AI overview
Artificial intelligence (AI) is transforming the role of locations managers in both scripted and unscripted film and television production. Traditionally, a locations manager identifies, secures and manages filming locations that align with the creative vision, logistical requirements and budgetary constraints of the project.
AI now offers powerful tools that help streamline the location scouting process, evaluate logistical challenges and even predict the costs associated with certain locations.
By analysing data from previous productions, AI can help locations managers find suitable options faster, considering factors like accessibility, lighting conditions, and the visual compatibility of locations with the project’s artistic goals.
AI also supports locations managers by automating aspects of data analysis and risk assessment. For example, AI-driven tools can evaluate weather patterns, traffic data and noise levels to assess the feasibility of a location.
Additionally, AI can generate reports on cost projections, safety requirements and environmental impact, allowing locations managers to make informed decisions. These tools save time and provide data-driven insights that enhance planning and coordination, allowing a locations manager to focus on creative problem-solving and on-the-ground logistics during production.
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How can I prepare for the future?
To remain competitive and effective, locations managers should build familiarity with AI tools that support data analysis, risk assessment and logistical planning. Developing skills in using AI-driven scouting tools, environmental analysis, and cost management systems will enhance efficiency and provide valuable insights for location-related decisions.
AI is starting to offer new and faster ways of analysing data to assist the process of making complex decisions, so working with others to collate useful historical data and archive it will enable you to respond to others even more quickly and accurately in the future.
AI can also play a role in this process, easing some of the data collection and labelling processes. With careful use, AI should in the future be able to act as an assistant giving you costings estimates and potential difficulties or benefits of a given location simply based on a potential script.
It can also be used in more unusual ways, such as image processing applied to satellite map data. It may be able to find existing places that closely match with your production's requirements, searching vast areas quickly.
Thought should also be given to the applications of virtual production and other similar location simulation tools. Understanding when and where these could be effective or efficient in a production could well influence other decisions.
Training opportunities, such as those offered by ScreenSkills and other online learning platforms, provide essential knowledge on integrating AI into production workflows. By adopting these technologies, locations managers can better manage the complexities of on-location production while staying aligned with the latest industry advancements.
Embracing AI in location management
AI introduces valuable tools for locations managers, offering solutions that enhance efficiency, improve planning and support sustainable practices.
While AI assists with data-driven insights and logistical analysis, the role of a locations manager - ensuring that each location aligns with the creative and operational vision - remains a skill rooted in human expertise.
AI complements this role by reducing administrative work and providing precise assessments, allowing locations managers to focus on creative problem-solving and on-the-ground coordination.
Embracing AI in a thoughtful way empowers locations managers to enhance their productivity while upholding the artistic and practical standards essential to successful film and TV production.
What AI tools can I use right now?
Locations managers can utilise a range of AI tools that assist in different stages of the production process, from scouting to post-production. Here’s an outline of AI applications currently available to support locations managers, along with options for privately hosting AI models for secure data management.
- Location identification and matching: AI tools can analyse location requirements and suggest potential sites based on parameters such as aesthetic fit, accessibility and budget considerations. By using databases of location images and profiles, AI helps narrow down choices and streamlines the initial scouting process.
- Confidential and customisable AI Models: Self-hosted AI models offer some new features, in comparison to simply uploading your work to the cloud and using ChatGPT, locations managers are now able to create their own AIs, hosting them locally on their own computers instead of using cloud services. This can enable a locations manager to work confidentially, or even develop their own unique AIs that have learnt from them directly, acting as an assistant that matches and understands their workflows. Meta (Ollama), Mistral AI and quite a few other models will allows this. It does take a bit of training and learning to be able to set it up, but it is possible to use your own past work as a training data set or a template to work from.
- Location suitability and assessment: An example application of this could be for instant and quick budget assessment. Perhaps a heavy piece of set will need to be hung from a roof in the location. An AI could give a some insight to the potential structural capability of a location roof and therefore viability of that roof to a locations manager. Previously they may have needed to wait for a structural engineer to deliver a costly and lengthy analysis. While this AI driven result cannot yet be relied upon for safety reasons, for quick budgeting and instant ballpark assessment it could be invaluable.
- Environmental and safety analysis: AI-driven tools can evaluate on site factors such as weather conditions, noise levels and environmental risks. These insights help managers ensure that each location meets safety standards and complies with any legal or logistical constraints.
- Traffic and accessibility monitoring: AI systems can assess traffic patterns, parking availability and access routes, helping locations managers to plan for crew movement, equipment delivery and accessibility issues. This can also reduce unexpected delays and logistical challenges during production.
- Cost analysis and budget predictions: AI-powered cost analysis tools evaluate past data on location expenses, helping managers estimate the budget impact of particular sites. These tools track expenses in real-time and highlight areas where costs may exceed projections, supporting managers in effective budget control.
- Impact assessment reports: AI systems can generate reports on the environmental and social impact of filming at specific locations. This data helps locations managers provide insights to production teams and align with sustainable production practices, offering a transparent assessment of any impact left on the location.
AI-driven location scouting: AI tools that analyse aesthetic, logistical and budgetary requirements to suggest locations that align with project goals.
Self-hosted AI models for privacy: AI systems that run locally to help managers process data without sharing sensitive information externally.
Environmental and safety analysis: AI tools that assess weather, environmental risks and noise levels to ensure each location is safe and meets legal standards.
Traffic and accessibility planning: AI-driven tools that monitor real-time traffic and parking availability, helping managers plan crew and equipment movement.
Cost and impact assessment: AI systems that evaluate costs and the environmental impact of locations, helping managers manage budgets and sustainability.
ScreenSkills offers a variety of training opportunities for people at all stages of their career. Explore all training, events and opportunities.
ScreenSkills resources:
- Locations manager job profile
Other resources:
- LinkedIn Learning has information on AI scouting techniques and environmental and risk analysis
- GitHub and YouTube have tutorials and guides on self-hosted AI models for privacy
- Coursera has resources on AI in logistics