{"resourceId":"depaul-staff-facing-financial-aid-agent-educause-20261110","versions":[{"version":"external-bce17869dc7b254305b9886fc20abe8a7d62e6b788618401a0ee7277b8b712f1","resource":{"id":"depaul-staff-facing-financial-aid-agent-educause-20261110","title":"DePaul's staff-facing financial aid AI agent cuts counselor prep from about 15 minutes to about one, per an EDUCAUSE Review case study","organization":"DePaul University with Salesforce (EDUCAUSE Review case study, Mission Partner collaboration)","sector":"Higher education administrative operations (financial aid office)","geography":"United States (Illinois)","publishedAt":"10 November 2026 (EDUCAUSE Review)","publicationDate":"2026-11-10","eventDate":null,"sourceName":"EDUCAUSE Review","sourceLabel":"Association case study, written with a vendor partner (Salesforce)","sourceUrl":"https://er.educause.edu/articles/2026/11/using-ai-to-reduce-administrative-burden-and-focus-on-students-at-depaul-university","evidenceClass":"public-sector-association","outcomeClass":"emerging","topics":["knowledge-work","data-security","operating-model"],"finding":"DePaul built a staff-facing financial aid agent on Salesforce Agentforce. In the first minute of an advising appointment it produces a financial-aid overview drawn from four separate sources (DePaul knowledge articles, federal guidance, Illinois guidance, and student data in Data360) plus an action plan. The case study says counselor administrative burden fell from fifteen minutes to about one minute, that processing time for financial aid tasks fell by an average of 90 percent, and that over one hundred hours were freed since launch in November 2025. Build took eight weeks over three iterations after a four-week discovery that interviewed every counselor and identified more than sixty counselor actions.","sledRelevance":"A concrete administrative-operations pattern with stated numbers: keep the agent staff-facing, ground it in existing governed data, and decide up front how freed time is reused. Fresh today (published 10 November 2026) and directly about back-office workload on campus.","evidence":"Self-reported by the institution through an interview with DePaul's VP for Information Sciences, published by EDUCAUSE in a Salesforce collaboration. No baseline method, sample size, accuracy or error-rate data, or audit are described; the 90 percent figure is an average with no stated definition of 'task'.","architectureImplications":"Agentforce agent over four disconnected sources; an existing Data360 environment, built earlier for a predictive-analytics project, served as the data foundation. A third-party implementation partner selected an LLM per task and connected the data sources.","governanceImplications":"The team states good data governance is a prerequisite, key data decisions should be made before starting, and the goal of increasing human connection should be discussed early with counselors. It also recommends extensive red-team testing, including checks on what the agent must not do.","securityPrivacyImplications":"Staff-facing design is presented as limiting privacy and security risk: a student-facing agent could expose other students' data in a malfunction. The case study gives no detail on logging, retention or access controls.","caveats":"Vendor-partnered case study with no independent verification; single institution; the financial aid office is student-adjacent, so relevance to wider back-office operations is partial; US federal and Illinois aid rules shape the design; no cost, error or staffing-impact figures.","streamIds":["campus-operations"],"roles":{"sales":"Sales framing: DePaul shows the buyer story most administrative leaders want, less time assembling data and more time with the person, but only through a vendor-partnered self-report. Interpretation: lead with a bounded discovery that maps counselor or staff actions and data sources, then size a staff-facing assistant. Ask how freed time will be reused. The source cannot support claims of savings, accuracy or headcount effects, and the 90 percent figure is undefined.","engineering":"Engineering framing: the design is a retrieval and summarization agent over four disconnected sources, resting on an existing governed Salesforce data layer. Validate data latency, source authority for federal and state rules, per-task model choice, access logging and red-team coverage for what the agent must not do. Test against a human-prepared summary sample before relying on the reported times. Evidence is a single self-reported deployment.","delivery":"Delivery framing: the operating change is a counselor workflow redesign, owned jointly by IT and the financial aid office. Work includes staff interviews, data decisions up front, iterative build, red teaming and a plan for reusing time saved. Acceptance criteria should be set locally, since DePaul published no error rates. Retain human review of action plans and train new staff on the agent output."},"roleDetails":{"sales":{"customerProblem":"Advisers spend appointment time assembling data from several systems and policy sources instead of resolving the student's problem.","stakeholders":["CIO or VP of information systems","Director of financial aid or equivalent service office","Registrar or student accounts lead","Data governance owner"],"discoveryQuestions":["How long does a counselor spend assembling context before advising today, and how was it measured?","Which data sources must be combined and who owns each?","How would freed time be reused and who decides?","Do staff have a say in what the agent does?"],"valueHypothesis":"A staff-facing assistant that prepares a case summary and suggested actions may shorten prep time and reduce repeat visits, if baselines are measured first.","potentialEngagement":"A bounded discovery and proof of value for one high-volume office: map staff actions and data sources, measure baseline prep time, and test an assistant on a sample.","unsupportedClaims":["Guaranteed time or cost savings","Accuracy or error rates","Headcount effects","That the 90 percent figure generalizes beyond DePaul financial aid"]},"engineering":{"fit":"Good fit where staff consult several disconnected sources per case and a governed data layer already exists; weaker without one.","architectureIntegration":"Agent over knowledge articles, federal and state guidance, and student records, with an LLM chosen per task; DePaul reused an existing Data360 foundation.","prerequisites":["Governed, integrated student data layer","Inventory of staff actions and authoritative policy sources","Data decisions on scope, aggregation and latency before build"],"constraints":["Federal and state aid rules vary by institution and jurisdiction","Reported platform is Salesforce Agentforce; other estates need separate evaluation","Student records are regulated education records"],"security":"Keep the agent staff-facing as DePaul did; review role-based access, logging and retention, which the source does not describe.","proofOfValue":["Compare agent summaries with human-prepared summaries on a sample","Measure appointment prep time before and after","Red-team for unauthorized data exposure and wrong guidance","Track repeat-visit rates"]},"delivery":{"work":"Run staff interviews, define the agent's tasks, make data decisions, build in iterations, red-team, launch and track time reuse.","dependencies":["Data foundation","Implementation partner or in-house skills","Office leadership buy-in","Policy source owners"],"ownership":"Office director owns the workflow and outcomes; IT owns the platform and data pipelines.","skillsAdoption":"Counselors need to learn to verify and adjust AI action plans; new staff onboarding may change.","governanceCheckpoints":["Goal and time-reuse agreed before build","Data decisions signed off","Red-team results reviewed before launch","Periodic accuracy review after launch"],"acceptanceCriteria":["Prep time reduced against a measured baseline","Agent summary matches human reference on an agreed sample","No unauthorized data exposure in red-team tests","Repeat-visit rate tracked"],"risks":["Self-reported results may not reproduce","Outdated policy sources produce wrong guidance","Time saved is absorbed by higher volume rather than reinvested","Staff distrust without involvement"]}},"retrievedAt":"2026-11-10T21:47:17-06:00","enrichedAt":"2026-11-10T21:48:17-06:00","enrichmentBasis":"retrieved source","accessibilityWorkforceImplications":"Counselors were interviewed during discovery and the time saved is framed as redirected to student contact, not headcount reduction. The article reports no staff survey or union input.","procurementImplications":"Platform plus third-party implementation partner; the article gives no pricing or licensing terms.","operatingModelImplications":"Joint IT and financial aid leadership, a technical lead who was a long-serving financial aid data analyst, and a partner. Reuse of the freed time is named as a deliberate management decision.","sourceVerification":{"openedUrl":"https://er.educause.edu/articles/2026/11/using-ai-to-reduce-administrative-burden-and-focus-on-students-at-depaul-university","referenceExcerpt":"has reduced processing time for financial aid tasks by an average of 90 percent","promptVersion":"sled-research-v4.0","model":null,"basis":"agent-reported inspection"}}}]}