Over the last few years, artificial intelligence (AI) has emerged as one of the most disruptive forces affecting organizations and individuals alike. According to a recent report from IDC, global spending on AI systems is expected to reach $97.9 billion in 2023. This spending translates to an annual growth rate of about 28.4% over the next few years.
This massive wave of change has a far-reaching impact on the way organizations function. Here are four trends:
Technology has automated several repetitive job tasks through technologies such as robotic process automation (RPA), AI and machine learning, robotics, and cloud-based products that deliver insights more quickly than humans can. According to a 2017 report by the McKinsey Global Institute, automation could displace as many as 800 million people around the world by 2030. In 2016, McKinsey also estimated that current technologies could automate about 45% of the activities people are paid to perform. The gig economy, too, has a role to play in this trend, since companies might find it more cost-effective to hire a temporary gig worker than to create an in-house position.
As automation frees up time and resources, it’s often consolidating job roles. As cloud-based software as a service (SaaS) tools provide deeper insights more quickly, and as these tools work together in a comprehensive and consolidated way, the amount of output expected from employees has increased. For example, digital marketers can rely on a steady state of information that tells them how their target customers are reacting to their ad or even their campaign, leading to faster decisions.
From a learning and development (L&D) perspective, this trend means ensuring that employees have access to role-based digital training so that they can succeed in this disruptive situation.
As our organizations shift toward AI and machine learning, it is crucial to bring the workforce up to speed. Again, role-based training is crucial here. For example, data scientists now need to know how to work with machine learning algorithms and insights, and developers need to understand the cloud ecosystem.
The 2018 World Economic Forum “Future of Jobs” report predicts that by 2022, more than half the workforce (54%) will require considerable re- and upskilling, a significant portion of which will require additional training that lasts over six months. What’s more, Gartner reports that 80% of employees lack the skills they need for their current and future jobs.
One of the most exciting aspects of the AI revolution is the plethora of opportunities to create new and innovative applications. This potential is magnified when organizations use AI and machine learning in conjunction with technologies such as the internet of things (IoT) or virtual or augmented reality (VR/AR). This confluence of technological innovation will create a number of new opportunities and job roles that do not even exist today. For example, it is possible to deliver packages to customers through a combination of drones, GPS and cloud-based technology. The technology experts behind these products are working in brand-new roles.
The World Economic Forum report states that while machines and algorithms are likely to displace about 75 million jobs by 2022, they will create about 133 million new roles. And, according to a 2018 Accenture report, while 46% of business leaders say that traditional job descriptions have become obsolete, companies that invest in AI and in “human-machine collaboration” may see a 38% increase in revenues and a 10% increase in employment levels by 2022.
As organizations work to prepare themselves for this onslaught of opportunities and growth drivers in the next phase of AI, L&D plays an increasingly central role. A well-crafted L&D plan that incorporates upskilling, reskilling and future-readiness can be a big success factor.
At the same time, there needs to be a certain amount of rethinking and innovation in terms of how to deliver this knowledge so that it sticks. Should you offer bite-sized online courses or long-winded classroom sessions? A blended approach that balances self-study with interactivity is often most effective. In fact, our data shows that a well-designed blended learning approach helps drive learner net promoter scores of over 75 and course completion rates of over 60%.
As AI and machine learning redefine industries and businesses, organizations need to empower their employees and equip them with the right tools and expertise to take on whatever happens next. Your organization’s future depends on it.