Artificial Intelligence (AI) strategy: 4 priorities for CIOs

Artificial Intelligence (AI) strategy: 4 priorities for CIOs

Extracting the value of artificial intelligence requires gaining quick wins while developing at enterprise scale. Consider focusing on these key AI areas

It’s an exciting and scary time to be a technology leader: Exciting for the endless opportunities offered by rapidly evolving digital technologies – and scary due to the associated feeling of FOMO (fear of missing out).

Consider Artificial Intelligence (AI). Driven by the desire to tap unprecedented volumes of data for a broad array of real-world applications, many organizations see AI as a magic wand that CIOs can swing to generate customer delight and executive exhilaration.

CIOs know better, of course. The challenges that come with any new technology hit technologists harder and faster than the optimism driving it. This is especially true with AI and related areas such as machine learning (ML), data science, deep learning, natural language processing (NLP), and cognitive intelligence. Not only is talent scarce in these fields, but their vocabulary and application development are also different.

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