Best examples of human-machine teaming


Best examples of human-machine teaming

Enterprise tech leaders weigh in on how people can partner most productively with AIs, bots, and algorithms

While just 5% of all current jobs are likely to fully automated in the years to come, 50% of all work activities today could be offloaded to machines, according to McKinsey research. What does that mean? People will work side-by-side with a wide variety of intelligent digital tools to augment their work.

Human-machine teaming is on the horizon, but what does it look like in practice across different industries? In this expert roundtable, we asked four leaders in digital innovation where they see the most promise with human-machine collaboration.

I watched a demonstration a couple years ago showing how a human/AI interface could work. A call was coming into a customer support center. While the agent was logging the call, NLP technology was processing the call in real time and using NLU to understand the conversation on the phone. When the caller explained the problem was with a washing machine, the AI element prompted the agent to ask for the model.

When the caller replied with the model, the AI started popping up common issues with that device, right in front of the assistant. The agent instantly had all the answers at their fingertips. The caller felt like she was speaking to a true expert in the issue that was causing them frustration. The AI was being used to augment, not replace, the agent.

Dave Wright

I am most excited about the impact these collaborations will have on healthcare. Notable examples include AI helping to improve cancer screenings by identifying hard-to-spot cancer cells in X-rays and MRIs, genetic testing and robot-assisted surgery.

Less attention has been directed at the ways in which AI and automation will transform caregiving work for older generations. In the future, I believe we will see a number of collaborations around healthcare for an aging population where the capabilities of healthcare professionals are dramatically enhanced with a combination of AI and robotics.

Already we’re seeing the development of home caregiver bots that can manage medication and provide empathic daily monitoring of the elderly. Computer vision can detect subtle health deterioration and diagnose previously unnoticed ailments, alerting a primary physician to schedule an appointment or prescribe medication in early stages.

While we see some of these applications in the market already, I expect the future will offer much greater personalization and new innovations that will reduce the strain on human caregivers and physicians and significantly improve quality of life for our graying global population.

Daniel Newman

One example of how humans and machines are already collaborating in the legal arena is in the use of big data to support evidence-based decision-making. AI will represent a force multiplier in augmenting work across the legal industry, from making low-cost legal representation accessible to poorer populations to automating routine paralegal tasks and even managing simple legal cases in the justice system.

In Estonia, the government is pioneering the use of AI to adjudicate legal decisions. They’re planning to deploy “robot judges” to handle small-claims disputes. The good news is that increasingly better-trained AI will go a long way toward clearing the backlog of cases clogging many court systems today, freeing human judges to focus their time and attention on more nuanced and complex legal cases.

Dr. Daniel Araya

The most exciting milestone in any human-machine collaboration is when everyone understands that AI and machine learning (ML) are tools to support humans. I saw this done right when [national security] intelligence analysts were able to leverage AI as a digital assistant to analyze vast quantities of data.

Once the organization demystified what its needs were for AI/ML, they put it to work to help identify potential trends and outcomes in their field. It was more than just improving efficiency of time and methods. They couldn’t have done the analysis without the assistants’ help, as it was simply too much data for humans to analyze in a given time period.

This is critical, because it provided leaders potentially lifesaving insights and advantages that they wouldn’t have had without the tools. As the team saw the results they were getting, and the time they were getting back to do more of what they loved (i.e., the actual analysis) versus rote tasks, they fully embraced the use of AI and ML.

Kathleen Featheringham


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