AI gets down to business

New tools and troves of data have CIOs turning to neural nets and machine learning to deliver real-world results. Here’s how six 2017 CIO 100 leaders put AI to work.

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Jon Valk

Long a staple of sci-fi novels and summer blockbuster movies, artificial intelligence and machine learning are fast becoming a dominant force in the enterprise, helping businesses across industries transform operations, revamp customer experiences, and carve out new revenue opportunities.

ciod aug cio100 primary 800x533JON VALK

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Already, many of the 2017 CIO 100 leaders are piloting AI and machine learning projects, taking a do-it-yourself approach to building predictive models and open platforms, working with consultants, or taking advantage of new AI-infused capabilities increasingly popping up in core enterprise systems like ERP and CRM. Across industries, the momentum is clearly building — International Data Corp. is forecasting worldwide revenue for cognitive and AI systems to climb to $12.5 billion in 2017, a jump of 59.3 percent over 2016. Moving forward, IDC is anticipating spending on cognitive and AI solutions to enjoy steady enterprise investment, growing at a compound annual growth rate (CAGR) of 54.4 percent through 2020 when revenues will hit upwards of $46 billion.

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While AI isn’t exactly a newcomer — it’s been around for at least a couple of decades — the technology has taken off this year for a number of reasons: Relatively cheap access to cloud-based computing and storage horsepower; unlimited troves of data; and new tools that make it more accessible for mere mortals, not just research scientists, to develop complex algorithms, notes David Schubmehl, research director for cognitive and AI systems at IDC. “All of this has created fertile conditions for AI to begin to flourish,” he says.

In fact, Schubmehl says AI and cognitive systems are taking root in the banking and finance industry to do better fraud detection, in retail scenarios for personalization and product recommendations, and in manufacturing to do predictive maintenance. At the same time, AI is creeping into enterprise software platforms, where it is used to make recommendations for how to segment a marketing campaign, for example, or to automate back-office functions like software updates and network monitoring, freeing IT from time-consuming housekeeping tasks to focus on value-added activities. “AI is really about automation of automation,” he explains. “It’s really the idea that programs or applications can self-program to improve and learn and make recommendations and make predictions.”

Schubmehl says IT organizations have to start thinking about AI (if they aren’t already) and working with line of business to identify possible use cases and pilot projects. They should also be evaluating the software vendors they currently use to ensure that AI and cognitive capabilities are part of those vendors’ product roadmaps, he adds.

At the same, CIOs cast a critical eye on AI and cognitive capabilities, Schubmehl cautions. Data quality is a big issue as companies move forward, as is privacy, he says. For example, if you’re making predictions or recommendations to a customer based on bad data or information that should be safeguarded, you inevitably open up an organization to risk.

“You need to get on board, but you have to understand what the positive impacts will be on the organization as well as examine the risk potential and liabilities,” he explains. “Think about whether you need to have a data quality or integration initiative to make data better before you under-take AI practices. None of this should be done in a vacuum.”

Read ahead to learn how six 2017 CIO 100 leaders are transforming their enterprises to capitalize on AI and machine learning.  >>>

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