Even as the whole of Silicon Valley grapples with historic inflation, a bank crash, and mass layoffs, Google's woes stand apart. The explosion of ChatGPT and artificial intelligence more broadly has produced something of an existential crisis for the company, a "code red" moment for the business. Yes," Sundar Pichai, Google's CEO, told The New York Times. But Google employees are encountering another problem: "They took away the dried mango," says a project manager at Google's San Francisco office, whom I agreed not to name to protect the employee from reprisal. At least at that office, the project manager said, workers are seeing less of long-cherished food items--not just the mango, but also the Maui-onion chips and the fun-size bags of M&Ms.

With companies continuing to shrink or outsource their human resources departments, it is tempting to augment that traditional business function with artificial intelligence. Data science holds so much promise for other fields that it makes sense for algorithms to replace imperfect human decision-making for hiring, firing, scheduling and promoting. But new research from Wharton professors Peter Cappelli and Prasanna "Sonny" Tambe flashes a cautionary yellow light on using AI in human resources. In their paper, "Artificial Intelligence in Human Resources Management: Challenges and a Path Forward," the professors show how limited data, the complexity of HR tasks, fairness and accountability pose problems for digital HR. The study, which was co-authored by Valery Yakubovich, professor at ESSEC Business School and senior fellow at the Wharton Center for Human Resources, also looks at how to remedy those problems. Cappelli and Tambe spoke about their research with Knowledge@Wharton. An edited transcript of the conversation follows. Knowledge@Wharton: You make the point that while AI is invading many different industries and sectors, there are some special concerns when it comes to using AI in human resources. Can you talk about what some of those challenges are?

AIOps looms large as a way to help push the DevOps envelope. As organizations journey down the path of DevOps maturation, sustainable IT operations and IT service management remains a challenge for many. Even advanced organizations that have managed to speed up deployment rates and improve software quality struggle to maintain the resilience of the infrastructure that supports those applications. To support DevOps speeds, a growing number of organizations are turning to AIOps--the use of artificial intelligence (AI) and machine learning in IT ops--to speed up analysis of IT problems and better automate incident handling. A new study out this week from OpsRamp shows that ops pros are able to reduce mean-time-to-resolution of incidents by as much as 50% through the use of AIOps.

This article is a post in a series on bringing continuous integration and deployment (CI/CD) practices to machine learning. Check back to The New Stack for future installments. With orchestration and monitoring playing such key roles in DevOps, the emerging trend of using artificial intelligence (AI) to support and even automate operations roles by delivering real-time insights about what's happening in your infrastructure seems an obvious fit. DevOps is about improving agility and flexibility; AIOps should be able to help by automating the path from development to production, predicting the effect of deployment on production and automatically responding to changes in how the production environment is performing. That's especially true as trends like microservices, hybrid cloud, edge computing and IoT increase the complexity of app infrastructures -- and the number of logs that you might have to look at to find the root cause of an issue, and the number of people who need to be in a conference call or chat room tracking down what's gone wrong and how to fix it.

Self-driving cars and medical advances may grab all the headlines, but artificial intelligence is being applied to any number of less high-profile applications across a variety of industries. Perhaps the most unsung and yet closest to home for IT organizations comes in the form of "AIOps." That could be because applying artificial intelligence to IT operations is still an emerging area, so it hasn't garnered much attention. But early indications are that this is a growing area. It's too small for the Magic Quadrant treatment at Gartner, but in August 2017, Gartner issued a Market Guide for AIOps platforms.

By the 1990s, the rules of talent management had to be rewritten. Businesses became less predictable and had to become agile. Employees went from being lifers to job hoppers, and consequently, companies were no longer able to keep long, detailed notes on employees. People constantly moving in and out of companies made hiring overwhelming, and businesses found they needed to be savvier to find the right person for the evolving work environment. The result is the growing prevalence of reliance on technology when it comes to finding and tracking candidates.

Big data and machine learning will play increasingly critical roles in improving information security, predicts Will Cappelli, a vice president of research at Gartner. Even today, the demand for the technologies is strong, he points out. "In terms of market size, Gartner estimates that in 2016 the world spent approximately $800 million on the application of big data and machine learning technologies to security use cases," he says in an interview with Information Security Media Group. "About 80 percent of that was big data; about 20 percent was machine learning." Enterprises are looking at these technologies as two components of a single architecture, he says.