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Daniel Faggella is Head of Research at Emerj. Called upon by the United Nations, World Bank, INTERPOL, and leading enterprises, Daniel is a globally sought-after expert on the competitive strategy implications of AI for business and government leaders.
Per Alibaba’s annual report, its revenue in 2021 exceeded 717 billion yuan (approximately 109 billion U.S. dollars), while its active yearly customers reached nearly 1.3 billion people. As of March 2022, Alibaba trades on the NYSE and has an approximate market cap of $225 billion.
Internal and external evidence suggests that Alibaba is aggressively researching and implementing AI and machine learning solutions. Such evidence includes:
In this article, we’ll examine how Alibaba has used AI applications for its business and industry through two distinct use-cases:
First, we will analyze how Alibaba uses its recommendation system framework’s algorithms to retrieve products based on customer search activity.
Alibaba is the largest e-commerce company by Gross Merchandise Volume (‘GMV’) globally (it more than doubles Amazon’s GMV numbers.) As with any e-commerce enterprise, it is vested in maximizing revenue by optimizing the virtual shopping experience and recommending the most comparable products.
Alibaba claims nearly 1.3 billion active global users across its ecosystem. Managing this amount of traffic while optimizing the shopping experience requires prodigious use of AI, particularly machine learning and cloud AI functions.
The idea behind Alibaba’s Recommendation System Framework (RSF) is simple: optimize traffic flow while updating and offering relevant products to TMall consumers that they want to buy. Moreover, to perform these operations in real-time, thereby increasing customer satisfaction clickthrough rate, and company revenue.
Prior to 2019, the company used a “relevance of recommendation” function, which calculates the “degree of similarity between the previous products clicked on and/or purchased with TMall’s inventory.” However, Alibaba changed this function to include “diversity of recommendation” and “discovery optimization” using what the company calls its Artificial Intelligence Recommendation (AIRec) engine. Notably, Alibaba claims that its AIRec algorithms outperform “self-managed” algorithms by 20-100%.
The following video goes into some more detail about Alibaba’s updated recommendation system:
The company claims that the AIRec engine can analyze and capture user behavior in seconds and provide personalized recommendations within milliseconds. Another way to the above from our research is: “We’re focusing more on offering customers more diverse products that (also) have a high clickthrough rate.”
As for the inputs to the model, Alibaba purports that their updated homepage RSF includes four “modules”:
Given the number of variables that impact subsidiary success or failure and the lack of a dedicated metric measuring AI impact on revenue, it’s difficult to quantify the bottom-line business impact. The company states that TMall has become the world’s largest ‘third-party online and mobile commerce platform…in terms of GMV (Gross Merchandise Volume).” The company also reports a 19 percent YOY revenue increase in its TMall operations from 2019 to 2020, when the newer algorithmic processes were implemented.
Cloud AI is expanding rapidly thanks to rapid advances in computing power, new AI tools, and software that delivers a more economical option for data storage. AI algorithms require significant computing power, the price of which is out of reach for most enterprises. The global cloud AI market is expected to increase by $10.22 billion between 2021 and 2026, including YOY growth in 2022 of 20.26%.
It isn’t just economics that inclines the user of AI towards the cloud – it’s also scalability. Alibaba is a good case study in this respect. The company states that their “Double 11” festival required the prolific use of cloud-based AI. “It was challenging for the (Alibaba) Group to deal with such copious amounts of computational resources and scale resources on demand by relying on an on-premises solution,” thereby requiring an “off-premises” (read: cloud-based) solution.
Alibaba holds an entirely “online global shopping festival” on ‘“Singles Day,” or November 11. In recent years, the company has extended the event to 11 days (Nov 1 to Nov 11). It is a massively ambitious undertaking that requires a profuse amount of computing power. Here are some purported numbers from the most recent festival:
Alibaba claims to have achieved this computational scale through AI cloud operations and related apps. Let’s discuss the distinct ways they accomplished this feat.
According to the company, the festival’s cloud infrastructure relied on three critical technologies. Here are those three technologies and the purported results:
It is difficult to gauge the business impact of the cloud AI variable in isolation. In theory, such massive infrastructural changes that Alibaba alleges that it made using 100% cloud AI resources may have a significant monetary impact. More quantifiably, the 2021 festival is reported to have earned approximately $84 million, while the ‘20 festival is reported to have earned $56 million – a 50% YOY increase.
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Founded in 1977 by a team of engineers led by Larry Ellison, Oracle became the world’s largest database management company by 1987. Today, Oracle claims a long list of innovations including:
Today, Coca-Cola is the world’s largest beverage company, selling over 500 soft drinks in more than 200 countries. In 2020, Coca-Cola had over 80 thousand employees worldwide.
Founded in 2003 as Tesla Motors, the electric vehicle and clean energy company based in California currently has a market cap of over $700 billion – making it more valuable than the top seven automakers combined. Today, Tesla is well-known for its electric vehicles but the company also produces products for sustainable energy generation and storage such as solar panels, solar roof tiles, and more to enable “homeowners, businesses, and utilities to manage renewable energy generation, storage, and consumption”.
Dick and Mac McDonald opened the first McDonald’s restaurant in San Bernardino, California in 1940. By the end of the decade, the restaurant added its now-famous French fries. Ray Kroc joined the growing organization in 1954, purchased it in 1961, and served as its CEO into the early 1970s. Over the next decades, the restaurant chain grew, adding its drive-thru concept, Hamburger University, and iconic menu items like its Filet-O-Fish, Big Mac, and Quarter Pounder sandwiches.
AI has made some inroads in the cybersecurity sector and several AI vendors claim to have launched products that use AI to help safeguard against cyber threats. At Emerj, we’ve seen many cybersecurity vendors offering AI and machine learning-based products to help identify and deal with cyber threats. Even the Pentagon created the Joint Artificial Intelligence Center (JAIC) to upgrade to AI-enabled capabilities in their cybersecurity efforts.
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