Improving operations through machine learning


The aim of this competition is to improve the efficiency and effectiveness of Innovate UK’s operational functions.

Innovate UK is looking for as many ideas as possible for how machine learning can help it make the best use of its existing data. You can find out more about the data that Innovate UK will provide to successful applicants in Annex A on the secure server (FTP) site. The FTP site will be available after you register.

Machine learning is a type of artificial intelligence. It allows computers to become more accurate in predicting outcomes without explicit programming, using algorithms that iteratively learn from data.

This is a 2 phase competition. A decision to proceed with phase 2 will depend on the outcomes from phase 1.

1. Phase 1: technical feasibility. Projects should last up to 4 months and you must complete your project by 31 March 2018. Projects can range in size up to a total cost of £50,000 including VAT. The total funding available is up to £250,000 including VAT.
2. Phase 2: prototype development and evaluation. In phase 2 the successful applicants will continue to develop and test their prototype. Projects should last up to 6 months. They can range in size up to a total cost of £250,000 including VAT. We expect the total funding available to be £500,000 including VAT.

Only successful applicants from phase 1 will be able to take part in phase 2. Innovate UK may change the budget and transfer funding between the different phases.


The aim of this competition is to improve the efficiency and effectiveness of Innovate UK’s operational functions.

You should show how you would develop and test a machine learning-based prototype application to:

  • improve operational efficiency
  • improve the efficiency and quality of decision-making, both pre and post-funding award

Innovate UK has identified 2 areas where machine learning could help it reduce costs and work more efficiently:

1. Assessor allocation: identifying key words from funding applications to allocate appropriate assessors.

2. Checking for undeclared re-submissions: searching for re-submissions, duplicate and reassessed applications.

Below are other possibilities but Innovate UK welcomes other ideas:

  • analysing competition queries, complaints and enquiries data to improve the quality of our communications
  • gathering information automatically from different styles of application form and representing that data in a meaningful and readily-accessible way
  • learning from project outcomes to better inform competition and project design
  • learning from economic exploitation data to better inform investment decisions
  • using monitoring reporting data to inform continuous improvement of monitoring services
  • reducing the potential for fraud
  • improving the accuracy of expenditure claims forecasting helping us to schedule projects more effectively

The aim is for efficiency savings to cover the cost of the project investment.

We will provide details of available data to help you develop your proposal. We will provide successful applicants with anonymised data for their feasibility study. We will also provide descriptions of our internal systems. Successful applicants will need to sign a non-disclosure agreement.

Key Dates:

Competition opens – 24 July 2017

Registration closes – 6 September 2017 12:00pm 

Competition closes – 13 September 2017 12:00pm 

Applicants notified – 9 October 2017

If you are interested in this competition?

Please email the Innovation SuperNetwork team at or complete the form below so we can help you and guide you through the application process.

(Direct link – please cut and paste into browser –

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Please fill in the form below to find out more about this and other Innovation Challenges.

Our work is only possible with the support of our local and national partners, including Innovate UK, the Knowledge Transfer Network, the North East LEP, Northumbrian WaterNorth East BIC, and Reece Innovation with part funding from the European Regional Development Fund.
Huge thanks go to all our partners.