Program Demand Forecasting: Approaches, Obstacles, and Hard-Won Insights

Institutions want to offer the right programs to meet both student and employer needs. The challenge is knowing, at any given time, what "right" actually looks like. According to an AACRAO survey of more than 330 undergraduate-serving institutions, 27% conduct no class or academic program demand analyses at all. Among those who do, 60% report having only "somewhat" the data they need to forecast well. The gap between intent and infrastructure shows up in familiar places for academic professionals: waitlisted students, under-enrolled sections, and programs that drift out of alignment with student need.

What separates institutions that forecast well from those that face challenges is a matter of data access, analytical tools, and institutional practices. To better understand the program demand forecasting landscape this article examines where institutions currently stand, what is getting in the way, and what the path forward looks like.

Defining Program Demand Forecasting and Where Adoption Resides

Program demand forecasting includes two core components. The first includes projecting class demand for programs currently offered, such as which courses students will need in an upcoming term and how many sections to offer. The second analyzes academic program demand: tracking enrollment trends across majors to guide decisions about staffing, curriculum, and resource investment.

The AACRAO survey found that 38% of surveyed institutions conduct both class and academic program demand analyses, while 28% focus on class demand only and 7% on academic program demand only. That leaves more than a quarter of institutions conducting no demand analysis of any kind. Notably, the survey found no statistical relationship between institutional size, type, or control that reflected whether an institution conducts these analyses, meaning the gap is distributed broadly across higher education rather than concentrated in any one segment.

Common Barriers to Effective Program Demand Projections

Barriers to effective program demand forecasting typically fall into three areas: tools, time, and institutional culture. From a tools perspective, 45% of institutions that conduct demand analyses reported that a lack of appropriate analytical tools prevents accurate projections.

Among institutions that conduct no analyses at all, that share rises to 54%. Access to data presents a related concern, with 45% of non-analyzing institutions reporting they simply cannot get to the data they need.

Time and staff capacity compound the challenge. More than half of institutions conducting demand analyses cited time as a limiting factor, and over a third pointed to insufficient staff expertise. Beyond these practical constraints, institutional culture also plays a role. Several respondents noted that academic leaders prefer to maintain control over scheduling decisions, that faculty preferences often take precedence over data, and that resistance to changing established practices slows progress.

Signs of Progress in Demand Forecasting

Despite the challenges, there are signs that the forecasting landscape is improving. Purpose-built tools and advances in AI are making it faster and easier for institutions to access, interpret, and act on demand data that previously required significant manual effort. What once took weeks of staff time to compile can increasingly be surfaced through platforms designed specifically for academic planning, lowering the barrier for institutions that have historically lacked the capacity to forecast well.

Institutional attitudes are also shifting. Higher ed leaders are placing greater emphasis on building schedules and programs that reflect what students actually need, driven by a growing recognition that market alignment and successful forecasting is both a student success issue and a financial one. As enrollment pressure mounts and the cost of misaligned offerings becomes harder to absorb, the case for investing in better forecasting tools and practices is becoming easier to make at a high level.

A Shifting Enrollment Landscape Makes Forecasting More Critical

Today’s enrollment environment differs from even a few years ago. WICHE projects that the number of high school graduates peaked at around 3.9 million in 2025, followed by a 15-year decline that will bring traditional-age enrollment down 13% by 2041. For academic operations professionals, this shift directly affects how programs are planned, resourced, and sustained.

At the same time, student preferences are evolving in ways that further complicate forecasting. Undergraduate certificate program enrollment saw the largest growth of any credential type in fall 2025, increasing 6.6%, reflecting a broader shift toward flexible, career-aligned pathways. Institutions that cannot anticipate demand shifts risk building schedules and programs around assumptions that no longer reflect the students they are serving.

Academic operations leaders who want to strengthen program demand projections should evaluate whether their current tools and data are sufficient for the decisions they need to make. Purpose-built forecasting tools can simplify the shift of moving from reactive scheduling and curricular management to proactive, data-informed planning that keep students on track.