Hi Tanner,
Great question - At my firm, we've been working extensively with Workday Adaptive Planning, which now has several embedded AI and machine learning capabilities designed specifically for predictive forecasting, anomaly detection, and automated variance and KPI analysis. These features allow higher education institutions to move beyond static budget models into more dynamic, data-driven planning.
For example, I recently presented a session at the NACUBO Planning, Budgeting, and Analytics Forum in September, where I demonstrated how Adaptive's AI engine generated a three-year forecast for:
- Enrollment and tuition revenue
- Financial aid and discount rate trends
- Full-time and adjunct faculty compensation and benefits
- Non-labor operating expenses
Within minutes, the AI produced a baseline projection, ran scenario variations, and then populated variance reports, charts, and KPIs automatically - all within the same planning environment. We also showcased a conversational AI chatbot built directly into Workday Adaptive Planning, which allows users to ask natural-language questions like "Why did instructional salaries increase by 8%?" or "Show me a five-year enrollment trend by college."
A key advantage of Workday's approach is that it's a closed-loop system - your institutional data remains private and secure. The AI models only learn from the data you authorize, never exposing it publicly. For many institutions, that's a major differentiator compared with open-source or external AI tools.
Workday Adaptive Planning can connect to your ERP (e.g., Banner, PeopleSoft, Workday Financials & HCM) for direct data integration, or supplement it with vetted external benchmarking data (IPEDS, CUPA-HR, NACUBO ratios, etc.) to enhance predictive accuracy or create checks and balances.
We've seen measurable benefits such as:
- Hours to weeks reduction in time spent producing forecasts and budget books
- Faster insight generation through automated visualizations and drill-throughs
- Improved data confidence via anomaly detection and variance explanations
Challenges to consider are ensuring data cleanliness and granularity before applying AI forecasting, and establishing clear governance around how the AI's output is used in decision-making. Institutions that define these parameters upfront see much stronger results.
If you're exploring options, I'd be happy to share the NACUBO deck or walk through how these AI capabilities align with institutional planning goals.
You can see a demo of the functionality on my linkedin page - "AI in Workday Adaptive Planning": https://www.linkedin.com/smart-links/AQEYkXLr7o_vMQ
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John McGrath
Director of Strategy & Innovation | Strada
Former Budget Director in Higher Education
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Original Message:
Sent: 10-20-2025 11:38 AM
From: Tanner Grubbs
Subject: AI and automation in higher ed
Hello colleagues,
I'm reaching out to learn more about how higher ed institutions are currently using AI and automation in their financial processes. As we explore opportunities to enhance efficiency and accuracy in our own operations, I'd greatly appreciate hearing about your experiences.
Specifically, I'm curious about:
- Which financial processes (if any) you've implemented AI tools for
- What types of AI solutions you're using (vendor products, custom-built, or hybrid)
- Any measurable outcomes or lessons learned you'd be willing to share
- Challenges or considerations that emerged during implementation
Whether you're in early exploration, mid-implementation, or have established AI-enabled processes, I'd value your insights. Even if you've decided not to pursue AI in certain areas, that perspective would be helpful too.
Feel free to reach out to me directly if you prefer. Thanks in advance for sharing your experience!
-Tanner
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Tanner Grubbs
Functional Systems Analyst Sr.
University of Kansas
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