AI Centre of Excellence (AICoE)

Project summary

There is an ever-increasing demand for Artificial Intelligence (AI) & Machine Learning (ML) to deliver innovation projects needed for achieving net zero targets.

Name Status Project reference number Start date Proposed End date
AI Centre of Excellence Complete NIA2_NGESO0021 Sept 2022 Dec 2022

 

Strategy theme Funding mechanism Technology Expenditure Third Party Collaborators
Net zero and the energy system transition NIA_RIIO-2 Digital Network £266,000 Capgemini
Summary

This project will assess the value and business impact of advancing the data science and AI/ML capabilities available to the ESO. It will determine whether an AI Centre of Excellence (CoE) model is the optimal route for building this capability and driving innovation in collaboration with external stakeholders. 

By engaging with key industry stakeholders and securing senior sponsorship, the ambition is to design a programme to deliver the CoE based on a set of potential use cases to drive innovation in business-as-usual activities and data science capabilities to support the Net Zero transition. 

Benefits

The existing machine learning capability within the ESO is unable to fulfil the current and future pipeline of projects requiring AI & ML to facilitate the net zero transition. This project is an opportunity to create a central resource for data science best practice, increasing collaboration and focused work across the industry. It will also create potential opportunities for training and upskilling, increasing the overall data science maturity within the ESO and therefore the ability to undertake more AI/ML projects to facilitate the net zero transition.

ENA smarter networks portal

Learnings

Outcomes

  • Delivered vision, mission and ambition for the AI Centre of Excellence. 
  • Delivered an AI Use case framework and engaged with the ESO business extensively to identify over 100 AI/ML opportunities. 
  • Delivered a roadmap to deliver for MVP phase and beyond. 
  • Positive engagement with key stakeholders including ESO exec on development of the AI CoE 
  • Delivered an academic partnership framework to assess and establish partnerships with academia. 
  • Delivered an MoU and MoA framework review 
  • Delivered options paper on Funding and operating models for the AI CoHackathon launched and completed on Kaggle. We had 251 entrants and 569 submissions. 
  • Collated open data and curated it for public consumption and use in ML Hackathon. 
  • Delivered an AI knowledge hub, with a robust design to be opened up externally when required. 
  • KPIs in place and UAT sessions delivered to iterate the design 

Lessons Learnt

  • Business engagement is key, having early and consistent stakeholder engagement with
  • ESO staff has been critical to the success of the AI Centre of Excellence so far. 
  • Strategic alignment has been critical to making sure the AI CoE serves the needs of the business and helps deliver on priorities. 
  • Knowledge hub requires significant buy in from the business to maintain, key stakeholder engagement was required and more time could have been dedicated there. 
  • Hackathon was incredibly successful, one lesson from this competition was to be firm with your goal and set expectations ahead of time and be explicit with them. We were clear with the goal of a hackathon to provide a dataset and problem for people outside of the energy industry to engage and get excited.