Data Science

Here Are Six Machine Learning Success Stories


Fewer technologies are hotter than artificial intelligence (AI) and machine learning (ML). Leading organizations are already harnessing the technology, which mimics the behavior of the human mind, to woo customers and bolster business operations. And the trend will only gain more traction in the years ahead, as AI and ML will be a top five investment priority for more than 30 percent of CIOs by 2020, according to Gartner.

Initial fears over AI and ML being used to displace jobs appears to be dissipating, with more than 67 percent of business executives surveyed by PwC saying that AI will help humans and machines work better together. Recognizing the opportunity to move the needle for their businesses, some CIOs are experimenting with, building and even patenting new AI and ML technologies. These IT leaders shared their ML use cases with CIO.com.

AI augments securities research

Putnam Investments, a provider of mutual funds, institutional investment strategies and retirement services, views AI and ML as essential for driving improved coverage of stocks by the financial services firm’s research analysts, CIO Sumedh Mehta tells CIO.com.

The analysts work closely with Putnam data scientists to create theses that help glean insights from large amounts of data, Mehta says. Putnam is also working on algorithms that will recommend the most important sales prospects.

“It’s a hugely disruptive and transformational power and the whole business driver for it is efficiency and productivity,” says Mehta of AI and ML.

Mehta, who relies on a combination of software engineers, data scientists, analytics and vendors, has created a data science center of excellence, which is essentially ground zero for AI and ML efforts that support business stakeholders. He says his “enlightened” business partners have embraced these approaches to achieve better automation.

The AI and ML work is part of Putnam’s broader digital transformation, which entails modernizing IT infrastructure with cloud computing and creating a single platform on which to run the business.

Key advice: Organizations should take their time and set expectations appropriately, understanding that the first few ideas will lead to new questions rather than answers. “There is no such thing as a eureka moment when it comes to AI,” Mehta says. “It’s not the case that suddenly your algorithm will yield insight you didn’t already know about.”

AI makes finances less taxing

Intuit is accelerating AI and ML efforts under Ashok Srivastava, who joined the financial software maker as chief data officer in October.

Intuit is using Amazon Web Services to help its QuickBooks Assistant chatbot better understand and process natural language, says Srivastava, who joined the company after building out Verizon’s big data platform. A growing area of focus is shepherding users through the hundreds of categorizations that inform Quickbooks.

“We’re dealing with over 1 billion transactions from QuickBooks and we can optimize the categorizations that occur with high accuracy,” Srivastava adds.

The company’s TurboTax uses AI to help users get their maximum refund by guiding them through the itemized deduction process, potentially saving users up to 40 percent of tax prep time and efforts retrieving documents.

The company is using ML and cloud technology from AWS to scale more rapidly, Srivastava says.

Read the source article at CIO.com.


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