CASE Insights on Generative AI in Advancement
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start
2025
© COUNCIL FOR ADVANCEMENT AND SUPPORT OF EDUCATION
Introduction
How to Use this Report
Pathways to Adoption
Research Findings
Table of Contents
How to Go Further
About CASE InsightsSM
Project Credits
Feedback
I am pleased to present CASE InsightsSM on Generative Artificial Intelligence, a timely and practical guide for our advancement community. Generously funded by the membership of the CASE 50 and informed by research with 21 institutions, this report offers a clear-eyed view of where we stand today and a thoughtful pathway toward what lies ahead. Artificial Intelligence is rapidly shaping the future. For advancement professionals working in education, it holds particular promise. When applied wisely, it has the potential to free up capacity for the strategic, relational work that defines our profession at its best. But this is not solely a question of technology. It is about culture, leadership, and readiness to innovate.
Why AI Matters for Advancement
This report identifies six key focus areas ranging from ethics to infrastructure, mapped across four stages of adoption. The report does not prescribe a single route forward but rather invites progress on your own terms. Woven throughout are practitioner voices offering practical examples and lessons learned, brought to life through a new, dynamic format.
As AI continues to reshape how we live, work, and connect, so too must we invest in its thoughtful and ethical application. The moment the ink has set on this report there will be further innovation through AI offering opportunity and risk. Our mission demands that we not only respond to innovation but that we aim to lead it.
CASE is your steadfast partner in this journey. It is a privilege to learn and explore together to shape a brighter, more impactful future for all.
Sue Cunningham President & CEO CASE
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Table of Contents
Artificial intelligence (AI) is rapidly reshaping industries, and higher education advancement is no exception. From transforming how we engage donors and alumni to enhancing data analysis, personalization, and operational efficiency, AI holds immense potential to strengthen our work and deepen our impact. Yet, with that promise comes responsibility—to thoughtfully examine the opportunities, risks, and ethical considerations unique to our field. This research, made possible by funding from CASE 50* member insititutions, represents an essential step in helping higher education institutions understand and navigate AI’s role in advancement. By providing timely insights, practical guidance, and considerations for future implementation, this project aims to equip us all with the knowledge we need to lead with integrity, innovation, and care, and to keep pace with the rapidly changing world around us.
CASE 50 Research Support
Katy Herbert Kotlarczyk, EdD Vice Chancellor for Advancement University of Colorado Boulder CASE 50 Steering Committee Member
*This project was funded by CASE 50, a global group of colleges and universities that consistently generates the highest levels of private philanthropy for their institutions, to help support the strategic adoption of AI across advancement.
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What areas should you focus on when considering the use of AI in advancement? What are the next steps that your advancement team should take to advance your adoption of these exciting (and sometimes intimidating) new tools? In this interactive report, CASE InsightsSM offers a comprehensive approach for adopting generative AI, based on findings from a year-long research study with advancement practitioners. At the heart of this report are the Pathways to Adoption, which highlight four stages of AI adoption across six focus areas. The detailed information we provide in each stage comes directly from our research. As you explore the report, you will find a variety of additional resources, including a deep dive into the research findings. We encourage you to provide feedback and recommendations to help shape our future CASE InsightsSM work in this area.
Introduction
Report Authors:
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Christy Moss Vice President of Membership & Marketing University of Illinois Alumni Association
Cara Giacomini, PhD Vice President of Data, Research & Technology Council for Advancement and Support of Education
Jeff Nesmith Video & Multimedia Production Manager Council for Advancement and Support of Education
Table of Contents
At the heart of the CASE InsightsSM on Generative AI in Advancement report are the Pathways to Adoption
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To better understand how advancement teams are engaging with generative AI, this framework outlines six key focus areas where adoption is taking shape. Each area is presented across four general pathways, from early-stage exploration to scaled integration. This model is not intended to prescribe a linear progression, but rather to help teams reflect on where they are, identify what is emerging, and plan for what comes next. Different areas may advance at different speeds, depending on institutional culture, leadership, and available resources.
The Pathway to Adoption of generative AI has several stages. You may be in different stages for the different focus areas involved in adopting generative AI. Throughout the report we will describe actions associated with each step and ways to progress to the next step in each focus area. In general, the steps along this pathway can be described as follows:
Pathways to Adoption
Larger-scale implementation, with activities in specific categories reaching more formal practices connected with team and organizational goals. Embedded in daily practice.
Early stages of implementation, with individuals and teams carrying out activities in the specific category, with some supporting resources.
The first step towards adoption, involving initial exploration, often individually or in small groups, with minimal direction or planning.
Expanded implementation to larger strategic and advancement-wide goals. Embedded in future planning.
Operationalizing
Scaling
Exploring
Experimenting
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STAGES
Exploring
Experimenting
Scaling
Operationalizing
Culture &Mindset
Innovation culture widespread; staff feel agency and shared responsibility
Early adopters (individuals or teams) experimenting and sharing wins
Broader interest growing; champions begin to influence peers
Curiosity and skepticism coexist; limited awareness
Policy & Governance
Advancement policies regularly updated; compliance mechanisms in place
Informal norms developing; awareness of risks increasing
No formal guidance or policy in place
Drafting acceptable use and compliance policies
Strategy &Vision
Strategy discussions underway; early alignment with advancement and institutional goals
Fully aligned with innovation, fundraising, marketing and communications, and/or engagement strategy
GenAI is a topic of interest; conversations emerging
Individuals or teams testing GenAI in isolated ways
Pathways to Adoption
Focus Areas
Ethics & Responsibility
Ethics language appears in policy drafts; disclosure statements considered
Responsible use frameworks adopted; transparency and bias regularly reviewed
Ethics not yet part of GenAI discussions
Emerging concerns about transparency and bias
Skills &Training
Ongoing development programs that are advancement specific; advanced skill-building supported
No formal training; individuals learn on their own
Informal resource sharing among staff
Identified need for training; peer learning encouraged
Infrastructure & Technical Capability
GenAI tools integrated into systems and workflows; vendor relationships established
Public tools accessed individually; limited institutional support
Requests for tool access; discussions with IT or CRM partners
Internal evaluation of tool integration or vendor partnerships
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As part of a larger inquiry into generative AI in advancement, a three-day online focus group gathered perspectives from 21 advancement professionals working across institutions and regions. The participants, primarily mid-career with 10+ years of experience, offered reflections on their current practices, challenges, and aspirations related to generative AI integration. The resulting transcript—over 130 pages of asynchronous discussion—was analyzed using inductive thematic analysis. The findings offer a grounded view of how generative AI adoption is unfolding across the advancement profession.
Research Goal
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Research Findings
Headlines from Leaders
Key Themes & Insights
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The goals of this research are to:
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Timeline
• November 2024: Trainings
• January and February 2025: Focus Groups
Training & Recruitment
• Online training with CASE 50 members on generative AI
Research Design
• Online training for CASE members on generative AI (439 participants)
• Recruitment of study participants from among training attendees
Data Collection
Research Menu
• Asynchronous online 3-day focus group with participants from 21 institutions
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• Focus groups with CASE 50 members (13 participants) on leadership and generative AI
Table of Contents
From focus groups we learned:
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1. Strategic Framing of Generative AI Adoption
Participants repeatedly emphasized the importance of aligning generative AI projects with institutional strategy. Early experiments often began in an ad hoc manner but are now evolving into more structured initiatives. Respondents highlighted the value of pilots, iterative planning, and stakeholder alignment.
Advancement professionals increasingly see generative AI not as a novelty, but as a strategic tool requiring intentional design and long-term planning.
Key Themes & Insights
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2. Growing Emphasis on Policy, Ethics, and Compliance
Concerns about data privacy, institutional policies, and ethical usage surfaced in nearly every thread. While some institutions have formal policies in place, many are still navigating ambiguity—especially around international standards and reputational risk.
There is a clear expectation that generative AI use must be governed by institutional policy, grounded in ethical considerations, and compliant with external regulations.
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3. Human Oversight Remains Central
Despite excitement about automation, participants emphasized the ongoing need for human review, particularly in creative and relationship-based work. Several noted that overreliance on generative AI led to disappointing results, and that human intervention is still necessary to ensure quality, nuance, and contextual sensitivity.
Advancement professionals see generative AI as an augmentation tool—not a replacement—and emphasize the need for structured human-AI collaboration.
Key Themes & Insights
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4. Culture and Mindset Shape Organizational Readiness
Participants were candid about their emotional responses to generative AI, including curiosity, overwhelm, and a desire for space to learn. Some described celebrating small wins and fostering a culture of experimentation, while others expressed concern about burnout and isolation.
Readiness to adopt generative AI is not solely technical—it is deeply cultural. Mindset, leadership vision, and internal storytelling shape how generative AI is received and sustained.
Key Themes & Insights
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5. Efficiency is the Common Entry Point
Many current use cases center around time savings, content drafting, and reducing repetitive work. These practical applications were often framed as early-stage wins that helped build organizational momentum.
Efficiency remains a compelling gateway to generative AI adoption—providing quick wins that can catalyze deeper innovation over time.
Key Themes & Insights
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6. Systemic Gaps are Hindering Implementation
Participants noted significant barriers in infrastructure, including limited training, outdated systems, CRM limitations, and lack of integration support. These challenges often stymied even the most enthusiastic teams.
Institutions are eager to advance their generative AI efforts, but gaps in training, technical capacity, and system integration continue to pose obstacles.
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From focus groups with leaders we learned:
Headlines from Leaders
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Research Report: CASE InsightsSM on Artificial Intelligence in Advancement in partnership with GiveCampus This March 2024 report shares insights from a previous CASE InsightsSM research study on artificial intelligence in advancement. Subject Guide: Artificial Intelligence (CASE Members only) This resource collection from the CASE Library introduces the basics of AI, takes a look at some of the latest trends and ethical questions, and provides information on how AI is being leveraged by educational institutions and nonprofit organizations.
Webinar Recording: AI in Advancement: Bridging Innovation, Policy, and Practice (Free for CASE Members)
Generative artificial Intelligence (AI) has emerged as a transformative tool with the potential to fundamentally alter the way integrated advancement is approached. This training highlights benefits, concerns, and opportunities beyond ChatGPT and Copilot. Custom GPTs and Custom Instructions Custom GPTs can help you with editing content, reporting, scheduling, and more. Setting your Custom Instructions can mean the difference between content tailored to your unique situation and generic output. Follow this step-by-step guide to set your Custom Instructions. The future of generative AI in Advancement Generative AI is poised to transform educational advancement. This is how language learning models like ChatGPT and ElevenLabs describe this phenomenon.
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CASE Insights
data | standards | research
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These are the five areas where CASE provides data, research, and frameworks for measuring Advancement activities. Our data collection is based around the CASE Global Reporting Standards. Creating and adhering to a set of methods, standards, and guidelines for reporting fundraising activities allows schools, colleges, and universities to represent the work of all institutions in transparent ways.
About CASE InsightsSM
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CASE Insights
data | standards | research
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Standards: CASE's Global Reporting Standards, which provide a common set of definitions for advancement professionals around the globe.
Research: topical analysis of the most relevant issues in advancement today, made possible by funders and educational partners.
Data: CASE's annual benchmarking surveys administered globally to shed light on trends within the educational advancement landscape.
About CASE InsightsSM
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CASE Insights
data | standards | research
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CASE is the world leader in helping advancement professionals in colleges, universities, and schools make data-informed decisions. Institutions of any size, type, or location around the globe will find CASE InsightsSM data, standards, and research are valuable tools to help drive success. Through your CASE membership, you already have access to free CASE InsightsSM resources, along with additional services to meet your institution's needs. Explore peer comparisons, detailed benchmarking data, research reports about emerging issues, and other options you can use to expand your institution's impact. Start your journey now. Find a variety of resources and build your expertise. Visit us online or connect with the InsightsSM Solutions team directly:
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Discover the many ways data can contribute to your institution's success with CASE InsightsSM foundational tools.
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Larger-scale implementation, with activities in specific categories reaching more formal practices connected with team and organizational goals. Embedded in daily practice.
Early stages of implementation, with targeted activities in the specific category by individuals and team, with some resources supporting the activity.
The first step towards adoption involves initial exploration, often individually or in small groups, with minimal direction or planning.
Larger-scale implementation, with activities in specific categories reaching more formal practices connected with team and organizational goals. Embedded in daily practice.
Early stages of implementation, with targeted activities in the specific category by individuals and team, with some resources supporting the activity.
The first step towards adoption involves initial exploration, often individually or in small groups, with minimal direction or planning.
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Operationalizing
Exploring
Experimenting
Operationalizing
Exploring
Experimenting
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Research Sponsor:
Research Team:
- Cara Giacomini, PhD, Vice President of Data, Research & Technology, Council for Advancement and Support of Education
- Jenny Cooke-Smith, Senior Director, CASE Insights Solutions, Council for Advancement and Support of Education
- Christy Moss, Vice President of Membership and Marketing, University of Illinois Alumni Association
- Gustavo Segui, Executive Director for Marketing, Advancement and Admissions,International School of Curitiba
- Isurus Market Research & Consulting
Project Credits
CASE Project Team:
- Grant Kollet, Executive Director for US-Canada
- Genji Lawson, Senior Design Director
- Jeff Nesmith, Video and Multimedia Production Manager
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Contact Us:
insights@case.org
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Insight:
Skills & Training
EXPERIMENTING
Our study found that efficiency-focused pilots are a low-risk entry point for most institutions.
Support for this stage
Use cases
Pathways forward
Leadership lens
Sticking points
Fun idea
Commentary
Insight:
Culture & Mindset:
OPERATIONALIZING
Even the most advanced teams view AI as a tool—not a replacement.
Support for this stage
Use cases
Pathways forward
Operationalizing this culture requires intentional storytelling and structured human-AI collaboration.
Leadership lens
Sticking points
Commentary
Expert Voice: The Future of Generative AI in Advancement
Content generated by ChatGPT AI Text-to-Speech by ElevenLabs
*AI-Generated Recording
Insight:
Strategy & Vision
OPERATIONALIZING
Institutions are evolving from pilot projects to enterprise-level planning—and realizing the need for formal structures to support AI at scale.
Support for this stage
Use cases
Pathways forward
Leadership lens
Sticking points
Fun idea
Commentary
Insight:
Culture & Mindset:
EXPERIMENTING
"Champions can’t operate in a vacuum."
Support for this stage
Use cases
Burnout and isolation were mentioned as risks when experimentation isn’t shared or supported.
Pathways forward
Leadership lens
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Commentary
Strategic Uses of AI
Use AI to Strengthen Strategic Focus
To sharpen your strategy, use AI to support each step of your strategic planning, from situational analysis to campaign development. Use AI to validate ideas, stress-test scenarios, and guide more confident strategic choices.
Use AI to Listen at Scale Through Smart Surveys
Create a survey focused on satisfaction, agreement with your brand values, and likelihood to recommend. Use AI to analyze open-ended responses and cross-reference satisfaction by segment. Instead of just counting scores, AI uncovers patterns and emotional tone.
Let AI Drive Deeper Campaign Development
When developing campaigns, use AI to test creative angles, headlines, and audience targeting. Start by feeding the AI key audience profiles and goals. Then, prompt it to generate variations, simulate responses, and even predict engagement. This turns campaign planning into an experiment-driven process.
Use AI to Map Strategic Sentiment Over Time
Track how people feel about your institution over time using AI-powered sentiment analysis. This lets you intervene early, adapt messages, and track whether strategic changes are truly landing with your audience.
Use AI to Power Ongoing Strategic Iteration
AI isn’t a one-time tool—it’s a partner in ongoing strategic refinement. Set up systems where AI continuously pulls in new data (e.g., survey results, campaign metrics, market shifts) and surfaces emerging insights.
- from Gustavo SeguiExecutive Director for Marketing, Advancement and Admissions International School of Curitiba
Culture & Mindset
At a Glance: Culture and mindset shape the emotional and intellectual readiness of a team to engage with generative AI. This area includes attitudes toward innovation, comfort with experimentation, and the sense of psychological safety around trying (and failing at) something new. It is the bedrock of adoption—and often the most underestimated.
Without a supportive and psychologically safe culture, even the best tools or strategies can fall flat. Cultural readiness influences every other aspect of adoption, from experimentation to policy alignment. Teams that feel empowered to learn together, take risks, and share openly are better positioned to adapt with agility and purpose.
While many educational institutions are beginning to offer cross-functional trainings, far less attention is paid to the cultural shifts required to sustain meaningful change. Without intentionally focusing on managing change, early adopters risk burnout from carrying momentum alone, while more hesitant team members may be left behind without the support they need to engage.
Insight:
Policy & Governance
OPERATIONALIZING
There is a clear expectation that generative AI use must be governed by institutional policy, grounded in ethical considerations, and compliant with external regulations.
Support for this stage
Use cases
Pathways forward
Leadership lens
Sticking points
Fun idea
Commentary
Insight:
Policy & Governance
EXPERIMENTING
Many institutions start their AI journey by convening a working group or taskforce to look at policy needs. Getting timely initial guidance in place can be a challenge.
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Use cases
Pathways forward
Leadership lens
Sticking points
Fun fact
Commentary
Infrastructure & Technical Capability
At a glance: Infrastructure and technical capability provide the foundation that enables generative AI integration at scale. This area includes access to tools, data systems, vendor partnerships, internal tech support, and the organizational ability to build or customize AI solutions as needs evolve. Appetite for new AI initiatives is often
shaped by what else is happening behind the scenes—especially large-scale projects like CRM conversions, website rebuilds, or data infrastructure upgrades. These competing priorities can influence both the pace and focus of generative AI adoption. Even the most enthusiastic teams can be limited without the right technical environment. Strong infrastructure and technical capacity ensure that generative AI moves from isolated experiments to embedded, sustainable practice. These foundations create reliability, scalability, and long-term innovation.
Insight:
Strategy & Vision
EXPERIMENTING
Many focus group participants noted they’d structure implementations differently if starting over—more iterative, more stakeholder aligned.
Support for this stage
Use cases
Pathways forward
Leadership lens
Sticking points
Fun idea
Commentary
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Infrastructure & Technical Capability
Insight:
EXPLORING
Public tools accessed individually; limited institutional support.
Support for this stage
Use cases
Pathways forward
This was a common starting point across institutions.
Leadership lens
Sticking points
Fun idea
Commentary
Infrastructure & Technical Capability
Insight:
EXPERIMENTING
When asked how much support they had received from IT, one participant responded, “ZEROOOOOOOOO."
Support for this stage
Use cases
Pathways forward
Leadership lens
Collaboration is essential for expanding AI use beyond individuals and small teams.
Sticking points
Fun idea
Commentary
Insight:
Policy & Governance
SCALING
There’s a growing expectation that AI usage must be institutionally governed and ethically anchored—this isn’t optional.
Support for this stage
Use cases
Pathways forward
Leadership lens
Sticking points
Fun fact
Commentary
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Infrastructure & Technical Capability
Insight:
SCALING
Widespread adoption of AI requires collaboration across teams and support from IT.
Support for this stage
Use cases
Pathways forward
Leadership lens
Sticking points
Commentary
Expert Voice: Managing Change — How to Get Started
Gustavo Segui Director of Marketing & Advancement Director of Admissions International School of Curitiba
Infrastructure & Technical Capability
ZOOMING IN
Out-of-the-Box Use
Light Customization
Agent-Based Integration
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Insight:
Skills & Training
EXPLORING
Staff capacity, leadership vision, and curiosity continue to shape pace and tone.
Support for this stage
Use cases
Pathways forward
Leadership lens
Sticking points
Fun idea
Commentary
Expert Voice: Creating an AI Policy for Advancement
Christie Horton Associate Vice President Talent, Culture & Human Resources Kansas State University Foundation Chris Miles Associate Vice President Information, Technology, & Infrastucture Kansas State University Foundation
Insight:
Skills & Training
SCALING
The appetite for AI is high, but many institutions lack the connective tissue—tech + people + policy —to unlock its full potential.
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Use cases
Pathways forward
Leadership lens
Sticking points
Commentary
Use Cases
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Expert Voice: Addressing Bias
Cara Giacomini Vice President - Data, Research & Technology CASE
Expert Voice: Investing in AI
Joe Manok Vice President, University Advancement Clark University
*AI-Generated Recording
Insight:
Ethics & Responsibility
OPERATIONALIZING
One participant noted,
Support for this stage
"AI use must be governed and anchored in ethics."
Use cases
Pathways forward
Participants expressed strong interest in clarity and accessibility.
Leadership lens
Sticking points
Fun idea
Commentary
Using Copilot as an Executive Coach
Use Case: I recently embarked on an unconventional professional development exercise: I used the Research Agent provided in MS Copilot as an “executive coach” to audit my own leadership and management behaviors. Prompt: “Based on everything you have access to from the last two years, please perform an audit of my work, leadership, and management behaviors. What do I do well? What could be improved? With whom do I communicate effectively? Are there people that need my attention that aren’t receiving it? Infer details based on my role, its typical functions, and how it should perform within an organization like the University of Texas at Austin. Use our org. chart to be precise and return the exact names of individuals or divisions. Try and define my footprint within the university and where it is either strong or lacking. Analyze my communication style and try and discern any hidden biases towards people, projects, or units that I may not be aware of. Are there any relationships that should be strengthened? Try and define my footprint outside of the university regarding professional and volunteer organizations. Give precise recommendations on how I could improve as an employee and as a leader.”
Report: The final report was thorough and read like something a management consultant or coach might prepare after weeks of interviews and research. The report didn’t shy away from tough feedback (delivered politely), but it balanced critiques with encouragement, acknowledging my strengths in those same areas.
- from John GoughAssistand Vice President University of Texas Austin
Insight:
Strategy & Vision
EXPLORING
Advancement professionals are framing GenAI as a strategic endeavor, not a one-off experiment.
Support for this stage
Use cases
Pathways forward
Leadership lens
Sticking points
Commentary
Insight:
Culture & Mindset:
SCALING
"Change management and internal storytelling matter."
Support for this stage
Use cases
Pathways forward
Teams thriving at this stage are investing in connection, communication, and shared purpose.
Leadership lens
Sticking points
Fun idea
Commentary
Expert Voice: Leading with Curiosity and Clarity
Jenny Cooke Smith Senior Director of CASE InsightsSM Solutions CASE
Skills & Training
At a glance: Skills and training are the bridge between curiosity and capability. As advancement professionals explore and expand their use of generative AI, access to structured learning opportunities becomes vital. This area encompasses informal learning, formal training, peer knowledge-sharing, and the development of role-specific competencies.
The best AI strategies can stall without confident users. Building skills and creating a shared foundation allows innovation to flourish. When staff understand how to use AI effectively—and when not to—they become empowered collaborators in transformation. Effective learning is tailored to the context. Advancement professionals benefit most from training that reflects their goals, systems, and use cases. Just as importantly, they need safe, low-stakes environments to experiment, ask questions, and build confidence together.
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Ethics & Responsibility
At a glance: Ethics and responsibility shape the boundaries—and possibilities—of generative AI in advancement. This area includes transparency, bias mitigation, disclosure, equity, and intentional alignment with institutional values. As AI becomes more powerful, so too does the need to use it with care, integrity, and accountability.
Ethics isn’t an afterthought—it’s the compass. Responsible use builds trust with stakeholders and protects the dignity of communities. Institutions that prioritize responsible innovation are better positioned to adapt sustainably and equitably.
While environmental sustainability is an emerging concern in the broader AI field, it’s rarely discussed within advancement contexts. As institutions move toward more responsible use, understanding the ecological impact of GenAI will become increasingly important.
Insight:
Skills & Training
OPERATIONALIZING
Our participants said, "Lack of training and integration support often hinder implementation—even among eager teams."
Support for this stage
Use cases
Pathways forward
Leadership lens
Sticking points
Commentary
Strategy & Vision
At a glance: Strategy and vision are the connective tissue between generative AI experimentation and institutional transformation. This focus area encompasses how AI is framed, prioritized, and integrated into long-term plans and leadership narratives. It influences everything from pilot selection to resource allocation and helps ensure AI efforts support the broader mission.
Without strategic clarity and a compelling vision, AI initiatives risk becoming scattered, superficial, or short-lived. When generative AI is positioned as a strategic asset—aligned with values and measurable goals—it can amplify innovation, fundraising outcomes, and community impact. As teams mature in their use of GenAI, the focus often shifts from efficiency—getting more done—to efficacy: ensuring that AI-driven efforts truly advance strategic goals. This evolution is a key marker of long-term, values-aligned integration.
Insight:
Culture & Mindset:
EXPLORING
"Readiness to adopt generative AI is not solely technical—it is deeply cultural."
Support for this stage
Use cases
Pathways forward
Leadership lens
Focus group participants noted curiosity, overwhelm, and a desire for space to learn.
Sticking points
Commentary
As we move forward, the most successful institutions will be those that view AI not as a tool, but as a catalyst for transformational leadership.
- Research Study Participant
Starting Point: Policy Creation
Review the section on “Ethical Principles” in the CASE Global Reporting Standards
Review institutional and regional privacy policies
Review existing technology-use policies or guidelines
Focus on clear communication and transparency about how AI is being used
Avoid entering institutional intellectual property (IP) or information about specific individuals when using any public AI
Consider convening a working group to advise on AI use
Expect your policy to evolve over time as tools and use cases advance
The success of our AI initiatives was not just about technology—it was made possible by a culture of innovation, a willingness to embrace new tools, and the institutional flexibility to experiment.
- Research Study Participant
Insight:
Ethics & Responsibility
EXPERIMENTING
Several noted that overreliance on generative AI led to disappointing results—human intervention is still necessary.
Support for this stage
Use cases
Pathways forward
Leadership lens
Sticking points
Fun idea
Commentary
Expert Voice: Using AI to Advance Strategic Enrollment Goals
Jenny Petty Vice President - Marketing Communications Experience & Engagement University of Montana
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Infrastructure & Technical Capability
Insight:
OPERATIONALIZING
Custom GPTs or low-code modifications and "vendor partnerships" begin to emerge during this stage.
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Use cases
Pathways forward
Leadership lens
Sticking points
Tailoring GenAI to Your Team: What Is a Custom GPT?
Insight:
Ethics & Responsibility
EXPLORING
Themes of human review, oversight, and judgment came up strongly—especially in questions about pitfalls.
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Use cases
Pathways forward
Leadership lens
Sticking points
Fun idea
Commentary
Click on this button to open a quote from an advancement leader or a research study participant
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Insight:
Strategy & Vision
SCALING
Strategic clarity helps translate AI excitement into impact—especially when supported by structure and storytelling.
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Use cases
Pathways forward
Leadership lens
Sticking points
Commentary
Insight:
Policy & Governance
EXPLORING
Initial explorations of AI can get stalled while waiting for someone to decide how AI can be used.
Support for this stage
Use cases
Pathways forward
Leadership lens
Sticking points
Commentary
Insight:
Ethics & Responsibility
SCALING
When asked about environmental sustainability, a focus group participant observed:
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Use cases
Pathways forward
“It is too early in the process for me to answer this particular question.”
Leadership lens
Sticking points
This area may naturally get more institutional attention as use of AI expands.
Commentary
Policy & Governance
At a glance: Policy and governance provide the ethical scaffolding for generative AI use. This includes institutional guidance, compliance with external regulations, and clear communication about what’s allowed—and what isn’t. As teams experiment and scale, policies help mitigate risk, ensure responsible use, and foster trust.
Clear policies and governance frameworks protect both the institution and its people. They empower users to explore AI confidently, while ensuring compliance with ethical, legal, and institutional standards. This clarity reduces confusion, builds trust, and supports responsible scaling across all areas of advancement. Yet in many institutions, there's uncertainty about where ownership should live—should policy originate within advancement, at the institutional level, or through individual team norms? This ambiguity can lead to inaction, as teams wait for someone else to lead the way.
CASE Insights on Generative AI in Advancement
Jeff Nesmith
Created on June 6, 2025
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CASE Insights on Generative AI in Advancement
SM
start
2025
© COUNCIL FOR ADVANCEMENT AND SUPPORT OF EDUCATION
Introduction
How to Use this Report
Pathways to Adoption
Research Findings
Table of Contents
How to Go Further
About CASE InsightsSM
Project Credits
Feedback
I am pleased to present CASE InsightsSM on Generative Artificial Intelligence, a timely and practical guide for our advancement community. Generously funded by the membership of the CASE 50 and informed by research with 21 institutions, this report offers a clear-eyed view of where we stand today and a thoughtful pathway toward what lies ahead. Artificial Intelligence is rapidly shaping the future. For advancement professionals working in education, it holds particular promise. When applied wisely, it has the potential to free up capacity for the strategic, relational work that defines our profession at its best. But this is not solely a question of technology. It is about culture, leadership, and readiness to innovate.
Why AI Matters for Advancement
This report identifies six key focus areas ranging from ethics to infrastructure, mapped across four stages of adoption. The report does not prescribe a single route forward but rather invites progress on your own terms. Woven throughout are practitioner voices offering practical examples and lessons learned, brought to life through a new, dynamic format. As AI continues to reshape how we live, work, and connect, so too must we invest in its thoughtful and ethical application. The moment the ink has set on this report there will be further innovation through AI offering opportunity and risk. Our mission demands that we not only respond to innovation but that we aim to lead it. CASE is your steadfast partner in this journey. It is a privilege to learn and explore together to shape a brighter, more impactful future for all. Sue Cunningham President & CEO CASE
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Table of Contents
Artificial intelligence (AI) is rapidly reshaping industries, and higher education advancement is no exception. From transforming how we engage donors and alumni to enhancing data analysis, personalization, and operational efficiency, AI holds immense potential to strengthen our work and deepen our impact. Yet, with that promise comes responsibility—to thoughtfully examine the opportunities, risks, and ethical considerations unique to our field. This research, made possible by funding from CASE 50* member insititutions, represents an essential step in helping higher education institutions understand and navigate AI’s role in advancement. By providing timely insights, practical guidance, and considerations for future implementation, this project aims to equip us all with the knowledge we need to lead with integrity, innovation, and care, and to keep pace with the rapidly changing world around us.
CASE 50 Research Support
Katy Herbert Kotlarczyk, EdD Vice Chancellor for Advancement University of Colorado Boulder CASE 50 Steering Committee Member
*This project was funded by CASE 50, a global group of colleges and universities that consistently generates the highest levels of private philanthropy for their institutions, to help support the strategic adoption of AI across advancement.
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Table of Contents
What areas should you focus on when considering the use of AI in advancement? What are the next steps that your advancement team should take to advance your adoption of these exciting (and sometimes intimidating) new tools? In this interactive report, CASE InsightsSM offers a comprehensive approach for adopting generative AI, based on findings from a year-long research study with advancement practitioners. At the heart of this report are the Pathways to Adoption, which highlight four stages of AI adoption across six focus areas. The detailed information we provide in each stage comes directly from our research. As you explore the report, you will find a variety of additional resources, including a deep dive into the research findings. We encourage you to provide feedback and recommendations to help shape our future CASE InsightsSM work in this area.
Introduction
Report Authors:
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Christy Moss Vice President of Membership & Marketing University of Illinois Alumni Association
Cara Giacomini, PhD Vice President of Data, Research & Technology Council for Advancement and Support of Education
Jeff Nesmith Video & Multimedia Production Manager Council for Advancement and Support of Education
Table of Contents
At the heart of the CASE InsightsSM on Generative AI in Advancement report are the Pathways to Adoption
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To better understand how advancement teams are engaging with generative AI, this framework outlines six key focus areas where adoption is taking shape. Each area is presented across four general pathways, from early-stage exploration to scaled integration. This model is not intended to prescribe a linear progression, but rather to help teams reflect on where they are, identify what is emerging, and plan for what comes next. Different areas may advance at different speeds, depending on institutional culture, leadership, and available resources. The Pathway to Adoption of generative AI has several stages. You may be in different stages for the different focus areas involved in adopting generative AI. Throughout the report we will describe actions associated with each step and ways to progress to the next step in each focus area. In general, the steps along this pathway can be described as follows:
Pathways to Adoption
Larger-scale implementation, with activities in specific categories reaching more formal practices connected with team and organizational goals. Embedded in daily practice.
Early stages of implementation, with individuals and teams carrying out activities in the specific category, with some supporting resources.
The first step towards adoption, involving initial exploration, often individually or in small groups, with minimal direction or planning.
Expanded implementation to larger strategic and advancement-wide goals. Embedded in future planning.
Operationalizing
Scaling
Exploring
Experimenting
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STAGES
Exploring
Experimenting
Scaling
Operationalizing
Culture &Mindset
Innovation culture widespread; staff feel agency and shared responsibility
Early adopters (individuals or teams) experimenting and sharing wins
Broader interest growing; champions begin to influence peers
Curiosity and skepticism coexist; limited awareness
Policy & Governance
Advancement policies regularly updated; compliance mechanisms in place
Informal norms developing; awareness of risks increasing
No formal guidance or policy in place
Drafting acceptable use and compliance policies
Strategy &Vision
Strategy discussions underway; early alignment with advancement and institutional goals
Fully aligned with innovation, fundraising, marketing and communications, and/or engagement strategy
GenAI is a topic of interest; conversations emerging
Individuals or teams testing GenAI in isolated ways
Pathways to Adoption
Focus Areas
Ethics & Responsibility
Ethics language appears in policy drafts; disclosure statements considered
Responsible use frameworks adopted; transparency and bias regularly reviewed
Ethics not yet part of GenAI discussions
Emerging concerns about transparency and bias
Skills &Training
Ongoing development programs that are advancement specific; advanced skill-building supported
No formal training; individuals learn on their own
Informal resource sharing among staff
Identified need for training; peer learning encouraged
Infrastructure & Technical Capability
GenAI tools integrated into systems and workflows; vendor relationships established
Public tools accessed individually; limited institutional support
Requests for tool access; discussions with IT or CRM partners
Internal evaluation of tool integration or vendor partnerships
Table of Contents
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As part of a larger inquiry into generative AI in advancement, a three-day online focus group gathered perspectives from 21 advancement professionals working across institutions and regions. The participants, primarily mid-career with 10+ years of experience, offered reflections on their current practices, challenges, and aspirations related to generative AI integration. The resulting transcript—over 130 pages of asynchronous discussion—was analyzed using inductive thematic analysis. The findings offer a grounded view of how generative AI adoption is unfolding across the advancement profession.
Research Goal
Research Design
Headlines
Research Findings
Headlines from Leaders
Key Themes & Insights
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Table of Contents
The goals of this research are to:
Research Goal
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Timeline
• November 2024: Trainings
• January and February 2025: Focus Groups
Training & Recruitment
• Online training with CASE 50 members on generative AI
Research Design
• Online training for CASE members on generative AI (439 participants)
• Recruitment of study participants from among training attendees
Data Collection
Research Menu
• Asynchronous online 3-day focus group with participants from 21 institutions
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• Focus groups with CASE 50 members (13 participants) on leadership and generative AI
Table of Contents
From focus groups we learned:
Headlines
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1. Strategic Framing of Generative AI Adoption
Participants repeatedly emphasized the importance of aligning generative AI projects with institutional strategy. Early experiments often began in an ad hoc manner but are now evolving into more structured initiatives. Respondents highlighted the value of pilots, iterative planning, and stakeholder alignment.
Advancement professionals increasingly see generative AI not as a novelty, but as a strategic tool requiring intentional design and long-term planning.
Key Themes & Insights
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Table of Contents
2. Growing Emphasis on Policy, Ethics, and Compliance
Concerns about data privacy, institutional policies, and ethical usage surfaced in nearly every thread. While some institutions have formal policies in place, many are still navigating ambiguity—especially around international standards and reputational risk.
There is a clear expectation that generative AI use must be governed by institutional policy, grounded in ethical considerations, and compliant with external regulations.
Key Themes & Insights
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3. Human Oversight Remains Central
Despite excitement about automation, participants emphasized the ongoing need for human review, particularly in creative and relationship-based work. Several noted that overreliance on generative AI led to disappointing results, and that human intervention is still necessary to ensure quality, nuance, and contextual sensitivity.
Advancement professionals see generative AI as an augmentation tool—not a replacement—and emphasize the need for structured human-AI collaboration.
Key Themes & Insights
Research Menu
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Table of Contents
4. Culture and Mindset Shape Organizational Readiness
Participants were candid about their emotional responses to generative AI, including curiosity, overwhelm, and a desire for space to learn. Some described celebrating small wins and fostering a culture of experimentation, while others expressed concern about burnout and isolation.
Readiness to adopt generative AI is not solely technical—it is deeply cultural. Mindset, leadership vision, and internal storytelling shape how generative AI is received and sustained.
Key Themes & Insights
Research Menu
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Table of Contents
5. Efficiency is the Common Entry Point
Many current use cases center around time savings, content drafting, and reducing repetitive work. These practical applications were often framed as early-stage wins that helped build organizational momentum.
Efficiency remains a compelling gateway to generative AI adoption—providing quick wins that can catalyze deeper innovation over time.
Key Themes & Insights
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Table of Contents
6. Systemic Gaps are Hindering Implementation
Participants noted significant barriers in infrastructure, including limited training, outdated systems, CRM limitations, and lack of integration support. These challenges often stymied even the most enthusiastic teams.
Institutions are eager to advance their generative AI efforts, but gaps in training, technical capacity, and system integration continue to pose obstacles.
Key Themes & Insights
Research Menu
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Table of Contents
From focus groups with leaders we learned:
Headlines from Leaders
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Research Report: CASE InsightsSM on Artificial Intelligence in Advancement in partnership with GiveCampus This March 2024 report shares insights from a previous CASE InsightsSM research study on artificial intelligence in advancement. Subject Guide: Artificial Intelligence (CASE Members only) This resource collection from the CASE Library introduces the basics of AI, takes a look at some of the latest trends and ethical questions, and provides information on how AI is being leveraged by educational institutions and nonprofit organizations. Webinar Recording: AI in Advancement: Bridging Innovation, Policy, and Practice (Free for CASE Members) Generative artificial Intelligence (AI) has emerged as a transformative tool with the potential to fundamentally alter the way integrated advancement is approached. This training highlights benefits, concerns, and opportunities beyond ChatGPT and Copilot. Custom GPTs and Custom Instructions Custom GPTs can help you with editing content, reporting, scheduling, and more. Setting your Custom Instructions can mean the difference between content tailored to your unique situation and generic output. Follow this step-by-step guide to set your Custom Instructions. The future of generative AI in Advancement Generative AI is poised to transform educational advancement. This is how language learning models like ChatGPT and ElevenLabs describe this phenomenon.
How to Go Further
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Table of Contents
CASE Insights
data | standards | research
SM
These are the five areas where CASE provides data, research, and frameworks for measuring Advancement activities. Our data collection is based around the CASE Global Reporting Standards. Creating and adhering to a set of methods, standards, and guidelines for reporting fundraising activities allows schools, colleges, and universities to represent the work of all institutions in transparent ways.
About CASE InsightsSM
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Table of Contents
Click on the links for additional CASE.org resources.
CASE Insights
data | standards | research
SM
Standards: CASE's Global Reporting Standards, which provide a common set of definitions for advancement professionals around the globe.
Research: topical analysis of the most relevant issues in advancement today, made possible by funders and educational partners.
Data: CASE's annual benchmarking surveys administered globally to shed light on trends within the educational advancement landscape.
About CASE InsightsSM
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Table of Contents
CASE Insights
data | standards | research
SM
CASE is the world leader in helping advancement professionals in colleges, universities, and schools make data-informed decisions. Institutions of any size, type, or location around the globe will find CASE InsightsSM data, standards, and research are valuable tools to help drive success. Through your CASE membership, you already have access to free CASE InsightsSM resources, along with additional services to meet your institution's needs. Explore peer comparisons, detailed benchmarking data, research reports about emerging issues, and other options you can use to expand your institution's impact. Start your journey now. Find a variety of resources and build your expertise. Visit us online or connect with the InsightsSM Solutions team directly:
About CASE InsightsSM
Email the InsightsSMSolutions Team
Visit CASE InsightsSM
Discover the many ways data can contribute to your institution's success with CASE InsightsSM foundational tools.
Stay up to date on active surveys, new reports, and learning opportunities.
Create greater impact for your institution with in-depth resources that will help you move to a data-informed strategy.
Insights for building solutions
Insights for getting started
Insights for today's opportunities
Larger-scale implementation, with activities in specific categories reaching more formal practices connected with team and organizational goals. Embedded in daily practice.
Early stages of implementation, with targeted activities in the specific category by individuals and team, with some resources supporting the activity.
The first step towards adoption involves initial exploration, often individually or in small groups, with minimal direction or planning.
Larger-scale implementation, with activities in specific categories reaching more formal practices connected with team and organizational goals. Embedded in daily practice.
Early stages of implementation, with targeted activities in the specific category by individuals and team, with some resources supporting the activity.
The first step towards adoption involves initial exploration, often individually or in small groups, with minimal direction or planning.
Jump to Pathways
Operationalizing
Exploring
Experimenting
Operationalizing
Exploring
Experimenting
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Research Sponsor:
Research Team:
Project Credits
CASE Project Team:
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Contact Us:
insights@case.org
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Feedback
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Insight:
Skills & Training
EXPERIMENTING
Our study found that efficiency-focused pilots are a low-risk entry point for most institutions.
Support for this stage
Use cases
Pathways forward
Leadership lens
Sticking points
Fun idea
Commentary
Insight:
Culture & Mindset:
OPERATIONALIZING
Even the most advanced teams view AI as a tool—not a replacement.
Support for this stage
Use cases
Pathways forward
Operationalizing this culture requires intentional storytelling and structured human-AI collaboration.
Leadership lens
Sticking points
Commentary
Expert Voice: The Future of Generative AI in Advancement
Content generated by ChatGPT AI Text-to-Speech by ElevenLabs
*AI-Generated Recording
Insight:
Strategy & Vision
OPERATIONALIZING
Institutions are evolving from pilot projects to enterprise-level planning—and realizing the need for formal structures to support AI at scale.
Support for this stage
Use cases
Pathways forward
Leadership lens
Sticking points
Fun idea
Commentary
Insight:
Culture & Mindset:
EXPERIMENTING
"Champions can’t operate in a vacuum."
Support for this stage
Use cases
Burnout and isolation were mentioned as risks when experimentation isn’t shared or supported.
Pathways forward
Leadership lens
Sticking points
Commentary
Strategic Uses of AI
Use AI to Strengthen Strategic Focus To sharpen your strategy, use AI to support each step of your strategic planning, from situational analysis to campaign development. Use AI to validate ideas, stress-test scenarios, and guide more confident strategic choices. Use AI to Listen at Scale Through Smart Surveys Create a survey focused on satisfaction, agreement with your brand values, and likelihood to recommend. Use AI to analyze open-ended responses and cross-reference satisfaction by segment. Instead of just counting scores, AI uncovers patterns and emotional tone. Let AI Drive Deeper Campaign Development When developing campaigns, use AI to test creative angles, headlines, and audience targeting. Start by feeding the AI key audience profiles and goals. Then, prompt it to generate variations, simulate responses, and even predict engagement. This turns campaign planning into an experiment-driven process. Use AI to Map Strategic Sentiment Over Time Track how people feel about your institution over time using AI-powered sentiment analysis. This lets you intervene early, adapt messages, and track whether strategic changes are truly landing with your audience. Use AI to Power Ongoing Strategic Iteration AI isn’t a one-time tool—it’s a partner in ongoing strategic refinement. Set up systems where AI continuously pulls in new data (e.g., survey results, campaign metrics, market shifts) and surfaces emerging insights.
- from Gustavo SeguiExecutive Director for Marketing, Advancement and Admissions International School of Curitiba
Culture & Mindset
At a Glance: Culture and mindset shape the emotional and intellectual readiness of a team to engage with generative AI. This area includes attitudes toward innovation, comfort with experimentation, and the sense of psychological safety around trying (and failing at) something new. It is the bedrock of adoption—and often the most underestimated.
Without a supportive and psychologically safe culture, even the best tools or strategies can fall flat. Cultural readiness influences every other aspect of adoption, from experimentation to policy alignment. Teams that feel empowered to learn together, take risks, and share openly are better positioned to adapt with agility and purpose. While many educational institutions are beginning to offer cross-functional trainings, far less attention is paid to the cultural shifts required to sustain meaningful change. Without intentionally focusing on managing change, early adopters risk burnout from carrying momentum alone, while more hesitant team members may be left behind without the support they need to engage.
Insight:
Policy & Governance
OPERATIONALIZING
There is a clear expectation that generative AI use must be governed by institutional policy, grounded in ethical considerations, and compliant with external regulations.
Support for this stage
Use cases
Pathways forward
Leadership lens
Sticking points
Fun idea
Commentary
Insight:
Policy & Governance
EXPERIMENTING
Many institutions start their AI journey by convening a working group or taskforce to look at policy needs. Getting timely initial guidance in place can be a challenge.
Support for this stage
Use cases
Pathways forward
Leadership lens
Sticking points
Fun fact
Commentary
Infrastructure & Technical Capability
At a glance: Infrastructure and technical capability provide the foundation that enables generative AI integration at scale. This area includes access to tools, data systems, vendor partnerships, internal tech support, and the organizational ability to build or customize AI solutions as needs evolve. Appetite for new AI initiatives is often
shaped by what else is happening behind the scenes—especially large-scale projects like CRM conversions, website rebuilds, or data infrastructure upgrades. These competing priorities can influence both the pace and focus of generative AI adoption. Even the most enthusiastic teams can be limited without the right technical environment. Strong infrastructure and technical capacity ensure that generative AI moves from isolated experiments to embedded, sustainable practice. These foundations create reliability, scalability, and long-term innovation.
Insight:
Strategy & Vision
EXPERIMENTING
Many focus group participants noted they’d structure implementations differently if starting over—more iterative, more stakeholder aligned.
Support for this stage
Use cases
Pathways forward
Leadership lens
Sticking points
Fun idea
Commentary
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Infrastructure & Technical Capability
Insight:
EXPLORING
Public tools accessed individually; limited institutional support.
Support for this stage
Use cases
Pathways forward
This was a common starting point across institutions.
Leadership lens
Sticking points
Fun idea
Commentary
Infrastructure & Technical Capability
Insight:
EXPERIMENTING
When asked how much support they had received from IT, one participant responded, “ZEROOOOOOOOO."
Support for this stage
Use cases
Pathways forward
Leadership lens
Collaboration is essential for expanding AI use beyond individuals and small teams.
Sticking points
Fun idea
Commentary
Insight:
Policy & Governance
SCALING
There’s a growing expectation that AI usage must be institutionally governed and ethically anchored—this isn’t optional.
Support for this stage
Use cases
Pathways forward
Leadership lens
Sticking points
Fun fact
Commentary
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Infrastructure & Technical Capability
Insight:
SCALING
Widespread adoption of AI requires collaboration across teams and support from IT.
Support for this stage
Use cases
Pathways forward
Leadership lens
Sticking points
Commentary
Expert Voice: Managing Change — How to Get Started
Gustavo Segui Director of Marketing & Advancement Director of Admissions International School of Curitiba
Infrastructure & Technical Capability
ZOOMING IN
Out-of-the-Box Use
Light Customization
Agent-Based Integration
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Insight:
Skills & Training
EXPLORING
Staff capacity, leadership vision, and curiosity continue to shape pace and tone.
Support for this stage
Use cases
Pathways forward
Leadership lens
Sticking points
Fun idea
Commentary
Expert Voice: Creating an AI Policy for Advancement
Christie Horton Associate Vice President Talent, Culture & Human Resources Kansas State University Foundation Chris Miles Associate Vice President Information, Technology, & Infrastucture Kansas State University Foundation
Insight:
Skills & Training
SCALING
The appetite for AI is high, but many institutions lack the connective tissue—tech + people + policy —to unlock its full potential.
Support for this stage
Use cases
Pathways forward
Leadership lens
Sticking points
Commentary
Use Cases
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Expert Voice: Addressing Bias
Cara Giacomini Vice President - Data, Research & Technology CASE
Expert Voice: Investing in AI
Joe Manok Vice President, University Advancement Clark University
*AI-Generated Recording
Insight:
Ethics & Responsibility
OPERATIONALIZING
One participant noted,
Support for this stage
"AI use must be governed and anchored in ethics."
Use cases
Pathways forward
Participants expressed strong interest in clarity and accessibility.
Leadership lens
Sticking points
Fun idea
Commentary
Using Copilot as an Executive Coach
Use Case: I recently embarked on an unconventional professional development exercise: I used the Research Agent provided in MS Copilot as an “executive coach” to audit my own leadership and management behaviors. Prompt: “Based on everything you have access to from the last two years, please perform an audit of my work, leadership, and management behaviors. What do I do well? What could be improved? With whom do I communicate effectively? Are there people that need my attention that aren’t receiving it? Infer details based on my role, its typical functions, and how it should perform within an organization like the University of Texas at Austin. Use our org. chart to be precise and return the exact names of individuals or divisions. Try and define my footprint within the university and where it is either strong or lacking. Analyze my communication style and try and discern any hidden biases towards people, projects, or units that I may not be aware of. Are there any relationships that should be strengthened? Try and define my footprint outside of the university regarding professional and volunteer organizations. Give precise recommendations on how I could improve as an employee and as a leader.” Report: The final report was thorough and read like something a management consultant or coach might prepare after weeks of interviews and research. The report didn’t shy away from tough feedback (delivered politely), but it balanced critiques with encouragement, acknowledging my strengths in those same areas.
- from John GoughAssistand Vice President University of Texas Austin
Insight:
Strategy & Vision
EXPLORING
Advancement professionals are framing GenAI as a strategic endeavor, not a one-off experiment.
Support for this stage
Use cases
Pathways forward
Leadership lens
Sticking points
Commentary
Insight:
Culture & Mindset:
SCALING
"Change management and internal storytelling matter."
Support for this stage
Use cases
Pathways forward
Teams thriving at this stage are investing in connection, communication, and shared purpose.
Leadership lens
Sticking points
Fun idea
Commentary
Expert Voice: Leading with Curiosity and Clarity
Jenny Cooke Smith Senior Director of CASE InsightsSM Solutions CASE
Skills & Training
At a glance: Skills and training are the bridge between curiosity and capability. As advancement professionals explore and expand their use of generative AI, access to structured learning opportunities becomes vital. This area encompasses informal learning, formal training, peer knowledge-sharing, and the development of role-specific competencies.
The best AI strategies can stall without confident users. Building skills and creating a shared foundation allows innovation to flourish. When staff understand how to use AI effectively—and when not to—they become empowered collaborators in transformation. Effective learning is tailored to the context. Advancement professionals benefit most from training that reflects their goals, systems, and use cases. Just as importantly, they need safe, low-stakes environments to experiment, ask questions, and build confidence together.
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Ethics & Responsibility
At a glance: Ethics and responsibility shape the boundaries—and possibilities—of generative AI in advancement. This area includes transparency, bias mitigation, disclosure, equity, and intentional alignment with institutional values. As AI becomes more powerful, so too does the need to use it with care, integrity, and accountability.
Ethics isn’t an afterthought—it’s the compass. Responsible use builds trust with stakeholders and protects the dignity of communities. Institutions that prioritize responsible innovation are better positioned to adapt sustainably and equitably. While environmental sustainability is an emerging concern in the broader AI field, it’s rarely discussed within advancement contexts. As institutions move toward more responsible use, understanding the ecological impact of GenAI will become increasingly important.
Insight:
Skills & Training
OPERATIONALIZING
Our participants said, "Lack of training and integration support often hinder implementation—even among eager teams."
Support for this stage
Use cases
Pathways forward
Leadership lens
Sticking points
Commentary
Strategy & Vision
At a glance: Strategy and vision are the connective tissue between generative AI experimentation and institutional transformation. This focus area encompasses how AI is framed, prioritized, and integrated into long-term plans and leadership narratives. It influences everything from pilot selection to resource allocation and helps ensure AI efforts support the broader mission.
Without strategic clarity and a compelling vision, AI initiatives risk becoming scattered, superficial, or short-lived. When generative AI is positioned as a strategic asset—aligned with values and measurable goals—it can amplify innovation, fundraising outcomes, and community impact. As teams mature in their use of GenAI, the focus often shifts from efficiency—getting more done—to efficacy: ensuring that AI-driven efforts truly advance strategic goals. This evolution is a key marker of long-term, values-aligned integration.
Insight:
Culture & Mindset:
EXPLORING
"Readiness to adopt generative AI is not solely technical—it is deeply cultural."
Support for this stage
Use cases
Pathways forward
Leadership lens
Focus group participants noted curiosity, overwhelm, and a desire for space to learn.
Sticking points
Commentary
As we move forward, the most successful institutions will be those that view AI not as a tool, but as a catalyst for transformational leadership.
- Research Study Participant
Starting Point: Policy Creation
Review the section on “Ethical Principles” in the CASE Global Reporting Standards
Review institutional and regional privacy policies
Review existing technology-use policies or guidelines
Focus on clear communication and transparency about how AI is being used
Avoid entering institutional intellectual property (IP) or information about specific individuals when using any public AI
Consider convening a working group to advise on AI use
Expect your policy to evolve over time as tools and use cases advance
The success of our AI initiatives was not just about technology—it was made possible by a culture of innovation, a willingness to embrace new tools, and the institutional flexibility to experiment.
- Research Study Participant
Insight:
Ethics & Responsibility
EXPERIMENTING
Several noted that overreliance on generative AI led to disappointing results—human intervention is still necessary.
Support for this stage
Use cases
Pathways forward
Leadership lens
Sticking points
Fun idea
Commentary
Expert Voice: Using AI to Advance Strategic Enrollment Goals
Jenny Petty Vice President - Marketing Communications Experience & Engagement University of Montana
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Infrastructure & Technical Capability
Insight:
OPERATIONALIZING
Custom GPTs or low-code modifications and "vendor partnerships" begin to emerge during this stage.
Support for this stage
Use cases
Pathways forward
Leadership lens
Sticking points
Tailoring GenAI to Your Team: What Is a Custom GPT?
Insight:
Ethics & Responsibility
EXPLORING
Themes of human review, oversight, and judgment came up strongly—especially in questions about pitfalls.
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Use cases
Pathways forward
Leadership lens
Sticking points
Fun idea
Commentary
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Insight:
Strategy & Vision
SCALING
Strategic clarity helps translate AI excitement into impact—especially when supported by structure and storytelling.
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Use cases
Pathways forward
Leadership lens
Sticking points
Commentary
Insight:
Policy & Governance
EXPLORING
Initial explorations of AI can get stalled while waiting for someone to decide how AI can be used.
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Use cases
Pathways forward
Leadership lens
Sticking points
Commentary
Insight:
Ethics & Responsibility
SCALING
When asked about environmental sustainability, a focus group participant observed:
Support for this stage
Use cases
Pathways forward
“It is too early in the process for me to answer this particular question.”
Leadership lens
Sticking points
This area may naturally get more institutional attention as use of AI expands.
Commentary
Policy & Governance
At a glance: Policy and governance provide the ethical scaffolding for generative AI use. This includes institutional guidance, compliance with external regulations, and clear communication about what’s allowed—and what isn’t. As teams experiment and scale, policies help mitigate risk, ensure responsible use, and foster trust.
Clear policies and governance frameworks protect both the institution and its people. They empower users to explore AI confidently, while ensuring compliance with ethical, legal, and institutional standards. This clarity reduces confusion, builds trust, and supports responsible scaling across all areas of advancement. Yet in many institutions, there's uncertainty about where ownership should live—should policy originate within advancement, at the institutional level, or through individual team norms? This ambiguity can lead to inaction, as teams wait for someone else to lead the way.