Sustainability AI Hackathon
Four sustainability-focused AI use cases: scenario analysis, BOM generation, LCA transformation, and data mapping
The Challenge
A full-day hackathon brought mixed teams together to get familiar with AI applications and implement their first AI project. Four sustainability use cases were offered: scenario analysis and optimization, data enrichment and BOM generation, LCA data transformation, and data mapping for LCA - each with clear deliverables and presentation slots.
Our Approach
The day started with kick-off and project descriptions. Each project included an intro to AI concepts, suggested algorithms, hands-on tutorials, code snippets, a pre-configured Python environment, and onsite support. Teams used Jupyter Notebooks and were encouraged to use Cline (VS Code + Docker + Dev Containers) for AI-assisted development.
Four use cases were offered: Scenario Analysis & Optimization, Data Enrichment & BOM Generation, LCA Data Transformation, and Data Mapping for LCA. Teams chose one and worked through the hands-on materials with mentors on hand, designing and implementing their solution from data loading through to a working prototype and learning effective prompting and iteration with Cline as they went.
The day closed with short presentations: each team covered scope, challenge, implementation, solution, and learnings. This reinforced both technical outcomes and how they had used AI-assisted development to get there.
The Results
The hackathon proved that mixed teams can go from AI basics to working sustainability solutions in one day when supported with clear use cases, code snippets, and mentors. Learning was boosted by AI-assisted programming: teams used Cline to iterate faster and picked up effective prompting as they built. Presenting scope, challenges, implementation, and further steps reinforced both technical and communication skills.
4 Teams
4 sustainability AI challenges Use cases
Boosted with AI-assisted programming Learning
Technologies Used
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