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Opinion: National AI Strategy and what it means for skills and talent

10 November 2021

The UK Government published its first National Artificial Intelligence (AI) Strategy in September 2021. What are the implications for businesses, and, more specifically, for the people that make up those businesses? We asked data science experts David Pool and Duncan Shaw for their thoughts.

While the vision of making the UK a global centre for AI innovation has been bubbling away for several years, this latest strategy undoubtedly sets ambitious goals. For the UK to be a ‘global AI superpower’ by the 2030s, the report acknowledges that ‘people’ and ‘data’ are key drivers. It lays out short-, medium- and long-term actions to deliver on what is set to transform the economy, industry and our day-to-day lives.

David Pool

David Pool is an entrepreneur with specialisation in Artificial Intelligence, Machine Learning, data analytics and business intelligence.

“The UK AI strategy is a recognition of AI as a foundational technology that supports the Government agenda to become a high productivity / high wage economy over the next decade. Add to this, quantum computing, synthetic biology, semiconductors, and renewable energy and the scene is set - with AI at the core. 

AI is moving rapidly from an age of experience and discovery to an age of data-driven implementation, so we need people with the skills to implement data projects at scale. Today, the UK is realising around 15 per cent of its digital potential with 7 in 10 organisations having just 10 per cent adoption and, worryingly, only 1 in 20 having 90% adoption, so there is huge potential for productivity growth. Also, the digitisation of industries means an opportunity for more geographically distributed talent. Data scientists can work remotely on projects as easily in Cumbria as in Canary Wharf, supporting the levelling up agenda for the UK too. 

Government long-term academic investment in AI, through Universities and institutions such as the Turing and the Big Data Institute, is now matched by commercial investment through the employee training levy, allowing companies to train a new generation of data scientists, able to apply the latest AI techniques to operational data and allowing companies to drive huge efficiencies and information-gain. Corndel is proud to play a key role in the strategy, with programmes tailored for all levels of commercially focused data scientists, helping every company to put its data to work in the most optimal and effective ways.”

Hear more from David Pool on LinkedIn.

Duncan Shaw

Dr Duncan Shaw helps business leaders transform their organisations using emerging data technologies.

“The recent National AI Strategy has many good points but there is always a danger of training people to ‘fight the last war’. Increasing AI availability and increased AI capabilities mean that most people’s work is actually becoming less technical. The success of the UK means that most people need to learn how to work with AIs, not how to develop them.

AI systems are automating away task after task. So most people are using less deeply technical computer science skills. You can see this in the development of higher level programming languages and No Code – the programming approach and software tools which use clever templates and graphical interfaces rather than code writing.

There will always be a role for computer scientists, developers and data scientists, especially in the short term. But it will become increasingly centralised around the main AI providers. These are non-UK firms like Google, IBM, Amazon and so on. Most organisations use AI-powered applications in the cloud, and they demand a different skill set.

This migration of human brain power from the automatable to ‘unautomatable’ is happening not just in the technology that underpins AI tools but also in the decision-making tasks that use them. This is reflected in Mckinsey’s analytics translator role, which connects modern analytics capabilities to the rest of the business.

You can also see it in courses like Corndel’s Data Professional course that teach how a deep understanding of each business is the basis for every analytics project. Skills like data story-telling, communications and Journey-based Thinking are the second pillar of data-driven organisations. The first pillar, AI-powered tools will be bought-in not home grown.

The upskilling requirements here are huge. Yes, a large number of computer scientists and data scientists are needed. But everyone else in the country needs upskilling to work with the AI systems that they create.

As more work is done by AIs the UK will need training up in the things that AIs can’t do better than humans, not the things that they can do better. Things like intuition, creativity, critical thinking, empathy and human judgement.

This is especially true for how these skills can help with developing AI technologies to guard against AI bias and to strengthen AI ethics. The strategy report mentions “trustworthiness, adoptability, and transparency” and the Alan Turing Institute will play a major central role in that, but where does the UK’s capacity come from to support this at scale?”

Hear more from Duncan Shaw on LinkedIn.

Read the National AI Strategy here.

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