· Artificial Intelligence · 5 min read
AI-Assisted Event Operations: How Machine Intelligence Will Transform Cashless Event Management in 2026
Over the last five years, the industry has focused heavily on improving the front-of-house experience — faster transactions, contactless systems, digital wallets, NFC wearables, and seamless entry.
But the next wave of innovation won’t happen at the bar or gate.
It will happen in the operations centre.
In 2026, AI will evolve into the core operational engine powering how large events, attractions, resorts, and marketplaces manage staffing, flow, supply, devices, and safety. Its role is not to analyse individuals, but to convert aggregate, anonymised operational data into real-time intelligence that improves the experience for everyone onsite.
1. Cashless Infrastructure Enables Real-Time Operational Intelligence
As more destinations adopt NFC wearables, tokenised payments, and mobile wallets, organisers now have access to something they never had before: a continuous stream of aggregate, depersonalised operational signals.
The shift toward tokenised payments is already well underway.
According to the Ticketmaster Trends Report (2024), over 70% of large and mid-sized events have adopted NFC wearables, mobile wallets or other contactless systems — creating the perfect foundation for AI-driven operational intelligence.
Every tap, scan, or device event generates a simple data point:
- A transaction happened
- At a specific time
- On a specific device
- At a specific location
This allows AI to understand:
- Where demand is building
- When crowd density increases
- Which service points are over- or under-performing
- Where flow bottlenecks are emerging
- How stock moves across a site
- When devices show early signs of degradation
Retail, stadiums, and theme parks already rely on similar systems — and events, attractions, and resorts will follow this path in 2026.
2. AI-Powered Queue & Flow Forecasting Will Become Standard
Queue times remain one of the biggest drivers of guest dissatisfaction across attractions, festivals, and live events. Multiple hospitality and visitor-experience studies consistently show that long waits increase abandonment, reduce spend, and create operational pressure points.
Today, most teams respond only once queues are visible.
AI changes this by analysing transaction velocity, movement patterns, and service-point throughput to forecast congestion 10–25 minutes before it occurs.
This allows operators to:
- Redeploy staff to where they’re needed
- Rebalance service points
- Prevent bottlenecks before they form
- Improve flow across high-density zones
The result is smoother operations and a significantly better guest experience.
3. Fraud & Anomaly Detection Without Guest Profiling
Operational AI can identify irregular patterns at the device or process level, such as:
- A device showing repeated failed attempts
- Unexpected refund or void patterns
- Unusual transaction velocity on a vendor’s POS
- Indicators of malfunction or misuse
These safeguards are increasingly important.
The Worldpay Global Payments Report (2025) notes a significant year-on-year rise in chargeback-related threats, reinforcing the need for automated anomaly detection that protects vendors without analysing individual guest behaviour.
All modelling is performed on transaction events, not personal data.
4. Dynamic Vendor & Outlet Performance Benchmarking
Predictive operations and performance monitoring are already widely used in hospitality, multi-site retail, and food-and-beverage environments. Applying the same operational intelligence to live environments, AI can continuously benchmark anonymised service-point metrics, including:
- Average transaction speed
- Throughput
- Queue abandonment
- Stock movement velocity
- Hourly revenue pace
- Device health indicators
If a bar, kiosk, or retail outlet begins falling behind expected patterns, AI can identify whether the cause is:
- Staffing imbalance
- Low stock
- A device issue
- Localised congestion
- Environmental conditions
This enables teams to intervene early — often before the impact becomes guest-visible.
5. Real-Time Demand Forecasting for Better Operational Decisions
Cashless ecosystems reveal predictable aggregate demand patterns — not personal behaviour.
AI models can interpret:
- Sudden increases in total spend velocity
- Product-level performance
- Time-of-day surges
- Weather-driven changes in activity
- Movement patterns across different zones
This supports:
- Better stock deployment
- Improved staff allocation
- Faster service delivery
- More consistent guest experience
- Better vendor fulfilment operations
All without touching identifiable guest information.
6. Predictive POS Health Monitoring Reduces Downtime
AI can detect operational issues before they affect service, including:
- Battery degradation
- Thermal overload
- Connection instability
- Early signs of hardware strain
- Devices operating outside expected norms
This keeps bars, kiosks, retail points, rides, and entry gates running smoothly during peak times.
7. The 2026 AI Operations Centre
By the end of 2026, large attractions, festivals, markets, and resorts will run AI-assisted control rooms that provide real-time operational recommendations:
- “Outlet D will exceed safe queue length in 14 minutes — redeploy two staff.”
- “Device 41 is showing abnormal thermal behaviour — replace now.”
- “Cold beverages are trending 32% above normal — restock zone A.”
- “Transaction density shows rising congestion at East Entrance.”
- “Velocity indicates a demand surge approaching the South Market.”
The system doesn’t track people — it identifies patterns, enabling smarter, faster operational decisions.
8. Security & Safety Through Operational AI
Operational AI plays a significant role in enhancing onsite safety by:
- Identifying congestion before it becomes unsafe
- Detecting device anomalies that could disrupt service
- Improving flow around high-density areas
- Reducing operational risks through early warnings
- Supporting emergency response by highlighting unusual patterns
These systems rely solely on aggregate, anonymised operational metrics.
They do not perform identity-level analytics or behavioural profiling. This approach aligns with global privacy standards and reinforces the safety and trust expected by modern destinations.
9. Privacy & Data Ethics
All AI capabilities described here use aggregate, depersonalised operational data, never individual guest identity or personal behavioural profiles. Operational AI focuses on:
- Device performance
- Total transaction volume
- Queue activity
- Outlet throughput
- Environmental patterns
This ensures full compliance with GDPR principles of minimisation and purpose limitation. AI improves operations — not guest tracking.
Where Glownet Fits Into This Future
Glownet serves as the operational layer powering onsite transactions and fulfillment for events, attractions, and marketplaces.
The platform already provides:
- Real-time device health monitoring
- High-volume operational event data
- Multi-zone and multi-vendor support
- Offline-first reliability
- Automated reporting and insights
The foundation for AI-driven operational intelligence
As the operating backbone, Glownet structures the rich, anonymised data required to power real-time AI systems — both online and offline.
This enables ethical, effective AI-driven optimisation across staffing, flow management, vendor performance, fulfilment, and device health.
By pairing AI with Glownet’s operational dataset, organisers and partners gain a predictive engine that makes destinations run safer, smoother, and more efficiently.
Conclusion
Cashless technology transformed how guests access experiences.
AI will transform how organisers deliver those experiences.
In 2026, the industry will shift from reactive management to predictive, AI-assisted operations — improving flow, safety, vendor performance, and guest satisfaction.
Destinations that adopt operational AI early will define the next generation of smarter, safer, more responsive visitor experiences.
