By Sara Castellanos 

Intel Corp. Chief Information Officer Archana Deskus says artificial intelligence has become more critical to the semiconductor maker during the coronavirus pandemic.

AI is helping Intel generate insights and increase the speed at which products are tested, which has been crucial as technology initiatives were accelerated in recent months, Ms. Deskus said. The Santa Clara, Calif.-based company has also benefited from investments in data analytics and machine learning algorithms over the past decade as it contends with the pandemic's effects on its business, she said.

"We've spent a decade building up a sturdy data foundation which is the basis for AI efforts, and when the pandemic hit, we were able to move quickly," Ms. Deskus said.

Intel is also using AI algorithms to identify problems with employees working remotely and for optimizing inventory throughout its supply chain, as the pandemic caused manufacturing disruptions in China, Ms. Deskus said.

By the end of 2024, 75% of organizations experimenting with AI today are expected to shift from testing AI algorithms to fully deploying them in business operations, according to a June report from technology research firm Gartner Inc. That is partly because AI provides companies with critical predictions that can drive revenue growth and reduce cost and risk, according to Gartner.

AI has become more important to enterprise chief information officers during the pandemic, and information technology executives are deploying AI projects much faster than they would have in the past, said Svetlana Sicular, vice president analyst at Gartner. Some IT executives have recently deployed AI-based chatbots, for example, to help call-center agents answer routine questions from customers during the pandemic.

"Some projects were implemented unusually very quickly and they will probably not return to those long cycles," she said. "Most [CIOs] will see that AI is efficient where they need it."

Ms. Deskus became CIO of Intel on Jan. 30 and oversees its global IT operations, including how technology is used in manufacturing. Her team includes more than 5,000 employees. She was previously CIO at Hewlett-Packard Enterprise and held CIO roles at Baker Hughes, Timex and United Technologies.

A critical use case for AI in the coming months will be to speed up product testing, Ms. Deskus said.

Intel's IT teams developed an AI-based hardware validation program composed of more than 50 machine learning algorithms, an effort that began about five years ago. The machine learning algorithms can automatically detect hardware bugs quicker than humans, because they are able to sift through terabytes of data to find anomalies, Ms. Deskus said.

"We can do this in a much more sophisticated way than we could previously," she said. Previously, the work of generating tests and selecting which tests to execute was performed manually.

The algorithms are currently testing all of the functionalities of Intel's future chips. Intel reported stronger earnings in its most recent quarter but it has signaled a delay in its development of superfast chips.

Earlier this year, Ms. Deskus oversaw the deployment of virtual private networks to support approximately 100,000 employees who began working remotely in early March. VPNs allow employees to work on their computers securely from home. Intel's technology team used machine-learning algorithms developed in-house as well as tools built by a network monitoring company to proactively identify issues with remote-work setups, she said.

The algorithms were used in part to detect problems with Wi-Fi connectivity and internet traffic routing. Intel helped employees optimize routing configurations and bandwidth use.

"We tried to help them get as similar of an experience as if they were in the office," Ms. Deskus said.

Intel used AI algorithms to help simulate various supply chain and logistics scenarios. For example, when supply chain disruptions for Intel products occurred at factories in China during the height of the pandemic, the company used machine-learning models to help identify alternative supply chain routes.

AI algorithms were also used to identify the root cause of quality issues on the manufacturing line in real-time to correct those problems right away. Previously, Intel relied on data analysis without machine learning to identify the root cause of problems after manufacturing had been completed.

Before the pandemic, automation wasn't a major part of Intel's manufacturing strategy, Ms. Deskus said. "The reliance on the [automation] technology and the pivot is happening much faster than if we didn't go through the crisis," she added.

Write to Sara Castellanos at sara.castellanos@wsj.com

 

(END) Dow Jones Newswires

September 01, 2020 18:03 ET (22:03 GMT)

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