In fundraising, bespoke AI models built exclusively on an organization’s own data, donors, and strategic priorities will consistently outperform collective, generalized AI models. In this article, BWF helps show how the competitive advantage in fundraising will come not from shared, generalized AI tools, but from organization-specific, purpose-built intelligence.

In this article, we will focus on the standards set by Family Educational Rights and Privacy Act (FERPA) and the Health Insurance Portability and Accountability Act (HIPAA) and the questions you should be asking as part of your overall Responsible AI strategy. With a focus on FERPA and HIPAA compliance frameworks, we delve into the considerations that institutions must prioritize when deploying AI-powered tools. 

Ready to start using AI for fundraising? This guide explores common AI fundraising use cases and strategies to make the most of your innovative solutions.

The fundraising sector is built on processes optimized for a world that no longer exists. Our systems, staffing models, and “best practices” were designed for an era of slow-moving information, predictable donor behavior, one-way institutional communication, and institutions as the primary source of expertise. AI has not disrupted those systems; it has simply revealed how outdated many already were. This article examines what that means as organizations grapple with resourcing, complexity, and what it truly means to work at the top of license, doing the work aligned with one’s training and expertise.

University presidents are making tough financial trade-offs and increasingly looking to advancement to do more than just fundraise. They’re expecting advancement to bridge the gap and help solve the institution’s broader financial challenges. Here’s the hard truth: You can’t meet tomorrow’s goals with yesterday’s structure, especially when budget growth and new hires are not realistic options. This article takes you through steps to automate the routine so your staff can level up.

Over the past five years, Salesforce has made nearly two dozen acquisitions to expand its capabilities and enrich the AppExchange ecosystem. In parallel, independent software vendors (ISVs) within the Salesforce marketplace have pursued their own growth strategies—sometimes through mergers that promise to fast-track innovation and improve functionality for nonprofit and higher education clients. The merger of Affinaquest and Kindsight is one such example. So what does that mean for you?

In today’s evolving philanthropic landscape, predictive AI has emerged as one of the most powerful opportunities to provide both scale and focus in fundraising. From predicting donor behavior to prioritizing outreach, from suggesting next-best-action to creating business simulations that align with a changing world, AI can transform how organizations build and sustain relationships with their supporters. Here are three key reasons why predictive AI is most effective when it can amplify human expertise and design thinking.

As leaders, the question isn’t whether you want better outcomes, it’s whether your current approach to decision-making is built to deliver them. The future of your organization’s philanthropic success depends less on your technology and more on your ability to make consistently better decisions. And that is where data science and business intelligence come in.

A CRM implementation is a foundational moment—one that requires strategic clarity, thoughtful planning, and a relentless focus on how information will drive decision-making across the organization. Ultimately, the success of a CRM isn’t measured by what it can do—it’s measured by what people do with it. This blog takes you beyond the tool into how to make the most of your CRM.