Human-Machine Collaboration: The Machine as Talent

Survey after survey shows that increased collaboration is one of the top five priorities for senior leaders.  Without increased collaboration, organizations cannot leverage their global talent effectively for continuous innovation and operational effectiveness.

Survey after survey shows that increased collaboration is one of the top five priorities for senior leaders. Without increased collaboration, organizations cannot leverage their global talent effectively for continuous innovation and operational effectiveness.

Now a new dimension to the collaboration imperative is being added – human-machine collaboration. Machines are beginning to be thought of as talent, and not just people.

This is made possible by cognitive technologies – machines that use speech recognition, computer vision, and machine learning to talk, see, read, listen and even learn by watching YouTube videos.  A 2015 study at the University of Maryland had robots learn to use kitchen tools by watching videos.

Is all of this too far into the future to be of interest? Not according to Deloitte’s Global Human Capital Trends 2015. Sixty percent of leaders in the survey rated the issue of ‘machines as talent’ as ‘important’ or ‘very important’. Only 5 percent of executives however, feel they have a detailed understanding of how cognitive computing will impact their workforce.  In short, organizations, management, and jobs will need to re-designed. 

An Accenture survey of 2,000 executives showed that 77 percent expect employees and intelligent machines to increasingly work side-by-side in a collaborative way. The report refers to this collaboration as a ‘blended workforce’.

To say that these machines are aimed at replacing human workers is far too simplistic. They will undertake some routine tasks, of course, but the real value will be in improved analysis and decision making.

In the past, nurse practitioners at Anthem Health Insurance spent hundreds of thousands of hours analyzing whether proposed treatments were consistent with Anthem’s policies. These decisions are complex involving in-depth knowledge of medicine, patient history, and the prescribing doctor’s rationale for the treatment.  A cognitive computing system now uses hypothesis generation and evidence based learning to generate confidence-scored recommendations that help the nurses make faster decisions.

Back in 1997, Garry Kasarov the world chess champion was defeated by IBM’s Deep Blue computer. Since that time, a new form of chess has developed – Centaur Chess – in which the human and machine play as a team. Chess experts say that the chess played is better than either humans or computers could have managed on their own.  Now that’s collaboration.

Interested in how introducing a cultural intelligence tool in your business could help to create a more borderless workforce? We’d love to show you our groundbreaking platform.