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The Engagement 4Cast
As we wrapped up 2025, one thing became clear: AI finally crossed the line from novelty to utility.
Not because the technology suddenly became magical—but because organizations stopped chasing hype and started using AI to solve real, everyday problems. In a recent fireside chat, we looked back on what actually changed this year, what didn’t, and what nonprofit teams should be paying attention to as we head into 2026.
The short version: AI works best when it stays small, specific, and human-centered.
Missed our Jan 2026 webinar on “Looking Back, Looking Ahead: AI, Data, and Nonprofit Digital Engagement?” Watch the recording here!
For the past few years, AI conversations were dominated by big promises: artificial general intelligence, full automation, sweeping transformation. In practice, most of that turned out to be noise.
What did work in 2025 were modest, focused uses of AI:
Across nonprofits and agencies alike, we saw teams quietly building the tools they wished already existed. Sometimes that looked like a custom script to clean up data. Sometimes it was AI reading across documents or emails to surface insights that would have taken hours to find manually.
The common thread wasn’t sophistication. It was usefulness.
One of the biggest shifts we saw was away from “off-the-shelf AI” and toward custom, context-aware tools.
Organizations didn’t want AI in the abstract. They wanted help with their data, their workflows, and their constraints. And that naturally pushed teams toward smaller, more bespoke solutions.
This is where AI actually shines. Not as a universal answer machine, but as a flexible collaborator that adapts to how work already happens.
As AI became more embedded, a familiar truth resurfaced: bad data leads to bad outcomes—faster.
AI didn’t eliminate the need for clean, well-structured data. It amplified it. The organizations getting the most value weren’t the ones chasing new tools. They were the ones investing time in understanding their data, connecting systems thoughtfully, and being clear about what questions they were trying to answer.
In many ways, AI simply made long-standing data issues impossible to ignore.
Looking ahead, personalization will continue to be a major focus—but it remains a delicate balance.
Effective personalization isn’t about naming someone’s pet or revealing everything you know about them. That crosses into “creepy” territory fast. Instead, it’s about relevance and context:
AI can help with this, but only when it’s guided by human judgment and restraint. The goal is to feel understood, not surveilled.
One of the hardest challenges organizations face is trusting AI-generated recommendations.
The answer isn’t blind faith; or avoiding AI entirely. It’s transparency.
When AI suggests an action, teams need to understand:
The most effective organizations treat AI like a junior analyst: helpful, fast, but never unaccountable. Results are tested. Assumptions are questioned. And if something doesn’t work, it’s adjusted or discarded.
Some of the most compelling uses of AI in 2025 had nothing to do with strategy at all. They focused on eliminating friction:
This is where AI delivers real human value—not by doing meaningful work for people, but by clearing space so people can do it themselves.
AI also lowered the barrier to building solutions. Non-developers started creating scripts, tools, and workflows tailored specifically to their needs—often solving problems that had lingered for years.
This “accidental technologist” role isn’t new, but AI supercharged it. When people can prototype ideas quickly and safely, organizations gain capacity without adding headcount.
AI isn’t free—from a cost, energy, or environmental perspective. But not all AI use is equal.
Text-based tasks and one-time automation tend to have relatively low impact. Image and video generation, on the other hand, are far more resource-intensive. The takeaway isn’t “don’t use AI,” but “use it intentionally.”
Build once. Automate thoughtfully. Avoid wasteful experimentation that doesn’t serve a clear purpose.
As we head into 2026, the organizations that succeed won’t be the ones using the most AI. They’ll be the ones using it best.
That means:
AI’s real value isn’t intelligence. It’s leverage.
And when used well, it gives teams back the one thing they never have enough of: time to do better human work.
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