The 2026 reality: Fragmented processes, limited resources, and rising risk

Over the last decade, many universities addressed urgent problems with disjointed point solutions. Program consolidations and institutional mergers compounded the issue, leaving universities with fragmented processes, siloed data, and staff spending time on redundant tasks.

Meanwhile, staff capacity remains stretched. Technology teams juggle legacy systems, new applications, and compliance demands. Even so, higher education leaders now prioritize AI-enabled efficiencies, recognizing the opportunity to shift toward automation and analytics that help them do more with less. This shift enables institutions to focus on their core mission: student success.

Funding and enrollment uncertainty intensifies the pressure. Leaders need to develop a digital strategy that incorporates measurable impacts and clear return on investment (ROI) while consolidating where possible and avoiding quick fixes that create technical debt and interoperability challenges.

A blueprint for modernization and AI adoption

Your institution can overcome many of these challenges to drive progress using a two-pronged approach.

1. Modernize core operations with unified applications, data, and AI for efficiency and compliance.
2. Create transformative impact with AI fueled by the context of your data.

Modernize core operations

Standardize processes on a unified platform backbone to accelerate financial close, streamline procurement, manage grants, and free up staff time for analysis. Integrated solutions such as SAP Business Suite deliver unified data, AI applications, embedded analytics, and cross-functional visibility to support these outcomes.

Establish an enterprise process standard

• Map the most important end-to-end business processes, including record-to-report, procure-to-pay, hire-to-retire
• Form a process council with leaders from finance, HR, procurement, research administration, facilities, and enrollment; task the council with resolving duplicate entries and standardizing processes.
• Design master data governance for people, the institution, suppliers, grants, assets, and locations; assign stewards, define data quality thresholds, and set change approval protocols.

Standardizing increases transparency into fragmented tools and processes. Integrated solutions can accelerate standardization using preconfigured process templates and shared data models.

Consolidate to the fewest necessary platforms

Consolidation is essential because each additional system adds overhead, complexity, and compliance risk. By consolidating, universities meet board expectations for ROI and reduce technical debt from fragmented, short-term purchases, and create a single source of truth. A cloud-based architecture enables faster deployment, scalability, and continuous innovation without the burden of legacy upgrades.

• Rationalize point solutions using a formal scorecard that evaluates regulatory fit, process coverage, total cost to serve, data lineage, API readiness, and auditability; eliminate redundancy and retain only tools that extend standardized processes.
• Use an integrated cloud application suite with embedded AI to automate cross-functional processes, reduce data silos, and enforce consistent controls. A unified suite helps you drive innovation by uniting core business applications, data, and AI capabilities. Integrating controls and audit trails into operational flows helps ensure compliance is built into every transaction.

Build “compliance by design” into the workflow

• Embed controls directly into processes, including segregation of duties, pre-encumbrance checks for grants, and exception routing for accounts payable and expenses.
• Automate audit evidence by capturing approvals, logs, and data lineage within workflows, making data retrievable for state auditors and federal program reviews.
• Align system data models to regulatory reporting structures.

Define and track modernization KPIs

• Cycle time and quality: Month-end close, AP “touchless” rate, requisition-to-PO cycle, on-contract spend percentage, supplier onboarding days
• Risk and compliance: Audit findings count, exception rates, late drawdown incidents, subrecipient monitoring coverage, maverick spend percentage
• Data health: Master data completeness, duplicate supplier rate, manual reclassification, reconciliation backlog days

Create transformative impact with AI

Use integrated AI-powered applications

Universities can solve complex problems faster with collaborative AI agents built into applications. AI agents use their unique understanding of your institutional context from data within the system to offer expertise or to automate processes. Context from system data enables AI agents to act as an orchestrator, triggering actions throughout workflows.

AI holds significant potential for modernizing student experiences and optimizing resources, but its deployment comes with inherent security and ethical concerns. To maximize benefits and mitigate risks, institutions must adopt robust governance frameworks, human oversight, and continuous monitoring. By defining human-in-the-loop checkpoints, approval thresholds, and post-deployment monitoring, institutions can take a measured approach to new technologies and the human implications of automation. AI literacy, policies, and training equip staff and faculty to use AI critically, creatively, and safely.

Successful AI and data insights are embedded, governed, and measured—driving cost reductions and capacity returns, helping meet ROI, and avoiding fragmented tool sprawl while maintaining compliance and transparency.

The following guidance can help your institution think through AI adoption.

Develop governance: Create a trusted AI program with risk and compliance governance processes that cover the entire institution’s use of AI. Assemble an AI committee that includes IT, compliance, general counsel, and the people likely to use AI regularly.

Monitor usage and deployment: Monitor testing, training, compliance, and legal risk. CIOs should monitor regulatory, legal, and reputational. Align system deployments and governance standards with regulatory guidance and develop mechanisms to identify, escalate, and manage issues.

Build a heat map with three axes—volume, variability, and compliance sensitivity—to select the initial use cases you want to use:

• High volume, low variability examples include invoice capture, expense audits, catalog requisitions, supplier data hygiene, and work order triage.
• Moderate volume, low variability examples include flagging risks and suggesting interventions, room optimization, and monitoring research invoice aging.
• Lower volume, moderate variability examples include agentic AI that orchestrates multistep tasks—for example, end-to-end travel reconciliation—and financial posting automations without human review.

An example of a use case that one university elected to advance was the reduction of student attrition. Georgia State University (GSU) needed to reduce summer melt—when students accept college admission, disengage over the summer, then skip fall enrollment. GSU created an AI texting assistant called Pounce to answer student questions through text messages and prepare them for their first day of classes. As a result, GSU reported a 22% reduction in melt in its first summer of implementation, contributing to improved enrollment yield.

Additional use cases to consider include:

• Facilities work order triage: Classifying requests, predicting parts and labor, and scheduling technicians with natural language processing; tracking first-time fix rate and backlog days; using embedded AI in facilities and safety processes, enabling real-time triage, predictive maintenance, and incident tracking
• Invoice and expense automation: Optical character recognition (OCR) and machine learning (ML) extracting line items, flagging duplicates and policy exceptions, and routing only exceptions to humans—helping to improve touchless rates and reduce cycle times
• Spend intelligence: Recommending contracted alternatives, flagging off-contract spend, and predicting lead times for critical lab and facilities items to prevent service disruptions
• Grants lifecycle intelligence: Monitoring allowability, burn rate vs. period of performance, and subrecipient risk; triggering early alerts to avoid de-obligations and audit findings
• Talent acquisition and workforce planning: Using skills inference and structured interviewing to reduce time to hire, improve classification accuracy, and test for and mitigate bias

AI is already helping institutions move faster and make smarter decisions, but knowing where to start isn’t always clear. With the proper safeguards, transparency, and oversight, institutions should feel confident about realizing their AI potential.

The 2026 mandate for higher education leaders

Institutions don’t have the luxury of time when it comes to modernizing, adopting AI, and optimizing their funding. The combination of fragmented processes, siloed data, capacity constraints, and financial pressure demands a plan that is both operationally conservative and strategically bold.

Integrated solutions can seamlessly connect your entire institutional value chain. These processes are flexible to adapt for industry nuances, and provide the capability to reduce costs, streamline processes, and free up staff capacity, while managing compliance and risk.

Together, governed modernization and AI create time, clarity, and control: time to focus on the mission, clarity in decision-making and reporting, and control over risk and cost. Follow this blueprint to enter the future with increased capacity, resilience, and options.

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