Artificial Intelligence
10 mins

How Ski Resorts use Artificial Intelligence

Ski resorts are leveraging Artificial Intelligence to improve the the skiing experience and management on the mountains.

Artificial Intelligence (AI) has found numerous applications in ski resorts and skiing, enhancing the experience for skiers and improving operational efficiency for resort managers. Here are some ways AI is being used:

1. Snow Management

Ski resorts use a wide variety of techniques to monitor, create, move and maintain snow.

Snow groomers  compact snow on the pistes and redistribute it, moving snow from abundant areas to places which are lacking sufficient snow to ensure an even cover.

AI can analyse terrain data, snow density, and usage patterns to suggest the most efficient routes for snow groomers to ensure an even distribution and optimal compactness across pistes.

Artificial snow can be created when temperatures are low enough to top up natural snow cover, usually at the beginning or end of the season.

AI can analyse weather forecasts, current snow conditions, and historical data to determine the best times to produce artificial snow. This ensures high-quality snow while reducing energy consumption.

AI can also monitor the quality of artificial snow being produced, adjusting machine parameters in real-time to ensure the snow has the desired moisture content and consistency.

Avalanche control requires snow to be monitored so that unstable snow can be moved before it becomes a hazard and small avalanches can be triggered using controlled explosions.

AI can predict areas with the highest risk of avalanches, using data from snowpack sensors, weather forecasts, and historical avalanche events. This can be used to decide where and when controlled explosions should be carried out to ensure they are done under the safest conditions.

Drones equipped with AI can be used to perform regular aerial surveys of high-risk zones, identifying potential weak spots in the snowpack. The drones capture high resolution images during their surveys, and these images are then processed in real time or post flight by AI algorithms which identify patterns, anomalies or changes that might be indicative of weak spots. Over time AI can be trained to recognise patterns which are more likely to be potential avalanche initiation zones.

Some drones are equipped with thermal cameras to provide thermal data which AI can analyse to detect temperature variations within the snowpack. Warm spots might indicate areas where the snow is melting internally, leading to instability.

To reduce human error and ensure data consistency, AI can automate the flight path of the drone, ensuring it covers the high-risk zones comprehensively and consistently every time.

Shade structures, or reflective materials, can be installed to slow down the melting process in some resorts.

AI can analyse sun trajectories, historical melting patterns, and terrain data to suggest the best locations for shade structures or reflective materials. Post-installation, AI can monitor the effectiveness of these structures, determining if they are slowing the melting process as intended. Over time, this data can be used to refine and improve the placement and design of future structures.

2. Safety and Rescue

AI can be used to assist with rescue operations and maximise safety in ski resorts.

Search and Rescue Operations:

Drone-based Search: Drones with AI capabilities can expedite search and rescue operations. They can scan vast areas faster than human search teams, using high-resolution cameras. Paired with AI, these drones can process captured images in real-time, identifying signs of lost or injured skiers.

Thermal Imaging: AI-enhanced drones equipped with thermal cameras can detect heat signatures, which is vital for locating individuals in challenging conditions, especially during night or low visibility situations.

Pattern Recognition: AI can be trained to recognize patterns typical of skiers, distinguishing them from wildlife or other potential false positives in the environment. This streamlines the search process.

Predictive Analysis: By analysing the last known location (eg from lift data), ski patterns, and terrain data, AI can suggest probable areas where a lost skier might be located, directing search teams more efficiently.

Automated Alerts: AI can process signals from wearable devices or smartphones to automatically detect if a skier has had an accident or remained stationary for an unusually long period, triggering an alert to resort safety teams.

Safety Monitoring:

Avalanche Prediction: As in (1) above, AI models can analyse a combination of data sources, including weather stations, snowpack sensors, and historical avalanche events, to predict areas with heightened avalanche risks.

Crowd Management: AI can analyse data from cameras placed around the resort to monitor crowd density. This helps in identifying overly congested areas, allowing skiers to be redirected or lift operations to be adjusted accordingly.

Equipment Analysis: AI can be used to reducing equipment-related accidents by analysing equipment wear and tear and predicting when maintenance or replacements are needed.

Slope Condition Monitoring: AI can continually assess slope conditions, identifying areas that might be icy or hazardous, and alert maintenance teams or provide warnings to skiers.

Lift Safety (see Lift Operations below).

3. Lift Operations

AI can be used to make lifts run efficiently, smoothly, safely and efficiently.

Facial Recognition for Access: Implementing AI-powered facial recognition systems can expedite lift access. Once guests are registered, they can quickly access lifts or other facilities without the need for manual ticket checks. This not only speeds up the process but also offers a touchless experience, which can be particularly valuable in peak times or health-conscious scenarios.

Peak Time Prediction: Using historical data, current resort bookings, and even broader factors like regional events or holidays, AI can predict peak times for lift usage. Resorts can then optimise lift operations, either by adjusting the speed of the lifts or allocating more staff, ensuring efficient movement of guests and reduced wait times.

Lift Queue Analysis: Cameras equipped with AI can monitor lift queues in real-time. If a particular lift line becomes excessively long, the system can send alerts, allowing the resort to take corrective actions, such as rerouting skiers to less busy lifts or opening additional lines.

Predictive Maintenance: AI can analyse data from sensors placed on ski lifts to monitor wear and tear, vibrations, and other parameters. By applying predictive analytics, the system can forecast potential malfunctions or component failures, prompting timely maintenance. This ensures lifts operate smoothly and reduces unexpected downtimes, enhancing safety and reliability.

Energy Consumption Optimisation: AI can analyse lift usage patterns, weather conditions, and energy prices to suggest the most energy-efficient operating schedules, helping resorts reduce energy costs without compromising guest experience.

Safety Monitoring: Using sensors and AI, abnormal behaviors or potentially unsafe situations (e.g., a person not seated correctly) can be detected in real-time, allowing for immediate intervention. (See our blog on AI Ski Lift Surveillance)

4. Skiers

Skiers can enjoy advances in AI to improve their skiing technique and experiences on the slopes.

Ski Performance Tracking: Wearables with AI capabilities offer skiers real-time feedback on their skiing technique, pinpointing areas for improvement. Read our blog on CARV to read how AI can act as your ski instructor.

Custom Trail Recommendations: By studying a visitor's skiing habits, preferences, and proficiency, AI can suggest trails or even recommend ski classes tailored to the individual.

Enhanced Ski Lift Experience: AI can predict peak times, adjusting ski lift operations to minimise wait times. Additionally, facial recognition technology enables faster access to ski lifts, streamlining the guest experience.

Local Weather Forecasts: Advanced AI-driven models refine the accuracy of weather predictions, aiding skiers in planning their day and assisting resort managers in making informed operational decisions.

Augmented and Virtual Reality in Skiing: Some pioneering ski schools are using VR to train novices in a controlled setting before they venture onto the slopes. On the other hand, AR technology can overlay vital information on ski goggles, offering skiers real-time data on aspects like speed, route guidance, or nearby attractions.

5. Resort Management

Resorts can use AI to maximise their revenue and improve the customer service they provide.

Dynamic ticket pricing : Resorts can maximise their revenue by using AI to dynamically adjust ticket prices. By evaluating factors like demand, prevailing weather conditions, and historical data, resorts can keep rates competitive whilst benefiting from real-time pricing optimisation.

Virtual Assistants and Chatbots: Modern ski resorts employ AI-powered chatbots on their websites to swiftly answer queries, assist with reservations, or provide up-to-date conditions, ensuring visitors have all the information they need at their fingertips.

Personalised Marketing Strategies : Resorts can create personalised marketing campaigns and target special offers by using AI to analyse customer data and segment visitors. This data-driven approach ensures that promotions are not only relevant but also effective in enhancing guest engagement.


As technology continues to advance, it's likely that the integration of AI in ski resorts and skiing will become even more prevalent, offering enhanced experiences and operational efficiencies.

September 5, 2023

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