nearly every real-world situation. Complexity studies emerged from efforts to make sense of chaos theory, where “chaos” doesn’t imply disorganised confusion but rather the interconnectedness within seemingly random events. Briggs and Peat (1999) capture this idea well:
“Chaos theory teaches us that we are always a part of the problem, and that particular tension and dislocation always unfold from the entire system rather than from some defective ‘part.’ Envisioning an issue as a purely mechanical problem to be solved may bring temporary relief of symptoms, but chaos suggests that in the long run it could be more effective to look at the overall context in which a particular problem manifests itself.”
This understanding of complexity sheds light on the limitations of traditional change management. Many conventional models rest on a Newtonian, linear causation view, assuming organisations work like machines that produce predictable results when given specific inputs. This linear, step-by-step approach sees organisations as closed systems, driven by stability and predictability, where change is achieved through controlled, sequential actions.
Organisations move from Machines to Living Systems
Looking toward the future of change management, insights from George H. Mead and Complex Responsive Processes (CRP) theory offer a different approach from traditional linear, cause-and-effect models. Rather than viewing organisations as rigid structures governed by fixed rules, these theories encourage us to see them as living systems, constantly shaped by dynamic interactions among individuals.
Mead’s concepts of the “I” and the “Me,” along with his idea of the “conversation of gestures,” highlight how each person actively contributes to shaping the organisation. The “I” represents the spontaneous, creative side of a person, while the “Me” reflects societal norms and organisational expectations. In every interaction, people bring both these aspects, helping to shape and reshape the organisation. The “conversation of gestures” refers to the ongoing, often unspoken exchanges in these interactions, where people convey subtle cues, sometimes without words, that reflect acceptance, resistance, or adaptation to change. As we discussed in our October 2, 2022, newsletter, it’s through these exchanges that change unfolds, not from a single directive but through countless small interactions that collectively steer the organisation’s course.
CRP theory, also featured in our October 2, 2022, newsletter, builds on this by proposing that organisations are not predictable systems with fixed parts. Instead, they’re seen as evolving, responsive processes. Here, causality is more fluid, not “If we do this, then we get that,” but rather a transformative approach where change emerges organically from daily interactions. CRP introduces the idea of “transformative teleology,” focusing on change that arises naturally from people’s everyday engagements.
In this context, leaders and managers take on a new role. Rather than top-down decision-makers, they help create an environment that allows change to evolve naturally. By fostering openness, dialogue, and collaboration, they enable employees to directly influence the organisation’s growth. Decision-making here is not about rigid plans but rather adapting to real-time feedback from interactions, allowing flexibility and responsiveness. Leaders support experimentation, understanding that change is unpredictable and often stems from local, small-scale adaptations rather than large, preplanned strategies.
This approach to managing change views organisations as evolving communities, with change unfolding moment by moment through interactions. It moves away from rigid structures and linear plans, embracing a process where people continuously adapt, engage, and contribute. In the future, this mindset could make organisations more resilient and adaptable, built not on static systems but on ongoing, responsive processes that evolve with their environments.
Technology as an Extension of Humankind
In an organisation embracing responsive, human-centred change, AI and other data sources are valuable tools that support, rather than dictate, how people adapt. Instead of controlling decisions, AI-generated insights, like market trends or sensor data, can provide real-time information that enhances our understanding and responsiveness.
Similar to how Mead’s “I” and “Me” explain the balance between individual spontaneity and social norms, AI can be thought of as an extension of the organisation’s “Me.” It offers reliable patterns and insights but doesn’t replace human creativity and judgment. In a Complex Responsive Processes (CRP) approach, AI inputs become part of the broader “conversation of gestures,” where data might indicate a market trend or operational need, but it’s only one piece of the adaptive puzzle. Leaders and employees interpret these signals, discuss them, and respond in ways that align with the organisation’s goals and values.
By making AI processes transparent and open to human interpretation, organisations can build trust and keep data rooted in human judgment. This setup allows the organisation to remain agile and responsive to change without losing its human-centred focus. AI and data inputs help foster real-time adaptation, reinforcing the organisation’s collaborative and values-driven approach to change.
An Example of Theory in Action
Readers who are new to such discussion around organisations as living systems may find the following example helpful. LEGOTM is a brand whose products are known pretty much worldwide. Their building blocks, related figures, and other paraphernalia are enjoyed by a wide range of ages from 2 to 92 years old.
In the following illustrative case, we are building a LEGOTM City. Sit back and enjoy the story.
LEGO City
Imagine you’re in charge of building and expanding a LEGO city. You start with just a few blocks and gradually add roads, houses, cars, and people until it becomes a busy, connected place. Every part has a purpose, and together they create something much larger and more complex than just a collection of individual pieces. General Systems Theory, or GST, is a bit like this approach. It helps us understand how different parts of a system, like an organisation, come together and affect one another. Just as in your LEGO city, each piece in an organisation is connected. For example, adding a road to your LEGO city might change how cars move, where buildings are placed, and how the city grows. In the same way, when we make changes within an organisation, GST reminds us to look at the whole picture, thinking about how a change in one area might impact others.
Imagine that one day, you decide to add a train station to your LEGO city. With GST, you’d know that a new train station might bring more people to certain areas, so you’d adjust the roads, add more houses, and maybe even set up new businesses nearby. GST helps change managers in the real world see these kinds of connections too, ensuring their changes support each other and make the whole system stronger. By focusing on relationships and interdependencies, GST can help them anticipate how small changes in one department or team might affect the entire organisation. They might, for instance, look for feedback as the change unfolds, making adjustments like adding or moving pieces in your LEGO city to keep things running smoothly.
But there’s a twist. While GST gives us a great start, if we rely on it too much, we might miss some crucial details. Imagine if you thought your LEGO city was set, with clear, unchanging boundaries and relationships, without considering that new buildings, roads, or even people’s preferences could change the city’s needs. Real organisations work like this, too; they’re dynamic, with shifting boundaries that blur between internal and external influences. If change managers don’t recognise this, they might create rigid designs that don’t adapt well to the unexpected or overlook how outside factors like competitors or social trends impact the system.
In a LEGO city, the connections between pieces are obvious and stable, but in a real organisation, people bring in emotions, motivations, and unpredictable responses. Not everyone will respond to change in the same way, and some may resist or adapt slower than others. If a change manager relies solely on GST, they might assume that people and processes will smoothly connect like LEGO blocks. Yet, without recognising the complexity of human behaviour, change managers might miss signs of frustration or miscommunication, just as if you didn’t notice that certain LEGO people might want more parks or better transportation. GST can make it easy to assume that changes will be predictable and controllable when, in reality, organisations need flexibility and resilience to manage new challenges as they arise.
To bring your LEGO city to life, you would add new elements, reshape layouts, and make sure every change still fits within the big picture. In the same way, effective change managers go beyond GST, blending it with more adaptive approaches that let them respond to surprises, build on feedback, and keep both individuals and the whole organisation in mind. By looking at change as a lively, interactive process rather than a set of predictable steps, they can make their “LEGO city” work well not just for today but for all the unexpected ways it may grow in the future.
How can the work of George H. Mead and CRP help our city?
Let’s dive even deeper into our LEGO city to explore how social dynamics and complex interactions work, with insights from George H. Mead and Complex Responsive Processes (CRP).
Imagine that in your LEGO city, it’s not just the buildings and roads connecting things, but also the people who live there. They have their own identities, interests, and roles in the city, interacting in ways that make the city feel alive. Mead's concepts of the "I" and the "Me" help us understand these roles.
The "I" is the personal, spontaneous part of each LEGO character, like when a LEGO character decides to take a different route to work or try out a new café. It’s the part of people that’s creative and unique, shaping decisions in ways that might surprise us. The "Me," on the other hand, is the part that follows the rules and fits into the community, thinking about how others see them.
For example, a LEGO character might choose to park only in designated spots because they know it’s the city’s rule and want to respect the shared order. These aspects of the "I" and "Me" mean that each LEGO person is both an individual and part of the whole, shaping their interactions in ways that affect the entire city’s vibe.
Now, when we think of changing something in the city, like building a new school or adjusting how transportation works, it’s important to recognise that each LEGO character has a role in making that change happen. If we ignore these "I" and "Me" perspectives and just move pieces around as if they’re inanimate, we might overlook how each LEGO person feels about and reacts to the change. Maybe some are excited, while others feel unsettled or ignored.
Complex Responsive Processes (CRP) highlights that these day-to-day conversations and actions are crucial, especially in change processes. According to CRP, change doesn’t come from a single, centralised plan, but from all the ongoing conversations, interactions, and small adaptations that happen along the way.
So, let’s say you want to introduce a new bus route in your LEGO city. If you only think about it as a system, you might look at the roads and buildings and assume everyone will quickly adjust. But with CRP, we see that change will actually unfold through conversations between the LEGO people, some will be excited about faster commutes, others might worry about more traffic, and some might not even notice it until they see the new bus stop in front of their favourite café. Every conversation, each little reaction, becomes part of the change process, shaping how smoothly (or not) the bus route is adopted in the city.
This is why connectionist change models work better in real life than the traditional, linear approaches. In a linear model, you might assume that if you just plan the steps for introducing the bus route and follow them, everything will work out as intended. But real change is less like a step-by-step build and more like an evolving LEGO city where everyone’s feedback, reactions, and daily interactions create the change over time. Connectionist models embrace this complexity, seeing each character as both an individual and a contributor to the whole, making change a collaborative, adaptive process.
In your LEGO city, embracing these principles of Mead’s “I” and “Me” and the CRP approach will lead to a more thoughtful design for any new additions. Instead of just adding pieces and assuming they’ll fit in smoothly, you’ll involve the LEGO characters themselves, learning what they want and how they’ll interact with the changes. Ignoring these aspects can make change feel forced and create a sense of disconnect. By moving toward a connectionist approach, we break away from rigid, one-way plans and instead encourage a city that grows through its interactions, where each LEGO character’s individuality and contributions drive lasting, positive change.
The Impact of External Factors in our City
Now, let’s expand our LEGO city by adding a bus company and other external groups, bringing even more complexity and showing how the principles of connectionism, AI, and technology can enhance this evolving system. Imagine that the LEGO city’s new bus route isn’t managed solely by the city itself, it’s run by a separate bus company with its own goals, resources, and plans. The bus company doesn’t just drop in a route; it needs to work closely with city planners, businesses, and residents to ensure that this new addition benefits everyone.
From a connectionist perspective, change here isn’t just about adding a few bus stops. It’s about constantly gathering input, interpreting feedback, and adjusting in real time as the city, the bus company, and other partners connect and collaborate. Let’s say that AI technology is introduced to help the bus company manage routes. With AI, the bus company can analyse traffic patterns, popular destinations, and passenger behaviours, adjusting routes dynamically based on real-time data. Maybe AI notices that a particular route is busiest on certain days and times, so it increases service during peak hours, benefiting commuters and local businesses alike. This data-driven approach allows for constant adaptation, with technology making the city more responsive and connected.
Through this connectionist model, AI also enables the city to work seamlessly with other organisations beyond the bus company. For instance, local businesses can receive updates on bus schedules, planning promotions or events to coincide with times when people are most likely to arrive. Schools and workplaces can coordinate with the bus company to suggest optimal times for pick-ups and drop-offs, while environmental groups work with the city to encourage routes that minimise emissions. Here, connectionism shows its strength: rather than each organisation working in isolation, AI and technology foster a networked approach, enabling all parties to adapt and respond to one another continuously.
However, imagine what would happen if we relied only on a linear, isolated change model. The bus company might create a route based on initial assumptions, without real-time data or constant feedback from the city, businesses, or residents. This could result in underused routes, traffic congestion, or even residents feeling frustrated because the service doesn’t meet their actual needs. The change could feel rigid, more like something that’s been imposed on the city rather than something the city and its people can shape and influence over time.
With a connectionist model, each organisation, whether internal or external, becomes part of a larger network that adapts together, turning change into a shared, responsive journey. AI, with its ability to process vast amounts of information, becomes a critical partner, helping each part of the system make better, quicker decisions. Instead of implementing change based on a set plan, the city and its partners adapt to one another’s needs, making the whole process feel more natural and effective.
In the end, embracing connectionism and using technology like AI turns change from a step-by-step instruction manual into a living, breathing process. In your LEGO city, the new bus route isn’t just another part of the city, it becomes an evolving element that learns from, adjusts to, and enriches everyone’s experience, with each organisation and resident playing a role in its success.
What can we learn from this story?
As explored through the story of Lego City, embracing connectionist change means viewing organisations as dynamic, interconnected systems rather than static hierarchies. Systems theory shows us that these structures are made up of countless interactions that shape organisational identity and growth. Unlike traditional linear models that attempt to enforce change top-down in rigid stages, the connectionist approach thrives on flexibility and real-time adaptation. George H. Mead’s concept of the “I” and the “Me” reflects this balance between individual agency and collective structure, with AI now playing an essential role in the organisational "Me." AI enhances the organisation's collective intelligence, providing data-driven insights that align individual actions with broader goals.
Complex Responsive Processes (CRP) remind us that change emerges through continuous interactions rather than fixed, linear paths. With AI embedded in the connectionist model, organisations can navigate today’s fast-paced landscape, respond swiftly, and cultivate a shared purpose, while avoiding the rigidity of linear change models. This adaptability helps organisations sustain creativity, agility, and authenticity essential for growth in a complex world.