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InformationWeek, serving the information needs of the Business Technology Community

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Most managers feel euphoria when implementing a technology meant to enhance the workflow of a team or an organization. But they often overlook the details that help implement the technology successfully. The same sentiment can occur for managers who oversee data scientists, data engineers, and analysts examining machine learning initiatives. Every organization seems to be in love with machine learning. Because love is blind, so to speak, IT teams become the first line of defense in protecting that euphoric feeling.


InformationWeek, serving the information needs of the Business Technology Community

#artificialintelligence

As COVID-19 vaccination rates rise, conversations about the future of work are picking up again. It's no longer the workplace of 2019; the landscape has changed significantly since then. The automated, digitized world of work that we knew would arrive "soon" is suddenly here, and many of those changes are here to stay. Chief information officers and IT leaders have a key role to play in facilitating employee adoption and encouraging buy-in for the future of an AI-enabled workforce. Organizations accelerated their digital transformation plans over the past year, or improvised along the way, to accommodate the rapid shift to virtual work.


InformationWeek, serving the information needs of the Business Technology Community

#artificialintelligence

AI is seeping into just about everything, from consumer products to industrial equipment. As enterprises utilize AI to become more competitive, more of them are taking advantage of machine learning to accomplish more in less time, reduce costs and discover something whether a drug or a latent market desire. While there's no need for non-data scientists to understand how machine learning (ML) works, they should understand enough to use basic terminology correctly. Although the scope of ML extends considerably past what's possible to cover in this short article, following are some of the fundamentals. Before one can grasp machine learning concepts, they need to understand what machine learning terms mean.