I am writing a story about how IT departments at higher education institutions are modernizing infrastructure for AI. I am not looking for vendors, just people who work at a college or university.

Question

Ashish Fernando · Accepted Answer

Higher education institutions are growing sensitive to student demands and pushing IT departments to take cognizance & recalibrate systems accordingly. IT teams are primarily investing resources in building scalable, cloud-first ecosystems that can handle AI's data-intensive workloads. From enrollment automation to AI-driven research analysis, no avenue has been left untouched by AI, with tech outflows pouring into high-performance computing clusters, GPU-powered servers, and hybrid cloud architectures. While modernization is not only reflected in infrastructure, the mindset is also a salient depiction. IT departments are also focusing on retraining staff, creating AI-efficient teams, and building cross-departmental partnerships to align technology with institutional objectives.

Mircea Dima · Answer

We began updating our infrastructure shortly after our initial AI pilots continued to fail not due to the quality of the model but because our systems could not support it. Our student records were scattered on several legacy systems and we had no trusted identity resolution across systems. Prior to working with either GPUs or compute clusters, we had been redesigning our ETL flows and centralising access through a data governance model over a six-month period.

This laid a firm foundation and we were able to see results right away: 40% faster inference and complete transparency into what teams are using AI models. It has also gotten rid of the issue of shadow infrastructure--no more rogue GPU instances or ad-hoc data access policies

The greatest myth is that AI infrastructure begins with hardware. It doesn't. You require clean and connected data and a system that does not break down when the demand goes high during a semester. All other things emanate out of that.

Nicolas Breedlove · Answer

Every playground design starts with questions: Who will use it? How do we make it safe? How do we make it fun for everyone? IT leaders in higher ed face the same process with AI.

They need to ask: Will this system support students with disabilities? Does it meet compliance standards? What upkeep will it require five years from now?

When we miss those questions, playgrounds can end up unsafe or unusable. In IT, skipping them can leave systems that fail students. Building the habit of questioning first makes upgrades last.

Lacey Jarvis · Answer

Running big projects has taught me that asking questions early saves time and stress later. Higher ed IT leaders need to lean on the same habit with AI.

The questions could be: Does this system meet accreditation requirements? Can it grow with the school for the next decade? How will faculty use it, not just students?

Skip those and you risk patchwork fixes that don't hold up. In my world, that means unsafe equipment. In IT, it means lost trust. When questioning is the culture, modernization becomes more than a buzzword.

John Elarde III · Answer

In my world, small questions make the biggest difference. Did we stock enough supplies? Did we clean every high-touch area? The same is true for higher ed IT teams preparing for AI.

They may need to ask: Will this system serve all students equally? Are we protecting sensitive data? What happens when the system scales? Skipping those checks is how you end up with shiny tools that don't work in practice.

When leaders encourage open questions, problems get solved faster. At my company, if we miss a simple check, the client notices. On a campus, if IT overlooks security or fairness, students will notice too

Josh Howarth · Answer

My job is spotting what's next, and that only happens when you ask the right questions. Higher ed IT should take the same approach with AI.

They need to ask What new trends in student learning are emerging? How will this infrastructure hold up when demand doubles in a few years?

If they only think about today, they'll end up with short-term fixes. If they keep asking forward-looking questions, they'll build systems that last. The schools that keep questioning are the ones most likely to stay ahead

Spencergarret Fernandez · Answer

Good Day,

Based on my own experience in higher education, the modernization of IT begins with the enhancement of cloud infrastructure, ensuring that data is secure, reliable, and easily accessed. Also, the support offered to faculty and staff to help them learn-how to effectively use new tools is crucial. If systems are designed allowing for phased upgrades that align with student and research needs, institutions can build a system that remains useful and relevant in the future.

If you decide to use this quote, I'd love to stay connected! Feel free to reach me at spencergarret_fernandez@seoechelon.com

Adam Bocik · Answer

In our experience helping brands modernize their infrastructure to support AI, the biggest challenge felt more about bringing the right people together and ensuring they are aligned as systems, than it did with the technology itself. Higher education institutions face significant challenges in adopting AI-enabled solutions -- and, often times, are running on legacy platforms that were not designed for the amount and complexity of data requirements we have today. Effective AI is about more than plugging in, however. Developing an ecosystem in which multiple sources communicate with one another, and stakeholders are confident in outputs of all kinds, is vital, as well! Our best experience has been that when brands start small with their AI use case, and there were well-defined use cases, there is some confidence developing in segments, and then scaling those applications institution-wide. For universities, this might look like piloting AI to improve admissions analytics, or student support, then expanding into broader academic or operational applications. What we have learned is that modernization succeeds when it has a staged approach, is built on transparent process, and is driven by solving human challenges, not simply for the sake of using a new technology.

Adrian Iorga · Answer

When you modernize anything, the first step isn't buying new tools, it's asking better questions. For higher ed IT, those questions might be: What do students expect from AI tools? Can our network handle the traffic? Where are the risks?

I've learned in moving that a missed question, like forgetting to ask about stairs, can throw off an entire job. Colleges face the same thing. If IT doesn't ask about bandwidth or data storage up front, the upgrade may stall later.

A shared space, physical or digital, makes it easier for people to ask these things before it's too late. AI itself may nudge staff toward the right questions. That keeps the focus on the student experience, not just the tech

I am writing a story about how IT departments at higher education institutions are modernizing infrastructure for AI. I am not looking for vendors, just people who work at a college or university.

5 Answers

Ashish Fernando

Mircea Dima

Nikita Sherbina

Adam Bocik

Spencergarret Fernandez

Related Questions

I am writing a story about how IT departments at higher education institutions are modernizing infrastructure for AI. I am not looking for vendors, just people who work at a college or university.

5 Answers

Ashish Fernando

Mircea Dima

Nikita Sherbina

Adam Bocik

Spencergarret Fernandez