Finding talent for those hard-to-fill AI jobsAI jobs are difficult to fill, enterprises need to upskill and recruit the right talent, while investing in new technology and tools
According to research by SnapLogic, the software company, although 93% of IT decision-makers in the UK and the US believe AI is a business priority, 51% acknowledge that they don’t have the right mix of skilled AI talent in-house to execute their AI programmes. Likewise, the McKinsey Global Institute has predicted that the US economy will be short 250,000 data scientists by 2024. As the technology grows in popularity, AI jobs will only get harder to fill.
“Fostering a talented data team should be a top priority for organisations looking to unlock the true potential of AI,” said Craig Stewart, SVP Product, SnapLogic. “Our research found the priority skills and attributes that organisations are looking for in their AI team are coding, programming and software development (35%), data visualisation and analytics (33%) and an understanding of governance, security and ethics (34%). But whilst recruitment may be the most obvious fix for these shortcomings, the skills gap is proof it’s not so straightforward.”
Upskilling and finding new-collar workers
According to Stewart, an alternative route to skilled employees, which does not require tapping the already limited talent pool, is upskilling.
“Employees that are familiar with working with large sets of data, such as those that work in analytics, may require little retraining in order to contribute meaningfully to AI projects,” he said. “Fostering the capabilities of existing employees has the added benefit of maximising skilled employees who already understand the company ethos and goals and will likely be motivated to broaden their skill sets.”
Jobs of this sort are often referred to as new-collar careers. A new-collar worker is an employee who develops the core soft skills required to fulfil a role through means outside of traditional education.
The term was coined by IBM’s CEO, Ginni Rometty, in relation to ‘middle-skill’ occupations in technology, such as cyber security analysts, application developers and cloud computing specialists.
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According to Rometty, speaking on CNBC: “For the future, there are many jobs that can be done without a four-year degree. In some of our centres in the US around a third of our folks don’t have four-year degrees.”
Indeed, while strong technical expertise will be a must, enterprises should be aware that AI initiatives require a host of other skills, such as creativity, business acumen and ambition.
Stewart added: “Businesses need to equip themselves with automation tools that will insulate them from the ongoing skills drought and ensure they’re not limited by the existing pool of talent. Certain technologies, such as AI-powered low-code platforms, get technology capability into the hands of more people across the organisation with various skills, expanding the number of people an organisation can call upon to engage in AI projects and drive the business forward.”
There are a number of AI courses that can help relevant employees upskill, as technology becomes more pervasive in the enterprise.
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Displace AI fears to optimise internal talent
Despite the skills gap in AI, this technology has a strong associated with destroying jobs. While reducing labour costs is attractive to business executives, it is likely to create resistance from those whose jobs appear to be at risk. However, according to James Bell, general manager, integrated global services at Long View, if businesses wish to scale AI effectively, they need to help their employees overcome these fears, and help them understand AI’s benefits, or else they will not be interested in upskilling.
“When kicking off a new AI initiative, we create a mission statement telling employees that ‘we want to provide as many healthy lives and prosperous careers as possible,’ then we align project goals to how it will impact employees in their day-to-day,” he said, speaking at IPsoft’s Digital Workforce Summit 2019.
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According to Bell, this process is all about displacing fear and helping employees embrace change.
“Once employees understand they are going to be given the opportunity to retrain and do more interesting work they get motivated,” he added. “For them to believe us, we needed to create a solid career path for them and provided the training that supports them. We took some of our senior operational leaders and created an automation services team, which allow for new career progression opportunity for several employees. It’s basically a task force or a SWAT team of people who go and look for those use cases and for those pieces, that have to be automated for our business to continue to grow.”
Partnering with universities
In order to address the skills gap, businesses must also learn to cultivate collaborative relationships with the academic community as opposed to treating it like a transactional, one-stop-shop for talented new hires.
Stewart said: “Businesses typically pursue AI technologies with a specific use-case in mind, adhering to customer requirements or business growth goals, whereas academia develops AI with more freedom to explore, testing theories and the limits of the technology. Both approaches, while different, are important to the future development of AI.
“Businesses should share their expertise and engage with academia by delivering guest lectures and sharing real-world data and insight by working closely with professors. Internships and other work placements for students provide the experience of real-world AI applications and allow them to apply theory to practical projects. Both environments contribute to the development of the talent pool and allow individuals to develop important skill sets.”
Here are a few examples of Universities and academic communities in the UK that have recently partnered with businesses on AI initiatives:
Anglia Ruskin University
Back in April 2019, Anglia Ruskin University launched four new courses, with the help of local companies, to help close the AI skills gap.
Students are now able to enrol in three Master’s courses (MSc Artificial Intelligence and Big Data, MSc Artificial Intelligence with Cyber Security, MSc Intelligent Systems and Machine Learning) as well as an undergraduate BSc (Hons) in Artificial Intelligence.
Dr Silvia Cirstea, deputy head of the School of Computing and Information Science, said: “Our new courses have been developed in conjunction with employers, many of which are based in and around Cambridge, and these employers have provided advice about future roles and opportunities, and what they will need graduates to be able to do.”
King’s College London
Back in May 2019, NVIDIA and King’s College London announced a new partnership focused on developing a new artificial intelligence (AI) platform to improve radiology workflows.
The platform, once complete, will help providers from the U.K.’s National Health Service (NHS) automate “the most time-consuming” aspects of providing imaging services.
“Together with King’s College London, we’re working to push the envelope in AI for healthcare,” said Jaap Zuiderveld, vice president of sales and marketing for Europe, Middle East, and India, NVIDIA.
The Alan Turing Institute
The Alan Turing Institute partners with a wide range of organisations, including government, charities, companies and universities to undertake world-leading research to change the world for the better.
Earlier this year, the Financial Conduct Authority (FCA) and The Alan Turing Institute launched a joint year-long project around the use of AI in the financial services sector.
The project will examine current and future uses of AI across the financial services sector, analyse ethical and regulatory questions that arise in this context and advise on potential strategies for addressing them.
The future of AI jobs
According to Gartner, leading organisations expect to double the number of AI projects in place within the next year.
Needless to say, AI jobs are going to increase. The rising number of AI projects means that organisations need to reorganise internally to make sure that AI projects are properly staffed and funded.