Building an AI-Ready Campus: How to Connect, Govern and Prove AI Success

AI has arrived on campus, and it’s not just students using it. This technology is rapidly reshaping nearly every aspect of higher education. From research initiatives and administrative operations to student services and institutional planning, universities are exploring how AI can improve outcomes across the institution. According to the EDUCAUSE AI Landscape Study, 57% of higher education institutions are prioritizing AI and see it as strategic, up from only 49% in the previous year.[1]

Despite the clear promise of this technology, most universities are just beginning their AI journeys. According to Gartner®, “While student adoption is widespread, the 2025 Gartner Education AI Strategy Survey highlights that only 1% of institutions currently report tangible returns or measurable cost savings from AI initiatives.”[2]

Research remains one of the most promising applications. AI can help researchers process massive volumes of data, identify patterns that would otherwise go unnoticed and accelerate discovery. But the same challenges that affect research also apply to AI deployments across the broader campus.

Many institutions struggle with AI adoption because:

• Their outdated infrastructure can’t scale quickly enough to support data-intensive AI workloads.
• They find it difficult to navigate complex compliance requirements and ensure grant eligibility.
• Their networks are hampered by data bottlenecks and high latency, impeding global collaboration.

Modernizing their infrastructure for AI adoption is difficult and costly because they’ve relied on legacy systems for so long now. On top of all this, they have to get alignment between business and IT teams and overcome cultural resistance to change. Stalling with an effective plan to implement newer technologies opens the door to shadow AI: individuals using the AI tool-of-the-day with no governance or oversight.

With all these challenges facing them, it’s no surprise that universities have been relatively slow to execute their AI strategies. But there are clear steps they can start taking today to address these challenges.

The Equinix AI-ready framework

To close the gap between AI ambition and AI results, institutions should focus on three priorities:

• Connect: Build the infrastructure and ecosystem connections that enable AI at scale
• Govern: Establish the controls, security and oversight needed to use AI responsibly
• Prove: Measure outcomes and demonstrate institutional value

Connect: Build the infrastructure and ecosystem connections that enable AI at scale

Of course, high-density, GPU-ready environments are an essential part of what it means for a data center to be AI-ready. But there’s much more to AI success than just compute capacity. Institutions need to connect data, models, partners and users across a distributed ecosystem.

Historically, universities built their research environments around local infrastructure, but this won’t work in the AI era. Researchers need access to specialized models, distributed datasets, cloud platforms, AI service providers, industry collaborators, and public-sector partners. The ability to rapidly connect to these resources is just as important as the resources themselves.

Additionally, institutions need to connect AI initiatives across campus, linking researchers, instructors, students, administrators and other internal stakeholders. This is true whether institutions are supporting research workloads, AI-powered student services, academic applications or operational automation. In every case, success depends on securely connecting data, applications, users and AI services.

Institutions need an AI-ready data pipeline that can quickly aggregate data from distributed sources. Otherwise, their hardware may process data faster than it can pull in new datasets, leading to low utilization and poor ROI.

Networks must be highly scalable to support large AI datasets. But they must also be agile, because the AI landscape never sits still. This is one reason that shifting to software-defined interconnection can be essential. Instead of spending weeks setting up physical connections, institutions can connect to new data sources with a few simple clicks.

Also, security and control are top priorities for institutions that handle sensitive AI datasets. They need to apply the appropriate security measures across their data center environments and network infrastructure to ensure that their data is protected wherever it ends up.

Colocation data centers can provide a mix of AI-ready capabilities and on-demand interconnection. For instance, Equinix customers can tap into advanced liquid cooling to enable GPUs, while also connecting quickly and easily with our dense ecosystem. Because our facilities are vendor-neutral, they make excellent interconnection hubs, bringing together thousands of partners and service providers in the same locations.

Watch the video below to learn how Children’s Cancer Institute worked with Equinix to overcome the infrastructure challenges of performing collaborative research on a global scale, working in partnership with the University of New South Wales (UNSW) and others.

Govern: Establish the controls, security and oversight needed to use AI responsibly

Many universities rely on grant funding and must work within the confines of the grant-based funding model. This means they need to demonstrate how their research provides good value for money. They also need to show that the research is conducted in a secure, compliant manner. How they use AI will determine whether or not they can meet these goals.

Ensuring effective data stewardship and complying with frameworks like NIST SP 800-171 and GDPR starts with maintaining control over AI datasets and the infrastructure on which they reside. As mentioned earlier, shadow AI is also a concern as it adds to the urgent need for security and compliance to prevent loss of control over data.

Institutions need the flexibility of cloud infrastructure, but the trouble starts when they unknowingly abdicate their security and governance responsibilities to the cloud providers. For instance, if they put data into cloud-native storage, they’d have no control over where that data is physically hosted, potentially making it impossible to meet data sovereignty requirements.

This is why a hybrid multicloud infrastructure model can be helpful. Hybrid multicloud enables an intentional approach to cloud services: Institutions can temporarily move certain datasets into the cloud, but do so on their own terms. Sensitive datasets that need special security provisions can remain entirely on private infrastructure such as a colocation environment, so that CIOs can see for themselves that the right security and governance principles are applied.

Watch the video to learn how Equinix customer Merck KGaA uses hybrid cloud to enable AI-driven research without compromising security.

Prove: Measure outcomes and demonstrate institutional value

What does success look like for an AI-ready campus? The answer includes infrastructure metrics such as compute capacity and the volume of data exchanged with ecosystem partners, but it doesn’t stop there. Institutions should evaluate AI impact across three dimensions: research, student experience, and operations.

• Research impact may include grant funding, publication output and collaborative research activity.
• Student impact may include student engagement, retention and access to AI-enabled services.
• Operational impact may include improvements in productivity, cost-efficiency and service quality.

Within these three dimensions, each institution will have their own definitions of success, based on KPIs that they measure in their own unique ways. Regardless of the details, all institutions must take a thoughtful approach to incorporating measurement into their AI strategies. This allows them to assess what’s working, determine what they could be doing better, and produce valuable data points that can be used in future grant applications.

Take the next step on your AI journey

Now that you understand the basic steps to becoming an AI-ready campus, the question is how you’re going to execute them. If you build the right infrastructure, governance and operating model, you can turn AI experimentation into lasting institutional advantage.

Success with AI comes from connecting the right ecosystem of data, partners and AI services, governing AI responsibly across the institution, and proving measurable value through research, student and operational outcomes.

Achieving these goals requires an infrastructure foundation that’s flexible, scalable and secure. Yet legacy on-premises environments weren’t designed for the demands of today’s AI workloads. Modernizing them can be complex, costly and time-consuming, making it difficult to move at the pace that AI adoption demands.

The right digital infrastructure partner can help you accelerate the journey without feeling overwhelmed. Equinix is well-suited to be that partner because we can help you:

• Quickly deploy infrastructure to get close to many different data sources and accelerate processing on a global scale
• Connect with an ecosystem of thousands of potential partners, including AI service providers and public sector agencies, to boost collaboration and enable seamless sharing of research data
• Access virtual interconnection solutions that enable direct, private connectivity to all the data sources and partners that matter most
• Get the control, security and operational resilience needed to meet stringent research and data standards

With these capabilities, we give institutions the agility and scalability they need to thrive in today’s fast-paced world of higher education.

Equinix helps organizations in highly regulated industries maintain control over their AI infrastructure, so that they can run AI workloads wherever they need to run while keeping sensitive data where it needs to be. To learn more, read the brief, “Leverage AI while protecting sensitive data.”

[1] EDUCAUSE, 2025 EDUCAUSE AI Landscape Study: Into the Digital AI Divide, Jenay Robert, Mark McCormack, February 17, 2025.

[2] Gartner, AI in Higher Education 2026 — How to Reduce Three Barriers and Enhance AI Maturity, Tony Sheehan, February 26, 2026.

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.