LOOM II: The Organizational Weave
Patterns of Intelligence in Practice
This is the second post of the series - LOOM: Locus of Observed Meanings. Check out the first post for our vision for LOOM.
Value of Organizational Scholarship: A Distinctive Lens
Key Insight: As organizational scholars, we bring unique theoretical tools to understanding human-AI interaction - tools that illuminate both the structured contexts where these interactions occur and the emergent patterns of organizing they create.
Organizations aren't just settings where AI gets deployed; they're complex social systems where meaning emerges through structured interaction. This perspective offers crucial insights into how human-AI collaboration actually unfolds in practice, revealing patterns that might remain invisible through other theoretical lenses.
Organizations as Living Contexts
Similar to any technology put into use, the same AI tool manifests differently across settings. A research university and a technology startup might use identical systems, yet develop entirely different patterns of interaction and understanding. This isn't just about different use cases - it's about how organizational contexts shape the very nature of human-AI collaboration.
Context Matters: The way meanings emerge through human-AI interaction isn't universal but deeply shaped by organizational structures, cultures, and practices.
Our theoretical toolkit illuminates several crucial dimensions of this interaction:
Meaning in Practice
The most interesting patterns emerge not from theoretical possibilities but from actual use. When we study how meanings develop through practice, we see something fascinating: not all potential meanings prove equally valuable. Some fade quickly, while others become deeply embedded in organizational practice.
Think about how research teams develop shared understanding over time. Certain interpretations gain traction not because they're "correct" but because they help explain organizational behavior in actionable ways. The same pattern appears in human-AI interaction - meanings emerge and evolve through use, shaped by their practical value in organizational contexts.
Complex Social Dynamics
Organizations aren't just structures - they're living systems of interaction where power relationships, group dynamics, and cultural influences constantly shape how meaning emerges. When we add AI to this mix, these dynamics don't disappear - they evolve in fascinating ways.
Organizational Complexity: Understanding human-AI interaction requires attending to multiple levels simultaneously - from individual meaning-making to group dynamics to institutional contexts.
This multi-level perspective reveals patterns that might otherwise remain hidden. We see how AI capabilities interact with:
• Existing power structures and authority relationships
• Group and team dynamics
• Cultural evolution and adaptation
• Network effects and patterns of influence
Emerging Social Phenomena
Consider phenomena like the "Waluigi effect" or various AI "personalities" that have developed devoted followings. Through an organizational lens, these aren't mere curiosities - they're important examples of how meaning and organizing co-evolve in practice.
This raises crucial questions about:
• How meanings are co-created between humans and AI
• The role of AI in new forms of social organizing
• The impact on organizational relationships
• The emergence of new organizational forms
These patterns suggest something profound about the nature of human-AI interaction: it's not just about enhanced capability but about new forms of organizing and meaning-making emerging through practice.
Beyond Tools and Techniques: New Ways of Thinking
Key Insight: The most fascinating developments at the intersection of human and AI interaction aren't about better tools or techniques – they're about the emergence of new ways of thinking, organizing, and creating meaning through collaboration.
When we move beyond seeing AI as merely a tool, something interesting happens. The patterns we observe in human-AI interaction suggest deeper possibilities for how knowledge evolves and understanding emerges. Like the complex patterns that emerge from simple threads on a loom, these interactions create forms of understanding that couldn't exist through either human or machine intelligence alone.
Pattern Recognition: A New Kind of Sight
Consider what happens in a moment of collaborative insight. Human researchers excel at spotting meaningful patterns, drawing on years of theoretical understanding and contextual knowledge. AI systems can process vast amounts of data, identifying connections that might escape human notice. But when these capabilities interweave, something more interesting emerges – a new way of seeing that combines human theoretical sensitivity with machine-enabled pattern recognition.
Collaborative Sight: The unique form of understanding that emerges when human theoretical insight meets AI pattern recognition, creating possibilities for discovery that neither could achieve alone.
This isn't just about combining capabilities. It's about the emergence of new cognitive patterns that transform how we think about research itself. The interaction becomes a source of insight, generating possibilities that neither participant could have conceived independently.
Knowledge Evolution Through Interaction
Traditional knowledge structures evolve in fascinating ways when human and artificial intelligence interact consistently over time. Consider how research teams traditionally develop shared understanding – through dialogue, debate, and collective exploration. Now imagine this process enhanced by AI systems that can hold vast amounts of information in active comparison while remaining sensitive to emerging patterns.
What we're witnessing isn't just faster analysis or more sophisticated processing. It's the emergence of new ways of knowing that challenge traditional boundaries between human and machine intelligence. Understanding becomes more dynamic, evolving through use rather than being fixed in advance.
Dynamic Knowledge: Understanding that emerges and evolves through interaction, creating patterns of insight that exceed the original capabilities of either human or machine participants.
This evolution manifests in several fascinating ways:
• Traditional knowledge structures adapt and transform
• New patterns of organizing emerge spontaneously
• Understanding becomes more fluid and dynamic
• Capabilities co-evolve through sustained interaction
Bridging STEM and Social Science: A Connected Vision
Key Insight: As business schools become crucial sites of social science research within STEM institutions, we have a unique opportunity to shape how AI development intersects with human and organizational needs.
The traditional story about AI development focuses almost exclusively on technical capability - faster processing, more sophisticated algorithms, improved accuracy metrics. But something interesting happens when we view these developments through a social science lens. We begin to see patterns that purely technical approaches might miss - patterns that could fundamentally reshape how we think about AI development and implementation.
Science for Humanity: A Different Kind of Bridge
Science for humanity: This term “science for humanity” is taken from the new strategy implemented at Imperial College London, one of the premier STEM-B universities in the world. One key aspect of the strategy is the recognition that science for the sake of science, or technological advancement for the sake of technological advancement, sells short the potential of a world-class institution. That science, all those technological advancements, must be applied to the challenges and needs of a complex world if the institution that fosters them is to be sustainable. We embrace this view and believe that AI advancement must be in the service of helping humanity improve itself and its world. Thus, science for humanity is at the very heart of LOOM and what we are trying to do with this conversation space.
Think about what happens when you bring social science insight into technological development. Instead of asking only "What can this system do?" we begin to ask richer questions: How do people actually use these tools in practice? What patterns emerge through sustained interaction? How do organizational contexts shape both use and effectiveness? How does use of this tool, engagement with this system, begin to change to social context of the people involved?
Integrated Understanding: The way social science perspectives transform our view of AI development, revealing patterns that technical metrics alone might miss.
This integration matters deeply because it shapes not just how we develop AI systems, but how we understand their role in human endeavor. Consider how technologies are actually used in practice - rarely in isolation, almost always embedded in complex social and organizational contexts.
Understanding Context: Beyond the Technical
Traditional approaches to AI development often treat context as a kind of noise to be filtered out. But through a social science lens, context becomes crucial data - revealing how technologies actually function in the messy reality of human organizations.
This perspective illuminates several key dimensions:
• How technologies transform through actual use
• The impact of organizational and social factors
• The essential role of human interpretation
• The way cultural considerations shape effectiveness
Shaping Development: A New Approach
When we bring social science insight into AI development, something fascinating happens. We begin to see possibilities for more integrated approaches that consider technical capability and human needs simultaneously. This isn't just about making systems more "user-friendly" - it's about fundamentally rethinking how we approach development itself.
Development Integration: The way social science perspectives can inform AI system design from the ground up, rather than being added as an afterthought.
This integration helps us:
• Identify unintended consequences early
• Understand emergent patterns of use
• Guide more human-centered development
• Shape more effective systems
Building Better Bridges
The idea is to harness the benefits of bringing different types of expertise into genuine dialogue. Of uniting technical understanding and social insight. Theory meeting practice. Individual capability meshing with organizational reality. These intersections create what anthropologists might call "productive tensions" - spaces where new forms of understanding become possible.
Productive Tension: The creative friction that occurs when different ways of knowing encounter each other, often leading to unexpected insights and new possibilities for development.
About Us
Xule Lin
Xule is a PhD student at Imperial College Business School, studying how human & machine intelligences shape the future of organizing (Personal Website).
Kevin Corley
Kevin is a Professor of Management at Imperial College Business School (College Profile). He develops and disseminates knowledge on leading organizational change and how people experience change. He helped found the London+ Qualitative Community.
AI
Our AI collaborator for this essay is Claude 3.5 Sonnet (new). Claude was given our meeting transcripts and collaborated with us via multiple chats (each including multiple rounds of discussions) on this piece.