Introduction to Artificial Intelligence (AI) and Data within Waste and Resource Management
Unlock smarter, more efficient waste and resource operations
Artificial Intelligence (AI) and data-driven technologies are rapidly transforming how industries operate, and the resources and waste sector is no exception. From improving operational efficiency to supporting sustainability goals, AI is already reshaping how organisations manage materials, routes, and infrastructure.
This introductory course provides a practical, sector-specific overview of how AI and data can be applied within resource and waste management. You’ll explore real-world applications such as smart sorting, predictive maintenance, and dynamic routing, alongside case studies demonstrating how UK organisations are using AI to improve performance and reduce environmental impact.
Designed for both newcomers and professionals looking to modernise their approach, this course will help you understand the opportunities, challenges, and future direction of AI in the sector.
Course Details
Format: Online
Duration: 4.5 hours
Entry requirements: None
About this course
AI and data are transforming how the sector operates, driving smarter decisions, greater efficiency, and improved sustainability outcomes. Gain the knowledge and confidence to understand these technologies and their role in shaping the future of resource and waste management.
Understanding AI in a sector context
Build a clear understanding of how AI and data technologies are being used within resource and waste management to improve efficiency and sustainability.
Identifying practical applications
Learn where AI can be applied in real-world operations, including smart sorting, predictive maintenance, and dynamic routing.
Insights from real-world case studies
Explore examples of AI adoption within UK resources and waste organisations and understand what successful implementation looks like in practice.
Benefits, risks, and ethical considerations
Develop awareness of the advantages and challenges of AI, including ethical considerations and responsible use of data-driven technologies.
Future-focused thinking
Gain insight into emerging trends and how digital innovation is expected to shape the future of resource and waste management.
Professional development
Receive a certificate of completion and contribute towards your Continuing Professional Development (CPD).
This course is suitable for professionals who are interested in understanding or adopting AI and data-driven technologies.
It is particularly relevant for:
- Waste and resource management professionals seeking to modernise operations
- Operational managers and decision-makers focused on efficiency and cost reduction
- Sustainability and environmental performance teams
- Local authority officers exploring digital transformation opportunities
- Professionals involved in logistics, routing, or facility management
- Individuals new to the sector who want an introduction to digital innovation in waste management
No prior knowledge of AI or data science is required, making this course accessible to all experience levels.
Course Structure
This course is made up of two parts: a self-guided e-learning module followed by a virtual instructor-led training session.
This eLearning covers:
- An introduction to AI in the waste industry
This session covers:
- Introduction to Data and AI in Waste Management
- Data Management in the Waste Industry
- Applications of AI in Waste Operations
- Case Studies and Real-World Applications
- Benefits and Challenges
- Future Trends and Preparing for an AI-Driven Sector
Upcoming courses
View the timings for virtual sessions below.
- Session 1 - 8th September - 09:00-13:00
Book your place
Active CIWM members are eligible for a 20% discount on this course. Your discount will be applied automatically at checkout when you are logged in and book using your CIWM member details.
Select your preferred date and time from the options below.
| Date | Duration | Location | Price | ||
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| No time slots are currently available for this course. | |||||
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