Colleges Want More Students. Their Back Offices Can’t Keep Up.

Higher education has invested in digital transformation for years. Colleges have adopted student information systems, customer relationship management platforms, online application portals and increasingly sophisticated communication tools. Yet many admissions teams still face significant operational challenges.

EDMO’s The State of Automation in Admissions Report 2026 offers a closer look at that contradiction. Our online survey of 40 higher education leaders at U.S. colleges and universities examined enrollment priorities, automation maturity, processing workloads, artificial intelligence and student communication.

The findings reveal an interesting divide. While 80% of respondents reported automating CRM and data synchronization and 75% reported automating student communication, only 12.5% reported automating transcript and credit evaluation. Identity and fraud checks were automated by just 17.5%.

These findings suggest that institutions have made considerable progress in automating routine administrative activities. However, some of the processes that require the most staff effort remain largely manual.

For business leaders beyond higher education, the distinction is important: introducing automation is not the same as removing the bottlenecks that constrain an organization.

The most difficult work is often the least automated

Among surveyed leaders, 70% identified staffing and workload as a major admissions challenge. Data silos and system integration followed at 50%, while 47.5% cited transcript and credit evaluation.

These pressures become easier to understand when looking at the processes that remain manual. Nearly two-thirds of respondents identified transcript and credit evaluation as largely manual. Document intake and indexing, along with identity and fraud checks, were each cited by 52.5%.

International transcripts present an additional challenge. Some 72.5% of respondents identified them among the most difficult documents to process, reflecting the complexity of evaluating different educational systems, grading scales and academic records.

The broader lesson is that organizations should examine where work becomes complicated, not simply where it is easiest to deploy technology.

For an admissions office, that may mean understanding how long a transcript waits for evaluation, how many times an incomplete document moves between teams and how frequently staff must correct or re-enter information.

Automating these processes requires more than digitizing documents. It requires workflows capable of managing inconsistent information, handling exceptions and connecting the systems involved.

Enrollment ambitions are moving faster than operational readiness

The survey also reveals a tension between institutional priorities and operational capacity.

Growing enrollment and reaching new markets was ranked the highest priority by 67.5% of respondents. Increasing yield and conversion emerged as the next major priority, with 55% assigning it second place.

Yet 47.5% rated their institution’s admissions automation maturity at three out of five. Budget constraints were cited as a barrier by 65%, followed closely by change management and staff readiness at 62.5%. Integration complexity affected 45%.

This combination creates a planning challenge. An institution may successfully increase applications but discover that its evaluation, verification and decision workflows cannot expand at the same pace.

That makes operational capacity an important part of any enrollment strategy.

Before committing to higher application targets, institutions should examine their processing capacity, identify their most time-consuming activities and assess whether existing systems can support additional volume.

Technology investments also need to account for staff readiness. A promising AI application offers limited practical value if it cannot integrate with existing systems or if employees lack the training and confidence to use it effectively.

Student communication is increasingly a capacity issue

Admissions operations are not limited to processing documents and making decisions. Institutions must also respond to students who need clarification about application requirements, financial aid, transfer credits and admission status.

The survey found that 47.5% of respondents reported average inquiry response times of two to 24 hours. Another 15% reported response times exceeding 24 hours.

Meanwhile, 72.5% said their institutions did not provide around-the-clock applicant support. Nearly half reported that 20–40% of student inquiries required staff intervention.

These findings raise an important question: how much time should admissions professionals spend answering repetitive questions when they also manage complex evaluations and consequential decisions?

Automated communication can handle routine requests, provide application updates and make information available outside traditional working hours. However, institutions must still establish clear escalation paths for questions that require personal assistance or professional judgment.

The objective should be to improve access to information while ensuring staff remain available for interactions where their expertise is essential.

Fraud concerns make human oversight essential

Growing fraud concerns add another dimension to admissions automation.

Within the surveyed group, 65% reported an increase in application or financial-aid fraud during the preceding two years. That included 40% who reported a significant increase and 25% who reported a somewhat increased level.

At the same time, 52.5% relied on manual identity verification, while only 15% reported using automated identity verification.

Institutions therefore face two competing pressures: processing applications more efficiently while maintaining reliable verification and appropriate safeguards.

The findings also suggest that admissions leaders are not looking to remove humans from these processes. When asked about the importance of human review of AI outcomes, 65% assigned a rating of four or five on a five-point scale.

This points toward a model in which technology assists with extracting information, identifying inconsistencies, routing exceptions and supporting administrative decisions, while people remain responsible for reviewing consequential outcomes.

For institutions introducing AI, oversight should be part of the original workflow design rather than an additional requirement introduced after deployment.

The real measure of automation is capacity

The most useful conclusion from this research is not that colleges need more AI. It is that institutions need a clearer understanding of where automation can meaningfully improve their operations.

Student communication and data synchronization are important, but automating these activities alone does not resolve the difficulties involved in evaluating transcripts, verifying applicant identities or managing complex applications.

Institutions should begin by identifying the work that consumes the most staff time, examining how information moves between systems and determining which activities require professional judgment.

They should then measure automation against operational outcomes: processing times, staff hours, exception rates, response capacity and the quality of human review.

For leaders in any industry, the principle is similar. The value of automation should not be judged simply by the number of tasks completed without human involvement. Its greater value lies in creating the capacity for people to focus on the work that matters most.

Higher education has invested in digital transformation for years. Colleges have adopted student information systems, customer relationship management platforms, online application portals and increasingly sophisticated communication tools. Yet many admissions teams still face significant operational challenges.

EDMO’s The State of Automation in Admissions Report 2026 offers a closer look at that contradiction. Our online survey of 40 higher education leaders at U.S. colleges and universities examined enrollment priorities, automation maturity, processing workloads, artificial intelligence and student communication.

The findings reveal an interesting divide. While 80% of respondents reported automating CRM and data synchronization and 75% reported automating student communication, only 12.5% reported automating transcript and credit evaluation. Identity and fraud checks were automated by just 17.5%.