I made generative AI my intern. You can, too. 

Most people talk about generative AI tools like it’s a shortcut. A magic wand. A cheat code. Expectations are high, leaving people disappointed when the tool doesn’t instantly deliver what they hoped for. But I see it the way I see every great early-career team member I’ve ever worked with: smart, eager, capable…and completely unaware of how we do things around here. 

That’s why I started treating generative AI like my intern. Not because I want to hand off my thinking or outsource judgement, but because, when used thoughtfully, AI becomes what every intern hopes to be: a fast learner, a reliable collaborator, and a practical way to get more done without adding more to your team’s plate.  

In my role leading systems design at a boutique consulting firm that partners with mission-driven organizations, I see people struggling with generative AI because they expect it to behave like a search engine. They toss in a half-formed prompt, get a half-formed response, and decide the technology “just doesn’t sound human.” 

But you wouldn’t give a brand-new intern one sentence of direction and expect a perfect deliverable, so you shouldn’t expect that from AI, either. 

Mission-driven organizations and nonprofits are dealing with multiple constraints and pressures that would break most corporate teams. These organizations are doing high-impact work with limited staff, limited dollars, and limited time. Their work is as crucial as their capacity is fragile. 

Generative AI tools like ChatGPT can help give them their most valuable resource back: time. But users need to know how to harness its potential. 

’Onboard’ generative AI tools like a staff member 

An intern brings enthusiasm, not institutional knowledge. They don’t know your CEO’s communication quirks, your brand voice, or which stakeholders care about texture vs. data. A good manager will teach them; it’s no different with generative AI tools. 

Organizations looking to leverage AI should treat the process with the same level of detail as onboarding a staff member. Share reference documents, brand guidelines, past deliverables, and preferred formats. Don’t assume these tools know what “good” looks like—tell them. (And yes, this requires the same enterprise-grade, privacy-protected systems that personnel onboarding demands). 

When a generative AI tool makes a mistake, don’t silently fix it and rage against the technology’s limitations. Take the time to highlight what didn’t work, explain the reasoning, and give more direction. When it comes to AI, feedback isn’t optional—it’s the whole point. 

It’s also the piece that most users skip. Explaining to AI why you made a decision doesn’t just train the system; it trains you as a user, forcing you to give clearer prompts, guidelines, and feedback. Recent studies from MIT and Microsoft have concluded that generative AI is making us lazy and lessening our cognitive abilities, in part because those tools don’t require users to engage deeply with the material, reflect, or revise. 

Strategy, judgment, creativity, and nuance are human skills, sharpened by experience and empathy—skills that can’t be replaced with technology. 

Delegate tasks to generative AI tools so you can focus on mission

Delegating tasks via these tools enables nonprofits and mission-driven organizations to work quickly and efficiently. These often-strapped teams aren’t looking to replace real people with AI; they’re looking for a way to make the workload humanly possible. Generative AI tools can take pressure off overstretched teams without compromising quality or mission. 

One large nonprofit our team at FLEX Partners works with has leveraged AI to manage logistics for events, mapping production schedules for film interviews with arrival and departure times for hundreds of attendees and cross-referencing multiday runs of shows. What would typically take staff hours of tedious spreadsheet work is produced in seconds, giving event organizers more time to focus on curating experiences. 

Another research-based nonprofit has trained a generative AI tool to crawl through thousands of regulations and data points on a daily basis, flagging potential hits for researchers who can do a deeper dive, saving precious staff time. It’s the type of work that would have been a research associate’s full time job; instead, researchers can hone their skills in deeper analysis and produce more meaningful work. 

In both cases, generative AI didn’t change what these organizations were doing—it simply reduced the amount of time and energy required to do it. And in environments where teams are already operating at maximum capacity, that matters. 

By thoughtfully training generative AI tools and delegating tasks that don’t require human skills, nonprofits can free up staff time to focus on what matter most: mission-driven work. 

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