Finding the Leverage Point

The nonprofit sector has a systems problem. And it's costing us our best people.

A few years into my first nonprofit role, I called my dad in tears.

I didn’t know what was wrong. I was in my early twenties, giving everything I had to an organisation I believed in, and I was falling apart. I didn’t have the language for it then. I didn’t know what burnout was. I just knew I wasn’t myself anymore.

I’ve since learned the word. I’ve also watched it happen to dozens of others — brilliant, passionate people who entered the nonprofit sector because they wanted to make the world better, only to be ground down by broken processes, impossible workloads, and systems that were never designed to support them.

Many of them left the sector entirely.

That’s not a staffing problem. That’s a systems failure.

The task no one questioned

A few years ago, I was consulting with a nonprofit during a discovery session about potential AI projects. In passing — almost as an afterthought — a staff member mentioned something about their campaign season workflow.

I asked them to stop and tell me more.

During their busiest period, staff were staying up late every night manually collecting every media mention of their organisation. Copying URLs into spreadsheets. Pasting journalist names, article titles, dates — one by one. All so they could run analytics at the end of the campaign.

They’d been doing this for years. No one had questioned it. It was just “part of the job.”

We automated the entire process. A Google News alert feed handled the bulk of it. For anything missed, staff could drop a URL into a simple input, and AI would extract the journalist’s name, the title, the date — populating the spreadsheet automatically.

The task that had been keeping people up at night during their most demanding season simply... disappeared.

This is what a leverage point looks like. Not a massive organisational overhaul. One small shift in how a task gets done — and suddenly, people get their evenings back.

The concept that changed how I see everything

The systems thinker Donella Meadows wrote a famous paper called Leverage Points: Places to Intervene in a System. Her core insight was that in any complex system, there are specific points where a small shift can produce enormous change.

I named my newsletter after this idea because I’ve lived it.

Over the past few years, I’ve watched small interventions — an AI tool, a prompt, an automation — create disproportionate results. Not incrementally better. Transformatively different.

But here’s the thing Meadows also warned about: the higher the leverage point, the harder the system resists change.

And nowhere is that resistance more visible than in the nonprofit sector.

The real barrier isn’t technology — it’s visibility

The biggest leverage points in nonprofits are hiding in plain sight. They’re buried in the tasks people have normalised. The manual data entry. The meeting notes no one has time to write up properly. The reporting processes held together with spreadsheets and sheer willpower.

Staff don’t flag these as problems because they don’t know there’s an alternative. They’ve never seen what’s possible.

This realisation is what shifted my entire approach. I’d been building AI solutions for nonprofits as a consultant — and getting great results. But I kept coming back to the same thought:

The people inside these organisations will always be better positioned to spot the leverage points than I am.

They know where the friction lives. They feel it every day. They just don’t know it’s solvable.

So I shifted from implementation to education. I built the AI Impact Hub with courses, tutorials, and resources — because the real leverage point isn’t any single automation. It’s giving people the knowledge to see opportunities they didn’t know existed.

That’s a meta-leverage point, if you like. Moving from intervening at the level of individual fixes up toward changing how people see their own work. Meadows would call that shifting the information flows of a system — and it sits much higher on her hierarchy than any single automation ever could.

Beyond efficiency: the possibilities no one is talking about

Most conversations about AI in nonprofits start and end with efficiency. Write emails faster. Summarise documents. Save a few hours a week.

That matters. But it’s not the real story.

I worked with an organisation that had a twelve-part framework for guiding people through a process of change — traditionally delivered as a book. They were curious about whether a chatbot might help.

I encouraged them to experiment with a tool called Lovable. They vibe-coded a working MVP of an interactive app — no developers, no grant funding for a tech build, no six-month procurement process. Just experimentation and a willingness to try.

They plan to take that prototype to funders.

A year or two ago, that would have required a significant grant just to reach the proposal stage. Now, a small team with the right tools can build a working proof of concept in days.

This is the part of the AI conversation that’s being missed entirely. It’s not just about doing existing work faster. It’s about doing things that were never possible before — apps, interactive tools, personalised experiences — with the resources nonprofits actually have.

The time from ideation to execution has dramatically shrunk. And I’m just not hearing enough about nonprofits seizing that opportunity.

The survival trap

So why aren’t more nonprofits acting on this?

Meadows wrote that the goals of a system are among the highest leverage points. In nonprofits, the stated goal is always mission impact. But the actual operating goal — the one that drives daily decisions — is often survival. Keep the lights on. Meet the grant deliverables. Retain the donors. Don’t burn out the staff (or at least, replace them when they do).

When you’re in survival mode, you can’t lift your head long enough to see transformative possibilities. Every hour is accounted for. Every task feels urgent. The idea of taking time to experiment feels irresponsible when there are fires to put out.

And the resistance runs deeper than just time. The nonprofit sector attracts a special type of person — people driven by passion, by wanting to make the world better. That’s what makes them extraordinary. But it also means the sector hasn’t traditionally attracted or developed deep technical talent. Leadership often doesn’t know what’s possible with AI, and what you don’t know exists, you can’t prioritise.

Layer on top of that a natural fear of change, the very real challenges of change management, and the fact that even when organisations want the outcome, they resist the process of getting there — because the cost is immediate but the benefits are delayed — and you have a system that naturally favours the status quo.

The result is a self-reinforcing loop. Too busy to learn AI, so you stay busy, so you never learn AI. The only way to break the cycle is a deliberate leadership decision to prioritise it.

Breaking the loop

The good news is that the flywheel effect on the other side is massive.

Start with something simple — meeting transcription tools so staff don’t have to manually take notes, draft summaries, and compile action items. That saves time. Use that time for deeper exploration. Which saves more time. Which opens up new possibilities.

One practical starting point: run a time audit. Ask staff to track how they spend their time for a week. Where are the hours going? Which tasks are repetitive, manual, and draining? That’s your map of leverage points.

I recently saw an online publication take an entire week off normal operations just for AI experimentation — every staff member, dedicated time, no other expectations. Imagine what a nonprofit could discover with even a fraction of that commitment.

But none of this happens without leadership making the call. Whether it’s top-down — leaders prioritising AI literacy as a strategic imperative — or bottom-up — staff requesting access to paid AI tools, training, and support — someone has to decide that this matters more than the next fire to put out.

Because every task you choose to do is a task you’re prioritising over one you don’t. The question is whether AI experimentation makes that list.

The 14% problem

Late last year, Infoxchange released a study showing that only 14% of the 800-plus nonprofits surveyed had an AI policy.

Not an AI strategy. Not training programmes. Not implementation plans. Just a policy — the bare minimum signal that an organisation is thinking about this at all.

That means 86% of nonprofits haven’t even started the conversation.

An AI policy might sound like a small thing. But creating one means leadership has acknowledged AI as a priority. It means they’re talking to staff about when and how to use these tools. It means the door is open.

Without it, the door isn’t just closed — most organisations don’t even know there’s a door.

The cost of staying the same

I think a lot about the idea that for change to happen, the pain of staying the same must outweigh the pain of changing.

That tipping point is coming — fast.

If you look at what’s happened with AI over the last three years and project forward, the organisations that haven’t invested in AI literacy, training, and experimentation are going to face serious consequences. Not hypothetically. Practically.

The nonprofits that are experimenting now — building prototypes, automating workflows, upskilling their teams — will pull away from the pack. They’ll write better grants. They’ll deliver more innovative programmes. They’ll attract more funding and support.

And the ones that don’t? Some of them will get wiped out.

That’s not fear-mongering. That’s the reality of what happens when a sector built on passion and manual effort meets the most transformative technology of our time and chooses not to engage with it.

Meadows warned that systems which cannot self-evolve are doomed on a highly variable planet. The nonprofit landscape is shifting rapidly. The organisations that refuse to experiment aren’t just missing opportunities — they’re becoming fragile.

What this is really about

At the end of the day, this isn’t a technology conversation. It’s a conversation about dignity.

The people who work in nonprofits are sacrificing a lot. Less pay. Fewer resources. Limited career progression. They do it because they believe in the mission. They believe the work matters.

The least we can do is not make the work harder than it needs to be.

Good systems empower people to do great work. They lift people up. They enrich the experience of working at an organisation. They free people from the soul-crushing manual tasks that lead to late nights, burnout, and eventually — too often — leaving the sector altogether.

I know this because I’ve lived both sides. I’ve been the person calling my dad in tears, not understanding why I was falling apart. And I’ve been the person watching an automation give a team their evenings back during campaign season. The distance between those two experiences is often just one small shift — one leverage point that someone finally decided to look for.

Every nonprofit leader should be asking themselves one question: Are our systems setting our people up for success, or are we burning through their goodwill and hoping the next hire will survive what the last one couldn’t?

The tools exist now. The leverage points are there. They’re hiding in the tasks your team has stopped questioning.

All it takes is someone willing to look.

Kyle Behrend is an AI, Automations & Systems Specialist with 15 years in the nonprofit sector, based on the Central Coast of NSW, Australia. He is the founder of AI Impact Hub and writes The Leverage Point on Substack — exploring how small shifts create transformative change for mission-driven organisations.

P.S. If you’re a nonprofit leader wondering where to start, try this: ask your team to name the one task they dread most each week. That’s your first leverage point. You might be surprised how solvable it is.