The Barrier to AI Isn’t the Tools

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AI Adoption for Nonprofits with Mimi Yeh

Carolyn Woodard talks with Mimi Yeh, engagement director at PTKO, about what’s actually getting in the way of meaningful AI adoption at nonprofits, and what organizations can do about it. Mimi focuses on strategy, human capital, and change management for mission-driven organizations, and she brings a grounded, people-first perspective to a conversation that too often skips straight to tools and tech stacks.

The conversation starts with an observation that will resonate with many nonprofit leaders: organizations are not short on AI awareness right now. If anything, there is too much of it. Leaders are being told that AI is transformative and urgent, while staff are left wondering what they are actually supposed to do and whether they are even allowed to try.

That gap, between ambition at the top and readiness on the ground, is where most AI initiatives stall. Mimi’s core argument is that AI is not a tools problem. It is an adoption and alignment challenge, and organizations that treat it that way are the ones making real progress.

Key Takeaways

Using AI at your nonprofit organization requires far more than feeding an AI some documents. Mimi notes that the hard part is not the work the AI does. It is the voice, the personality, the values that need to be built in, and that requires input from people who live and breathe the organization’s mission. If you want AI to reflect your culture, the humans in your organization have to be engaged and involved in how you implement AI.

Resources

PTKO Consulting

Presenters

Mimi Yeh


Mimi Yeh is an Engagement Director at PTKO, a strategy consulting firm serving mission-driven organizations in the Metro DC area, New York, and beyond. With three decades of experience spanning the nonprofit, federal government, and global consulting sectors, Mimi specializes in human capital, change management, and organizational performance. Her background includes serving as Vice President of Human Capital at Connected DMV, a regional nonprofit in the DC metro area, and as Managing Director at Accenture.

Mimi’s work focuses on the people side of complex, strategic initiatives: helping organizations build the internal alignment, governance structures, and adoption practices that make new capabilities actually stick. She leads PTKO’s change management and strategy engagements, and brings a grounded, mission-aware perspective to questions about how technology, including AI, gets implemented in ways that reflect an organization’s values and culture. Mimi graduated from Tufts University and holds an MBA from Georgetown University’s McDonough School of Business.

Carolyn Woodard


Carolyn Woodard is currently head of Marketing and Outreach at Community IT Innovators. She has served many roles at Community IT, from client to project manager to marketing. With over twenty years of experience in the nonprofit world, including as a nonprofit technology project manager and Director of IT at both large and small organizations, Carolyn knows the frustrations and delights of working with technology professionals, accidental techies, executives, and staff to deliver your organization’s mission and keep your IT infrastructure operating. She has a master’s degree in Nonprofit Management from Johns Hopkins University and received her undergraduate degree in English Literature from Williams College.

She was glad to have this conversation with Mimi Yeh from PTKO Consulting about Nonprofit AI Management Advice.


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Transcript

Carolyn Woodard: Welcome everyone to the Community IT Innovators Technology Topics podcast. I’m Carolyn Woodard, your host, and I’m very excited today to be talking with Mimi Yeh from PTKO. So, Mimi, would you like to introduce yourself?

Mimi Yeh: Yes. Thank you, Carolyn. And thank you for having me on the podcast. I am an engagement director at PTKO. We are a small consulting firm. We have presence in the Metro DC area, as well as in New York, and one staff member out all by themselves on the West Coast, as well as some individuals in North Carolina.

We are a strategy consulting firm, and we do also help out with some technical aspects of work that is important to nonprofits and mission-driven organizations. That can include things like CRM selection or other software selection, building out roadmaps that help to improve the tech stack that organizations use as an enabler of achieving their mission and their vision and their goals.

I specifically focus on more strategy, human capital, and change management efforts. The way that I usually describe it to people is: process and technology are incredibly important; they are huge elements of what make an organization work well. But until we are all taken over by machines and robots, we have to have the human aspect considered just as much as the other two. So I really focus on the people aspect of changes and adoption of new capabilities and new technologies.

The Gap Between Leadership Ambition and Staff Readiness

Carolyn Woodard: And I think in the nonprofit sphere and in philanthropy, it’s all about people. We’re people organizations and we care about our communities and the people in our communities, and we have all these relationships and long-standing relationships. So I’m really interested to hear your experience.

Can you give us a little bit of an overview on AI and what you’re seeing?

Mimi Yeh: Yeah, and I’m really glad that we’re having this conversation because so far, what we’re seeing is that AI can go in a hundred different directions really quickly. What we’ve been observing in the past, I would say eight months, but maybe even longer than that, with AI really making a more specific and significant presence in nonprofits and organizations, is that organizations are not lacking an awareness of AI at this point. If anything, there’s too much awareness going on.

I think that a lot of leaders are hearing things like AI is transformative and you need a strategy and you need to act now. And then staff for their part are sitting there thinking, well, wait, what am I actually supposed to do with this? And am I even allowed to use it?

So what’s happening, Carolyn, is that there’s this growing gap between leadership ambition and staff readiness. And when that happens, that’s usually when new and exciting initiatives can tend to stall. Because from the way that we look at things, AI really isn’t a tools problem or a tools challenge. It is an adoption and an alignment challenge. It’s really about having a shared understanding of both the problem statement, but also the opportunity.

I think it’s about having some clear guardrails for the organization about what AI means to them and how they see AI as an enabler, not the final end-all be-all, but as an enabler. And then it’s really about having confidence across the organization about what to use AI for, how to use it, and that it’s okay to experiment and try out some new things.

I think the teams that are making progress are not necessarily the ones that waited and picked out the best tools. Rather, they’re the ones that created a safe and structured way for people to engage with AI in a space where it makes sense for AI to play a role.

Carolyn Woodard: It’s definitely what we’re hearing from a lot of our clients as well. And I think this gap is very deceptive, because we’re finding that AI requires a lot of change management. But it was sold to us all as: you just ask it a question. There’s no upfront anything. You already have it in your tool and you just ask it and it tells you. So it’s kind of wormed its way in as an easy solution, but the amount of change management around it to be really intentional about it is just kind of invisible. So I love that you’re bringing that up.

Mimi Yeh: Yeah, thank you. And I think that’s right. With many new products, they have this wrapper around them that describes things to nonprofit leaders and different individuals as the easy button that you push and it’s going to fix all your problems, and you don’t have to worry about anything.

We’ve seen this with other technologies in the past, and we’ve also seen how difficult and complex it is to manage and operate in a space that doesn’t have some basic governance: some standards, maybe even some ethics and policies that are in place. It’s not intended to over-police the use of AI within your organization, but it is really intended, again, to give staff members and leaders a common understanding of what AI means to get your work done and how to engage with AI in a way that’s going to be consistent with the organization’s values.

Addressing Skepticism: Environment, Job Loss, and Mission Alignment

Mimi Yeh: One of the consistent messages or pushback areas around AI that I hear from a lot of nonprofits is: oh, it’s so detrimental to the environment, or it has such a negative climate impact. How can I feel okay using a tool that has such a damaging effect on the climate and on the world that we live in?

And again, it comes back to that bigger question of: let’s not treat AI as a separate standalone item that has to face a different set of scrutiny and criteria. It should be the same consistent criteria and evaluation factors that are used when you’re looking at everything in your tech stack, or even everything in the way that we operate and live and breathe. Do we take planes instead of cars? Do we eat beef instead of chicken? There’s no intention here to say that one item is good and one item is bad. It’s more to say, let’s take a macro view at all of those different choices that we make. Where’s the right balance where maybe there’s an impact on climate, and there is also an end result that significantly improves efficiency, operations, effectiveness, or really the ability for that mission-driven organization to achieve the outcomes and the results that they hope to.

Carolyn Woodard: Yeah, someone said it to me this way: as an organization, have you been concerned about how much electricity you use in the sense that, like, oh, we’re just not going to use electricity today? It’s one of those things that’s all around us and it’s in everything, and you can’t operate as a modern functioning nonprofit without having lights on.

Mimi Yeh: Yes.

Carolyn Woodard: So I think that was a good analogy because I think AI is going to be in a similar place, where it’s just going to be everywhere, or are you going to go to the well to get water to wash your clothes? We all have pipes and plumbing. It’s kind of similar to that. I think AI is getting into that role. It’s not there yet, but it’s getting there.

Mimi Yeh: Yeah, yes, absolutely.

Carolyn Woodard: So are there other reasons that nonprofits are skeptical about AI aside from the environmental one, which is really landing with people? I mean, there are lots of things to be skeptical about, but that one seems to be taking a big part of people’s mental space.

Mimi Yeh: Yes. I do hear a lot of, I think there’s this kind of blend of skepticism and concern. The sustainability and the energy and environmental impact is definitely pretty close to the top of the list.

And then the other concern that comes across is this idea of job replacement: this idea that AI will eventually create job loss and that will have such a negative impact on communities, on the nonprofit industry as a whole, or the philanthropic field as a whole.

I think that skepticism sounds negative, but it can be looked at in a slightly positive way, only in that it helps to highlight the kinds of questions that organizations should be thinking about and should have a plan for.

When people ask about equity or the long-term consequences, they’re really thinking about things like: what happens to our data? Is using AI in alignment with our mission? Are there ways in which the use of AI might create some harm?

Those are all legitimate questions. They’re also great feeders and starting points for the whole structure and governance around AI that we were talking about earlier. They’re great ways to shape out the policy that organizations would like to have, the practices and guidelines they’d like to put in place, and maybe some security measures about what type of data is allowed to be fed into an AI tool or bot. What kinds of things might this organization decide we will never put in there?

And when we think about alignment to the mission, it can get into things like what’s acceptable use of AI versus what might we as an organization decide is not acceptable for us. I think at an event that you and I attended, we listened to a marketing and communications lead for a nonprofit organization. He said that one of the policies his organization has is they will never use AI to generate images that tie to their mission that would get displayed publicly on their website. And I think that’s a great example of identifying the guardrails around AI use. It can do all kinds of things: you have Nano Banana, you’ve got all kinds of technical tools that have gotten much, much better at generating very lifelike images.

And that director understood that to generate an artificial image of a mission-driven event or population or constituency is something that carries so much risk and can have such a bad impact on that organization’s brand and value and identity, that they made very deliberate steps to say that is not okay.

I think it’s by addressing a lot of those big questions that hopefully that skepticism starts to lower and people get more excited about the productive and really valuable use of AI.

Getting Started: Governance, Risk Management, and Practical Guidance

Carolyn Woodard: If you have leaders who are still on that skepticism side and maybe more reluctant: I agree with you, caution can be a good thing and being fully informed is a very good value to have, so skepticism can be coupled with those values.

But if you have someone who is really reluctant, do you have practical ways to get started? What kind of advice do you give leaders who just don’t even know where to start with governance or policy?

Mimi Yeh: This is going to sound trite, but I do think it’s really the right answer. Thinking about governance for AI ought not to be very different from thinking about governance for other technical tools and other solution sets within your organization. The reason that the organization exists is to achieve a particular mission or to support a particular constituency.

Thinking about it from that standpoint and understanding the ways in which different tools should be enablers of getting things done and contributors: AI is never going to be the silver bullet, or it should not be the silver bullet, nor should any other major technology out there. It is the direct services, the processes, the advocacy, the ability to convene a strong network and community of individuals who can all contribute to an outcome. That is the nature, I think, of many nonprofits’ work.

So creating guidelines around how to get that work done should be the central focus and element of an organization. And then it’s really about pressure-testing the different possible uses of AI against those existing guidelines and guardrails. The solutions and ideas that organizations already came up with when it comes to big data, when it comes to LLMs in general, and even when it comes to using a CRM, there are particular policies around acceptable uses of different technologies and tools. AI really should not be that much different.

Once an organization has a good sense of how they want to use AI and for what purposes, and then thinking through what are the risks that might be associated with each one of those, that’s a good starting point to building out the guidelines and the SOPs and the policies of use.

Carolyn Woodard: Yeah. So whatever process you would use for other types of governance, whether you talk to your board, have a committee, make a draft and share it, or however you get started, just get started.

Mimi Yeh: Yeah. And I think that when we link back to our earlier conversation around skepticism in the use of AI, a lot of times that skepticism comes from a place of risk management. It comes from a place of concern that AI, my goodness, it’s so powerful, it could do all these things.

I think a really useful practice is to list out all of the risks and concerns that people have today, rather than shutting them down, rather than saying, well, that could never happen. I mean, it could happen. So let’s list all those risks out first. And then, similar to many other organizations, you would evaluate what is the impact of each of those risks if they came to realization.

If it transitioned from a risk to an issue, how big is that impact going to be, whether it’s on the organization, on the constituencies, on partners, on whoever it is? What’s the impact and what’s the probability? What’s the likelihood that something like that could happen? And then I would probably take the top risk items that have high probability and high impact, or medium probability and medium impact, and start thinking through what type of governance structure, what type of policies, what type of guardrails do we want to put in place to help mitigate or reduce the likelihood that that risk could happen.

Funders, Capacity Building, and the Human in the Loop

Carolyn Woodard: I have another question for you, related to this, which is: what are you seeing from funders, or what are you hearing from your clients that they’re hearing from their funders?

Because I’m getting this sense, you talked earlier about the silver bullet and some of these unrealistic expectations of AI as a transformative agent. And I am hearing that from some clients and some funders: there are grant proposals out now, requests for proposals around how are you going to use AI to do your mission completely differently.

Mimi Yeh: Yeah.

Carolyn Woodard: And I’m wondering, are you hearing that too? And how are you advising clients to interact with their funders around this?

Mimi Yeh: Yeah, we are hearing that a fair amount. And I understand and can empathize with the point of view of the funders. I think that they’re looking for ways to exponentially or significantly help nonprofits achieve their mission.

I do think that they can consider AI as something like a steroid shot that just suddenly rockets that nonprofit into the high-performing space, or just gives them something that turbocharges everything that they do.

I think that there’s a danger of unintentionally creating pressure on that nonprofit. It’s like they’re being told: you should be using AI, you should be innovating, why aren’t you doing this, everybody else is doing this.

And I have to say it: without sounding like a broken record, it comes back to the fact that if you don’t have the right support structure in place, it can lead to some rushed decisions being made, it can lead to unclear policies and therefore people unintentionally going rogue or taking on practices that might be contradictory to what leadership had hoped, or honestly, people can just feel overwhelmed by it all.

I think that a more helpful role that funders could play is providing the ability and the resources to help organizations define their basic governance. There is a space to actually use AI, and we are seeing it with one of our clients: supporting experimentation, not perfection, not getting everything right in stage one, but trying out use of AI and understanding its potential in some structured use cases.

And by doing that level of experimenting and piloting, working alongside that tool development to also think about what is our position on AI, what is our point of view around AI specifically for our organization. And then I do think that it’s just as much about funding capacity building as it is about funding the tool.

So hopefully funders will understand and will support the nonprofit’s ability to put together the adoption structure, to maybe provide some investment in training, but not in training on how to use AI as a tool, but how to understand AI as an embedded part of the organization and the way that it can operate. AI adoption is a capability. It’s not a one-time project. It’s going to keep happening, it’s going to keep evolving, and it’s going to keep needing support and resources to make it happen.

Carolyn Woodard: Yeah. I heard it said that – I’m hoping also that there’ll be more funding toward AI literacy because, as you said, making good decisions requires knowing what you’re making a decision about.

Mimi Yeh: Right. Yes. That’s right. And one of the clients I was referring to earlier, we are helping them to build an AI tool that can be used internally by the staff in their organization.

There’s more to creating an AI tool than giving it a corpus of hundreds and hundreds of artifacts and documents. There’s a voice to that AI tool. There is a personality to that AI tool. And that’s the part that really requires a lot of thinking, and it requires the input of not just subject matter experts, but people who are living and breathing the work of that organization and people who understand how hard and complex it can be to do that nonprofit’s work.

So when people are pinning their hopes on AI to fix all of that, it can help with some of it, but it depends so much on the prompts, the thought process, the inputs that are used, and then the whole human-in-the-loop experience of creating that generative and eventually stronger and more closely aligned tool, closer to the culture and the ethos and the values that you would like everybody in your organization to know and to build upon.

If you want to use AI for that, it can do it, but it’s going to need a whole lot of very structured input and very clear guidance. And that’s something that the human has to put in there.

Carolyn Woodard: I love it. Thank you so much for wrapping up how that human in the loop ideally works. I just want to thank you so much for your time today, Mimi. This was just a wonderful conversation, and I really appreciate being able to talk with you about it.

Mimi Yeh: Thank you, Carolyn. I appreciate it too.

As advocates for using technology to work smarter, we’re practicing what we recommend. This transcript was drafted with the assistance of AI, and is not a verbatim transcript. The content was edited for clarity, and was reviewed, edited, and finalized by a human editor to ensure accuracy and relevance.

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