Data impacting the workplace: Optimizing the learning process

Posted by Ido Namir on December 19, 2018.

Acknowledging that efficient, learning AI technologies are due to a base of large and relevant data, a whole new approach toward human work and employment is emerging.

Alternative workers
Collecting, organizing, and gaining access to information has now become an important aspect of job requirements, and a new process of re-skilling is taking place. Such skills do not require being in an office, especially when virtual communication is so convenient. Therefore, the alternative workforce has become more relevant than ever.

For example, in industrial plants, where tasks such as machinery programming can be completed remotely, contingent workers are now welcomed. Similarly, since high-level communication platforms are available from our own computers, jobs that are solely in front of a screen are now in higher demand. Accordingly, employers trying to improve their outcome by collecting and gathering relevant information for their learning AI (machine learning – ML) technologies may find an alternative workforce sufficient.

Introducing a new level of technology with smart ML tools also impacts job requirements. Capabilities that leverage AI technologies are crucial at work, such as the ability to perform complex problem-solving and other cognitive skills. Furthermore, since today’s technologies can perform different functions on information and some can even execute whole processes, it is essential to adjust the workplace. For instance, fast and enhanced social collaboration using digital workplace tools can help collect more relevant and larger amounts of data to optimize the AI learning processes.

Ido Namir is a Knowledge Management Center of Excellence Leader and Partner with Deloitte Consulting LLP specializing in planning and executing organizational knowledge management strategies and tools.

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