AI is changing more than what computers can do and how we communicate and interact with technology. AI is changing the very nature of work, of hiring, reinforcing the imperative for life-long learning, and serving as a catalyst for organisation-wide change.
How to Prepare a Generation of AI-first Workers
The NHS is set to lose an estimated 350,000 staff by 2030 in the UK—a quarter of its workforce. It’s a Catch 22 situation: staff are leaving because of their intolerable workloads, caused by an already acute level of staff shortages.
While the National Health Service (NHS) has no magic wand to summon up a small army of suitably qualified staff, they are looking to Artificial Intelligence (AI) to help. AI has the power to automate many of the repetitive, manual functions clinicians love to hate. It can also help inter-department information and data sharing, and ease the workloads on stressed employees.
AI’s impact will be felt across a wide range of healthcare sectors from personal medicine and research, to diagnostics and logistics. But reaping the benefits of AI requires a number of industry-wide organisational and cultural shifts. Both healthcare providers and patients need to develop a culture that enables AI to augment traditional practices while easing the rollout of new technology.
However, integrating new technology comes with many challenges. AI developers need to tackle the issues both providers and patients have with the adoption of AI and build the right tools to accommodate the changing medical landscape—issues that extend well beyond the NHS.
All enterprise organisations need to prepare for the cultural and technological changes necessary to usher in this new wave of AI technology. Successful businesses need to facilitate a commitment to proper training to upskill and reskill as needed, whether that be through books, videos, or live online training. But, are we ready for the AI revolution?
The changing world of work
The world of work is changing at a rate never before seen in history. New jobs are being introduced that didn’t exist just a few years ago; community manager, social media manager, virtual reality consultant, data scientist, machine learning engineer, and UX manager to name a few.
With this new workforce comes the demand for new skills, fresh approaches, and a desire for continual learning. Research by the World Economic Forum shows machine learning and algorithms creating the potential demand for 133 million new jobs across all industries. Additionally, research by US job recruitment site Glassdoor indicates data scientists have the number one job in the US, in terms of job satisfaction, salary, and the number of job openings.
While encouraging information for data scientists, this also translates into massive skills shortages for businesses, governments, and research organisations that have not historically had to fill these positions. Research suggests there are, at best, no more than 300,000 AI specialists worldwide, but millions of job openings available. This makes hiring for these roles a major challenge for organisations trying to keep up with the pace of technology.
In fact, research from O’Reilly shows the AI skills gap is the biggest barrier to AI adoption. Data challenges, company culture, hardware, and other company resources all have a role to play too, but IT and tech workers need to rise to the occasion.
Academic and training programs simply can’t keep up with the pace of innovation and change in the IT sector. Not only do AI professionals need official training, but they need on-the-job experience too—particularly as the technology quickly advances with new tools and new things to learn.
But the barriers to entry are set high. Many companies want candidates with post-graduate qualifications. Some even only consider PhD level candidates, along with two- or three-years’ work experience. On top of that, companies need ‘soft skills’ so applicants can relate to customers and their colleagues. It’s an incredibly daunting list of criteria to even be considered for many data analytics and AI positions.
Recently, we have seen a rise in data science programs and machine learning at many universities. Investing in educational programs that target areas such as AI, cloud computing, and biotech is essential if we are to bridge the technology skills gap. That said, traditional institutions and industry partners need to work together to teach new technology relevant to the ever-changing job market.
For both employers and employees—current and prospective—the key requirement for a successful technology evolution is a commitment to lifelong learning. The World Economic Forum Future of Jobs report can validate this with findings that show more than half (54%) of all employees will require significant reskilling and upskilling in the next three years. Both businesses and their workforces need to be ready.
The rise in online training platforms has helped keep professionals up-to-date, while allowing them to learn new skills from scratch. Fellowship programs in some topic areas also help academics and PhD students in sciences and engineering transition to technology careers. More and more training programs, whether online or in a traditional teaching environment, are becoming available, but buy-in from both organisations and individuals is a must.
Gender equality and diversity
We have long documented the stats on the gender disparity in the IT sector. In fact, according to The United Nations Educational, Scientific and Cultural Organisation
(UNESCO) female graduates in STEM subjects make up only a third of the IT workforce. An even larger gender gap exists when it comes to the world of AI.
Ironically, AI and machine learning have failed to address the gender imbalance—and have arguably perpetuated it further. The recruitment bias has fallen against women, and although troubling, is not surprising given recent history. Computers have read so many applications written by men, they look for the same traits when recruiting for the next generation.
Machines are not infallible when it comes to filling the IT skills gap. However, if we want to create AI technologies that work for everyone, they need to be representative of all people—not just those who have been in the industry longer.
By addressing some of the gender biases in the industry, the community will become more inclusive and better-equipped to face new challenges. As we enter the implementation stage for AI technologies, new training and educational platforms are vital to help reskill workers over the next year. The talent pool is only set to grow and become more diverse, but organisations must take the appropriate steps to ensure that happens.
Beyond gender equality, diversity is another challenge for the AI space. Not just physical diversity, but cognitive, social-economic status, academic, cultural, industry, and other dimensions of diversity. One of the best ways to ensure that bias and fairness issues are addressed in AI is to ensure organisations have processes that require review from diverse perspectives.
We not only need to train a new army of AI technologists, but also ensure they truly represent the society in which we live in.