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AI Doesn’t Have to Be Too Complicated or Expensive for Your Business

Despite the vast potential of (), it hasn’t caught hold in most industries. Sure, it has transformed consumer internet companies such as Google, Baidu, and Amazon — all massive and  data-rich with hundreds of millions of users.

Copyright by hbr.org

SwissCognitive, AI, Artificial Intelligence, Bots, CDO, CIO, CI, Cognitive Computing, Deep Learning, IoT, Machine Learning, NLP, Robot, Virtual reality, learningBut for projections that will create $13 trillion of value a year to come true, industries such as manufacturing, agriculture, and healthcare still need to find ways to make this technology work for them. Here’s the problem: The playbook that these consumer internet companies use to build their systems — where a single one-size-fits-all system can serve massive numbers of users — won’t work for these other industries.

Instead, these legacy industries will need a large number of bespoke solutions that are adapted to their many diverse use cases. This doesn’t mean that won’t work for these industries, however. It just means they need to take a different approach.

To bridge this gap and unleash ’s full potential, executives in all industries should adopt a new, data-centric approach to building . Specifically, they should aim to build systems with careful attention to ensuring that the data clearly conveys what they need the to learn. This requires focusing on data that covers important cases and is consistently labeled, so that the can learn from this data what it is supposed to do. In other words, the key to creating these valuable systems is that we need teams that can program with data rather than program with code.

Why adopting outside of tech can be so hard

Why isn’t widely used outside consumer internet companies? The top challenges facing adoption in other industries include:

  1. Small datasets. In a consumer internet company with huge numbers of users, engineers have millions of data points that their can learn from. But in other industries, the dataset sizes are much smaller. For example, can you build an system that learns to detect a defective automotive component after seeing only 50 examples? Or to detect a rare disease after learning from just 100 diagnoses? Techniques built for 50 million data points don’t work when you have only 50 data points. […]

Read more: hbr.org