Imagine this: In the year 2032, an independent school CFO arrives at work and checks in with the school’s operational AI agent.
It instantly provides a summary of cash flows into and out of school accounts, transactions that need attention, budgets that are currently over or under YTD targets, and the predicted flow of gifts in the development pipeline.
It also provides a report on employee strengths and areas for growth, at both the individual and aggregate levels, all based on data employees have passively generated while executing their daily responsibilities. Supervisors use this information to help their direct reports set goals and to guide the selection of professional development priorities.
When a vacancy arises or a school leader creates a new role, the operational agent scans the internet for candidates on the job market and provides a list of candidates whose skills, experience, location and salary expectations align with the open position. After supervisors approve candidates, the agent proactively contacts the candidates with individualized messaging tailored to the candidate’s profile.
The Future Is Here
Over the past several years, there has been much discussion about what the future of learning looks like for students in the age of AI. There has been less discourse around how AI could reshape the operational side of schools.
While the future described above may currently seem like science fiction, we are already seeing the precursors to these activities. Schools are already building their own custom, closed LLMs to create school-specific chat bots or mounting existing AI chat bots like Gemini onto an aggregated data lake. Vendors already offer products that use AI chat bots to build custom, interactive dashboards that visualize and analyze data about admissions trends or academic outcomes.
Whether schools use AI tools to run leaner or to free up employees to spend more time on human-to-human interactions may ultimately be the real choice rather than the decision to adopt AI tools at all.
As with all new technologies, some schools will view this future as a utopia and rush to embrace it, while others will see a looming dystopia and attempt to resist it. Having seen what many cutting edge schools are already doing, in my role as executive director of CIRIS, I will venture to say that resistance may be futile in a competitive educational marketplace.
Today and in the near future, AI tools may help public schools encroach on independent schools’ strengths by better individualizing the student experience and opening a new frontier of private educational institutions like AI-powered micro- or home schools. This increased market competition could apply sufficient pressure to the already beleaguered independent school financial model to force schools to consider adopting cost-saving AI tools simply to stay open. Whether schools use AI tools to run leaner or to free up employees to spend more time on human-to-human interactions may ultimately be the real choice rather than the decision to adopt AI tools at all. In this future, making sure that your school has options around AI that align with your mission depends on what you do today.
The Time To Clean Data Is Now
Like human-powered data analysis, all AI tools rely on the availability of high-quality source data to be effective. To even have the option to leverage the AI tools of tomorrow, schools need to address the quality of their data today.
The bad news is that most schools drastically overestimate the quality of the data in their systems because they can execute day-to-day tasks required for running a school. Unfortunately, task-oriented data and analysis-ready data can be very different from each other, and having the former is not sufficient to conclude that the latter exists.
Because different databases on campus are used for different tasks, depending on which system is queried, a school leader may even get different answers to the basic question, 'How many employees work at the school right now?'
On one hand, because there are established standards and conventions around accounting and financial reporting, much of the data schools maintain in these areas is already ready for analysis and AI. On the other hand, when it comes to employee hiring, growth and attrition, school data is usually sparse, inconsistent or non-existent. This is despite the fact that nearly every school is focused on attracting and retaining quality faculty and staff.
Right now, most schools would struggle to execute more complex data analysis projects. Because different databases on campus are used for different tasks, depending on which system is queried, a school leader may even get different answers to the basic question, “How many employees work at the school right now?”
The good news is that the data availability, quality and governance obstacles institutional researchers have been wrestling with for the last 5-10 years are the same ones that schools must address in order to have the option to successfully adopt emerging AI tools.
A New Guide to Cleaner Data
In the newly published resource “ Clean Data, Clear Vision: An Independent School Data Strategy Handbook,” the Center for Institutional Research in Independent Schools (CIRIS) lays out a blueprint for data collection and governance to help schools accumulate the data they need to fuel impactful analysis and to be prepared for an AI-infused future.
Each chapter centers on a specific area of school operations and begins with a list of strategic questions many schools currently seek to answer through human-generated analysis. The chapters then lay out the data building blocks schools need to answer critical questions, including recommendations for collection methods, formatting and storage. Developed in consultation with NAIS, the standards around data schools report out for benchmarking are aligned with existing definitions, while also extending to many non-reportable, yet extremely important internal metrics.
In the chapter on human resources and professional development data, “ Clean Data, Clear Vision” explores crucial topics like:
- The importance of clearly defined job descriptions.
- Methods for distinguishing between and tracking headcount vs. total FTE’s.
- Documenting professional development costs, usage and impact.
- Accurately recording the reasons for employee attrition.
- Assessing the full cost of employee turnover.
While the exact shape of the future of schools in the age of AI is still murky, this nascent era has already underscored at least one truth: The data accumulating in school systems is a valuable institutional asset, just like the physical plant or tech stack, and it requires intentional investment and maintenance if it is to be effectively leveraged.
Download the free resource: “ Clean Data, Clear Vision” from the CIRIS website, which also lists relevant programming for CIRIS member schools.
