BridgeAI Innovation Exchange: Empowering AI Innovation

To lead a project your organisation must be either a UK registered small or medium sized enterprise (SME), or research organisation.

Subcontractors are allowed, and can be from anywhere in the UK; you must select them through your usual procurement process.

An applicant is not allowed to apply for more than two challenges in this competition.

Grant funding in this competition is awarded as Minimal Financial Assistance (MFA) – applicants must not have received over £315,000 (including this grant) in the last three financial years including the current one.

Innovate UK welcome and encourage applications from people of all backgrounds and are committed to making our application process accessible to everyone. This includes making reasonable adjustments, for people who have a disability or a long-term condition and face barriers applying to us.

You can contact us at any time to ask for guidance.

We recommend you contact us at least 15 working days before this competition’s closing date to allow us to put the most suitable support in place. The support we can provide may be limited if you contact us close to the competition deadline.

You can contact Innovate UK by email or call 0300 321 4357. Our phone lines are open from 9am to 12pm and 2pm to 5pm UK time, Monday to Friday (excluding bank holidays).

Your project must:

• have a grant funding request between £25,000 and £50,000
• last between three months and five months
• carry out its project work in the UK
• intend to exploit the results from or in the UK
• start by 1 September 2025
• end by 28 February 2026

This competition aims to accelerate AI adoption across the UK, by fostering collaboration between challenge holders in sectors and developers who need data rich environments to advance Artificial Intelligence (AI) and Machine Learning (ML).

Your project must:

• develop a technically viable AI-driven proof of concept
• collaborate closely with the challenge holder to ensure effective delivery
• use real-world or synthetic data for testing and refinement
• define measurable success metrics such as productivity gains, cost reductions, or efficiency improvements
• ensure practicality and usability within the challenge holder’s business environment
• provide a clear scalability plan for future expansion
• include training, documentation, or guidelines for knowledge transfer
• comply with sector specific regulations, data governance, and AI ethics standards

Your proposal must:

• demonstrate how the solution enhances productivity, efficiency or competitiveness
• address ethical considerations, data privacy, and transparency in AI development
• present a structured project plan with milestones, timelines and resource allocation

Challenge: Small to medium businesses in the UK cycling industry struggle to access and interpret market intelligence from the Market Data Service (MDS), limiting their ability to make informed decisions. Current dashboard tools are complex, and only larger businesses with analyst resources fully utilise them.

Objective: Develop an AI powered solution that simplifies access to MDS insights for non-technical users, allowing them to make data driven decisions on inventory, sales, and supply chain optimisation.

Solution requirements:

• create a user-friendly AI powered tool, for example, using a chatbot interface, that allows businesses to query market data in natural language
• reduce search and analysis time by 25% within six months
• increase active MDS users fivefold, particularly among smaller businesses
• integrate with AWS Quick sight and Snowflake for real time market benchmarking

Impact: Expands data driven decision making across the cycling industry, improving efficiency in supply chain management, reducing over or under ordering, and increasing profitability for businesses of all sizes.

Challenge: Manual site reinstatement processes across United Living’s telecoms, oil and gas, and property services projects lead to inefficiencies, delays, and compliance risks. Site assessments, hazard identification, and quality control are labour intensive and prone to human error, increasing project costs and environmental impact.

Objective: Develop an AI driven solution that automates site assessments, detects quality defects in real time, and integrates geospatial data to optimise reinstatement processes.

Solution requirements:

• use AI to analyse topographic survey data and validate designs against project specifications
• implement an AI powered photogrammetry tool to detect defects and flag compliance risks
• automate risk assessments and document site conditions in a central repository
• integrate with Microsoft Fabric and Azure for seamless data processing and reporting

Impact: Improves site reinstatement efficiency, reduces rework and compliance issues, enhances sustainability, and minimises costs associated with delays and safety hazards.

Challenge: Encouraging people to shift from car use to active travel (walking, cycling, and public transport) is resource intensive and difficult to scale. Identifying the right individuals at key moments of life transition, when they are most open to change, is currently a manual and inefficient process.

Objective: Develop an AI powered segmentation and engagement tool to identify individuals likely to switch travel modes and deliver personalised interventions to encourage sustainable transport choices.

Solution requirements:

• use mobile phone data and demographic insights to segment and target individuals based on travel habits
• deliver geographically tailored travel information through a user-friendly dashboard
• measure the percentage of people who shift travel modes after engagement
• ensure compliance with GDPR and ethical AI guidelines

Impact: Enables large scale, cost effective behaviour change interventions that reduce car journeys, lower CO2 emissions, and promote healthier, more sustainable transport options.

Challenge: England’s planning system is struggling to process the high volume of applications needed to meet government targets of 1.5 million new homes by 2029. A lack of automation, reliance on manual data entry, and fragmented policy frameworks create bottlenecks, increasing legal risks and delays.

Objective: Develop an AI powered planning co-pilot that streamlines policy interpretation, automates document analysis, and assists in application assessments.

Solution requirements:

• aggregate and analyse national, regional, and local planning policies, alongside submitted application data
• provide an AI powered tool to assist planners in making faster, more consistent decisions
• reduce application processing time and improve accuracy in policy compliance
• ensure transparency and regulatory compliance in AI driven decision making

Impact: Increases planning efficiency in both the private and public sectors, enabling faster approval of developments, reducing legal disputes, and helping meet housing and infrastructure targets.

An online briefing event will be held on 2 April 2025.

Register for the applicant briefing event.

A recording and slides will be available afterwards.

For further support and enquiries, contact Innovate UK Business Connect’s BridgeAI team.