The rapid adoption of AI is accelerating the broader digital skills gap but what skills are required, how might they be developed, and what specific actions should businesses take?

Most people see AI as beyond their comprehension due to its technical complexity and mathematical foundation. Added to that, the speed of change and development of new technologies is extreme, so the only way to keep up is to be constantly learning – and for now at least, that means developing skills in hard subject areas.

The easiest way to view the AI skills landscape is to think of it as a pyramid with exceptionally high levels of skills and knowledge required for developing AI solutions at the top, and lower levels of skills to consume AI intelligently at the bottom. The layers in between are where most working people will find themselves – either managing AI or using it to augment their work.

Figure 41. AI skills continuum

Skills for Building AI Solutions.

At the sharp end of AI development people are required to design systems that program themselves, so the level of abstract thinking, skills and knowledge needed is at or around PhD level – even if AI developers don’t have a PhD, their thinking needs to be broad and deep, and they need to be capable of significant abstraction.

The core skills needed to build AI systems are mathematics (particularly statistics and probability), data manipulation, and computer science. AI processes are being increasingly automated – e.g. AutoML allows the automated selection of the mathematical models that are applied to the data, and other tools can be used to automate workflows and other processes like feature selection in the data. However, there is still a need to be able to frame problems in the right way and understand the maths and technology to make good use from these tools.

It’s also worth noting that between 60% and 80% of the AI process is the preparation of data. So, skills like data wrangling, cleaning, model validation and data visualization will long be in high demand. As governments in the West are increasingly demanding more openness, having to ‘show your workings’ will drive demand for strong communication skills amongst AI solution developers. If an AI solution works, an AI solution developers need to explain why it is effective, and should also be able explain why it’s not effective when it’s not working.

Peering further into the future, we envisage Bio-Inspired AI fundamentally changing traditional development paradigms towards Goal Directed Design, or GDD. This will enable developers to tell the computer what is needed instead of what to do, and this has implications for the kinds of skills that will be needed in all kinds of software development.

Skills for Managing AI Solutions.

Whilst AI solution developer skills are rare, in reality only a very small number of people will be required to develop AI solutions. As AI reaches into the workplace, most people will be either managing the use of AI to produce value, or they will use AI to augment their work.

For most companies the ‘heavy lifting’ involved in implementing AI will be about defining the problems and opportunities, choosing and implementing the right solution components, and helping people incorporate the power of AI into their work.

AI can be used in an exceptionally wide range of scenarios, from swarms of microscopic robots to big data processing in massive server farms. So, understanding the scope, scale and range of benefits that AI can bring to business is an essential first step. The most important question to start with is “what problems can AI solve – for our customers, and for the company”.

A key skill required in all businesses is to recognise where AI can potentially add value. To take advantage of AI, there must be people in the organisation who know what AI can do, and understand what products and services are required. There must also be people who can visualise AI use-case scenarios, gain support, obtain resources, implement AI solutions, and manage change. Deep domain expertise will add a lot of value to the AI development process. 

Skills for Using AI to Augment People.

It’s likely that as time goes by, increasing numbers of workers will use AI to augment their work. For this, high level appreciation of AI and what it does, along with specific skills for working with AI-based solutions will be necessary. Across all levels in the organisation a broad appreciation of AI, and plans for its use in the organisation, is essential to dispel fears and to orientate people.    

Each business function will have different ways in which AI will be used to augment people, and will therefore have specific skills development needs – for example:

Marketing – using AI for recommendation, advertising and forecasting

Customer service – use AI for pre-emptive action, automated ‘always-on’ services, and personalised offers

Operations – use of sensors in products and processes, linked to AI for monitoring and predicting service requirements  

Logistics – demand planning, optimising the delivery processes, and self-driving vehicles

IT – boosting security, monitoring and resource optimization

Finance – extracting data, generating insights, faster invoice categorisation and payment matching 

HR – automating screening and reducing bias, performance management, improved workplace learning

Skills for Intelligently Consuming AI.

There’s also a real need to educate everyone to use AI in a smart way. This means people generally being savvy enough to not be persuaded by AI assisted fake news, or aggressive marketing, and being able to analyse how algorithmic decision making affects them. Without a basic understanding of how AI works, there’s a risk that many people will simply have AI done to them.

In a business context, this means ensuring that you end-user customers understand how to use your AI enhanced products, and for them to be informed about how it works, and how data is used.