After decades of navigating constant, rapid evolution, IT leaders may have thought that the field was finally settling into something of a sustainable groove. The advent of personal computing, and virtualization, now maturing with hyperscalers’ public clouds and their own organization’s private clouds were all well in the rearview mirror. IT professionals had just finished supporting the unprecedented level of digital transformation, which sustained companies through the COVID-19 pandemic by supporting the rise of remote work.
Then came artificial intelligence.
“The AI piece is just upending everything,” Jean S. Bozman, president of Cloud Architect Advisors, told The Forecast in a video interview.
“People thought they had things under control, and now this is happening. It started out with ChatGPT in 2022. That was fun and easy to learn. ChatGPT could write short poems like haikus and generate videos based on word pictures and phrases. But that’s not an enterprise use case. It was the launching pad to a much bigger opportunity. Now, enterprises are leveraging AI and machine learning (ML) to literally take the digital heartbeat of their business – and to draw inferences from the data in their specific industries and markets. As AI surges forward, organizations will need to find a way to mix AI with everything else they’ve been developing over the past decade to forge brand-new paths to revenue and profits.”
As a tech journalist and analyst, Bozman has seen several generations of innovation upend the apple cart. She’s had a front row seat at the biggest technology events through the years, including those of Apple, Amazon Web Services (AWS), Google, HP and HP Enterprise, IBM, Intel, Microsoft, Oracle, Red Hat and NVIDIA. She has interviewed many tech leaders over two decades, and written about the evolution in IT hardware, software and customers’ software-defined infrastructure. Her analysis shows the combined impact of these technologies on today’s new and emerging business cases. Her commentary crystalizes trends, bringing CIOs and IT professionals closer together about identifying new products and services that will become critical to their business.
“I think AI was almost a shock to people,” she added. “All of a sudden, it’s in your face. You have to do something. The hard part is harmonizing the data from different sources. You can do it at the edge, you can do it in the cloud, you can do it from your office, but it has to be harmonized so that it doesn’t drag the whole network down.”
To get value from AI, Bozman said, organizations cannot simply experiment with and adopt the commercial off-the-shelf applications that already exist. Rather, they will need to build out use cases, infrastructure, and talent tailored to their specific business environments.
Laying the IT Foundation for AI
Bozman noted that although many organizations have had a significant AI presence in the past, the technology has only recently garnered mainstream attention.
“AI was in the background doing data management, finding financial fraud, looking at medical records,” Bozman said. “That wasn’t sexy enough. Now, it is sexy. AI is in my office, I can see it. It’s tremendously exciting to people. There are ways that generative AI tools can fit into the enterprise – people writing things, people being creative. But there’s a lot of rigor that has to come with that. We need controls and guideposts.”
Over the past five to 10 years, Bozman said, many organizations have already embraced a hybrid multicloud model.
“In the beginning, we used to talk mostly about the hyperscalers,” she said. “But now, everybody is doing this. It’s been an evolution, and I think it’s an admission that data is everywhere. The data is not just sitting in one monolithic place.”
IT leaders face the additional challenge of architecting their hybrid multicloud environments in a way that will efficiently feed data into AI engines to support specific business outcomes. It’s a challenge that grows more daunting, Bozman noted, as data stores continue to grow.
“We’ve always known that there was data gravity,” she said. “It’s in the nature of data to grow, and the more it grows, the more gravity it has. If the data is in the wrong place, if it isn’t optimized, you’re going to have to drag large amounts of data over the network. You don’t want to have to drag the data back and forth, because that creates latency. People are thinking about this more carefully now because of the dramatic emergence of AI.”
Bozman noted a significant difference between the infrastructure needed to train an AI model and that needed to support a business or sector-specific “inference,” a term that refers to the process of using an AI model to analyze new data and make predictions or conclusions based on what it has learned.
“With inference, it becomes easier to get your arms around, rather than trying to take in everything that was ever written, just to write a haiku,” Bozman said.
Meeting the Moment with Enterprise AI
Costs and talent are among the top AI-related challenges facing organizations, Bozman said. These, of course, are not new. Bozman noted that industry observers have been bemoaning a shortage of talent since at least the emergence of hyperscale public cloud offerings – a problem that has only been exacerbated by the shift to hybrid multicloud models and the rise of artificial intelligence.
“A lot of the AI work is going to come from the same kind of people who have been doing the open-source work,” Bozman said.
“But it’s applying a new rigor to it. Traditional IT has been running for 40 years or more. A lot of these new developments are a sudden shock to systems that have been reliably in place. When you talk about things like backups and security, those were always done a certain way. All that is having to be adjusted.”
Bozman said the current moment is full of promise, but she stressed the importance of collaboration as organizations attempt to fully take advantage of existing technologies while also paving a path to the future.
“To me, the really exciting thing is seeing all of the innovation and hyperscale momentum preserved, but also brought forward to have a little bit more rigor in how it’s implemented in the enterprise,” she said.
“People are going to have to work together more, which is why we’re seeking a more unified environment. When we ride on the highway, that’s a unified system. We all have to agree: left lane, right lane, switching, signaling, all those things. If everybody is driving in their own way, it’s going to be pretty chaotic.”
Calvin Hennick is a contributing writer. His work appears in BizTech, Engineering Inc., The Boston Globe Magazine and elsewhere. He is also the author of Once More to the Rodeo: A Memoir. Follow him @CalvinHennick.
Ken Kaplan and Jason Lopez contributed to this story.
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