Enterprise AI Analysis
The Influence of Distributed AI in Trust and Collaboration for Search-and-Rescue Teams
Artificial intelligence (AI) is increasingly deployed in high-stakes domains such as search-and-rescue (SAR), where detections or classifications can shape how teams share information, build trust, and make time-critical decisions. This paper investigates how teams of SAR professionals incorporate AI into their teamwork, highlighting both benefits and challenges. To support this study, we developed the Council of Wizards, a multi-agent Wizard-of-Oz technique that simulates distributed AI systems, enabling scalable and controlled evaluation of collaborative dynamics. Using this novel method, we conducted an experiment with 24 subject-matter experts (SMEs) who reviewed SAR video footage as small teams and made group decisions, with or without AI support. Quantitative results showed that AI-assisted teams reached consensus faster than controls. Qualitative feedback revealed how participants interpreted trust cues, adapted strategies, and sometimes struggled with overload or conflicting detections. Findings illustrate how AI shapes teamwork in SAR and provide design implications for trustworthy distributed human-AI interactions.
Executive Impact: Key Findings
Our analysis reveals critical performance improvements and insights for AI integration in high-stakes environments.
Faster Decision-Making
SME Participants
AI Systems Simulated
Statistical Significance (TTD)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Decision Speed and Trust
AI Collaboration Dynamics
Methodology and Implications
55.5s
AI-assisted average decision time vs. 134.3s for controls
AI-Assisted vs. Control Group Performance
| Feature | AI-Assisted Teams | Control Teams |
|---|---|---|
| Decision Speed |
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| Trust & Confidence |
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| Perception of AI |
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Enterprise Process Flow
AI-Assisted Perception
→
Shared Object Detection
→
Trust Assessment Communication
→
Collaborative Decision-Making
→
Faster Consensus
The Council of Wizards Technique in Action
The Council of Wizards (CoW) technique simulates distributed AI collaboration, enabling SAR experts to evaluate layered AI support in a controlled, realistic environment.
Challenge: Traditional Wizard-of-Oz setups are often limited to single-user or pair-participant scenarios, failing to capture the complexity of multi-user contexts and distributed AI systems in high-stakes environments like SAR.
Our Solution: CoW assigns each "wizard" to simulate a distinct computer vision system's perception stream and trust model. This multi-agent approach allows synchronized cue production and information exchange across distributed human-AI teams. It supports various sensing modalities (AR HUD, canine harness, drone) and integrates image recognition with trust assessments.
Outcome: This method facilitated a scalable, repeatable empirical study, recruiting 24 SAR SMEs. It allowed investigation of how AI influences team sensemaking, trust calibration, and consensus formation, leading to faster decision-making consensus in AI-assisted teams.
CoW Technique vs. Conventional Field Studies
| Aspect | Council of Wizards (CoW) | Conventional Field Studies |
|---|---|---|
| Environment |
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| Scalability & Repeatability |
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| Focus |
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Trust Monitoring
AI framework feature to override system-generated trust values with human judgment
Calculate Your Potential AI ROI
Estimate the annual time and cost savings your enterprise could realize by implementing distributed AI solutions for enhanced collaboration and decision-making.
Estimated Annual Cost Savings
$0
Estimated Annual Hours Reclaimed
0
Your AI Implementation Roadmap
A structured approach to integrating distributed AI for enhanced team collaboration and trust.
01. Discovery & Strategy
Assess current sensemaking workflows, identify high-impact AI opportunities, and define trust calibration metrics specific to your team's needs.
02. Prototyping & Simulation
Utilize multi-agent Wizard-of-Oz techniques to simulate distributed AI scenarios, test human-AI collaboration dynamics, and fine-tune system designs.
03. Pilot Deployment & Feedback
Deploy AI-assisted perception systems in a controlled pilot environment, gather real-time feedback from SME teams, and iterate on trust signals and UI elements.
04. Scaled Integration & Training
Integrate distributed AI across your operational infrastructure, provide comprehensive training for human teams, and establish ongoing monitoring for performance and trust.
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