The IBM Data Science and AI Elite team showed that PostNord can predict non-deliveries of traceable items depending on address, weather condition, sizes and time of delivery. By leveraging AI, it’s possible to reduce non-deliveries by 50 percent annually, beneficial for both customers and PostNord
James Fisher & Sons had hearty ambitions to build predictive maintenance capabilities for its customers' subsea cables -- but lacked the right data to do so. In a creative pivot, the IBM Data Science and AI Elite team delivered more than what the heritage engineering company bartered for --
Segmentation, targeting, positioning – how does an organization optimize these strategic approaches in the context of retention? Which factors should the segmentation take into consideration? This is where good information management and analytics come into play. Explore IBM’s solutions today:
Making the case for AI, or any nascent technology for that matter, can be a struggle for companies today. While large enterprises know they need to be fast, agile and innovation-obsessed to survive disruption, their age-old policies, antiquated systems, disconnected data and entrenched corporate
Today’s data science and analytics teams are often composed of individuals with a variety of skill sets, educational backgrounds, levels of exposure to open source tools and professional needs. Here’s a typical breakdown:
Business professionals need straightforward ways to first discover and then
Today, “doing more with less” is a key principle driving business strategy across many resource-intensive industries. Organisations are looking to get more out of artificial intelligence (AI) and machine learning (ML) than just great insights. They need access to recommendations that help simplify
Seizing the AI opportunity to tap new sources of energy inspired one ExxonMobil leader to take a collaborative approach to its big data problem. Now she’s been recognized by IBM as a top woman AI leader.
AutoAI, a powerful automated AI development capability in IBM Watson Studio, won the Best Innovation in Intelligent Automation Award, chosen by a panel of 13 independent judges yesterday for the AIconics AI Summit in San Francisco.
68 percent of surveyed businesses recently responded that they use machine learning (ML) or plan to do so in the next three years. AI technologies rapidly are becoming how businesses distinguish themselves from competitors. But choosing the best way to implement AI isn’t always a straightforward
In a previous blog, I explained how data science capabilities, massive parallel processing (MPP)
and usability improvements in data warehouse appliances can help the bottom line—and why old-fashioned architectures might not cut it. But what does that look like in practice?
Research firm Quark +
In part one of the Capitalogix data science story, I focused on their strategic need for a data platform that supports speed, data variety and custom-built algorithms to find advantages for their business. A key success driver: they worked to make life better for the people on the front lines of
With the automated AI and ML advancements, you may find yourself wondering--what are the overall impacts to business? How will all of this technological progress impact the ways we run our business and perform our jobs?