Nonprofit talent development host shares how AI can speed up the training process and why it still takes time

Good training takes time, and in a world where AI can generate content in seconds, that truth is more important than ever.

In this episode, we unpack why effective learning and development strategies in nonprofits can’t be rushed, even when tools promise speed and efficiency. If you’ve ever felt pressure to produce training quickly or wondered why your programs aren’t driving real change, this episode will shift your perspective.

▶️ Key Points:

00:00 Why it takes time to create a good training

05:57 Where AI can speed up training design

07:18 The importance of testing and iteration

AI Can Build Your Training Fast

There’s a story making the rounds in L&D circles right now, and you’ve probably heard a version of it: someone loads their content into an AI tool, and minutes later, a fully built eLearning pops out. It looks polished. It has objectives, activities, and a clean layout. It’s—by every surface-level measure—a training.

And leadership sees this and thinks: “Great. So why does it take your team 6 months to build something?”

If you’re a nonprofit L&D or HR professional, that question probably lands in a familiar, frustrating place. You’re expected to produce faster, do more with less, and somehow still be accountable for whether any of it actually works. You’ve probably already been operating from this place. And now there’s a new pressure layered on top: the assumption that AI has solved the “good training takes time” problem.

It hasn’t. And episode 184 of the Learning for Good podcast is about why good training still takes time, even in our AI-powered world.

The AI Trap: Development Speed vs. Training Effectiveness

I want to be clear: this is not an anti-AI argument. AI is a genuinely useful tool in the L&D process. It can help you brainstorm, draft, repurpose content, and accelerate the production phase of your work in ways that weren’t possible even a few years ago.

But here’s the trap: watching someone use AI to build something fast…and assuming that speed is always achievable and always sufficient.

The instructional designer who built that effective eLearning quickly? They already had the content. They already knew the audience. They’d already done the thinking. They were experienced with the tool. The fast part came after all the slow, important work had been done. That context matters enormously, and it’s almost always missing from the “AI is so fast” story.

When organizations start believing that AI alone can drive training creation—that you can skip the pre-work and go straight to production—you end up with training that looks complete but doesn’t change behavior. It’s a polished product built on an incomplete foundation. And in nonprofit settings, where every dollar and every staff hour matters, that’s a waste of resources.

The real risk isn’t that AI is bad. It’s that speed becomes the metric when behavior change is the actual goal.

How Effective Learning Designers Spend Their Time

Here’s a useful way to think about where your training time actually goes. Imagine a pie chart. It shows the total time it takes to create effective training, and it’s divided into four slices. Most conversations about AI and training efficiency are focused on just one of them.

Building Trust and Credibility with Your Nonprofit Stakeholders

This is the slice that rarely shows up on a project plan, and it’s one of the most important. If your stakeholders don’t trust your expertise, you’ll stay in order-taker mode indefinitely. You’ll continue to be handed a list of topics and told to build something, without any opportunity to ask the questions that would make that training actually work.

Building trust takes time. It means showing up consistently as someone who understands the organization’s challenges, not just someone who produces learning content. It means being in conversations early so that when the training request arrives, you’re already positioned as a partner rather than a training factory.

When you have that trust, everything changes. Stakeholders stop handing you a solution (“build me this training”) and start bringing you a problem (“we’re seeing this gap; can you help?”). That shift doesn’t happen because you produce faster. It happens because you’ve invested in the relationship. And that takes time.

Understanding Your Training Audience and Their Context

This is another slice that AI cannot do for you—at least not yet or not totally, and not without significant human direction. Understanding your audience means understanding what their day actually looks like. Things like:

What pressures are they under?
What already prevents them from doing the thing you’re training them to do?
What support exists, and what’s missing?

In nonprofit organizations, this is especially critical because your staff are often stretched across multiple roles, navigating complex community relationships, and working against mission-driven urgency that makes it hard to slow down for anything, including training. If you don’t understand that context, your training won’t fit into it. It will feel disconnected from the reality your learners are living.

This is your needs analysis. It takes time. It involves conversations, observation, and honest questions about what’s actually going on. But it is the foundation on which everything else is built. Skip it, and you might build something fast, but you won’t build something that works.

Creating the Training (Design and Development)

This is the slice of your pie where AI genuinely helps and where it will continue to help more over time. Writing objectives, building activities, creating resources, mapping the learning journey: these are production tasks that AI can accelerate significantly, especially when the pre-work has been done.

Think of this as the smallest slice of the future pie. Not because it doesn’t matter, but because it’s the part of the process that is genuinely getting faster. The goal isn’t to resist that. It’s to make sure the time you save here gets reinvested into the slices that AI can’t replace.

Testing, Iteration, and Measuring Training Results

This is the slice most L&D professionals under-invest in, and it’s the one that makes everything else defensible. Launching training is not the finish line. Knowing whether it worked, and adjusting when it didn’t, is.

This is where you collect data. This is where you can point to behavior change or identify where it’s not happening yet. This is where you build the credibility that earns you more influence in the next conversation with leadership. A completion rate tells you someone clicked through the training. Measurement tells you whether anything changed because of it.

When you invest in this slice, you stop being a training factory and start being a true partner. That’s a fundamentally different position to be in, and it’s earned through the willingness to ask hard questions about impact, not just output.

Scenario:

Imagine a program director comes to you needing onboarding for a cohort of new case managers starting in three weeks. Leadership is watching. Turnover has been a problem, and everyone agrees the onboarding experience needs to improve. You have access to AI tools and a library of existing content.

You could open an AI tool right now and have a 90-minute onboarding module ready by the end of the day. It would look professional. It would cover the topics. And it would almost certainly miss the real problem entirely. Why? Because the reason case managers are leaving at month three isn’t that they didn’t receive information. It’s that they didn’t feel equipped for the emotional weight of the work, or they didn’t know who to turn to when things got hard.

That insight only comes from talking to current case managers, supervisors, and/or to people who left. It comes from the needs analysis. AI didn’t do that work. And if we skip the needs analysis, we fall into this trap of faster solutions that aren’t actually effective.

How to Help Stakeholders Understand that Good Training Takes Time

A lot of the pressure to move faster on training doesn’t come from bad intentions. It comes from leaders who genuinely don’t understand what good training design requires. They see AI produce something in minutes and assume the bottleneck has been removed.

Your job is to help them understand what is actually required to create change. When you can articulate what goes into the pie, and why each slice matters for results, you change the conversation from “why does this take so long?” to “what do we need to protect in this process?”

Rushing the process creates something that looks complete but produces no change (with or without AI). And when that training fails to move the needle, who gets held accountable? Usually you. That means the argument for taking time upfront isn’t just about good design. It’s about protecting your credibility and your capacity to do the work well.

Taking time upfront also reduces rework. Every hour spent on a thoughtful needs analysis is an hour that could prevent three hours of rebuilding something that didn’t land. That’s a business case leadership can actually hear.

Using AI Effectively in Learning Design

Once you have the foundation—the stakeholder relationship, the audience understanding, the clear behavioral outcome—AI is genuinely powerful.

A few ways we can use AI more effective as learning designers:

Brainstorm learning activities aligned to your objective.
Draft scenario-based content for review and refinement.
Identify existing content that could be repurposed rather than rebuilt from scratch.
Accelerate the production of materials once the design is solid.
Help format, edit, and structure content you’ve already developed.

The design and development slice of the pie is getting smaller with AI’s help. The time you save there should flow back into the work that actually determines whether training succeeds: understanding your audience, building stakeholder trust, and measuring what changes.

To learn more about why good training takes time, tune into episode 184 of the Learning for Good podcast.

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