AI-Assisted Modelling
How can AI reduce the effort, time, or expertise required to create MADE models, especially when teams are starting from existing diagrams, documents, or inherited system information?
A full-day, free-thinking innovation sprint exploring how AI can improve, accelerate, and transform the way we build, use, integrate, and extract value from MADE.
This is an internal innovation day focused on the future of AI within MADE. The aim is to challenge assumptions, explore bold ideas, and turn real user pain points into practical product opportunities.
This day is about looking at MADE from new angles — bold imagination, grounded engineering, and practical AI opportunities that can change how customers work.
The session details will be added later. For now, the day is structured to move from shared context, to broad ideation, then focused concept development.
Arrive at the office.
Purpose, themes, team setup, expected outputs, and ground rules.
Build a tower, free standing that can support the weight of a marshmallow
Generate ideas broadly across the AI within MADE opportunity areas.
Reset, compare early thinking, and prepare for the next round.
Informal discussion and cross-team idea sharing.
Prioritise, sharpen, and develop the strongest concepts into practical proposals.
Final reset before teams prepare their concept outputs.
Present your chosen idea back to the judges.
Wrap-up and next steps.
The day will focus on four broad opportunity areas drawn from customer research, persona pain points, workflow constraints, and MADE adoption challenges.
How can AI reduce the effort, time, or expertise required to create MADE models, especially when teams are starting from existing diagrams, documents, or inherited system information?
How can AI make it easier to connect MADE with engineering artefacts, customer data, PLM and MBSE sources, spreadsheets, or operational data?
How can AI turn MADE data and outputs into faster, clearer, role-specific decisions for safety, reliability, systems, maintainability, and management stakeholders?
How can AI make MADE easier to learn, navigate, query, and extract value from for new, occasional, or non-modeller users?
Each team should leave the day with a clear, explainable concept that can be reviewed, challenged, and potentially developed further.