UPDATED 17:20 EST / NOVEMBER 02 2021

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Google relies on engineering expertise, AI savvy to help companies optimize business outcomes

Opting for inefficient and poorly designed solutions can make digital transformation a costly nightmare.

But it isn’t always easy to spot well-designed and engineered solutions from generic solutions with an industry-specific label stuck on top.

“It’s really important to dig down, to identify is this just a horizontal solution or has a company done the real hard engineering work to solve the problem?” said Lisa O’Malley (pictured), senior director of product management, Google Cloud AI industry solutions, at Google LLC.

O’Malley spoke with David Nicholson, host of theCUBE, SiliconANGLE Media’s livestreaming studio, for a digital CUBE Conversation. They discussed the importance of specialized industry solutions and how Google’s engineering expertise and work with artificial intelligence is helping companies optimize their business outcomes.

Is it a horizontal solution in hiding?

While some providers think it’s OK to take a horizontal solution and pose it as an industry-specific one, Google prefers to “do the hard engineering work,” according to O’Malley. The company digs in to discover the root problems and key outcomes required within a specific sector and sets about to address them “at a level that makes a difference and transforms their industry,” she added. Instrumental in this are Google’s security, data, analytics and AI capabilities.

“We don’t want to create bespoke solutions for individual customers; we like to take industry-wide problems and think about them a different way,” O’Malley said.

One example is in visual inspection to identify defects on a manufacturing line. Google’s machine learning and AI expertise allow its solution to deliver a 10x reduction in defects with approximately 300x less training data, according to O’Malley.

According to O’Malley, the questions a customer should ask when looking to purchase an industry-specific solution are: Has it been engineered from the ground up to solve a specific industry problem? Can it be demoed it in a real-world example? How much is original code versus reference architecture? Does it work out of the box, or does it require major implementation with system integrator spend? And, most importantly, is the cost transparent, and does it connect with the value received?

Choosing a solution that is AI/ML-enabled is another important factor.

“AI is a tool that we should use wisely,” O’Malley said. “Careful application of AI and machine learning can benefit everybody in transforming their industries, whether that’s through increasing top-line revenue, taking cost out of the system or generally being more efficient.”

Here’s the complete video interview, part of SiliconANGLE’s coverage of Google Cloud Next and one of many CUBE Conversations from SiliconANGLE and theCUBE:

Photo: SiliconANGLE

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