Zoe Webster, director of AI and data economy, Innovate UK

Path to power

I developed an interest in artificial intelligence (AI) in childhood after reading Isaac Asimov books. I completed a BEng in computer science and followed that with a MSc in intelligent systems to learn about other disciplines relevant to AI, especially philosophy of mind and cognitive psychology.

In 2000, I joined DERA (part of which became QinetiQ) to continue the research but also to explore its application in a range of fields. There I had the opportunity to develop and demonstrate cutting-edge machine learning algorithms in a range of applications, including retail, security and health, in which many of the data challenges are incredibly similar.

Later, I moved on to work at the newly-formed Technology Strategy Board, a government agency charged with accelerating business innovation (it is now called Innovate UK and is part of UK Research & Innovation).

During my time at Innovate UK, I’ve seen a wide portfolio of projects having led our Enabling Technologies, Manufacturing and Strategy teams for a time. I am now working with colleagues to find ways to identify, support and accelerate high growth-potential innovation in the UK, based on cutting-edge AI and data research and technology.

What is the proudest achievement of your career to date?

I meet innovators from all walks of life and get to play my part in introducing them to funding opportunities and partners who can help them develop and commercialise their ideas. When you then get to hear about their success a few years later, that makes me incredibly proud. They did all the hard work, but hopefully I had some influence on the path they undertook.

Who is your role model or the person you look to for inspiration?

I am inspired by the innovators and entrepreneurs I meet as part of my role, especially those without established networks who are trying to break through. The innovators we supported in our Women in Innovation programmes, who tackled grand challenges from reducing cold and damp in Britain’s homes to revolutionising the factory floor, are great role models for anyone looking to take forward an idea.

Did 2019 turn out the way you expected? If not, in what ways was it different?

2019 was a very busy year for our whole organisation as we look to maximise the opportunities that the formation of UK Research & Innovation provides for the economy and society. Most of my time was focused on the people and culture aspects of the job and that is set to continue in 2020. Data has a role to play here, too.

What do you expect 2020 to be like for the data and analytics industry?

More businesses across the UK economy are realising the potential that data and analytics could have on their productivity and competitiveness. Some sectors are more engaged than others (eg finance and health), so there is a need for the data and analytics industry to engage with new markets. In the past couple of years, there has been a lot of discussion about ethics and responsibility in data and AI. I expect this to continue into 2020 but, hopefully, with more practical approaches for businesses to use.

Data and technology are changing business, the economy and society – what do you see as the biggest opportunity emerging from this?

There are opportunities in areas such as healthcare, including earlier diagnosis of disease, and transport, through smarter infrastructure, and the creative industries, by way of the personalisation of content. A massive opportunity for society is the use of data and technology to mitigate climate change. This could be through better monitoring of what is going on, through geospatial intelligence, or faster and more accurate simulation and modelling tools, to analyse the likely impact of policy or technological solutions. We need to be able to do all this without adding to the burden on the environment.

What is the biggest tech challenge organisations face in ensuring data is at the heart of their digital transformation strategy?

A lot of businesses talk to me about the cost of data engineering – the effort required to access data and to make it AI-ready. This takes specific skills and a lot of time and resource. Having said that, I firmly believe this is an area ripe for innovation so I am exploring that opportunity with colleagues to see how we can help businesses use analytics more easily.