How to close the AI skills gap in your organisation

Artificial intelligence (AI) has the potential to dramatically raise productivity among managers in almost all industries and types of organisations.  

But only those with the right skill sets are positioned to take full advantage of this revolution, making the upskilling of staff essential.  

AI will change many aspects of our lives just like the internet has and all staff involved need to be trained in how to use it, while some must understand enough to direct it.  

Closing the AI skills gap involves at least six pathways: 

• Internal training as continuous professional development (CPD) - where the leadership team leads by example.  
• Knowledge exchange with universities and individual training providers.  
• Sending employees on short programmes, boot camps, or curated university courses designed in collaboration with business for targeted learning.  
• Pilot projects with AI vendors – start small and then scale it up. For any adopting company, there is a lot at stake, so pilots are a good risk moderator.  
• Emulation of ‘best in class’ – often disseminated by management consultants.  
• Engage with the AI community from academia or industry in a continuous dialogue – including attending AI events and summits, where it is possible to learn about various challenges and approaches (few SMEs do this). It is important to see your organisation as part of a bigger picture.

Ensuring your organisation can make the most of AI tools

Skills gaps must be addressed both on the AI solution provider and user sides.

On the provider side, AI research is advancing, and freshly qualified graduate staff are available to apply the latest techniques and approaches, helping upgrade the skills of existing qualified staff.  

These employees will come from business focused educational establishments such as Warwick Business School, along with computer science graduates.  

The biggest companies like Google and Microsoft, which have their own R&D departments, are hiring widely and putting a lot of money into upskilling internally.  

Then there are a range of companies that either buy AI solutions from providers or have a small AI division themselves.

Those who use the AI in companies must be taught the possibilities and limitations of AI.

Why in-house teams must understand AI technology

In particular it is crucial to involve those employees who possess an intricate understanding of how the business operates.  

They are the essential cogs of the firm, holding the contextual knowledge necessary to run the business and are actively involved in day-to-day operations, ultimately comprehending the challenges faced by customers.  

Enrolling employees in AI training boot camps or internal training programmes becomes imperative to maintain a competitive advantage in an age where AI is disrupting every sector. 

Adapting AI tools to any business requires contextual knowledge from which it can support, replicate, and automate some of the simpler tasks. This is a supervised process requiring input from employees, who can use the AI to support their work, freeing them up for further supervisory tasks.  

Not only does such knowledge help identify and collate the right data to feed the AI, it also enables staff to fine tune data and decide on tasks, while the simpler repeatable jobs are taken care of. Those tasked with directing the AI require the most upskilling.

Why relationships are key when implementing AI

Effective communication is key between those providing and using the AI.

Technical knowledge of the AI product needs to be adapted to the business environment, so both groups must understand some common AI terminology, such as the difference between ‘supervised learning’ and ‘unsupervised learning’.  

The two groups need to work together to understand what can be shared and what needs doing. Technologists talk to technologists, and the business side talks business – and the two must come together.  

Existing staff already in the company’s ecosystem can become well versed in the problems of AI product development and implementation in their workplace. Improved training can help illustrate what the AI solution could look like in their company.

When upskilling existing staff, AI training must address the ‘why’ as well as the ‘how’.  

Employees need to know the AI’s ability depends on the data it is fed on, and that when it gets bad data it will not work as intended. There is a whole debate around the biases integrated in many AI solutions.  

The three big generative AI tools - or large language models (LLMs) - crawl the internet for data and tend to spell out a series of points in response to questions.

This may not solve the issues you need it to solve, although it can probably help. The user needs to ensure the right data is available to get the best results.

Overcoming employee fear to close the AI skills gap

Managing fear of AI is also important. Many employees may be concerned that their jobs are at risk.

Training can balance these worries and enhance productivity. Emphasise that AI tools are trained to perform specific tasks and there is a lot that AI cannot do.

AI can free up of employees to do something more creative, as long as there is upskilling both in the use of AI and broader business goals.  

For example, WBS research improved healthcare AI processes for image analysis and registrations and so reduced routine workload, freeing up crucial healthcare resources that could be used more productively elsewhere.  

In the US, half of war veterans have mental problems and no healthcare support – but an AI chatbot agent can provide a plan that previously required a therapist.  

The AI must have access to medical records as part of its data set, but as long as all the right data is available, it can provide an improved service by reducing the time and costs involved.  

It is the creativity of the user that determines how AI systems are used.

As Al and algorithms are refined, they will be able to address more and more problems. This will make the user ever more productive. However, it also requires the user to understand the potential gaps, risks, and biases.  

In a data-driven world, the ability to source the optimal data and maximise AI’s potential will deliver a critical competitive edge. Upskilling is essential to enable staff to achieve this. 

Those who embrace these changes and upskill effectively, stand to gain a significant competitive advantage.

Further reading:

Who will benefit from AI in the workplace and who will lose out?

How to reduce AI bias

Working on the jagged frontier: How companies should use generative AI

How to use ChatGPT as a tool in business strategy

Neha Gupta is Assistant Professor of Information Systems Management and Analytics at Warwick Business School. She teaches Artificial Intelligence for Business and Programming for Business Application on the BSc International Management and BSc Management, BSc International Management, and BSc Accounting and Finance.

Learn more about harnessing AI to give your organisation a competitive advantage on the four-day Executive Education course Business Impacts of Artificial Intelligence at WBS London at The Shard.

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