This is why AI has yet to reshape most businesses

  • “Last September, a data scientist named Peter Skomoroch tweeted: “As a rule of thumb, you can expect the transition of your enterprise company to machine learning will be about 100x harder than your transition to mobile.” It had the ring of a joke, but Skomoroch wasn’t kidding.
  • Among the biggest obstacles is getting disparate record-keeping systems to talk to each other. That’s a problem Richard Zane has encountered as the chief innovation officer at UC Health, a network of hospitals and medical clinics in Colorado, Wyoming, and Nebraska
  • It took a year and a half to deploy Livi, largely because of the IT headaches involved with linking the software to patient medical records, insurance-billing data, and other hospital systems.
  • When Genpact, an IT services company, helps businesses launch what they consider AI projects, “10% of the work is AI,” says Sanjay Srivastava, the chief digital officer. “Ninety percent of the work is actually data extraction, cleansing, normalizing, wrangling.”
  • To develop a system like this, “you have to bring your domain experts from the business—I mean your best people,” she says. “That means you have to pull them off other things.” Using top people was essential, she adds, because building the AI engine was “too important, too long, and too expensive” for them to do otherwise.”

https://www.google.com/amp/s/www.technologyreview.com/s/612897/this-is-why-ai-has-yet-to-reshape-most-businesses/amp/

Build, buy, or both? The AI implementation conundrum – Pedro Alves, Ople

“Let’s assume you are in ecommerce, and you’re interested in predicting the likelihood of a customer checking out her shopping cart. If you are making the prediction based on factors like the previous purchase history and time spent on site, you will get a reasonable outcome. But if you are looking at factors like the number of red cars on the street at the time or the number of windows in your office, you will never get a reliable answer.

This example is somewhat extreme, but the point is that AI is not magic. Moreover, it requires your expertise because the answers to what matters are often industry- and even company-specific. In other words, you need to rally your teams and figure out the questions and answers you seek before embarking on any AI venture.”

https://www.google.com/amp/s/venturebeat.com/2018/10/01/build-buy-or-both-the-ai-implementation-conundrum/amp/

Retailers & AI – CSA

“In a move to step up their customer experiences, more retailers are embracing artificial intelligence (AI). This was according to the third quarterly ‘2017 E-commerce Performance Index,’ a report from SLI Systems.
  • 54% of companies reported they are using or plan to add AI in the future
  • 20% expect to add AI within the next 12 months

The most popular applications for AI — among both existing retail users and those that plan to use AI within the next 12 months – are:
  • 56% – personalized product recommendations
  • 41% – customer service requests
  • 35% – chatbots

Of those planning to implement AI:
  • 13% plan to build their own technology
  • 60% will buy existing technology
  • 27% expect to blend build and buy”