GE’s intelligent systems: Creating an AI, machine learning standard
The business potential in artificial intelligence technologies has enterprises across industries prioritizing the discovery of machine learning opportunities to leverage their next big innovation. In the surge toward modernization, some companies run the risk of overlooking the fundamentals of operational success, specifically the teams that must work with each other and the technology itself, as traditional production processes rapidly change.
“Even if you have really good data science teams, it’s incredibly hard to go from white board into production,” said Jeff Erhardt, vice president of intelligent systems at General Electric Co. “How do you take concepts and make them work reliably, repeatably, scalably over time?”
Following GE’s acquisition of machine learning company Wise.io, where Erhardt served as chief executive officer, Erhardt has been working to implement his former company’s processes at scale within the multinational conglomerate.
Jeff Erhardt recently sat down with Jeff Frick (@JeffFrick), host of theCUBE, SiliconANGLE Media’s mobile livestreaming studio, for a discussion at theCUBE’s studio in Palo Alto, California. They discussed modernization challenges, including AI and machine learning.
A humanistic approach to a systematic journey
Erhardt discovered his first challenge applying AI tech at GE in the integration of disparate processes across the company’s many verticals. Maintaining optimal performance over time for customers required more streamlined and better-defined workflows, and Erhardt took a top-down approach to aligning data-driven processes throughout the massive, industrially diverse organization.
Through GE’s journey to become a thoroughly data-driven company, Erhardt discovered some of AI’s more popular pitfalls, such as the novelty problem that surfaces when businesses are rushing to incorporate new tech. He advises organizations to assess the value-producing, data-driven workflows within the business to determine where to define existing data and how to strategically implement it.
“Then … we can overlay machine learning as a technology to intelligently automate or augment those processes. … It’s gonna force you to standardize your infrastructure, standardize those workflows, quantify what you’re trying to optimize for your customers,” Erhardt said.
These modernization challenges aren’t unique to GE. As an increasing number of enterprises grapple with the new standard of digital transformation, Erhardt advises modernizing people and tools to bridge inefficiency gaps that can break down the data cycle.
“There are bigger challenges than simply, ‘Are the tools easy enough to use?’ It’s very much more a software engineering problem than it is a data access or algorithmic problem. … The algorithm piece is something like five percent of an overall production machine learning implementation,” he said.
Erhardt looks forward to applying his learnings to the mass of data at GE and creating an industry standard around applying AI to customer success. Looking ahead, he’s working to consolidate knowledge, standardize it within an application workflow, and move into the complex, adaptive, intelligent systems powered by AI machine learning.
“Know your business; get the people right; understand that it’s a systematic journey,” he concluded.
Watch the complete video interview below, and be sure to check out more of SiliconANGLE’s and theCUBE’s CUBE Conversations.
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