Nicholas Blechman for HBR

When most people think about humans and robots working together, they tend to assume that it’s the human giving the instructions. Robots, after all, are good at following commands or rote tasks, not the messy world of management, right?

In fact, artificial intelligence is quickly encroaching on key aspects of management, like task assignment and performance evaluation.

Consider BuildDirect, a home improvement products platform that delivers heavyweight goods directly to customers.(Disclosure: My firm is a current or former investor this company, as well as the others mentioned in this article.) The company leverages data and algorithms to tell its manufacturers which products should be produced and delivered to a particular geographical area in North America (since conversion drops dramatically with heavy products the greater the distance from the end customer). These manufacturing and logistical choices used to be made qualitatively by merchandisers. It turns out that the results are much better when humans are removed from the decision-making process. Now, algorithms make the decisions and instruct humans throughout the supply chain on which products to make, in what quantities, and where to deliver them.

Then there’s VirtuOz, a virtual agent technology acquired by Nuance 18 months ago: its technology progressed to where it could answer 80-90% of customers’ questions through automation. That’s just the sort of work we’re already used to machines doing. But the system could also delegate in a “smart” way. It could recognize when and why users were getting frustrated or upset — without users explicitly saying so — and so could pass them along to the right agent prepared to answer the right question. Human involvement was key to the entire customer service system working properly, but it was technology that decided what work should be automated and what required a human touch.

VirtuOz is far from the only technology delegating to humans. A machine learning-based software-as-a-service (SaaS) company, WorkFusion – cited in Martin Ford’s new book, Rise of the Robots – breaks high volume, complex data work into discrete tasks and algorithmically assigns the work to appropriate machine and human resources. For example, data can enter banks in a significant number of different formats, which often presents a challenge for transcription and translation for accurate data consumption. Recently, the WorkFusion platform was tasked by a global bank with learning and automating the PDF extraction of SSI (Standard Settlement Instructions) details into text format in line with SSI best practices. The platform improves human productivity by leveraging a combination of internal, outsourced, or crowdsourced workers. Customers control which types of workers contribute to each step in the workflow. WorkFusion uses advanced statistical analysis to ensure the accuracy of human output, and the platform uses that quality data to train algorithms to automate that work in the future. Over time, humans are engaged only when algorithms face new obstacles or challenges for any particular task.

These systems are being initially adopted where the work is easily understood and broken down into pieces digitally. In the near term, managers will still be needed for different kinds of non-repetitive work that requires complex problem-solving, strategic judgment, and experienced insight. But that’s just a starting point. Machines will learn over time to delegate more and more complex work.

Used correctly, artificial intelligence can improve managers’ productivity by allowing them to focus on more high-value work. Businesses should prepare for this eventuality by thinking about which tasks can be improved through a combination of machines and humans initially, and then automated over time. They should also be thinking about how to train their workforces for this kind of reality. Most of all, they shouldn’t assume that automation will leave the practice of management be. Managers may pride themselves on their ability to determine who should be doing what, but at least in many realms, machines are learning to do this just as well if not better.