Research Paper Analysis
Designing Interfaces that Support Temporal Work Across Meetings with Generative AI
This paper addresses the challenge of fragmented knowledge work by proposing a framework and interfaces that leverage Generative AI to support "temporal work" – the crucial acts of looking back (retrospection) and ahead (prospection) across multiple meetings and projects. By integrating AI-driven insights with user interaction, it aims to enhance continuity, coherence, and intentionality in the modern workplace.
Executive Impact
Traditional meeting tools often miss the broader context of temporal work. This research reveals significant opportunities to enhance knowledge worker productivity and intentionality by integrating Generative AI across the timeline of work, rather than just isolated meetings.
706+
Instances of Temporal Work identified across 8 meetings
3
Novel Interface Concepts designed to bridge temporal gaps
1 Framework
Proposed for grounding AI design in real-world temporal needs
1 Sandbox
Tool developed for exploring multimodal Generative AI capabilities
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Enterprise Process Flow
Define Temporal Work Framework
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Explore Generative AI Capabilities (Sandbox)
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Design Interface Concepts
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Build Interactive Prototypes
Expanding Meeting Support Beyond Individual Sessions
| Feature/Benefit | Traditional Meeting Support | Temporal Work + Generative AI Approach |
|---|---|---|
| Focus | Individual meetings, efficiency, immediate summaries. | Across meetings & projects, intentionality, continuous context. |
| Time Scale | Short-term (in-meeting, immediate post-meeting recap). | Short-term to long-term (days, weeks, months, years) for retrospection & prospection. |
| AI Integration | Basic summaries, action items within a single meeting. | Multimodal content, cross-meeting insights, support for complex prospection/retrospection. |
| User Engagement | Passive consumption of generated recaps or notes. | Active engagement (editing recaps, navigating project history, setting intentions). |
| Outcome | Improved individual meeting efficiency and record-keeping. | Enhanced continuity, coherence, and intentionality across all knowledge work. |
Key Interface Concepts for Temporal Work
Problem: Existing tools miss the temporal continuity of knowledge work, leaving gaps in supporting users across meetings and projects, hindering intentionality and long-term reflection.
Proposed Solution Concepts:
- Instant Recaps: Deliver quick, actionable summaries immediately post-meeting, blending AI-generated content with user edits for real-time reflection and planning for near-future actions.
- Adaptive Meeting Handoff: Dynamically present relevant information for both just-concluded and upcoming meetings, adapting to available time and user needs, to bridge temporal gaps between sequential interactions.
- Project Browsers: Offer a long-term, calendar or chat-based overview of projects, surfacing goals, progress, and themes across months or years to facilitate deep retrospection and prospection.
Result: These interfaces facilitate a 'chain of intentionality and reflection' that transcends individual meetings, enabling more proactive and coherent knowledge work across various timescales and contexts.
Calculate Your Potential AI Impact
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Your AI Implementation Roadmap
Our phased approach ensures a smooth and effective integration of temporal AI solutions into your existing workflows, tailored to your enterprise's unique needs.
Phase 1: Discovery & Strategy
In-depth analysis of current temporal work patterns and existing meeting support tools. Define specific goals and tailor AI solutions to your enterprise needs.
Phase 2: Sandbox & Concept Prototyping
Leverage our Generative AI Content Sandbox to explore capabilities with your data. Develop initial prototypes for Instant Recaps, Adaptive Handoff, or Project Browsers.
Phase 3: Integration & Pilot Deployment
Seamlessly integrate selected AI interfaces with your existing communication and collaboration platforms. Conduct pilot programs with key teams for feedback and refinement.
Phase 4: Scaling & Continuous Improvement
Roll out solutions across your organization, providing training and ongoing support. Continuously monitor performance and iterate based on user feedback to maximize impact.
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