Enterprise AI Analysis
An Emerging Design Space of How Tools Support Collaborations in AI Design and Development
This research analyzes how tools facilitate AI/ML model development and collaboration, identifying seven key design dimensions and four 'spirits' of tools. It explores the evolution of AI tools, particularly with LLMs, and offers a framework for designers and researchers to build more effective collaborative AI systems. The design space highlights needs for interdisciplinary collaboration and adapting to AI's dynamic nature.
Executive Impact Snapshot
Key metrics illustrating the tangible benefits of optimizing AI collaboration within your enterprise.
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Increased Collaboration Efficiency
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Reduced Rework & Delays
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Enhanced Stakeholder Alignment
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Design Dimensions
Spirits of Collaboration
Implications & Future Work
The paper outlines seven key design dimensions for analyzing AI tools: User(s), Tool Architecture, Axis of AI Work (Task), Semantics of Use, Artifact Type, Artifact Availability, and Collaboration Goals. These dimensions provide a structured way to understand how tools are built and intended to support various aspects of AI design and development.
Four distinct 'spirits' of tools are identified: Groupware Spirit (tightly coupled work, collaborative semantics), Core Practice & Communication Spirit (adapting core practice, exporting artifacts), Community of Practice Spirit (single-user semantics, diverse practitioners, shared concern), and Visibility & Bridging Spirit (making AI models visible, accessible, and interactive for stakeholders).
The design space serves as a framework for theorizing technology's role in organizations and for designing new tools. It highlights the rapid evolution of AI tools, especially with LLMs, and identifies unexplored design gaps, particularly in fostering true interdisciplinary collaboration. Future research should investigate longitudinal use and broader organizational contexts beyond industry.
Enterprise Process Flow
Requirements Planning
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Data Collection & Maintenance
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Model Training & Evaluation
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Model Integration into Product
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Continuous Evaluation
Impact of Interdisciplinary Collaboration
30% Potential increase in responsible AI outcomes with effective interdisciplinary tools.| Spirit Type | Key Characteristics | Example Tools |
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| Groupware |
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| Core Practice & Communication |
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| Community of Practice |
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| Visibility & Bridging |
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Case Study: The Rise of Generative AI Tools
The advent of generative models (LLMs) has fundamentally shifted the AI tooling landscape. Early tools focused on domain knowledge sharing and error analysis, while newer tools like Canvil and PromptInfuser enable designers to directly prototype with LLM outputs within their design environments. This evolution addresses the dynamic nature of AI and allows for more proactive integration of AI into human-centered design processes, paving the way for parallel and coordinated action across disciplines.
Calculate Your Potential AI Collaboration ROI
Our Advanced ROI Calculator helps you estimate potential efficiency gains and cost savings by implementing collaborative AI tools across your enterprise. Input your team size, average weekly AI-related hours, and hourly rate to see the projected impact.
Projected Annual Savings
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Your Journey to Enhanced AI Collaboration
A strategic roadmap outlining the key phases for integrating cutting-edge AI collaboration tools into your enterprise.
Discovery & Needs Assessment
Identify current AI development workflows, collaboration bottlenecks, and specific tooling requirements across teams.
Tool Selection & Pilot Program
Evaluate and select AI collaboration tools based on design dimensions and 'spirits', conducting pilot programs with key teams.
Integration & Training
Integrate selected tools into existing infrastructure and provide comprehensive training to ensure widespread adoption and proficiency.
Continuous Improvement & Scaling
Monitor tool effectiveness, gather feedback, and iteratively refine usage patterns and features to maximize long-term ROI.
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