While Aker BP got the help it needed from a small Austin, Texas-based AI software firm, SparkCognition, some bigger oil-and-gas companies are working with giants in the tech industry. Exxon Mobil Corp. in February started a partnership with Microsoft Corp. to deploy AI programs to optimize its operations in the Permian, or West Texas Basin. The oil giant also recently installed an AI program to gain insights from data coming from millions of sensors that monitor its global refineries. Total SA, meanwhile, is linking up with Google Inc. to better interpret seismic data, and is set to increase its investment in AI to squeeze more hydrocarbons from existing assets.
The WSJ has broken down some of the technology several retailers are trying out in response to the Amazon Go concept - see my blog post in Jan 2018
How many are experimenting?
"A global survey of about 400 retailers conducted in June by research and advisory firm International Data Corp. found that 28% are testing or piloting cashierless systems, said Leslie Hand, vice president of IDC’s Retail Insights division."
In 2017, Ping An, China’s second-largest insurer and its biggest non-state-owned company by revenue, rolled out a “Superfast Onsite Investigation” system—enabling policyholders to submit claims by simply opening a smartphone app and answering a few questions. But the app’s niftiest feature offers the option to not even wait for an inspector. Instead, customers can snap photos of a damaged vehicle and send them to a Ping An computer, which can respond with a repair estimate in three minutes or less. If the customer accepts the estimate, then wancheng! (“Done!”) Ping An can transfer funds immediately.
Last year, Ping An’s customers used this feature to settle 7.3 million claims, or 62% of the total. The service saves the company more than $750 million each year by reducing bogus claims and human error. But its simplicity belies the extraordinary sophistication of the artificial intelligence and data-processing operations that make it possible.
Ant Financial’s Alipay and Tencent’s WeChat have changed the way many people live their financial lives. They are one-stop shops that enable half a billion Chinese to access a dizzying array of services, from payments, loans, investments, and credit scores to taxi rides, travel bookings, and social media.Because so much is sold via these apps, Alibaba and Tencent know the health (or lack thereof) of many small businesses across China. As a result, they can lend to companies that banks might consider too risky. Likewise, people with no traditional credit score can get cheap loans because Ant Financial has their payment and purchase history.
The doors opened, and with that, Nordstrom finally had a horse in New York City’s high-end retail race, competing with stores like Saks Fifth Avenue, Bloomingdale’s, and Bergdorf Goodman.
But the 47,000-square-foot emporium is also a sophisticated shopping-tech laboratory—one of several where Nordstrom is urgently experimenting with ways to make upscale, high-touch retail more competitive in an e-commerce-driven era. The mazelike layout, spread over three floors, showcases subtle but important technological enhancements. Some are super-practical—such as self-service bins where shoppers can drop off returns (including online purchases) and get credit almost immediately. Others have a wow factor: In the suits section, customers can see how bespoke jackets would look on life-size avatars of themselves, projected on screens, a digital take on the adage, “Measure twice, cut once.” And yet others pamper you: If you’ve “reserved” a product online to try on before you buy, there’ll be a fitting room waiting for you—with your name on the door.
BusinessWeek has a cover story on Palantir including this on its Foundry product
“Palantir’s newest product, Foundry, aims to finally break through the profitability barrier with more automation and less need for on-site engineers. Airbus SE, the big European plane maker, uses Foundry to crunch airline data about specific onboard components to track usage and maintenance and anticipate repair problems. Merck KGaA, the pharmaceutical giant, has a long-term Palantir contract to use Foundry in drug development and supply chain management.”
Why are Palantir products so complex?
A personal opinion from Ethan Bond at Quora because the tools are used in highly sensitive applications.
“Let’s take the example of a minor feature I’ve been building out recently: commenting. This is exactly what it sounds like. We want to allow users to comment on datasets and analysis artifacts to collaborate with each other. We want them to be able to @mention other users to invite them to take a look at some analysis.
Sounds easy, right?
A few issues: Let’s say a user mentions another user in a comment on a dataset. We can’t notify recipients about datasets that they don’t have access to, as it leaks information about the existence of the dataset. We also can’t tell the commenter exactly who has what amount of access to the dataset, because it’d leak privilege information about the recipient. Now let’s assume the recipient does have access. We’d like her to receive an email about this comment. But that email can’t contain any information about the contents of the dataset because data can’t be sent over email. So now you might get an ambiguous “X User commented on Y Dataset.” If a user is getting too many notifications, she might want to unsubscribe – but for security reasons, in order to do that she’d likely have to log in through her employer’s network which you often can’t do (easily) from your phone. To alleviate this we might want to batch the notifications, but given the nature of our clients, some notifications are truly urgent and we don’t have a way to automatically and reliably differentiate urgent from non-urgent notifications. Now let’s say the commenter’s company is merging with another company, and as part of the merge, both userbases (Company A and Company B) will have access to the same datasets, but they won’t have the ability to know that the other company’s userbase exists until after the merger is completed.
A bit more complicated than your standard commenting feature, eh?”
Zillow’s initiative is called the “Instant Offers” marketplace, and it’s been in a test phase since mid-2017. Homeowners who want to sell receive offers from investors and an analysis of their home’s value, if the home were to be listed on the traditional real estate market instead. Zillow says it will aim to buy, renovate, and then re-sell homes in 90 days or less.
“The process offers transparency, convenience and choice for sellers, and gives them certainty over their sale price and timing,” Zillow said in a release.
What it offers for existing industry participants is less clear. “The program gives real estate agents the opportunity to acquire new listings by connecting them with motivated sellers who have taken a direct action to sell their home,” Zillow’s release said.
The financial services industry is going high-tech, and so the behemoth corporate organizations behind the big-name banks, asset managers and insurance companies are facing a fundamental challenge: how to attract, train and retain a very different sort of talent. “We are a tech company,” Goldman Sachs CEO Lloyd Blankfein has said on a number of occasions — admittedly, Wall Street execs have been saying this for years — but it’s finally beginning to ring true. Goldman now employs around “9,000 individuals in various engineering roles,” according to a recent company report, out of the company’s 33,000 or so employees. More than one in three new recruits are STEM majors.
Gone are the days when salespeople, traders, bankers and managers ruled Wall Street — in 2000 Goldman had 600 equity traders; now it has two. The technological shift is forcing finance companies to reexamine their recruiting practices and reinvent their corporate culture to bring a little Silicon Valley style to Wall Street.
After visiting companies including Apple Inc. and Facebook Inc. three years ago to understand how their developers worked, the bank set out to create its own computing cloud called Gaia that went online last year. Machine learning and big-data efforts now reside on the private platform, which effectively has limitless capacity to support their thirst for processing power. The system already is helping the bank automate some coding activities and making its 20,000 developers more productive, saving money, Zames said. When needed, the firm can also tap into outside cloud services from Amazon.com Inc., Microsoft Corp. and International Business Machines Corp.
JPMorgan will make some of its cloud-backed technology available to institutional clients later this year, allowing firms like BlackRock Inc. to access balances, research and trading tools. The move, which lets clients bypass salespeople and support staff for routine information, is similar to one Goldman Sachs Group Inc. announced in 2015.
This is apparent as Callahan surveys the 21st-century banking terrain: digital competitors are massing on every front—from fintech start-ups to new divisions of global institutions—while the speed of every banking process and customer interaction accelerates daily. All this change requires a focus on agility, Callahan says, which in turn demands a cultural rewiring.
At the helm of Citi’s digital transformation, Callahan is helping drive new thinking across the bank. He points to Citi’s digital lab for start-up innovations, powerful new apps for customer smartphones, and, internally, a push to expand capabilities across cloud computing and big data and analytics that enable automation and machine learning. In an interview with McKinsey’s James Kaplan and Asheet Mehta, Callahan describes what it takes to mobilize digital change at one of the world’s leading financial institutions.
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