The AI productivity challenge

Artificial intelligence, in particular the subfields of machine learning and deep learning, have shown remarkable progress in recent times. There is a general consensus that over time every business will need to start using AI if they are to be competitive and successful. AI is not something for the future nor something only for the large technology titans but rather a technology that is increasingly entering the mainstream.

The data scientist has emerged as an essential resource
— Björn Brinne, Head of Data Science at Peltarion

Why read this

This is an introductory paper on how to drive productivity in the areas of machine learning and deep learning. The paper highlights some of the key barriers to getting good outcomes in these projects and then outlines some potential functions or capabilities organizations should consider as they look at technologies to help support their AI programs.

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Björn Brinne
Head of Data Science


Björn is the Head of Data Science at Peltarion and has over a decade of experience working in data science. Before joining Peltarion, Björn worked at companies such as Truecaller, King and Electronic Arts. He holds a PhD in theoretical physics from Stockholm University and has contributed to many research papers across a range of academic fields, including computer science, string theory and computational biology.

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