Most nonprofit AI projects never start with a decision. They start with a tool. A new report on AI operations at nonprofits makes that specific. Among nonprofits that already have at least one AI initiative running in production, just 12% say their most recent AI initiative began with a clearly defined problem. The rest started elsewhere: 37% adopted a vendor's recommended use case, another 37% chose a platform first, and 13% never settled on a scope at all ( Coastal / Oxford Economics, "The AI Operations Report 2026: Nonprofit," accessed September 23, 2026). That's not a story about slow adoption. Every organization in the sample already has AI live somewhere. It's a story about what happens when "live" arrives before "why." A note on the source before we go further: the report's page shows only a 2026 copyright, no month or day for when it was published or fielded. That's worth saying plainly rather than dressing it up with a date...

Because almost none of the AI your team uses ever touches a goal. Two independent surveys published this year, run by different organizations, using different methods, land on nearly the same number. Nonprofit AI adoption is close to universal. What it changed is not. I keep hearing versions of the same sentence from EDs. "Everyone on my staff uses ChatGPT now." Said with a kind of relief, like the AI problem is handled. Then I ask the next question. Has it changed what your team delivered this quarter? Most go quiet. How widespread is AI use at nonprofits right now? Nearly universal, and rising fast. Bridgespan Group and NTEN surveyed 917 nonprofit staff and executives between late April and mid June 2026, about 59.7% executives and 40.3% staff ( Bridgespan, "Choosing Your AI Path," published 2026-08-20, accessed 2026-09-15 ). Virtuous, in a separate survey of 346 nonprofits, found that 92% have adopted some form of AI ( Virtuous, 2026 Nonprofit AI Adoption Re...

What does the data actually show? Board support tracks with two different things: how deeply staff use AI, and how the organization performs financially. The survey fielded responses from May 1 through May 14, 2026, across nonprofits and educational institutions with at least $500,000 in annual revenue, spanning arts and culture, education, human services, health, and environmental organizations. The margin of error sits at plus or minus 4.4 percentage points at 95% confidence, a disclosed methodology rather than a vague "survey says." Two numbers stand out. Organizations whose boards strongly endorse AI report extensive use 59% of the time, against 14% where the board supports it "with restrictions." Those same board-endorsed organizations adopted AI agents at 42%, compared to 28% for the restricted group. Revenue growth followed the same split. 92% of board-endorsed-AI organizations reported revenue growth over the past twelve months, against 81% for organiza...

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...

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...

Before we talk about solutions, let's look at the numbers. Replacing an employee isn't just a cost, it's a serious risk to your business. For C-level executives and investors, the math is sobering: The cost of replacing a single manager or technical professional can be as high as 200% of their yearly salary . But what if it also killed your company's valuation? According to a recent study by Russell Reynolds Associates, CEO turnover at big companies is at an all-time high ( Russell ). This kind of churn, which often points to bigger problems inside a company, is a major red flag to investors. It signals a lack of stability and a potential failure to deliver on promised growth. Your best employees aren't just assets; they are the foundation of your company's value. Why Your Best People Leave Why do people leave? It's not usually just about one thing. Here are the top problems that drive away your best people. Bad Management. Poor managers are the number one...