Running AI without FinOps? Here’s why institutions are overpaying

Key points:

• Higher-ed leaders should adopt a financial strategy that enables their AI ambitions without compromising financial health
• The rise of AI-native universities: OpenAI’s vision for every student
• Amid uncertainty, IT leaders outline priorities for higher ed
• For more news on AI strategies, visit eCN’s AI in Education hub

While AI holds immense potential to revolutionize the educational experience, universities risk derailing their innovation if they do not manage cloud costs effectively. As we approach 2025, 71 percent of IT leaders report that cloud spending is straining their budgets, yet 87 percent have not prioritized the discipline of financial operations, or FinOps, to govern these costs.

The AI promise in higher ed

AI tools, such as machine learning algorithms for personalized tutoring or data analytics platforms for research, are growing in adoption. The benefits are clear, but so too are the risks associated with unchecked spending.

Higher education institutions are uniquely positioned to benefit from AI. However, to unlock its full value, universities must integrate FinOps discipline alongside their AI initiatives, before costs spiral out of control. Let’d dive into this topic and learn how Penn State and Oregon State University are aligning cloud spend with FinOps best practices.

The impact of cloud spending

Cloud computing has become the backbone of many AI implementations. Universities are leaning heavily on platforms like Microsoft Azure, Amazon Web Services (AWS), and Google Cloud to run their AI-driven applications. However, these platforms are usage-based, meaning costs can rise exponentially as AI initiatives scale. This makes it crucial for higher education CIOs to adopt FinOps practices, which are cloud financial management strategies that provide a clear understanding of how cloud resources are being used and managed.

When cloud costs spiral without proper oversight, IT teams can lose sight of cost efficiency, especially when experimenting with AI projects that demand significant computing power. In higher education, this can result in wasted resources and limited ability to scale effectively. Without embedding a FinOps discipline into AI strategy from the start, universities risk derailing their own digital transformation efforts.

Taking a page from Oregon State University’s College of Business

Consider the example of Oregon State University’s (OSU) College of Business, which faced a dilemma: continue with a complex VMware Horizon setup or transition to a more scalable and flexible solution with embedded FinOps best practices. The decision to move to Azure Virtual Desktop transformed OSU’s computer labs into a highly flexible, scalable, and cost-effective system.

OSU was able to reduce operational costs by automating cloud resource scaling, ensuring that virtual machines were only running when needed. This automated approach meant that resources were consumed only when students logged into the system, significantly lowering unnecessary cloud spending. By shifting from a capital expenditure model (CAPEX) to an operational expenditure model (OPEX), OSU gained predictable budgeting, and the IT team could direct resources toward other strategic priorities. This includes the ability to allocate budget toward enhancing student experiences.

Penn State University’s cloud spend strategy

Similarly, Penn State University sought to provide equitable access to resources for its World Campus students through AVD. Initially, the IT team struggled with manual scaling and resource allocation. However, once the team implemented a cloud management platform, it significantly reduced manual intervention and achieved more efficient resource scaling, ultimately saving 71 percent on its AVD costs. The ability to leverage automated cloud scaling meant that Penn State could handle over 1,000 active users, all while driving significant operational cost savings.

For higher ed CIOs, Penn State’s journey highlights the importance of data visibility and governance in controlling cloud costs, especially when scaling AI-driven solutions. The integration of FinOps ensured that the university’s IT operations were financially sustainable, allowing them to continue innovating without the looming fear of runaway costs.

How CIOs can build a balanced AI strategy

IT leaders in higher education didn’t get a PhD in cloud pricing, and are at risk of falling in a trap of overspend without proper guidance. The key to pairing AI initiatives with FinOps discipline is to integrate financial management processes early into the AI adoption lifecycle. Here are a few steps to consider:

1. Automate resource scaling: Cloud platforms offer significant flexibility, but this flexibility must be governed to ensure that resources are scaled based on actual need. Automated scaling ensures that compute power is available when necessary, but not wasted during idle times.
2. Monitor cost visibility: Universities must implement cloud financial management capabilities that provide visibility into usage patterns. With detailed reporting, higher ed institutions can spot inefficiencies and adjust workloads or service plans accordingly.
3. Establish clear governance policies: Establishing governance for cloud spending is crucial. Having clear guidelines on who can provision resources, how they are monitored, and when they are shut down ensures that AI initiatives are sustainable in the long term.

A dual focus on innovation and financial responsibility

By embedding FinOps practices into AI strategy from the outset, CIOs in higher education can ensure that innovation and cost management go hand-in-hand. With cloud spending spiraling out of control in many institutions, now is the time for higher ed leaders to adopt a financial strategy that enables their AI ambitions without compromising their financial health.

About the Author:

Amol Dalvi is the VP of Product at Nerdio. With more than 15 years of experience leading product and engineering teams, he is a seasoned software product executive with rich expertise in Microsoft, Cloud, and SaaS. He oversees both Nerdio Manager for MSP and Nerdio Manager for Enterprise products.