Higher education leaders face one of the most complex strategic challenges in decades: deciding which academic programs to grow, redesign, or retire.
Enrollment patterns are shifting. Employer skill needs are evolving rapidly. And institutions must balance mission, student success, and financial sustainability.
The reality is simple:
Academic program decisions can no longer rely solely on intuition.
Instead, institutions are increasingly adopting data-informed program evaluation frameworks that combine student demand signals, labor market intelligence, and institutional economics.
Let’s outline a practical framework for making stronger academic program decisions and explain how colleges and universities can move from anecdotal decisions to evidence-informed strategy.
What Is a Data-Informed Academic Program Strategy?
A data-informed academic strategy uses student demand, labor market intelligence, and program economics to guide decisions about launching, growing, or redesigning academic programs.
In practice, this means universities draw on multiple sources of evidence, including student demand signals, labor-market trends, and institutional program economics, to evaluate which programs should expand, evolve, or be phased out.
At Gray Decision Intelligence, we’ve built a platform that integrates multiple high-quality data sources to help institutions view these signals together, giving academic leaders a clearer, data-informed perspective on academic program evaluation.
What Is Academic Program Evaluation?
Academic program evaluation is the process universities use to assess the performance and future viability of degree and certificate programs.
Institutions analyze factors such as:
- Student demand
- Workforce demand
- Enrollment trends
- Instructional costs
- Career outcomes
The goal is to determine whether programs should expand, evolve, or be phased out in order to support both student success and institutional sustainability.
Why Academic Program Decisions Are Getting Harder
Several major trends are reshaping academic program strategy.
A warning: making decisions based on popular narratives rather than evidence can lead institutions in the wrong direction.
1. The Demographic Cliff vs. the Perception Cliff in Higher Education
Much of the conversation in higher education has focused on the so-called “demographic cliff,” the idea that declining birth rates will inevitably cause a long-term collapse in college enrollment.
It is true that the Western Interstate Commission for Higher Education (WICHE) projects declines in the number of high school graduates in some regions during the coming decade (Knocking at the College Door). However, birth rates alone have historically been a poor predictor of higher education enrollment patterns.
A much stronger predictor is economic conditions. When unemployment rises or industries change, adults often return to college to gain new skills and credentials. Throughout modern history, economic transitions, from the Great Recession to the rise of digital industries, have repeatedly driven waves of adult reskilling and continuing education.
At the same time, higher education faces what might be better described as a “perception cliff.” Public confidence in higher education has declined in recent years, with Gallup reporting that confidence in colleges dropped from 57 percent in 2015 to about 42 percent by 2025.
Much of this perception challenge is fueled by the narrative that college is no longer a worthwhile investment. Yet the real data tells a different story.
According to the Education Data Initiative and the Association of Public and Land-grant Universities (APLU), the average student loan debt for a bachelor’s degree is approximately $31,000, which is now lower than the average price of a new car. Unlike a vehicle, which depreciates the moment it leaves the lot, a college degree is a long-term capital asset.
The narrative about the worth of a college degree has to change, and one way to do that is to share data-backed evidence, increase student outcomes by making academic program decisions based on that evidence, and get loud about it.
Research from the U.S. Social Security Administration estimates the median lifetime earnings premium of a bachelor’s degree at roughly $900,000 compared with a high school diploma.
The implication for academic leaders is clear: the real strategic challenge is not simply fewer students. It is ensuring that institutions offer programs aligned with evolving student goals and workforce opportunities.
Colleges that evaluate programs using data-informed insights into student demand, labor market needs, and institutional economics are far better positioned to navigate demographic shifts while continuing to attract learners seeking opportunity and economic mobility.
2. Labor Markets Are Evolving Faster Than Curricula
According to the U.S. Bureau of Labor Statistics, many of the fastest-growing occupations in the next decade are concentrated in areas such as:
- Healthcare
- Data science
- Cybersecurity
- Advanced manufacturing
- Renewable energy
Source: U.S. Bureau of Labor Statistics – Occupational Outlook Handbook
Universities must continuously evaluate whether their academic portfolio aligns with these evolving workforce needs.
But wait! What if we look more deeply at the data?
While many of the fastest-growing occupations are in technical fields, that does not mean other disciplines lack labor market value. In fact, humanities graduates often perform strongly over the course of their careers.
Research from the Georgetown Center on Education and the Workforce shows that humanities majors commonly earn $58,000–$73,000 annually, with many advancing into leadership roles where earnings increase significantly over time. Humanities graduates also move into a wide range of professional fields, including law, management, communications, consulting, and public policy.
The rise of artificial intelligence may actually increase the importance of these disciplines. As recent reporting and industry commentary have noted, organizations are placing greater value on skills that machines struggle to replicate, such as critical thinking, ethical reasoning, narrative communication, and contextual judgment.
These capabilities are central to disciplines such as history, philosophy, literature, and languages.
Analysis from Gray Decision Intelligence (graydi.us) reinforces this point. When institutions examine national completions data, labor-market outcomes, and long-term career trajectories, humanities programs often contribute meaningfully to both student economic mobility and a balanced academic portfolio.
The strategic challenge for universities is not choosing between STEM and the humanities. It is building data-informed academic portfolios that recognize how technical and humanistic disciplines together prepare students for an AI-shaped economy.
3. Students Are Increasingly ROI-Focused
Students and families are asking more direct questions about the economic value of degrees.
Research from the Georgetown Center on Education and the Workforce shows that lifetime earnings vary significantly by field of study.
Source: Georgetown CEW – The Economic Value of College Majors
This puts increasing pressure on institutions to demonstrate that programs lead to real career opportunities and economic mobility. So use data and get loud.
The Three Signals Every Academic Program Decision Should Consider
A common mistake institutions make is relying on only one type of data when evaluating programs.
For example:
- Labor market demand alone
- Historical enrollment alone
- Faculty interest alone
Effective academic planning requires combining three critical signals.
1. Student Demand Signals
Student demand reveals what learners are actively interested in studying.
Important indicators include:
- Enrollment trends
- National completions data
- Degree search trends
These signals answer a fundamental question:
Are students actually interested in this program?
2. Labor Market Demand
Labor market intelligence helps determine whether programs align with real workforce opportunities.
Key indicators include:
- Job postings data
- Regional hiring demand
- Wage growth trends
- Employer skill requirements
- Industry expansion patterns
When student demand and labor market demand align, institutions often identify the strongest opportunities for program growth.
3. Institutional Program Economics
Even programs with strong student interest and workforce demand must make sense economically for the institution.
Academic leaders must understand the financial dynamics behind each program, including:
- Faculty capacity and teaching load
- Student Credit Hour (SCH) cost
- Class size and course delivery models
- Equipment, lab, and facility requirements
- Long-term contribution to institutional sustainability
At most institutions, Student Credit Hours are the core driver of instructional economics. SCH analysis shows how faculty time is allocated, how resources are distributed, and how instructional costs are absorbed across the academic portfolio.
Yet many colleges struggle to clearly see how SCH production varies across programs, and how those differences affect sustainability.
Why Program Decisions Often Fail
Many institutions still rely on fragmented information and internal debate when making program decisions.
Common challenges include:
- Siloed data: Institutional research, enrollment management, and academic leadership often operate with separate datasets.
- Slow decision cycles: Traditional program review processes can take years while labor markets evolve rapidly.
- Limited forward-looking signals: Historical enrollment data alone cannot predict future demand.
A Modern Framework for Academic Program Strategy
A growing number of universities are adopting a data-informed framework for academic portfolio management.
This approach combines multiple datasets into a single strategic perspective.
The process typically includes:
Step 1: Identify emerging opportunities
Analyze labor market growth, employer demand, and industry investment trends.
Step 2: Validate student interest
Examine national completion data, search trends, and enrollment patterns.
Step 3: Evaluate institutional capability
Assess faculty expertise, facilities, and accreditation requirements.
Step 4: Model program economics
Understand revenue potential and instructional costs.
Step 5: Prioritize strategic investments
Focus resources on programs with the strongest long-term potential.
How Gray Decision Intelligence Supports Data-Informed Program Strategy
Gray Decision Intelligence is a higher-education analytics platform that helps universities evaluate academic programs using integrated insights across:
- Student demand analytics
- Labor market intelligence
- Competitive program analysis
- Institutional program economics
- Academic portfolio strategy
Instead of relying on fragmented reports, academic leaders can evaluate program strategy using a single integrated evidence base.
Academic program decisions have always been complex—but today they are mission-critical.
Demographic shifts, workforce disruptions, and rising student expectations are forcing institutions to rethink how programs are evaluated.
By combining student demand signals, workforce intelligence, and institutional economics, universities can make data-informed decisions that support:
- Student success
- Institutional sustainability
- Regional workforce development
Platforms like Gray Decision Intelligence help bring these insights together, empowering provosts and academic leaders to make more confident academic program decisions.
