Tal Frankfurt is the Founder and CEO of Cloud for Good, a Salesforce partner that creates transformational value with technology.
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A few weeks ago, I was watching a colleague from Generation Z try to find information on a nonprofit's website. He clicked through two pages, scanned for a few seconds, paused and then closed the tab.
The entire interaction lasted maybe 10 seconds. Nothing was technically wrong with the site. The information was there. But the path to understanding required a little too much searching, a few too many clicks and just enough friction to lose attention.
That moment stuck with me because it reflects a broader shift happening across the nonprofit and education sectors. The next generation of donors, students and stakeholders is not necessarily less committed to causes. If anything, Gen Z is deeply values-driven.
But they engage with organizations through a very different set of expectations.
Research backs this up. The Blackbaud Institute found that 84% of Gen Z supports nonprofits. The barrier is not commitment. It is friction. McKinsey notes this generation does not lack focus. They are simply more selective with their attention. And if an experience feels slow or confusing, they simply move on.
At the same time, we are entering a moment where AI, both generative and predictive, is reshaping how organizations operate and communicate. These shifts are beginning to collide, and they will change how organizations build trust.
Trust has always been the foundation of the impact sector.
Donors trust that their contributions create meaningful outcomes. Students trust institutions to guide them through complex systems. Communities trust that missions translate into measurable results.
But increasingly, trust is no longer built only through mission statements or annual reports. It is built through everyday interactions. And those interactions are now digital.
Think about the experience most people have with consumer technology today. If you browse for a product online and leave it in your cart, that item might reappear later in a social media ad or in a follow-up email. The next time you visit the site, the product may already be waiting for you.
That experience is not accidental. It reflects years of investment in data infrastructure, CRM systems and analytics designed to understand behavior across channels.
Retail companies know that friction costs them revenue, so they have spent decades removing it. In the impact sector, the experience is often very different.
Stakeholder data lives across disconnected systems, information is scattered across webpages and documents, and answering a simple question requires navigating multiple departments.
For organizations trying to build long-term relationships with Gen Z, that fragmentation is becoming increasingly visible.
This is where AI changes the equation.
For leaders in the impact sector, this shift is about adopting AI tools and redesigning how their organizations organize and share knowledge.
Generative AI can dramatically reduce the friction between a question and an answer. Predictive AI can help organizations anticipate needs, personalize communication and identify patterns across engagement channels.
We are already seeing early examples of this shift.
One of our clients, a university serving more than 21,000 students, implemented multiple use cases of both predictive and generative AI to support financial aid teams.
Financial aid, as I've seen in our work with this client, is one of the most complex and emotionally charged areas of the student experience. Policies are nuanced, documentation requirements are extensive, and students often arrive with urgent questions. Their approach is pragmatic. Rather than immediately launching a student-facing AI tool, the university started internally with a staff-facing assistant that helps financial aid teams navigate policies, documentation and case management.
The operational impact was significant. Tasks that previously required five to 20 minutes of manual research could be resolved in under a minute.
But the real value is not just speed. It is consistency.
When staff have faster access to accurate information, responses can become more reliable, processing times can decline, and teams can handle more cases without sacrificing accuracy. And now, when systems expand to student-facing experiences for that client, they can do so on a foundation employees already trust.
That pattern, starting internally, proving reliability, then expanding outward, is, in my view, likely to become a common model for AI adoption across the impact sector.
AI can make interactions faster, but speed alone does not create trust. In fact, it can expose weaknesses.
If an AI assistant pulls from outdated policies, inconsistent data or fragmented systems, it will surface those problems instantly. What used to take 20 minutes can now produce a wrong answer in two seconds.
That is why the real work behind AI adoption is not the model. It is the data.
Our client succeeded because, before launching their AI program, they completed a comprehensive data initiative. Once definitions were standardized and sources became reliable, automation delivered real value.
Based on this experience, and my other work with clients in this space, there are three practices I'd urge fellow leaders to adopt.
First, audit your data before selecting any tool. Understand where information lives, who owns it and whether it is consistent across systems.
Second, standardize your definitions. If two departments describe the same program differently, AI will reflect that confusion.
Third, before any AI project begins, ask this: If a staff member needed to answer a question accurately in under a minute, could they? If the answer is no, the data is not ready. AI does not fix broken foundations. It reveals them. AI will magnify whatever already exists inside an organization.
When someone asks a question, they expect a clear answer, not a search result, a PDF or a page of policy language. Organizations must start treating institutional knowledge as infrastructure, ensure that policies and program data are structured and accessible, and use AI to make that knowledge easier to navigate.
For Gen Z, the experience of interacting with an organization defines whether they trust it. AI will not solely determine whether organizations earn that trust. But it will make the answer visible much faster.
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