What duty does a nonprofit board have to oversee AI? A board's existing fiduciary duties, care and loyalty, now explicitly cover AI. Gene Takagi, one of the most cited voices in nonprofit law, laid this out plainly in a piece published August 16, 2026. The duty of care means a board must actively understand how AI is being used inside the organization, not wait for staff to bring it up. The duty of loyalty means weighing AI's efficiency gains against any harm it could cause to the mission, to beneficiaries, to staff, or to donor trust. That is a real shift. Until recently, "we're experimenting with AI" was a staff-level update, mentioned in passing if at all. Takagi's argument makes it a governance matter. A board member who never asked how AI touches the organization is not neutral on the question. They are behind on a duty they already hold. Source: Gene Takagi, Governance and AI: Fiduciary Duties , Nonprofit Law Blog, published August 16, 2026. How big...

What's actually happening I have sat in enough of these conversations to recognize the pattern. A director gets a demo from a vendor at a conference, or a peer organization mentions a tool that "changed everything," and the instinct is to move fast. Fast feels like leadership. So the organization buys the platform, or signs up for the pilot, and only then does anyone ask what it's for. This is the same mistake I see nonprofits make with strategic plans. A forty-page document gets written, approved by the board, and filed. Nobody translates it into what changes on a Tuesday. The plan is real. The connection to daily work is not. AI tools are landing on top of that same disconnect, except now the artifact isn't a binder, it's a subscription. I'm starting to hear a version of this at the board level too. Funders are asking nonprofits how they use AI, and a board wants an answer that sounds current. That pressure creates the same shortcut: adopt somethi...

What actually got taken Ask what AI absorbed first in a small nonprofit, and the list is consistent. First drafts of donor letters. Grant report boilerplate. Meeting notes. Volunteer scheduling emails. Social copy. The intake summary nobody wanted to write. Look at what those have in common. Every one of them is a task with an edge. You start it, you finish it, and you can point at the thing you made. The letter exists. The report is submitted. The notes are in the folder. Those tasks were tedious. They were also, for a lot of people, the only proof they had that they did anything that day. A program coordinator does not go home holding an outcome. Outcomes take months and belong to the whole organization. What she goes home holding is the twelve things she finished. When AI takes the twelve things, the tedium leaves and the proof leaves with it. Nobody planned that. It is a side effect, and side effects are the hardest thing to notice because no line item shows them. Why it la...

This spring, two researchers set out to answer one question about Florida nonprofits: why do some of them use generative AI, and others don't. Wanzhu Shi and Lauren Azevedo surveyed 168 Florida nonprofit organizations. They also sat down with 14 Florida executive directors and asked them to explain their own decisions, in their own words. The study ran in Nonprofit and Voluntary Sector Quarterly this past March, a peer-reviewed journal (journals.sagepub.com, accessed 2026-08-12). The full findings sit behind a paywall, so I can't tell you which factor mattered most. But I can tell you what they chose to measure, and that alone is worth sitting with. They used something called the Technology-Organization-Environment framework, TOE for short. It's a standard way researchers study why any organization adopts new technology, not just AI. It looks at three things: whether the pressure to adopt comes from outside the organization, what the culture inside the organization already ...

McKinsey just published one of the most honest things about AI adoption in years. Nearly 8 in 10 companies report using AI, and nearly the same number report no significant impact on earnings. They called it the "gen AI paradox." Companies are everywhere on AI. And almost nowhere on results. Here's what the data actually shows (McKinsey State of AI, 2025): → 88% of companies deploy AI in at least one function → Only 6% of companies see more than 5% of EBIT directly attributed to AI → Nearly two-thirds have not begun scaling AI across the enterprise → Fewer than 30% of CEOs personally sponsor their company's AI agenda The technology is not the problem. The leadership approach is. The real diagnosis Most companies bolted AI onto existing processes. Copilots on top of old workflows. Chatbots next to manual work. Assistant tools that assist nothing at scale. McKinsey calls this "horizontal" adoption. Wide, visible, and nearly impossible to connect to reven...

  Here are 5 employee motivation statistics from 2025: Employee engagement in the U.S. fell from 36% in 2020 to 31% by 2024, reflecting a consistent decrease in motivation and workplace connection. McKinsey Gen Z (42%) and Millennials (40%) are most motivated by work that aligns with their values and sense of purpose, while Gen X (34%) and Baby Boomers (32%) tend to focus more on financial security and long-term career stability. This highlights how motivation differs across generations. Deloitte Employees in hybrid work environments report the highest motivation levels (47%), compared to 39% for those working remotely and 34% for those in on-site roles. Own Labs 69% of employees say they would put in more effort if their contributions were acknowledged, showing that even basic recognition can significantly boost motivation. Gallup 90% of employees say that being recognized for their work encourages them to work harder and stay motivated. Achiever   It’s important to remember...