Sometimes, it’s centuries. There is archaeological evidence of antibiotics being used more than 1,500 years ago and written evidence of them being used 500 years ago. Yet it is only in the last 80 years that their use has become widespread.
Even with all the tools at our disposal, adopting a new technology is a huge challenge. Seventy per cent of digital transformation initiatives fail to meet their objectives. It is people who bridge the gap between a new technology’s potential and its real-life impact. But people are often the most neglected part of the solution, with only 3-6 per cent of AI budgets spent on employee training and artificial intelligence (AI) literacy.
How do we fix this?
The government’s AI strategy sets out the challenge clearly. It recognises that “a highly skilled, AI-fluent financial services workforce” will ultimately need different skill sets at different levels of an organisation – including boards, specialists and frontline teams. Most importantly, it prioritises “equitable access to reskilling and training” both regionally and for all socio-economic backgrounds.
That is the challenge. The mindset needed to meet this challenge must be based on two elements. First, an acknowledgement that technology, including AI, enables organisations and individuals to serve a purpose. However, adopting AI is not an end in itself.
Second, is the conscious decision to prioritise positive behaviours above technical know-how. Technical knowledge will progress with time, helping to iron out flaws and limitations in early models. In contrast, the wrong behaviours will stifle learning, giving permanence to missed opportunities and substandard solutions. But getting behaviours right is about more than wishful thinking and a sunny disposition. A professional approach to moulding behaviour demands precision and consistency in the way that we think about behaviour.
To facilitate a strategic approach to behaviours, many sectors have developed “professional maps”. For example, the Chartered Insurance Institute has developed a professional map for personal finance and insurance. As individuals progress to more senior roles, the demands on them to promote, as well as display the right behaviours, increases. This can also be identified and catalogued. For example, expectations of a junior member of staff might be to “handle personal and business information sensitively and confidentially, including when using AI tools”, while someone in a leadership role should be expected to “actively promote transparent data and AI practices that respect privacy and uphold public confidence”.
Many of the behaviours on our professional map that are relevant to technology go a long way beyond building and maintaining machines and software. They include:
Customer focus – to anchor professionals in sustainable value, rather than sexy presentations to investors.
Curiosity – to repeatedly come back to discover what technology is capable of as it develops, and to understand how it is being used in both official and informal ways across the workplace.
Drive to Deliver – the resourcefulness and determination to take responsibility for an outcome and ensuring its delivery without ever blaming third parties.
Impact – knowing how to use technology in a way that achieves better customer understanding, and not just delivering more information.
Integrity – understanding the ethical dimensions of technology – especially with the rapidly evolving role of AI –
and how to confront poor practice, such as social scoring or “emotion recognition” for pricing.
Insight – not just in how technology works, but in how it interacts with consumer psychology and behaviour.
Inclusivity – to understand the impact of technology on every potential customer so that the systems are designed to work for everyone.
Professional bodies are well-placed to lead the task of identifying and developing the right behaviours across an entire sector. With hundreds of thousands of practitioners making up their collective membership, they can facilitate progress by:
Designing qualifications and training tools, and accrediting and delivering continuing professional development.
Setting standards of good AI practice for firms and individuals – an example being the CII framework “Responsible AI: from Policy to Practice”, which was created from practitioner input. It sets out “red lines” between what is and isn’t acceptable and rejects activities such as social scoring, behavioural manipulation and emotion recognition for eligibility or pricing.
Developing robust benchmarking programmes by aggregating anonymised experience across firms to reflect back to the sector elements of strong AI policies, AI maturity curves and what is and is not working in practice. Fostering regional solutions through established local networks of practitioners throughout the UK.
For financial services, avoiding the common mistakes involved in implementing new technology involves two key elements: first, putting purpose before technology; and second, setting out a comprehensive map of the behaviours needed to drive successful adoption.
Crucially, these elements must form the foundation of the government’s AI partnership with business. Anything less risks leaving us waiting for decades – or even centuries – for new technology to reach its potential.
Related
