Whether you’re hiring or applying for new roles in emerging technologies, it’s important to know where tech is headed and how companies are adapting hiring and skilling strategies.
We’ve culled a few insights from the World Economic Forum’s The Future of Jobs Report to get you up to speed on what you need to know about the tech jobs of tomorrow. This article is the second in a four-part series after our analysis of blockchain opportunities, with upcoming articles on cloud computing and big data analytics.
Machine learning: The core of the Fourth Industrial Revolution
Perhaps more than any other emerging technology, machine learning sits at the core of the Fourth Industrial Revolution for its potential to drastically change the nature of tasks, job roles and necessary skills.
Machine learning platforms automate the task of finding meaningful patterns in data, making it easier to get insights from extremely large data sets. That, in turn, supports the application of artificial intelligence (AI), automation and vast innovation across industries.
As more tasks become automated, companies will need to evolve existing roles and create new ones to meet the changing demand. To weather this next industrial revolution, every company needs to invest in its workforce to adapt to machine learning instead of being overcome by it.
Machine learning’s impact across industries
For industries considering the potential impact of machine learning on business processes and job roles, it’s not a question of if — but how much — they’ll put machine learning to use to drive growth and innovation.
Companies are rapidly growing AI initiatives to advance autonomous driving, data security, fraud detection and personalization of the retail experience, among numerous applications. And it doesn’t appear to be slowing down.
The Future of Jobs Report found that 73% of all companies are planning to adopt machine learning in some form within the next three years. That impact will be most immediately felt in the information and communications technology (ICT) sector, with 91% of survey respondents planning to adopt machine learning by 2022.
The two other sectors shifting rapidly to adopt machine learning are the automotive, aerospace, supply chain and transport sector and the consumer sector, with 87% and 82% of companies in these industries expecting to adopt machine learning in some form by 2022.
By some estimates, new technologies (such as machine learning) may displace 75 million jobs over the next three years. Yet, the potential for new roles to emerge is even larger, representing a predicted 133 million jobs — a significant net growth in employment.
Changing roles and new jobs in machine learning
According to The Future of Jobs Report, this massive shift toward machine learning adoption will require reskilling of at least 54% of the current workforce, as well as broad education and training support to accommodate the new roles.
Companies overwhelmed by the prospect of adopting machine learning technology at a large scale should focus on finding value and time savings in critical organizations where adoption will be most seamless, and use those successful test cases as a roadmap. For example, starting with automating simple, repeatable tasks in IT will free humans from repetitive work and empower them to devote time to strategic and creative activities. Those activities will then fuel continued technological innovation.
In other words: Human creativity, deep work and cognitively demanding tasks will get a boost as manual administrative asks such as data entry, bookkeeping and accounting are handled through automation.
While the reality of automation becoming mainstream brings up very real concerns around job displacement, companies can prepare by devoting resources and attention to the growth of key machine learning-related roles, including data analysts and scientists, AI and machine learning specialists, process automation experts and human-machine interaction designers. Complementary roles such as robotics engineers, blockchain specialists and information security analysts will also grow as a result.
What’s next for machine learning?
Taking advantage of machine learning requires methodical planning and skill redevelopment. When evaluating the potential of automation for your company via machine learning and AI, here are three industry-specific considerations to keep in mind:
Machine learning in information and communication technology
Machine learning could accelerate organizations’ ability to automate the operation and maintenance of ICT networks and services, making emerging 5G networks more efficient, for example. But the uses extend beyond simply network expansion and efficiency into other areas of business; a mix of machine learning and data science best practice could help organizations optimize pricing models to help maximize profits as well as improve threat detection capabilities.
As with all industries that deal with communication, the question remains: How will you responsibly hand over historically human-driven functions like communication to machines without dehumanizing them?
Machine learning in automotive, aerospace, supply chain and transport
Companies will increasingly rely on data scientists and AI and machine learning specialists to take advantage of opportunities to improve the customer experience and drive greater efficiency and productivity across the supply chain.
In addition to feeding the growth of autonomous driving, the automotive, aerospace, supply chain and transport industry will use machine learning in other revolutionary ways—including predictive maintenance of large machines and equipment, delivery forecasting and human-robot collaboration.
Machine learning in consumer
Will companies be able to use machine learning to identify what customers want before the actual customer does?
With a clear roadmap that incorporates skilling and reskilling the workforce, machine learning could accelerate the ability of companies in this industry to predict buying behaviors, anticipate and avoid customer churn and personalize the shopping experience while also driving greater productivity overall.
Each organization that takes the time to better understand the potential of machine learning in its field and develop a clear strategy to evolve value chain in response will find they are better prepared to take advantage of machine learning’s vast potential. Getting real about the ability to meet these new skills in their local labor market, and creating a skilling approach to help address the workforce shift will also be just as important.