AI Glossary
Decoding AI for Fundraisers - A Language Guide
This glossary breaks down common terms and technologies in artificial intelligence, data automation, and predictive modeling
Affinity Scoring
Affinity scoring measures how closely a prospect’s interests and behaviors align with your organization’s mission. In healthcare fundraising, AI models analyze patient interactions, philanthropic history, and engagement patterns to prioritize individuals most likely to support your cause.
AI-Powered Fundraising
AI-powered fundraising uses artificial intelligence to streamline donor discovery, automate workflows, and surface real-time insights. By applying machine learning to EHR and CRM data, healthcare organizations can identify high-potential prospects and build stronger, more efficient fundraising pipelines.
Application Programming Interface (API)
An application programming interface (API) allows different software systems to communicate and share data seamlessly. In healthcare fundraising, APIs enable real-time integration between CRMs, EMRs, and AI-powered fundraising platforms, eliminating data silos and ensuring donor information stays accurate and up to date.
Data Enrichment
Data enrichment enhances donor profiles by supplementing existing CRM records with new attributes such as contact details, wealth markers, or philanthropic activity. Automated enrichment ensures fundraisers always work with the most accurate, actionable prospect data.
Data Hygiene
Data hygiene refers to the process of cleaning and maintaining donor records to eliminate duplicates, outdated information, and incomplete fields. Strong data hygiene is critical for healthcare philanthropy teams relying on EHR and CRM systems to guide fundraising strategy.
Engagement Scoring
Engagement scoring ranks donors and prospects based on their level of interaction with your organization. AI-driven scoring considers signals like event attendance, email opens, giving history, and patient experience to help fundraisers focus on the right relationships at the right time.
Generative AI
Generative AI creates new content such as donor outreach templates, stewardship messages, or predictive campaign scenarios. In healthcare fundraising, generative AI can help personalize communication at scale while keeping human fundraisers focused on relationship-building.
Large Language Model (LLM)
A large language model (LLM) is a type of artificial intelligence trained on massive datasets of text to understand and generate human-like language. In fundraising, LLMs can support donor communication, draft personalized stewardship notes, and assist in crafting campaign messages that resonate with healthcare supporters.
Machine Learning (ML)
Machine learning (ML) is a subset of AI where algorithms learn patterns from historical data to make predictions. In grateful patient fundraising, ML models improve donor segmentation, forecast campaign performance, and uncover hidden giving potential in EHR and CRM data.
Predictive AI
Predictive AI applies statistical modeling and machine learning to forecast future donor behavior. By analyzing trends in giving, patient history, and wealth indicators, predictive AI surfaces prospects most likely to donate and informs the timing of outreach.
Real-Time Scoring
Real-time scoring updates donor likelihood metrics continuously as new EMR, CRM, or engagement data flows in. Instead of relying on static wealth screens, healthcare fundraisers benefit from nightly or even live updates that reflect donor intent and readiness to give.
Stewardship Automation
Stewardship automation uses AI and workflow tools to streamline donor follow-up and acknowledgment processes. From personalized thank-you notes to milestone reminders, automated stewardship ensures no donor is overlooked while saving fundraisers valuable time.
Wealth Screening
Wealth screening evaluates a donor’s financial capacity to give by analyzing public records, real estate holdings, and philanthropic history. Combined with AI-driven affinity and engagement scoring, wealth screening helps fundraisers prioritize prospects who have both the means and the motivation to give.
