AI in event logistics: Key barriers and success factors

The global event logistics industry, long driven by creativity and human coordination, is entering a phase of structural transformation. Artificial intelligence (AI) now offers predictive scheduling, supplier optimisation, and resource forecasting capable of reducing errors and cutting waste. Despite the potential, adoption remains uneven, restrained not by technical limits alone but by deep organisational and cultural barriers.

A new peer-reviewed study, Artificial Intelligence Adoption in Event Logistics: Barriers, Critical Success Factors, and Expert Consensus from a Delphi Study, published in Logistics, finds that AI’s greatest operational impact lies in early-stage event planning, while resistance to change, industry readiness gaps, and integration challenges continue to slow widespread implementation.

AI delivers the strongest impact in pre-event planning

The study confirms that AI’s most immediate and measurable benefits appear in the early stages of event logistics. Through both qualitative interviews and expert ranking exercises, pre-event planning and design emerged as the top area where AI adds operational value. Scheduling and supplier coordination followed closely.

These findings reflect AI’s strengths in forecasting, optimisation, and pattern recognition. Event logistics require accurate coordination of vendors, equipment, transport, catering, and staffing within compressed timelines. AI tools can simulate venue layouts, generate 3D spatial configurations, predict resource demand, and optimise supplier arrival times to reduce bottlenecks.

Experts agreed that structured and repetitive processes are best suited to AI automation. Catering and resource management also ranked highly, as AI systems can forecast food and beverage quantities, reduce waste, and allocate supplies more efficiently.

On the other hand, live event functions such as crowd and queue management, while recognised as useful, did not rank as highly as planning functions. Post-event feedback analysis and sustainability impact assessment were placed lower in priority. This suggests that AI’s current strengths lie in preparation and coordination rather than dynamic, real-time adjustments during live operations.

The study highlights a key distinction between event logistics and traditional supply chains. Events are temporary and often one-off projects. Unlike continuous manufacturing or distribution systems, they lack stable data histories and long-term operational baselines. As a result, AI performs best in areas where structured data already exists and predictable patterns can be modelled.

Interestingly, sustainability functions received relatively low prioritisation despite being emphasised during interviews as an emerging area of interest. This gap suggests that while environmental responsibility is strategically important, AI tools for sustainability tracking in events remain underdeveloped or insufficiently integrated into mainstream logistics platforms.

Resistance to change and industry gaps slow adoption

Despite AI’s operational potential, adoption remains limited by significant barriers. Through the Delphi process, experts identified resistance to change as the most critical obstacle. Cultural and organisational inertia outweighed purely technical limitations.

Event professionals, particularly those in creative and design roles, expressed concern about job displacement. AI tools capable of generating layout simulations, branding visuals, and planning templates may threaten traditional workflows. Fear of automation replacing human creativity contributes to scepticism and hesitation.

The lack of industry-specific AI products also emerged as a major challenge. Many available AI tools are designed for general supply chain management, marketing, or enterprise planning rather than the unique demands of event logistics. As a result, event organisers often adapt generic platforms, increasing integration complexity and reducing usability.

High costs further complicate adoption. AI implementation requires investment in software, infrastructure, and training. Small and medium-sized event companies, which often operate within tight budget constraints, may prioritise venue costs and staffing over digital transformation initiatives.

Data limitations represent another structural barrier. AI systems depend on high-quality historical data to generate reliable predictions. Because many events are unique in scale, audience composition, and logistics configuration, consistent data accumulation is difficult. Experts noted that AI systems may struggle in entirely new event contexts where past datasets are limited.

Accuracy and trust concerns also surfaced. Event logistics involve safety, security, and financial decisions. Professionals remain cautious about relying solely on algorithmic recommendations without human oversight. In high-stakes scenarios, such as crowd control or vendor coordination, even minor errors can have significant consequences.

These barriers are amplified by the temporary and project-based nature of events. Unlike permanent logistics networks, events require rapid setup, execution, and teardown. This leaves limited time for system testing and iterative refinement, increasing perceived risk.

Infrastructure, integration and change management drive success

The research identifies clear pathways to successful AI adoption. The Delphi consensus highlighted technological infrastructure, system compatibility, and change management as the most critical success factors.

AI solutions must integrate seamlessly with existing event management systems, including registration platforms, catering software, venue management tools, and transport coordination systems. Interoperability is essential. Experts emphasised that plug-and-play solutions with flexible architecture are more suitable than rigid enterprise systems.

Investment in digital infrastructure also matters. Reliable connectivity, cloud integration, and data processing capacity form the foundation of AI deployment. However, infrastructure alone is insufficient.

Human and organisational readiness play an equally decisive role. Training and knowledge development were consistently ranked as vital. Many event professionals lack formal exposure to AI applications. Structured training programmes can reduce fear, build competence, and foster acceptance.

Change management emerged as a central theme. AI implementation is not merely a software upgrade but a transformation in workflow and decision-making culture. Leadership must articulate clear objectives, align AI initiatives with strategic goals, and engage staff throughout the transition.

Data governance also surfaced as a key enabler. AI systems in events process sensitive attendee data, including registration details and behavioural patterns. Ensuring privacy, transparency, and regulatory compliance builds trust and reduces resistance. Ethical oversight strengthens long-term adoption prospects.

The second round of the Delphi study demonstrated strong expert convergence on these success factors. Consensus increased significantly compared to the first round, validating the structured, iterative method used in the research. The growing alignment among experts reinforces the reliability of the prioritised factors.

A sector-specific framework for AI in event logistics

Event logistics differs fundamentally from continuous supply chain environments. It is temporary, resource-intensive, and highly time-constrained. Coordination involves multiple short-term stakeholders, from suppliers and contractors to venue operators and security teams. These characteristics influence both the feasibility and sequencing of AI adoption.

The research suggests that organisations should begin with AI integration in planning and scheduling before expanding to real-time operational management or sustainability analytics. Early wins in structured processes can build confidence and demonstrate value.

For technology developers, the study signals a market gap. There is demand for AI tools designed specifically for event logistics rather than adapted from other industries. Solutions must prioritise rapid deployment, user-friendly interfaces, and compatibility with diverse vendor systems.

For policymakers and industry associations, the findings highlight the need for clearer governance frameworks and data standards tailored to event environments. Establishing shared benchmarks for sustainability measurement and data security may accelerate AI maturity.

Limitations and Future directions

The authors acknowledge limitations. The qualitative phase involved a small number of interviewees, and the Delphi panel consisted of 10 experts. While sufficient for consensus building, broader international samples could enhance generalisability.

Future research could examine longitudinal implementation outcomes, comparing events that adopt AI extensively with those that do not. Comparative studies across corporate, cultural, and sporting events may reveal sector-specific variations in AI readiness.

The study also opens avenues for deeper investigation into sustainability integration. The relatively low prioritisation of sustainability analytics suggests technological immaturity rather than lack of strategic importance. Developing event-specific carbon tracking and environmental optimisation tools could bridge this gap.