To brace for a continuous wave of AI-powered tools and techniques, data center managers must acquire new skills and technologies to stay at the top of their game. That’s according to industry experts and senior executives who told The Forecast how AI is reshaping IT departments.
“You can already see companies restructuring,” Jeremiah Owyang, a leading industry trendspotter now at Blitzscaling Ventures working with AI startups.
“There's quite a few enterprise software companies who are downsizing, and they are clearly stating that they are shifting to AI and they are putting in the teams that can lead in AI.”
He pointed to the growing number of so-called AI-first companies that fundamentally integrate artificial intelligence into its operations, business strategies and decision-making processes as well as the products they create. The fast-growing list of AI-first companies includes DataRobot, Duolingo, and Box.
In an interview with Tech Brew, Gartner distinguished VP analyst Arun Chandrasekaran said these companies are sending a message to investors and employees.
“It’s a way to signal to the investors that we’re not going to be lagging behind. We want to be the disruptor, we don’t want to be the disrupted one,” Chandrasekaran said. “It’s also a signal to their own employees that this is going to come fast and furious, and you’ve got to be ready for this journey.”
Owyang said it’s a wake up call. AI is here and will quickly change how things are done across different industries.
“It's really critical that, as a tech worker, you lean into AI and lead with that within your company and for your career,” he said.
Even as enterprises aim to drive efficiencies and accelerate business with AI, reports show there is a major “AI gap” between senior leaders and frontline workers that must be filled before successful rollout of AI across business units. Left unbridged, that divide could jeopardize the success of enterprise AI specifically and digital transformation more broadly.
Only 26 percent of organizations say they are extremely confident in their overall AI capabilities and knowledge, according to DataRobot’s 2024 Unmet AI Needs survey. Furthermore, just 26 to 29 percent (gen AI and predictive AI, respectively) said they were confident in their ability to work with AI tools and develop useful solutions for their business. 28 percent said they were confident in their ability to push AI apps into production.
To address these shortcomings, Owyang encourages up-and-comers and old guard alike to explore the AI ecosystem and get hands-on experience with AI tools.
“Employees must get educated about AI,” he said. “There’s a plethora of information out there. YouTube videos. Newsletters. Plus, you can just ask AI.”
Beyond baseline familiarity with consumer-grade AI applications like chatbots and AI agent builders, Owyang recommends taking formal classes. Options for getting up to speed on AI range from entry-level classes on machine learning and automation on platforms like Udacity and Coursera to AI masterclasses for CIOs and IT decision makers.
He also advises participating in conferences and AI hackathons, which provide hands-on learning and opportunities to network with experts.
“Attend many events,” he said. There are many of them happening all around the world. Keep in mind, every worker is responsible for their continued growth.”
Build AI Skills In House
Cloud engineer Debo Dutta, Chief AI Officer at Nutanix, manages a team of software engineers aimed at developing AI capabilities that power products and boost productivity for the company. He said that since AI is new, it’s important to jump in and learn by doing along with everyone else.
“Enterprises need to be training their people to understand the AI workload, to manage the AI infrastructure effectively and to protect it,” he told The Forecast.
“For everyone involved, that means learning how AI works and also how AI systems work.”
AI applications must access good data, and often that data is proprietary to the company. Dutta refers to a company’s data as valuable intellectual property (IP), so it’s wise to build AI applications and run them on their IT platform in order to keep access to the data protected from others. Once the application and data pieces are in place, the IT platform must make data center resources available. For some enterprises, that means IT resources that span across on-premises data centers to multiple clouds and out at edge locations.
“Enterprises will need to enable their engineers to build better GenAI apps on top of this infrastructure for the business to run faster,” Dutta said.
Dutta spearheads Nutanix’s own AI education efforts, delving into the latest research and reeducating staff. To get the ball rolling, he kickstarted an internal training program and invited the company’s top software engineers to bone up on evolving AI systems and techniques.
“We all learned the basics of gen AI together,” he said. “It was a great equalizer for everybody. We found that the whole team picked up GenAI skills very rapidly.”
“It is not hard to build that skillset, but you have to be deliberate and have a strategy. We noticed that there would be a skills gap and we acted on it.”
The efforts quickly translated to more efficient operations, illustrating the benefits of organizations growing AI skills organically. His team was able to take the lessons they learned and apply them in real time, hammering out GenAI tools to help the customer support, sales teams and service engineers improve productivity. Dutta said engineering is currently working on internal tools to help improve the software writing workflow.
“We already knew how to build systems, so we were able to push the envelope on the job,” he said. “Now we are proliferating this skillset across the board.”
Dutta believes that AI app development skills and awareness will become increasingly important to the profession as high-quality AI infrastructure becomes the norm.
“I think what will happen is we will reach a place where enterprises become way more efficient compared to a decade ago.”
Invest in Cloud Native Skills
Building and managing AI applications requires a strong understanding of modern IT infrastructure and tools. These applications are built in containers using cloud-native Kubernetes technologies that allows them to run across different IT infrastructure.
“Virtually all apps that are being written today are running on cloud native technologies, that's true of all the AI apps,” said Dan Ciruli, senior director of product management for cloud native software at Nutanix.
“There are lots of new tools that come with that, and they're very different from the tools in the ecosystem that have existed for the last twenty years.”
Furthermore, nearly 90% of organizations report that at least some of their applications are now containerized, according to the 2025 Nutanix Enterprise Cloud Index.
“You need people hip to the modern concepts, the cloud native concepts for networking, for storage, for security, and really, for observability, for how you assess the health of a thing that's running,” Ciruli told The Forecast.
“Luckily, there's a lot of room to solve this problem,” he continued. “One way to solve it is through retraining and people learning new skills. People need to be educated on these new tools and concepts so they really understand them.”
To learn the ropes, Ciruli recommends taking classes from the Cloud Native Computing Foundation (CNCF), which offers accreditation programs for Kubernetes administrators, app developers, and more. They work with vendors who will come in and edify employees on the likes of containerized workloads, and can assist organizations in launching their own internal training programs.
“As new people enter the workforce, they will already have these skills. But for those of us who have been in the workforce for a while, we're going to have to learn some new concepts,” he said.
Jason Johnson is a contributing writer. He is a longtime content and copywriter for tech and tech-adjacent businesses. Find him on Linkedin.
Ken Kaplan contributed to this article.
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