How might we use wearable, hands-free technology to seamlessly capture and structure technician insights at the point of work, improving the accuracy, consistency, and usefulness of asset data across Maximo.
Challenge
Technicians interacting with physical assets are a potential goldmine of untapped operational insight. But valuable data-like observations, asset specifications, and fault symptoms - is often lost or inconsistently captured due to manual, time-consuming processes. This lack of structured input undermines asset records, limits operational intelligence, risks missed interventions and slows down preventative action.
We want to reimagine how asset data is collected, shared, and used - using hands-free technologies like video & voice enabled wearables, computer vision and AI to reduce friction, increase accuracy, and bring technician knowledge directly into systems like Maximo.
What will we do?
This sprint will explore the use of smart capture tools—including wearable video and voice-enabled tech—to transform the way data is collected at the asset. We’ll prototype a workflow that allows technicians to speak observations aloud as they work, with comments transcribed and entered directly into asset management systems as technician notes. Simultaneously, we'll test how video and image capture combined with computer vision supported by AI could extract key information (like model numbers, serials, and installation dates) from nameplates and equipment labels.
The team will investigate how these technologies can be integrated into day-to-day tasks, reducing manual input while improving the richness and consistency of asset data.
How we will do it?
Sprint participants will collaborate to map out current technician workflows and identify moments where data is often lost or inconsistently captured. We’ll use real-life asset interactions as inspiration to design and prototype a smarter capture process - blending voice, video, automation and AI. A range of use cases will be explored, from fault reporting to asset onboarding.
We’ll also explore interoperability challenges and how this data can flow directly into platforms like Maximo without disrupting existing workflows. Participants will test different user journeys, design touchpoints, and define key requirements for adoption, privacy, and governance.
Target Outcomes
By the end of the sprint, the team aims to deliver:
- A prototype workflow for capturing technician voice notes and translating them into structured asset comments.
- A suggested approach for extracting asset details from images using computer vision, AI and other technology enablers.
- Recommendations for integrating new data streams into existing systems like Maximo.
- A prioritised set of features and success metrics for further development.
- A clear outline of the use cases with the highest value and feasibility.
Who will benefit from this sprint?
Operatives and Technicians will benefit from a more intuitive, hands-free way to capture insights - reducing admin and improving accuracy. Asset intelligence teams will gain access to more consistent, richer data for analytics, maintenance planning, and lifecycle decisions. Digital teams will have the opportunity to explore and shape how emerging technologies are applied in a frontline context.
