For years now, experts in the fields of machine learning (ML) and artificial intelligence (AI) have predicted that the technologies would lead to a significant disruption of today’s workforce. In general, most have insisted that it’s repetitive task and low-skill jobs that are at risk, but that doesn’t really cover the full impact that AI is about to have on workers and businesses all around the world. In reality, AI is likely to alter as many jobs as it replaces, leaving a wholly reconfigured labor landscape in its wake.
The key to figuring out where the biggest impacts will be is to understand the conditions that AI and ML systems require to flourish, and where they’re more suited to a complementary role. For businesses, it’s a required exercise in future forecasting that many can ill-afford to get wrong. Here are the factors that they should be paying attention to when looking ahead to the AI-powered future, so they’ll be able to plan accordingly.
Data, Not Skills
When most people think of repetitive task jobs, they likely imagine an assembly line or a shipping fulfillment center. While some of those jobs do certainly fit the bill, it isn’t quite that simple. In reality, AI and ML systems can already do far more than move items from one place to another reliably. They’re already capable of performing much more complex tasks, which involve conditional variation based on factors like external input, context, and desired outcome. The key is how much data they have to use as an input, and what the required level of output precision is. For example, AI can already handle things like routine medical diagnostics, because of the sheer number of existing medical records available to study. In fact, an AI built by a UK firm has already passed a test given to human doctors that assess their diagnostic capabilities. That doesn’t mean, however, that doctors are about to be replaced by machines.
The reason that doctors make such a useful example for those attempting to forecast where AI is going to have the greatest impact is that they’re a perfect illustration of a role that will be hybridized, rather than replaced. That’s because their jobs also require a fair amount of soft skills and human interaction that isn’t handled well by AI and ML systems. For example, diagnosing a patient with an aggressive form of cancer may be easy for an AI, but delivering the news to the patient and providing the required emotional support may not be. That’s the dividing line that most businesses will need to pay attention to when they make their assessment of where AI fits into their strategic plans. It is the jobs that require few interpersonal and creative skills that will be absorbed by AI, and the others will be augmented by it, at most.
Since AI and ML systems excel at data-rich tasks, they will be able to replace some workers that fall on the higher end of the skills spectrum as well. This is especially true for jobs that require industry-specific expertise that’s not subjective. The IBM Watson-powered gemologist Rosi illustrates this quite well. Since precious stones like diamonds can be judged by specific, defined qualities, an AI was able to master the skill of selecting the right stone for a customer by learning alongside human experts. It’s an object lesson for businesses that proves that jobs that many considered safe from AI encroachment may be affected all the same. An identical lesson may be drawn from existing AI advances into industries like insurance and financial management. The bottom line is, if there’s enough available input to teach a machine to produce reliable results, it won’t be long until the machine is adept enough to be a viable option in the real world.
The Key Takeaway
For businesses that are just now starting to gauge how they expect ML and AI systems to impact their future plans and labor needs, the main point here is to focus not on the subjective difficulty of the work under examination. Instead, focus on what soft skills the work requires, and the organic process that led current staff to their level of expertise in their jobs. Jobs that require little human interaction and that one can master through an iterative approach are the ones that will be ripe for disruption, no matter how ‘high-level’ they seem to be. In short, it may be today’s CEOs that should be nervous, but the front-line customer service representative that fields calls from dissatisfied customers may be in the clear – but only time will tell.