PSAV (now known as Encore) is a global leader in event production and audiovisual (AV) services. The company provides comprehensive technical solutions for meetings, conferences, and trade shows, encompassing everything from stage design and lighting to complex sound engineering and digital signage. With a workforce of 5,001-10,000 employees and operations spanning countless venues worldwide, PSAV manages a vast, mobile inventory of high-tech equipment and a complex logistics network to deliver seamless live experiences for its corporate, association, and venue partners.

Why AI matters at this scale

For a company of PSAV's size and operational complexity, margins are heavily influenced by logistical efficiency. The manual processes of scheduling thousands of skilled technicians, routing millions of dollars of equipment, and designing custom technical proposals for each event are ripe for optimization. AI presents a lever to transform this complexity from a cost center into a competitive advantage. At this scale, even single-digit percentage improvements in labor utilization, equipment readiness, or sales cycle speed can translate to tens of millions in annual savings and increased capacity, directly boosting profitability in a service-intensive industry.

Concrete AI opportunities with ROI framing

1. Predictive Logistics for Equipment and Crew: By applying machine learning to historical event data, weather patterns, and venue logistics, PSAV can forecast exact equipment and staffing needs. This reduces costly over-provisioning and prevents last-minute shortages that jeopardize events. The ROI is direct: lower capital tied up in idle inventory, reduced freight costs, and optimized labor spend. A 10% reduction in these operational expenses would have a massive bottom-line impact.

2. Generative AI for Technical Proposals: Responding to RFPs is a time-intensive process requiring technical sales engineers. A fine-tuned large language model (LLM) can ingest an RFP and generate a first draft of a system design, equipment list, and narrative proposal. This accelerates the sales cycle, allows engineers to focus on high-value customization, and ensures consistency. The ROI is measured in increased proposal volume, faster client response times, and higher win rates.

3. Computer Vision for Event Quality Control: Using AI to analyze real-time feeds from cameras and microphones during events can automatically detect technical issues like audio feedback, poor lighting on a speaker, or screen failures. This enables proactive intervention before the client notices. The ROI is protected revenue—preventing service failures that lead to credits or lost future business—and enhanced reputation for flawless execution.

Deployment risks specific to this size band

Implementing AI in a large, established organization like PSAV carries specific risks. Integration Complexity: Connecting AI models to legacy enterprise resource planning (ERP), field service management, and scheduling systems is a significant technical challenge that can stall projects. Change Management: With a large, dispersed workforce of field technicians and operations managers, securing buy-in and training staff to adopt AI-driven workflows is crucial. Resistance to new tools that override long-held experiential judgment could undermine adoption. Data Silos: Operational data is often fragmented across regional divisions and software systems, making it difficult to create the unified, clean datasets required for effective AI. A failed AI pilot due to poor data quality could poison the well for future initiatives. A focused, data-first pilot project with clear executive sponsorship is essential to mitigate these scale-related risks.