Cloud Computing for AI Infrastructure: What K–12 IT Leaders Need To Know

What Makes Infrastructure ‘AI-Ready’ for Schools?

As schools build AI infrastructure, they need to understand how they plan to use the technology, says Attaie.

“This means identifying how AI can enhance teaching, learning and operations before making any purchasing or implementation decisions,” he says.

AI readiness also requires proper training, Attaie notes. For example, IBM’s SkillsBuild program teaches tech professionals how to gain practical skills in AI, and the watsonx Challenge ensures that staff and educators know how to use hybrid cloud and AI tools, he adds.

“Training should cover AI literacy, prompt engineering and ethical AI use,” Smith says. “With the right skills, educators can effectively use AI’s capabilities while protecting student well-being.”

AI readiness in schools is also defined by reliable high-speed connectivity, Smith says. Bandwidth needs are increasing with the volume of data that schools produce.

LEARN MORE: Access exclusive data about the AI landscape.

Hybrid Cloud vs. On-Premises for K–12 AI Tools

A school district’s particular use cases, resources and security requirements dictate whether a hybrid model or on-premises is best, according to Attaie.

“Hybrid cloud environments often provide greater flexibility and scalability for AI tools, allowing schools to leverage both local and cloud-based resources as needed,” he says.

Smith notes that this scalability allows schools to experiment with AI tools without expensive hardware upgrades. Meanwhile, on-premises systems can bring costly purchases of new servers and time-consuming installation, she adds.

“Together, hybrid cloud and AI-ready infrastructure give districts the best of both worlds: the agility to experiment with new AI tools and the structure to manage them responsibly,” Smith says.

Cloud services on a subscription model bring cost efficiency, according to Smith. “This reduces ongoing maintenance costs and enables faster adoption of new functionality,” she says. “On-premises systems, however, demand significant upfront capital for hardware and software, along with higher maintenance requirements.”

Choosing cloud infrastructure lets educators readily access AI capabilities, including machine learning, natural language processing and generative AI, without requiring specialized hardware such as GPUs or tensor processing units, Smith says.

“Deployment and updates are also faster in the cloud, with automatic updates helping schools stay current,” she says. “On-premises environments may experience delays due to manual update processes and limited resources.”

“Hybrid cloud models are increasingly popular in education because they balance innovation with control,” Smith says. “With a hybrid, organizations are also able to take advantage of current on-premises infrastructure such as data centers, servers, networks, storage, etc.”

However, she notes, scaling AI workloads on-premises is slow and expensive due to the need for physical infrastructure upgrades.

“Cloud-based systems, by contrast, shift computing and storage to external providers such as Oracle Cloud Infrastructure, AWS, Microsoft Azure, and Google Cloud,” Smith says. “These platforms allow organizations to easily access advanced AI capabilities such as large language models, ML and analytics without seriously expanding current data centers, servers and networks.”

Data Privacy Considerations for Cloud-Based AI in Education

Being AI-ready means that school districts need strict data governance policies that explain what type of data is collected, how it’s stored and who can access it, Attaie explains.

To protect student data, Smith advises, K–12 schools should partner with tech vendors who comply with privacy laws such as the Family Education Rights and Privacy Act (FERPA), which safeguards the rights of parents concerning kids’ school records, and the Children’s Online Privacy Protection Act Rule, which protects the exposure of information online for children under 13.

“Here’s where COPPA compliance becomes critical for technologists: You must be able to completely expunge and delete a student record if requested by the custodian or when a student ages out,” advises Stef Mills, senior technical product director at Digital Promise.

Mills notes that many vendor contracts are unclear regarding deletion of student data. “How do we verify true deletion of student data when a tool is discontinued or a deletion request is made?” Mills asks.

In addition, schools should implement ethical guidelines for AI.

“Beyond technical measures, establishing clear ethical guidelines is crucial,” Smith says. “These guidelines should promote fairness, transparency and accountability in AI decision-making. Such safeguards ensure that AI tools serve students’ best interests while maintaining public trust.”

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A hybrid cloud setup provides flexibility for protecting sensitive student data. Schools may keep student, teacher or human resources data on-premises for compliance or performance reasons while AI-driven analytics could run in the cloud, according to Smith.

“This allows these districts to leverage existing infrastructure and protect sensitive data locally while taking advantage of the cloud’s flexibility, scalability and computing power,” she says.

By taking a hybrid cloud approach, school districts keep sensitive or regulated data such as student records or special education files in local environments, while turning to the cloud to run high-demand workloads, including analytics, automation and collaboration tools, she explains.

“The result is an environment that balances security, compliance and scalability,” Smith says.

Recktenwald prefers on-premises data sets for protecting school information.

“Because we have FERPA to deal with, I personally like to keep anything to do with student records as close to where I can control it as possible,” Recktenwald says. “But we always want to be mindful of that.”

Calculating the ROI of AI-Ready Infrastructure

To calculate the ROI for AI infrastructure, school districts must conduct quantitative and qualitative evaluation, according to Attaie.

“Traditionally, these initiatives are led at the district level through collaboration between a variety of leaders, which may include CIOs, CTOs, COOs and chief academic officers,” Attaie says. “ROI can be measured through improved student outcomes, operational efficiencies, cost savings and time reductions in administrative processes.”

“The return on investment for AI-ready infrastructure in K–12 settings should be measured not only in financial terms,” Smith says. “It also includes operational improvements and educational outcomes.”

Although there are direct links between expenditures and academic progress, the modern infrastructure enables informed decisions that deliver operational efficiency and student success, according to Smith. 

How To Get Started With a Hybrid Cloud for AI

To get started with hybrid cloud for AI solutions, K–12 schools should ensure they have clear governance and ethics policies that outline use for AI, Attaie advises. In addition, partnering with trusted technology providers such as CDW and IBM allows districts to form phased implementation plans.

Smith recommends a phased approach to adopting hybrid cloud infrastructure for AI, followed by assessing readiness.

“Beginning with small-scale initiatives is crucial,” Smith says. “Schools should launch pilot programs in a single department or grade level to test AI tools and gather feedback.”

In the end, the infrastructure options are not set in stone, according to Mills.

“The answer isn't one-size-fits-all,” Mills says. “It’s about finding the right balance between AI ambitions and hybrid cloud readiness while never losing sight of our biggest obligation: protecting student data and ensuring we can truly honor the regulations meant to keep students safe.”