In 2018 Eni wanted to build a solution able to support more effectively and efficiently maintenance activities and related business objectives, leveraging Artificial Intelligence to empower ENI with a Predictive Maintenance platform.
My engagement: To design the digital solution of IoT service by the means of service and UI design methodologies.
The IoT portal as it was before my intervention:
First I discovered and understood reality and only after I thought deeply and defined what needs to be done. Before key decisions, I recommended validating the concept with potential users. To support successful deployment I’ve supported the technical team with detailed UI design and UX consultancy.
1. Discover: What the service should be about?
2. Define: Core experience of the product
3. Design: Bringing the experience into live
2 Weeks 2 Workshop
What the service should be about?
4 Weeks 8 Meetings
Core experience of the service:
The phase produces the concept definition in iterative manner.
The core elements of
the concept was also described in a concept
• Core concept: concept description, key components, main flows and
• KPI’s: for business goals and user experience (if applicable)
• Initial visual style: Key views visualized, describing the visual concept
based on Eni brand
• User/internal stakeholders feedback summary: from validation sessions
Validate and finalise
Does the concept get an instant buy in? How the concept should
be finalised to support optimal user experience?
• Plan the validation, define recruitment screener, recruit users
• Facilitate validation session (during 1 day onsite at Eni, remote sessions)
• Finalising of the concept based on the recommendations
• Light validation summary, incl. recommendations, for review prior finalisation
• Finalised concept
4 Weeks 4 Sprint
With concepts created and validated with stakeholders, I’ve created
detailed designs over 1 week design sprints while
conducting stakeholder playbacks at the end of each design sprint.
• Detailed UI designs for selected UI views
• Sketch files
• Detailed design
• Internal reviews and working sessions with stakeholders for feedback and to
• Support of deployment team when problems that affect user experience arise
The entire project was based on the work conducted by Capgemini Team, including Data Scientists, SMEs from the Energy sector, and Designers, in particular:
2 months developing and testing Predictive Maintenance models, based on the chosen anomaly detection approach
1 year collecting, cleansing, and analyzing data to define relevant features.
The design team (1 product designer + 1 facilitator) objectives:
Direct observation and preliminary interviews with ENI internal users, to gather insights and envision a “user-centric” solution.