The Innovation Highway project will utilise AI and machine-learning to optimise the full innovation value chain. The platform developed will help facilitate collaboration amongst networks, and other sectors such as water companies so they can innovate together. AI-empowered algorithms will simplify the identification, mapping, assessment and selection of problems and ideas, reducing manual processing time and enhancing effective decision making; this will support identifying and prioritising projects that will deliver the highest benefits. The platform will also help networks automate the development of cost benefit analysis.
Benefits
1. Reduction in time needed to process ideas
This benefit is based upon the time saved engaging with innovators unnecessarily and sifting through ideas prior to selection of what should and shouldn’t be approved and developed into a project. This would be informed by insights from previous projects, identification of similar historical ideas, and potential solutions that already exist.
The calculation of this benefit is based on the average number of ideas that have been rejected over 2021/2022. The project will reduce the time taken by an innovation engineer to review and reject these ideas by 60%. This is a recurring annual benefit.
2. Reduction in number of ideas unnecessarily progressed through governance
The project will create a 60% efficiency saving from investing resource in taking ideas through governance that then get rejected. The annual saving is based on the average number of ideas developed through Gate A and Gate B (these are UK Power Networks’ internal governance stage gates) that did not commence into full projects over 2019-2022.
3. Reduction in cost to develop project CBAs
The project will create a 60% efficiency saving via the AI tech flex that would look to automate the development of CBAs, reducing the need for human involvement. The cost saving is based on the average time spent creating CBAs over 2019-2022, assuming a continuous annual cost saving after deployment.
4. Uneconomic Project Spend Reduction
Over ED1, based on E6 reports, there was a total of £4.95 million spent on projects that returned no benefits. The project will reduce the spend on projects that return no benefits by 40% by focusing on projects that are more likely to deliver value through better problem identification and ideation. This will result in more projects having benefits.
For UK Power Networks, this could result in a £0.83m saving over RIIO-ED2
Base cost (PV) £3.76m
Method cost (PV) £2.93m
For UK Power Networks, this could result in a £1.63m saving over RIIO-ED3
Base cost (PV) £4.00m
Method cost (PV) £2.37m
Similar benefits are anticipated for the other participating networks, where the value will be based on their relevant size compared to UK Power Networks.
These benefits would apply across the entire innovation portfolio whether funded via the Network Innovation Allowance, the Strategic Innovation Fund or business funded innovation. Further refinement and communication of benefits will be undertaken during the Feasibility stage (Stage 1) of the project.
Learnings
Outcomes
The project has undertaken two phases, and valuable outcomes and lessons have emerged from each of these steps:
Stage 1
The primary outcomes of the first stage were that it answered a number of key questions that gave participating networks the confidence to continue into the Discovery phase. This included establishing how the vision for Ideaonomy relates to the needs of users, how Ideaonomy is unique in relation to competitors, the value that it will deliver to both networks and consumers, plus a better understanding of what it could cost once built.
Stage 2
Stage 2 built on the outcomes from Stage 1. The project made significant strides in refining the product vision and strategy, de-risking the investment proposition. The development and testing of a working prototype of the Challenge Module provided valuable insights for future development.
A further outcome was a plan for moving forward: A strategy for Phase 2 funding via SIF was developed with a foundation for subsequent Alpha and Beta phases, helping to map out user stories for the entire end to end platform, an enormous achievement. The Alpha plan looks towards a usable platform incorporating search, discussions, content privacy and user management, with the addition of the option of either problem matching, solution assessment, or an AutoCBA capability.
Lessons Learnt
There are a number of key learnings from the project:
User Centricity: The project demonstrated the importance of building a platform that reflects real-world user workflows and goals. This was highlighted through the User Story Map's structure, which is based on user needs and journey. This type of customer engagement and planning can be exploited further to ensure that future phases develop the product in a way that closely aligns to user needs. It should also be a primary consideration when scoping future projects.
Strategic Focus: Prioritising essential features for early user validation and risk mitigation. This can be used in future to ensure that too much time and resource isn’t engaged on developing features that ultimately aren’t useful.
Flexibility in Innovation Processes: Recognising that innovation is non-linear, the platform must accommodate workflows where problems and projects may stop, revert, or skip phases as needed. Future projects which seek to improve the innovation journey should account for this in their design.
Critical Role of AI Integration: Integrating AI enhances functionalities like intelligent matching and idea generation but requires careful implementation to ensure transparency and mitigate biases. Projects which look to capture user input, and ideation can benefit greatly from this inclusion. Both from an efficiency and creativity perspective.
Stage3: Alpha will see further module development and testing to develop a commercial-grade minimum viable product (MVP). This will very much be around user trials in which key stakeholders use the solution and provide feedback on how well it suits their needs, what gaps exist, and what improvements could be made. This form of trialling ensures alignment of the project’s key tenet of aligning with user requirements.
Problems with the trialled Methods
The methods deployed through the first two phases of the project have proved effective in achieving the objectives that were defined and delivered against the success criteria outlined. There are therefore no issues with the trialled methods to report.
Likelihood that the Method will be deployed on a large scale
The elements which will allow the project to move towards future deployment have been put in place by the initial two phases of the project, so progression to large scale deployment is, at this stage feasible. However, on conclusion of Stage 2: Discovery, SP Energy Networks have decided not to move to Stage 3: Alpha and this alongside Northern Powergrid’s earlier withdrawal, would leave UK Power Networks as the sole remaining initial project partner. Continued participation is therefore currently under review.
Effectiveness of any Research, Development or Demonstration
Research, development and demonstration have all been key elements within the project thus far. Development and demonstration were crucial to the production of the prototype, and the resulting feedback elicited from stakeholders upon demonstration were fundamental in achieving the objectives and success criteria, in particular ensuring alignment with user needs and development of the future roadmap. External resources used in the shaping of the project deliverables were:
