Collaboration between knowledge workers and machines
New AI capabilities that can recognize context, concepts, and meaning are opening up surprising new pathways for collaboration between knowledge workers and machines. Experts can now provide more of their own input for training, quality control, and fine-tuning of AI outcomes. Machines can augment the expertise of their human collaborators and sometimes help create new experts. These systems, in more closely mimicking human intelligence, are proving to be more robust than the big data-driven systems that came before them. And they could profoundly affect the 48% of the US workforce that are knowledge workers—and the more than 230 million globally. But to take full advantage of the possibilities of this smarter AI, companies will need to redesign knowledge-work processes and jobs.
Knowledge workers—people who reason, create, decide, and apply insight into non-routine cognitive processes—largely agree. Of more than 150 such experts drawn from a larger global survey on AI in the enterprise, almost 60% say their old job descriptions are rapidly becoming obsolete in light of their new collaborations with AI. Some 70% say they will need training and reskilling (and on-the-job-learning) due to the new requirements for working with AI. And 85% agree that C-suite executives must get involved in the overall effort of redesigning of work roles and processes. As those executives embark on the job of reimagining how to better leverage knowledge work through AI, here are some principles they can apply:
Let human experts tell AI what they care about. Consider medical diagnosis, where AI is likely to become pervasive. Often, when AI offers a diagnosis the algorithm’s reasoning isn’t obvious to the doctor, who ultimately must offer an explanation to a patient—the black box problem. But now, Google Brain has developed a system that opens up the black box and provides a translator for humans. [read more]
Using AI to Make Knowledge Workers More Effective