How to address the AI skills gap

The AI skills gap has been increasingly documented in recent months as awareness and expectations of the technology continue to grow. In fact, 93% of US and UK organizations consider AI to be a business priority, but more than half of them do not have the required in-house talent to execute their strategy.

As a relatively new discipline, attracting and retaining employees who are able to successfully drive business results via AI is undoubtedly something of a challenge.

It might sound obvious, but delivering a tangible ROI from AI requires data science talent and engineering know-how to ensure that ML models can be easily deployed into product, and that they deliver real business value. However, finding this talent is hard; academic ability isn’t the only factor you have to take into account here – your data scientists need to be commercially-focused, too, with a keen eye for business outcomes. You also need to create a technical environment in which data scientists can operate – which is no easy task – to try and shorten the time to value associated with your AI project.

What’s more, is that not all data scientists are born equal. The term ‘data scientist’ is still something of a ‘catch-all’ for those with data skills. However, each will have their own particular strengths; some will be better at data wrangling, others machine learning, others at communicating the value of data, and so on. What we’re saying here, is that simply hiring a data scientist isn’t going to be enough – a business needs to know what type of data scientist they are hiring, and how to assess that particular skill set.

Another factor that makes AI recruitment a challenge is the sheer amount of competition in the market. A recent PwC report found that only 4% of UK companies have implemented AI successfully, yet there is already a skills shortage despite educational improvements and more training opportunities in the AI space than ever before. AI talent gets snapped up quickly, with data scientists generally found in city centres, tech hubs or close to reputable universities. If you’re a more traditional business, based in a warehouse of factory in a less-than-glamorous location in the middle of nowhere, unfortunately you’re going to be seen as less of an attractive proposition for data scientists or AI engineers.

Closing the AI skills gap

The proliferation of AI in business is only going to continue, so businesses need to take action to address this AI skills gap if they want to keep up with the competition.

This, of course, is easier said than done. Businesses have spent billions on building and training and data science and AI talent in-house, but this isn’t always sustainable – in terms of both cost and time to value. It’s incredibly expensive and time consuming to develop an AI capability in-house, and often doesn’t deliver the best results.

Of course, some businesses do succeed from in-house AI; they tend to be larger enterprises, with the ability to both hire data scientists and develop their existing employees’ AI know-how through a mixture of upskilling and retraining.

This method, though, isn’t doable for many businesses – especially if they need to get value from their AI projects quickly. Thankfully, there is another way…

We may be biased here, but working with an AI company such as ourselves can often be the most cost-effective method of driving results from the technology. Not only do we provide you with our pioneering, results-focused AI System and solutions, you get access to some of the sharpest minds in the market, too.

Our team of data scientists, engineering experts, and customer success specialists have a proven track record of delivering value from commercially-focused AI projects, and delivering it quickly. No disruption in your day-to-day, no lengthly recruitment processes or costly training courses; just accessible AI that is focused on generating results for your business.

If you’d like to know more about our approach to AI for business, we’d love to hear from you.