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#AI@Work: Think Both, and…

Not either or…According to Robot Ready, “The future of work is now and standing still is not an option. In order to shape the work of the future, organizations have a tremendous opportunity to redesign and cultivate this mindset of ‘both, and’ earlier in the learning process” (Robot Ready, 2019). Learning needs to be ongoing and complete. It needs to encompass those skills needed in the workplace and those skills that support the continuing evolution of humans. Humans need to learn and continue to learn both technical skills and human skills. This learning will take place in the classroom, on the job, in training, online and in all other environments humans inhabit.

Get ready to learn. The future is not one of stagnation but of continued growth. This growth will require the ability to integrate technical and human skills. We will continue to discover what businesses, organizations and humans need in the workplace. New skills will have to be learned and relearned. More new skills will be added to the inventory of both traditional education and workforce training. The skills gap needs to address people looking for good work. It needs to include companies looking for talent, educators and learning and development specialists.
Agile is the key to our future. How we get there is through continued improvement in AI and continued human learning. Agile is about being awake, alert, vigilant and prepared. Continuing change and growth are the foundations of an agile workplace. The agile workplace will require radical change by institutions and organizations. Changes in the way we do things and the mindset we bring to the tasks. Humans can look forward to a future where AI will bring vast improvements. There will be improvements in productivity, freedom from boring work and in quality of life.

AI must be carefully programmed and monitored. It has the ability to increase inequalities in the workplace. And in the home, legal and judicial systems. Sexism, racism and other unrecognized biases can be built into machine-learning algorithms. The underlying intelligence will shape the way people are categorized and addressed. These risks perpetuate an already vicious cycle of bias. It could support, for example, systematic bias among poorer and nonwhite populations.

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