Why AI matters at this scale

Best Crowd Management, operating as Whelan Event Services, is a large-scale provider of crowd management and security services for events across the United States. With over 10,000 employees, the company specializes in ensuring safety, managing logistics, and providing staffing solutions for conventions, trade shows, concerts, and sporting events. Founded in 2009 and based in Minnesota, the company has grown to handle complex, high-attendance gatherings where precise coordination is critical.

For a company of this size and in the event services sector, AI presents a transformative lever. The core business challenges—optimizing a massive temporary workforce, predicting and responding to dynamic crowd behavior, and mitigating safety risks—are inherently data-rich problems. At a 10,000+ employee scale, even marginal efficiency gains translate into significant cost savings and service improvements. Furthermore, the event industry is increasingly competitive, with clients demanding higher levels of safety, real-time reporting, and operational transparency. AI enables a shift from reactive to proactive management, turning vast amounts of operational data (from ticket scans, video feeds, and staff reports) into predictive insights that enhance decision-making and resource allocation.

Concrete AI Opportunities with ROI Framing

1. Predictive Staff Deployment: By applying machine learning to historical event data, weather forecasts, and real-time ticket entry rates, the company can accurately forecast crowd density per zone and minute. This allows for dynamic, optimized deployment of security and usher personnel. The ROI is direct: a 10-15% reduction in overstaffing for large events could save hundreds of thousands annually in labor costs while improving coverage accuracy.

2. Computer Vision for Proactive Safety: Deploying AI-powered video analytics on existing venue camera systems can automatically detect potential safety incidents—such as overcrowding, perimeter breaches, or medical emergencies—and alert command centers in seconds. This reduces response time, potentially preventing costly lawsuits and reputational damage. The ROI includes lowered insurance premiums and strengthened client contracts due to demonstrated superior risk mitigation.

3. Intelligent Post-Event Analysis: Automating the synthesis of data from radios, incident reports, and movement patterns into a comprehensive dashboard provides clients with unparalleled insights. AI can highlight recurring bottlenecks and quantify staff performance. This transforms a service commodity into a strategic partnership, improving client retention and allowing for premium service offerings. The ROI is seen in increased contract value and reduced manual reporting labor.

Deployment Risks Specific to Large, Distributed Operations

Implementing AI in a company of this size band (10,001+ employees) presents unique challenges. First, integration complexity is high due to likely disparate, legacy systems across different venues and regional teams. A phased pilot approach is essential. Second, change management for a large, often non-technical field workforce is critical; AI tools must augment, not complicate, their workflows to ensure adoption. Third, data governance and privacy are paramount when handling video and attendee data; solutions must be designed with privacy-by-principle, potentially using edge processing to anonymize data at the source. Finally, the talent gap may require partnering with specialized AI vendors rather than building in-house capabilities from scratch, shifting the risk to vendor selection and management.