Scott Koegler

Scott Koegler

Scott Koegler is Executive Editor for PMG360. He is a technology writer and editor with 20+ years experience delivering high value content to readers and publishers. 

Find his portfolio here and his personal bio here

AI is Moving Into the Office

Artificial intelligence (AI) is increasingly incorporated into our workplaces and daily lives. With this, it is expected to fundamentally upend the way office operations are performed with major concerns over how it will adversely affect employment. Despite the few concerns about the potential of AI to affect and disrupt office operations, it is poised to improve operations and significantly increase workers' productivity. From autonomous robots to virtual assistants, here are some ways that AI is moving into the office.

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Artificial Intelligence Mistakes to Avoid

Artificial intelligence (AI) is changing industries for the better. It is altering the way businesses operate and has hundreds of use cases. However, developing and implementing an AI project to help build a business poses a great challenge to companies and slows adoption. According to research, it is estimated that 85% of all AI projects will fail or deliver poor outcomes in 2022. On the other hand, 70% of companies reported no or minimal impact from AI, while 87% of the AI projects did not even enter into production. With these worrying statistics, here are some artificial intelligence mistakes to avoid as you seek to venture into this new area.

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Take Advantage of Machine Learning

Machine learning (ML) could not have come at a better time than now. As businesses move towards a new era of digitization, ML is needed to help in various operations of organizations, starting with data processing to customer support. Machine learning algorithms learn from data and find different hidden patterns and insights, which is crucial for businesses to make critical decisions. ML and artificial intelligence tools and algorithms have gained fame over the past few years in business circles and the analytics community due to their potential to help sift through massive amounts of data from different sources. With growing data volumes, higher and faster computational power and affordable data storage, machine learning has become more feasible now than ever.

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