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AI Prompt Library

This is your quick-reference library of ready-to-use AI prompts for common workplace tasks at the Universities of Wisconsin. No need to start from scratch every time!

⚠️ Before Using These Prompts

  • Know your data classification – Is this public information or protected data?
  • Follow your institution’s AI policy – Check which AI systems are approved for your work
  • When in doubt, ask – Contact your supervisor or IT department

Key Principle: These prompts work with any AI collaborator, but YOU choose the appropriate system based on your data sensitivity. Only use approved AI for sensitive data and always follow your institution policy.

How to Use This Library

Step 1: Start a Document and Save this Table

CategoryPrompt NamePrompt TemplateWhen to UseExample Variables
Email CommunicationProfessional Email ResponseI need to respond to an email about [TOPIC]. The sender is [RELATIONSHIP TO YOU]. Key points to address: [LIST KEY POINTS]. Tone should be [TONE]. Draft a professional response.High-volume inquiries; need consistent toneTOPIC: program application deadline; RELATIONSHIP: prospective student; KEY POINTS: deadline is firm, exceptions rare; TONE: friendly but clear
Email CommunicationEmail SummarySummarize the key action items and decisions from this email thread: [PASTE EMAIL THREAD]Long email chains; need quick overviewPASTE actual email thread
Email CommunicationMeeting Follow-Up EmailDraft a follow-up email after [TYPE OF MEETING] with [ATTENDEES]. Key decisions: [DECISIONS]. Action items: [ACTION ITEMS]. Tone: [TONE]Post-meeting documentationTYPE: discovery session; ATTENDEES: Economic Engagement team; DECISIONS: pilot AI for business outreach; ACTION ITEMS: John Doe to review data privacy
Data AnalysisSpreadsheet Formula HelpI’m working in [EXCEL/GOOGLE SHEETS]. I need to [WHAT YOU WANT TO DO]. My data structure: [DESCRIBE COLUMNS]. What formula should I use?Complex data calculationsWHAT YOU WANT TO DO: calculate average application processing time by program; DESCRIBE COLUMNS: Column A is program name
Data AnalysisSurvey Data InterpretationI have survey data with [NUMBER] responses about [TOPIC]. Key findings I’m seeing: [LIST FINDINGS]. Help me identify patterns and craft 3-5 key insights for a summary report.Making sense of survey resultsNUMBER: 150; TOPIC: AI tool preferences; FINDINGS: 65% concerned about data privacy, 80% interested in training
Data AnalysisData Visualization SuggestionI need to present data about [TOPIC] to [AUDIENCE]. The data shows [DESCRIBE DATA]. What’s the best visualization type and why?Preparing presentations or reportsTOPIC: AI adoption rates across universities; AUDIENCE: leadership; DATA: adoption percentages over 6 months
Meeting FacilitationMeeting Agenda CreatorCreate a meeting agenda for a [DURATION] [TYPE OF MEETING] about [TOPIC]. Attendees: [WHO]. Goals: [GOALS]. Include time blocks.Planning any meetingDURATION: 60-minute; TYPE: discovery session; TOPIC: AI opportunities in admissions; WHO: 5 admissions staff; GOALS: identify pain points, explore solutions
Meeting FacilitationIcebreaker GeneratorSuggest 3 engaging icebreaker questions for a [TYPE OF MEETING] with [NUMBER] people who [DESCRIBE GROUP]. Keep it professional and relevant to [TOPIC].Starting meetings; building psychological safetyTYPE: Community of Practice; NUMBER: 15-20; DESCRIBE: varying AI experience levels; TOPIC: prompt engineering
Meeting FacilitationMeeting Notes TemplateCreate a structured template for taking notes during [TYPE OF MEETING] that captures: decisions, action items, attendees, key discussion points, and parking lot items.Standardizing documentationTYPE: AI discovery session
DocumentationProcess DocumentationI need to document the process for [TASK/WORKFLOW]. Steps include: [LIST STEPS]. Audience: [WHO WILL USE THIS]. Create clear, step-by-step documentation.Creating procedures for othersTASK: scheduling a discovery session; STEPS: check calendar, send Form, compile responses; WHO: future AI team members
DocumentationReport Executive SummaryBased on this information [PASTE FULL CONTENT], create a 3-4 paragraph executive summary that highlights: key findings, recommendations, and next steps. Audience: [WHO].Distilling long documents for leadershipWHO: university presidents or UWSA leadership
DocumentationPolicy Draft HelperHelp me draft policy language for [TOPIC]. Key requirements: [REQUIREMENTS]. Tone should be [TONE – clear/formal/accessible]. Similar policies to reference: [ANY EXAMPLES].Policy development workTOPIC: AI tool usage in student communications; REQUIREMENTS: data privacy, human oversight; TONE: clear and accessible
Project PlanningProject Timeline CreatorCreate a project timeline for [PROJECT NAME]. Start date: [DATE]. End date: [DATE]. Major milestones: [LIST MILESTONES]. Identify dependencies and suggest reasonable timeframes.Planning initiativesPROJECT: Enterprise AI agreement implementation; START: January 2026; END: June 2026; MILESTONES: vendor selection, pilot launch, training series
Project PlanningRisk AssessmentI’m planning [PROJECT/INITIATIVE]. Potential risks I’ve identified: [LIST RISKS]. Help me assess impact and likelihood for each, and suggest mitigation strategies.Planning any new initiativePROJECT: Software Enterprise rollout; RISKS: data privacy concerns, resistance to change, insufficient training
Project PlanningStakeholder Communication PlanCreate a communication plan for [PROJECT]. Stakeholders: [LIST GROUPS]. Key messages: [MESSAGES]. Suggest timing, channels, and format for each group.Major announcements or changesPROJECT: new AI policy; STAKEHOLDERS: faculty, staff, students, leadership; MESSAGES: what’s changing, why, how it affects them
Student/Stakeholder CommunicationFAQ GeneratorBased on this information [PASTE INFO], generate 8-10 frequently asked questions and clear answers. Audience: [WHO]. Tone: [TONE].Creating support resourcesWHO: international students; TONE: friendly and reassuring
Student/Stakeholder CommunicationProgram DescriptionWrite a [LENGTH] description of [PROGRAM/INITIATIVE] for [AUDIENCE]. Key benefits: [BENEFITS]. Unique features: [FEATURES]. Include call-to-action.Marketing programs or initiativesLENGTH: 150-word; PROGRAM: Direct Admit Wisconsin; AUDIENCE: high school counselors; BENEFITS: guaranteed admission, clear pathway
Student/Stakeholder CommunicationTranslation HelperTranslate this complex [POLICY/CONCEPT] into plain language for [AUDIENCE]: [PASTE CONTENT]Making technical content accessiblePOLICY: FERPA compliance requirements; AUDIENCE: staff with no legal background
Content CreationNewsletter SectionCreate a [LENGTH] newsletter section about [TOPIC] for [AUDIENCE]. Tone: [TONE]. Include a compelling headline.Regular communicationsLENGTH: 200-word; TOPIC: new AI prompt library launch; AUDIENCE: UWSA staff; TONE: enthusiastic and practical
Content CreationSocial Media PostCreate a [PLATFORM] post about [TOPIC]. Character limit: [LIMIT]. Include relevant hashtags. Tone: [TONE].Social sharingPLATFORM: LinkedIn; TOPIC: AI Community of Practice success; LIMIT: 300 characters; TONE: professional but warm
Content CreationPresentation OutlineCreate an outline for a [DURATION] presentation on [TOPIC] for [AUDIENCE]. Key points to cover: [POINTS]. Include suggested time for each section.Preparing talks or workshopsDURATION: 45-minute; TOPIC: prompt engineering basics; AUDIENCE: staff with beginner AI experience; POINTS: what is a prompt, persona-task-context formula, hands-on practice
Training & EducationTraining Module OutlineDesign a [DURATION] training module on [TOPIC] for [AUDIENCE]. Learning objectives: [OBJECTIVES]. Include interactive elements.Developing educational contentDURATION: 90-minute; TOPIC: AI for email efficiency; AUDIENCE: admissions staff; OBJECTIVES: reduce email response time by 30%, maintain personalization
Training & EducationQuiz GeneratorCreate a 5-question quiz to assess understanding of [TOPIC]. Difficulty level: [BEGINNER/INTERMEDIATE/ADVANCED]. Include answer key with explanations.Reinforcing learningTOPIC: responsible AI use; DIFFICULTY: beginner
Training & EducationCase Study CreatorDevelop a realistic case study for [TOPIC] that shows [SCENARIO]. Include discussion questions that prompt critical thinking about [THEMES].Teaching through examplesTOPIC: AI in student recruitment; SCENARIO: balancing automation with personal touch; THEMES: ethics, effectiveness, student experience
Research & AnalysisLiterature Review SummarySummarize the key themes, findings, and gaps from these sources about [TOPIC]: [LIST SOURCES OR PASTE ABSTRACTS]Background research for projectsTOPIC: AI adoption in higher education administration
Research & AnalysisCompetitive AnalysisCompare [ORGANIZATION/APPROACH A] and [ORGANIZATION/APPROACH B] on these dimensions: [LIST DIMENSIONS]. Present as a comparison table.Understanding landscapeA: UW-Madison AI policy; B: UW-Milwaukee AI policy; DIMENSIONS: scope, restrictions, training requirements
Research & AnalysisTrend AnalysisBased on this data [PASTE DATA], identify 3-5 key trends over time. Explain what might be driving these trends and potential implications.Making sense of patternsDATA: AI tool usage statistics over 6 months
Problem SolvingRoot Cause AnalysisWe’re experiencing [PROBLEM]. Symptoms: [DESCRIBE SYMPTOMS]. Context: [RELEVANT BACKGROUND]. Use the ‘5 Whys’ technique to help identify potential root causes.Diagnosing issuesPROBLEM: low Community of Practice attendance; SYMPTOMS: 30% drop in last 2 months; BACKGROUND: sessions moved to virtual only
Problem SolvingSolution BrainstormingWe need to solve [PROBLEM]. Constraints: [LIST CONSTRAINTS]. Generate 10 creative solutions that range from quick wins to longer-term strategic approaches.Creative problem-solvingPROBLEM: staff don’t know what tasks AI can help with; CONSTRAINTS: limited training budget, varying skill levels, no dedicated AI support staff
Problem SolvingDecision MatrixHelp me create a decision matrix to evaluate [OPTIONS] based on these criteria: [CRITERIA]. Weight the criteria by importance: [WEIGHTS].Making structured decisionsOPTIONS: different enterprise AI vendors; CRITERIA: cost, features, data privacy, ease of use, support; WEIGHTS: data privacy is highest priority
Personal ProductivityTask PrioritizationHere are my tasks for [TIMEFRAME]: [LIST TASKS]. Constraints: [TIME/RESOURCES]. Help me prioritize using the Eisenhower Matrix (urgent/important).Managing workloadTIMEFRAME: this week; LIST: prepare discovery session
Personal ProductivityEmail Batch ProcessingI have [NUMBER] emails in my inbox about [GENERAL TOPICS]. Help me create categories and draft template responses for common themes.Clearing inbox efficientlyNUMBER: 45; TOPICS: discovery session scheduling, AI tool questions, policy feedback
Personal ProductivityMeeting PrepI have a meeting about [TOPIC] with [ATTENDEES] in [TIMEFRAME]. Goals: [GOALS]. What should I prepare? Create a prep checklist.Getting ready efficientlyTOPIC: Enterprise AI proposal presentation; ATTENDEES: John Doe, Jane Johnson; TIMEFRAME: 2 days; GOALS: get approval for pilot

Step 2: Use a Prompt

  1. Find a prompt that matches your task
  2. Read the “When to Use” column to confirm it fits
  3. Copy the “Prompt Template”
  4. Replace the [BRACKETED SECTIONS] with your specific information
  5. Look at “Example Variables” for inspiration
  6. Paste into an AI tool of choice!*

The “Persona + Task + Context” Formula

Many prompts use this proven structure:

  • Persona: “I am a…” or “You are a…” (sets the role/expertise)
  • Task: What you need the AI to do
  • Context: Background information, constraints, audience, tone

Example: “I am a program coordinator at the Universities of Wisconsin. I need to draft a follow-up email after a meeting with campus partners. Key decisions: pilot new initiative for student support. Action items: review requirements and timeline. Tone: enthusiastic but professional.”

Tips for Success

Be Specific

Don’t: “Help me with emails”

Do: “I need to respond to 20 similar emails about program deadlines. Draft a template I can personalize.”

Iterate

Don’t expect perfection on the first try! If the output isn’t quite right:

  • Add more details
  • Clarify the tone
  • Provide an example
  • Ask the AI to revise specific parts

Customize Prompts

These are STARTING POINTS. Feel free to:

  • Add your own variables
  • Adjust the tone
  • Combine multiple prompts
  • Create variations that work for your style

Save Your Wins

When you find a variation that works really well:

  • Add it to your library!
  • Share it with your team
  • Note what made it effective

Categories Explained

Email Communication: High-volume correspondence, follow-ups, professional messaging

Data Analysis: Spreadsheets, surveys, identifying patterns, creating insights

Meeting Facilitation: Agendas, icebreakers, note-taking, follow-ups

Documentation: Process docs, summaries, policy language

Project Planning: Timelines, risk assessment, stakeholder communication

Student/Stakeholder Communication: FAQs, program descriptions, plain language

Content Creation: Newsletters, social media, presentations

Training & Education: Learning modules, quizzes, case studies

Research & Analysis: Literature reviews, competitive analysis, trend identification

Problem Solving: Root cause analysis, brainstorming, decision-making

Personal Productivity: Task management, email batching, meeting prep

Common Mistakes to Avoid

  1. Too Vague: “Write something about AI” → Not enough guidance
  2. No Context: AI doesn’t know your audience, constraints, or goals unless you tell it
  3. Expecting Mind-Reading: The more specific you are, the better the output
  4. Copy-Paste Without Reviewing: Always review and personalize AI outputs!
  5. Forgetting Tone: Specify if you want formal, casual, friendly, technical, etc.

Quick Reference: Most Popular Prompts

Based on common workplace needs, these are likely your most-used:

  1. Professional Email Response – For handling high-volume inquiries and correspondence
  2. Meeting Agenda Creator – For structured meetings and planning sessions
  3. Survey Data Interpretation – For making sense of feedback and assessments
  4. Meeting Notes Template – For standardizing documentation
  5. FAQ Generator – For creating support resources

Examples in Action

Example 1: Email Response

Prompt Used: Professional Email Response

Filled In: “I need to respond to an email about program application deadlines. The sender is a prospective student. Key points to address: deadline is March 1st and firm, late applications go to different process, encourage early completion. Tone should be friendly but clear.”

Result: Professional, consistent response ready to personalize

Example 2: Meeting Prep

Prompt Used: Meeting Agenda Creator

Filled In: “Create a meeting agenda for a 75-minute planning session about improving departmental processes. Attendees: 4 team members with varying experience levels. Goals: identify current challenges, explore potential solutions, gather input on priorities. Include time blocks.”

Result: Structured agenda with appropriate time allocation

Example 3: Making Data Digestible

Prompt Used: Survey Data Interpretation

Filled In: “I have survey data with 187 responses about workplace technology preferences. Key findings I’m seeing: 72% interested in training opportunities, 58% concerned about data privacy, 45% currently using tools unofficially, 89% want clearer guidelines. Help me identify patterns and craft 3-5 key insights for a summary report.”

Result: Clear narrative insights for leadership presentation

Remember: AI is a Collaborative Partner

Think of these prompts as starting conversations with an AI colleague, not commands to a robot. The best results come from:

  • Clear communication
  • Iterative refinement
  • Your human judgment and expertise
  • Treating AI as a thought partner, not a replacement

Version: 1.0 (December2025) Created for: Universities of Wisconsin Wisconsin Idea in Action: Extending knowledge to serve Wisconsin

November Central Applications Maintenance Release:

Waiver Correction v3.1
Release Notes:
Adjustment Fix: Resolved an issue affecting waivers that were applied and then modified. PeopleSoft calculates amounts differently for adjusted waivers than for those applied in full. This update ensures correction logic is applied properly for both scenarios.
Unapplied Credits Fix: This version also includes a fix for unapplied credits on waivers where only part of the total waiver amount is applied to charges. In the previous version, the FUND_CD (account) field was left blank; this update ensures the field is now populated correctly.
Link to Download: UW_CA_WAIVER_CORRECTION_ATP_v3.1.zip

PIP NRT_3 6 Update
Release Notes:
Modified the extract to display names in mixed case instead of converting them entirely to uppercase.
Updated the extract to use “WORKDAY” (replacing “UWHRS”) as the identifier for the HRS system in supplemental data extracts.
Corrected a bug that prevented updates to financial aid data from being processed during real-time changes.
It replaces PIP_NRT_3_6_Update_20250926, which caused issues by incorrectly identifying the HRS system as “Workday” in mixed case.
Link to Download: PIP_NRT_3_6_Update_20251007.zip

To view items ready for an application’s next maintenance release:
Incidents: SIS Team’s Reported Bugs Dashboard
Service Requests: SIS Team’s Enhancement Requests Dashboard

October Central Applications Maintenance Release

October Central Applications Maintenance Release:

Refunding Inbound Version 1.4

Release Notes:
Ivanti Ticket #115436Resolved an issue that occurred when no files were found to process.
Previously, an error message was triggered even though the process completed successfully (exit = 0).
The message has now been updated to a warning, indicating that no files were processed while still allowing the process to end successfully.
File Layout ConsistencyAdjusted file layout field lengths to match the record structure for consistency.
This update has no impact on delimited file layouts.
Project/App Engine Property UpdatesUpdated comments on Project/App Engine properties to more easily identify the currently installed version.
Link to Download: UW_SF_REFUNDS_INBOUND_V1.4.zip

FWS Version 2.1.3
Release Notes:
Fixed an issue where two payrolls (on-cycle and off-cycle) shared the same end date.
The process has been updated to use the maximum amount and corresponding pay date to ensure accurate processing.
Link to Download: ATP FWS Interfaces Version 2.1.3 – 20251003.zip

PIP NRT_3.6 Update
Release Notes:
Updated name formatting – The extract now provides names in mixed case instead of converting them entirely to uppercase.
System label update – The extract now uses “Workday” (replacing “UWHRS”) to identify the HRS system for supplemental data extracts.
Financial aid data fix – Corrects a bug which ignored updates of financial aid data when processing the real-time changes.
Link to Download: PIP_NRT_3_6_Update_20250926.zip

NOTE
Please note that the latest versions of PASS and POST-PASS, along with the Waiver Correction updates, are currently in the testing phase and will be released soon. Stay tuned for more details!

To view items ready for an applications next maintenance release:
Incidents: SIS Team’s Reported Bugs Dashboard
Service Requests: SIS Team’s Enhancement Requests Dashboard

AI Literacy 101: What Every Professional Should Know in 2025

The artificial intelligence revolution isn’t coming—it’s here. From the research assistant helping draft emails to the scheduling tool optimizing meeting times, AI has quietly woven itself into the fabric of modern work life. Yet many professionals find themselves using these powerful tools without a full understanding of how they work, what they can and can’t do, or how to use them effectively and safely.

As AI becomes as common as spreadsheets or email, developing AI literacy isn’t just helpful—it’s essential. Whether you’re a faculty member exploring new research methodologies, an administrator streamlining operations, or a student preparing for your career, understanding the fundamentals of AI will help you work more effectively while avoiding common pitfalls.

What Is AI, Really?

At its core, artificial intelligence refers to computer systems that can perform tasks typically requiring human intelligence. But today’s AI—particularly generative AI like Copilot, Claude, or ChatGPT—works differently than you might expect.

Think of AI as a sophisticated pattern recognition system. These tools are trained on massive datasets that contain text, images, code, and other types of information. They learn to identify patterns in this data and use those patterns to generate responses, complete tasks, or make predictions. They’re not searching a database for the “right” answer—they’re predicting what response would be most appropriate based on the patterns they’ve learned.

This distinction is important because it explains both the remarkable capabilities of AI and its significant limitations.

Core Concepts Every Professional Should Understand

Training Data: The Foundation of AI Knowledge

Every AI system learns from training data—the information used to teach it patterns and relationships. For large language models, this typically includes books, articles, websites, and other text sources, usually with a specified cutoff date for knowledge.

What this means for you: AI systems know what they were trained on, but they may not have information about recent events, your specific organization’s policies, or specialized knowledge in niche fields. Always verify important information, especially if it relates to current events or your specific context.

Hallucinations: When AI Gets Creative with Facts

One of the most important concepts to understand is “hallucination”—when AI generates information that sounds plausible but is actually incorrect or fabricated. This isn’t a bug; it’s an inherent characteristic of how these systems work.

Why it happens: AI generates responses by predicting what should come next based on patterns, not by accessing a reliable database of facts. Sometimes, this process produces convincing-sounding information that is simply not true.

What this means for you: Never assume AI-generated information is accurate without verification, especially for:

  • Statistics and specific data points
  • Citations and references
  • Technical specifications
  • Historical facts or dates
  • Legal or medical information

Bias: AI Reflects Human Patterns

AI systems learn from human-created data, which means they can perpetuate or amplify human biases present in that data. This can affect everything from language translation to hiring recommendations.

What this means for you: Be particularly careful when using AI for decisions that affect people—hiring, evaluation, resource allocation, or content that will be widely shared. Consider diverse perspectives and human oversight for these applications.

Context Windows: AI’s Limited Memory

AI systems can only “remember” a limited amount of information at once, referred to as a context window. Once you exceed this limit, the system starts “forgetting” earlier parts of your conversation.

What this means for you: For long documents or complex projects, you may need to break work into smaller chunks or periodically remind the AI of important context from earlier in your conversation.

Evaluating AI Outputs: Your Critical Thinking Checklist

Developing the ability to critically evaluate AI responses is perhaps the most important skill for AI literacy. Here’s a practical framework:

The FACT Check:

  • Factual accuracy: Can you verify the information from reliable sources?
  • Appropriate tone and context: Does the response fit your needs and audience?
  • Complete and relevant: Does it address your actual question without unnecessary tangents?
  • Timely and current: Is the information up-to-date for your purposes?

Red flags to watch for:

  • Responses that seem too confident about uncertain topics
  • Information you can’t verify through other sources
  • Advice that contradicts established best practices in your field
  • Content that doesn’t quite match your specific context or requirements

Getting Better Results: The Art of AI Communication

Effective AI use is as much about communication as it is about understanding the technology. Here are key strategies:

Be specific and clear: Instead of “help me write something,” try “help me write a professional email declining a meeting request while suggesting alternative times.”

Provide context: Share relevant background information, including your role, target audience, and objectives.

Iterate and refine: Use AI’s responses as starting points for further development. Ask follow-up questions, request revisions, or build on initial outputs.

Set boundaries: Clearly state what you do and don’t want included in responses.

Verify and personalize: Always review outputs for accuracy and make them authentically yours.

Looking Ahead: Building Sustainable AI Practices

As AI capabilities continue to evolve rapidly, the most important skill isn’t learning specific tools—it’s developing the judgment to use AI effectively and ethically. This means:

Maintaining your expertise: Use AI to enhance your skills, not replace your critical thinking and domain knowledge.

Staying informed: Keep up with your organization’s AI policies and best practices in your field.

Being transparent: When appropriate, let others know when AI has contributed to your work.

Thinking ethically: Consider the broader implications of your AI use on colleagues, students, and your profession.

Your AI Literacy Journey Starts Now

AI literacy isn’t about becoming a technical expert—it’s about developing the knowledge and judgment to use these powerful tools effectively, safely, and ethically. Like any literacy, it develops through practice, reflection, and continuous learning.

Start small: Choose one AI tool and explore it thoughtfully. Pay attention to where it helps and where it falls short. Build your understanding gradually, always keeping human judgment at the center of your decision-making process.

The professionals who thrive in an AI-enabled workplace won’t necessarily be those who use AI the most—they’ll be those who use it most wisely. With the foundational knowledge covered here, you’re well-equipped to begin that journey.

September Central Applications Maintenance Release

As we shared in the recent SIS Tech Managers meeting, we are officially moving to a monthly release cadence for our central applications. Going forward, updates will be rolled out on the first Monday of each month (or the following business day if the first Monday is a holiday). Each release will bring important maintenance updates and bug fixes to keep us running smoothly.
 
PASS 2.8.4
Release Notes:
Added new features for manipulating the JSON on the Webhook Test screen
Fixed an issue where the string “true” was being interpreted as a boolean value and converted to “Y” for yes. (Incident #114086)
Added a notification feature to alert users when delete events are received (Service Request #71501)
Link to Download: PASS_Version2.8.4.zip

POST-PASS 1.8.4
Release Notes:
Bug Fixes
Fixed the “Compare Only” setting for students that was preventing new employees from being added (Incident #113605).
Changed how Possible Matches are written to resolve an issue where rollbacks erased the matches displayed in the grid.
Documentation Updates
Added a note to the ‘Update Student Employee Personal Information’ option (page 13 of the installation guide).Updated the Testing Guide.Revised the Getting Datasection to reference the new UI (instead of noting no UI).
Added a new Testing the Configuration/Mapping
Link to Download: Post Pass Version 1.8.4.zip

Supervisory Org Importer Version 1.4
Release Notes:
Version 1.4 has 1 SR/Incident resolutions contained within:
Ivanti ticket #79544: The previous version deletes from the custom Sup Org table and commits before reloading the data.  This version will not be committed immediately.  This way, if there is a connection error to the API or some other error in processing, the program will rollback the delete, and the table will not be empty.
Link to Download: UW_HR_SUPORG_IMPORTER_v1.4.zip

Refunding Inbound Version 1.3
Release Notes:
Version 1.3 has 2 SR/Incident resolutions contained within:
Ivanti Ticket #112313:  Instead of erroring the whole process if an input file is not found, the process will exit successfully, and a message will be written to the log indicating that no file was found
Ivanti Ticket #113605:  Text field lengths have been increased to 100 characters to accommodate longer text data coming from Workday.  THIS WILL REQUIRE A TABLE ALTER OF PS_UW_SF_RFND_IN.
Link to Download: UW_SF_REFUNDS_INBOUND_V1.3.zip

GL Outbound Version 1.8
Release Notes:
Version 1.8 has 2 SR/Incident resolutions contained within:
Ivanti Ticket #112094: This incident was identified by OSH.  Whitespace was getting inserted into character fields where numeric operations were being performed.  This whitespace caused the numeric operations to fail.  The “TRIM” function was added to all of the numeric operations to ensure whitespace was removed before the numeric operations were performed.
Ivanti Ticket #83689: This SR was logged by Stout.  Using the ACCOUNTING_DT field on the output file was causing errors with their journal loads at WD.  We have added an option to the run control page to select the date to be used on the output file.  The field will default to ACCOUNTING_DT, so most campuses should NOT need to change their run control setups.  THIS WILL REQUIRE A TABLE ALTER of PS_UW_GLOUT_RC.
Link to Download: UW_SF_JRNL_OUT_WD_V1.8.zip

To view items ready for an application’s next maintenance release:
Incidents: SIS Team’s Reported Bugs Dashboard
Service Requests: SIS Team’s Enhancement Requests Dashboard

Central Applications Maintenance Release Schedule

We are utilizing a maintenance release schedule for our central applications. Instead of releasing individual fixes and improvements on an ad-hoc basis, we’ll bundle items together and release them in a more structured timeline. This approach will help us deliver improvements more consistently while minimizing disruptions to each application. It also provides clearer timelines for campuses and gives us a better way to prioritize and plan more effectively. All applications included in our Detailed Central Applications Service Catalog (above) will follow this schedule.

Maintenance Release Schedule

Starting in September 2025:

  • Central applications that have maintenance work completed will be released on the first Monday of each month.
    • If the first Monday is a legal holiday, we will release the information on the next available business day.
  • These releases will mainly include non-urgent items that have been identified.
  • If high-level issues are identified, they will be addressed on an ad-hoc basis.

To view items ready for an application’s next maintenance release:

Central Application Bug Status Breakdown

We are utilizing the ticket Status field to note the progress a ticket has. Here is a breakdown of what each ticket status means with respect to the development cycle:

Note: Incidents and Service Requests have different Status values available for use – you can determine if your ticket is an Incident or a Service Request based on the prefix (IN vs SR)

Ticket TypeTicket StatusDevelopment Cycle Status
Incident
(IN ######)
Logged :
Active :
Waiting on Resolution** :
Resolved/Closed :
Items that are waiting to be developed
Development in progress
Development completed, waiting for the official version release
Development version released
Service Request
(SR #####)
Submitted :
Active :
Pending Approval** :
Fulfilled/Closed :
Items that are waiting to be developed
Development in progress
Development completed, waiting for the official version release
Development version released

**When a development item is completed but is awaiting its official release, you will be able to view the version it will be released in at the start of the description of your request. ** 

This information can also be found in the Frequently Asked Questions section at the bottom of the Student Information System Services page.

Currently submitted bugs can be viewed on the SIS Team’s Reported Bug Dashboard.

Currently submitted enhancement requests can be viewed on the SIS Team’s Enhancement Requests Dashboard.

Workday Report

In a world where artificial intelligence (AI) is rapidly transforming the workplace, a new global study commissioned by Workday and conducted by Hanover Research offers a refreshingly optimistic perspective: AI isn’t here to replace us—it’s here to elevate us.

Based on insights from 2,500 full-time workers across 22 countries, the report explores how AI is reshaping work by enhancing human creativity, leadership, learning, trust, and collaboration.

The Human-Centered Promise of AI

The study reveals that 83% of respondents believe AI will enhance human creativity and lead to new forms of economic value. Rather than automating people out of relevance, AI is viewed as a tool that frees individuals from routine tasks, enabling them to focus on higher-order skills such as ethical decision-making, emotional intelligence, and strategic thinking.

Five Principles for Thriving with AI

The report is anchored in five core principles that define how organizations can thrive in an AI-enabled future:

1. Creativity, Elevated

AI acts as a creative assistant, helping individuals generate ideas and solutions more quickly and effectively. It enables people to bring imagination to their roles—whether in administrative workflows or product innovation.

2. Leadership, Elevated

AI supports empathetic leadership by providing real-time insights into team dynamics and freeing up time for human connection. It helps leaders make more objective decisions and focus on what matters most—their people.

3. Learning, Elevated

AI enhances learning by identifying skill gaps, personalizing development, and democratizing access to knowledge. It empowers organizations to build agile, future-ready teams.

4. Trust, Elevated

Transparency and responsible AI practices are essential. A striking 90% of respondents agree that AI can increase organizational accountability, but trust must be built collaboratively across sectors.

5. Collaboration, Elevated

AI breaks down data silos, enabling seamless collaboration across departments and between humans and machines. It fosters a new kind of teamwork where AI augments human potential.

Key Findings at a Glance

  • 93% of AI users say it allows them to focus on strategic tasks.
  • Top irreplaceable skills: ethical decision-making, empathy, relationship-building, and conflict resolution.
  • Biggest challenges to AI adoption: uncertainty about ROI, data privacy, and integration complexity.
  • Most impactful missing skills: cultural sensitivity, adaptability, and strategic planning.

A Call to Action

The report concludes with a clear message: the AI revolution is not just technological—it’s deeply human. Organizations must:

  • Embrace human-centric leadership.
  • Foster collaboration between people and AI.
  • Invest in upskilling and reskilling.
  • Promote transparency and accountability.

AI is not the new face of work—it’s the force that allows our human talent to shine brighter.