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In part 1, Mimi, George and Carolyn outline an AI maturity model that nonprofits can use to prompt reflection on where their organization falls on the model and where they want to get to. Mimi and George describe the phases of ad hoc, experimental, systematic, strategic, and pioneering. In pt 2, Mimi and George discuss intentional ways to move beyond experimental, and take audience Q&A.
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Your nonprofit has done early work on AI. You have a policy, a pilot program, an AI champion, or a small group of staff who are actively using AI tools.
How does AI go from experimental to being quiet, boring, and everywhere?
How do you move to AI that is genuinely embedded in how your organization works? What comes next?
This webinar is for nonprofit staff and leaders who are ready to go beyond the first steps. If you are still exploring whether AI is right for your organization, we have resources that can help you with that decision. In this session we talk about moving forward and focus on what successful, sustainable AI adoption looks like in practice.
Join Community IT with Mimi Yeh, PTKO Consulting, and George Danilovics of AHIP for a webinar on using an AI maturity model at your nonprofit. Mimi and George will walk through an AI maturity model designed to meet nonprofits where they are, including what distinguishes organizations at each stage and how teams move along that spectrum at their own pace.
The session will address the shift from experimentation to operationalization: what it takes to scale AI use across departments, how to build baseline AI competencies across your staff, and what it looks like when AI tools are as routine as sending an email or using a spreadsheet.
If your organization is still early in your AI journey, our Mission-Aligned AI Adoption Model for Nonprofits is a good place to check before joining us.
Community IT is proudly vendor-agnostic, and our webinars cover a range of topics and discussions. Webinars are never a sales pitch, always a way to share our knowledge with our community.
The session will include time for a dedicated Q&A. You are invited to submit your specific questions regarding moving from AI pilot to AI practice for nonprofits when you register.
As with all our webinars, this presentation is appropriate for an audience of varied IT experience.
Mimi Yeh is Engagement Director at PTKO. She is a senior strategy executive with 30 years of experience, with a focus on human performance, change management, stakeholder engagement, and organizational strategy design. She brings leadership experience in consulting and nonprofit sectors, blending high performance with stewardship and servant leadership.
George Danilovics serves as the Vice President of Information Technology at AHIP. He brings over 25 years of experience directing technology strategy and operations. Prior to his current role, he led technology teams and digital transformation efforts for both U.S.-based and international membership associations. He is currently focused on the strategic adoption of artificial intelligence while modernizing technology operations to ensure they fully support the organizational mission.
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 five 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 loved talking with Mimi and George about an AI maturity model for nonprofits, and this presentation answers a lot of questions we are getting from clients who are ready to take those next steps on their AI journeys.
This is one piece of a larger picture. Explore our AI for Nonprofits guide for the full context.
Carolyn Woodard: Welcome everyone to the Community IT Innovators webinar. This is the AI Maturity Model for Nonprofits: How AI Becomes Quiet, Boring, and Everywhere. We have experts from PTKO and AHIP with us today. If your nonprofit has gotten into AI and you have a policy, you’ve done a pilot program, you have an AI champion, a small group of staff who are actively using AI tools, you’re probably wondering what comes next. So, how do you move from that experimentation phase to genuinely embedding AI in how your organization works?
Today we’re going to talk to Mimi Yeh from PTKO and George Danilovics from AHIP about what best practices they can share with us about finding your place on that AI maturity model and moving to where you want to be on that model.
My name is Carolyn Woodard. I’m the outreach director for Community IT. I’ll be the moderator today. I’m very happy to hear from our experts, but first I want to go over our learning objectives.
Today we’re going to focus on these themes: What is the AI maturity model, and where does your nonprofit fit? How do you build AI as a standard core competency across your workforce? And what does strategic AI implementation look like? What are the emerging best practices?
And now I’d like to let Mimi and George introduce themselves. Mimi?
Mimi Yeh: Thank you so much, Carolyn, and welcome to everybody. Thank you for joining us. I’m Mimi Yeh, and I’m an engagement director at PTKO. Most of my work focuses on helping organizations improve how they work, and I typically do that through strategy, organizational change management, and technology adoption. AI is probably the newest example of a challenge that looks on the surface like it’s all about technology, but at the heart of it, it’s really about people, processes, and building new capabilities. So thank you for joining us.
George Danilovics: Hello, everyone. My name is George Danilovics. Thank you all for joining this afternoon. I am the VP of technology at AHIP. In addition to leading the technology team, I’ve been guiding AHIP along our AI adoption journey.
Mimi Yeh: I’m going to tell you a little bit about what we do at PTKO. One of the reasons I really enjoy co-presenting with George is because we come at AI from complementary perspectives and points of view. As George just mentioned, he leads technology efforts and blends strategic direction from within an organization.
At PTKO, I work alongside nonprofits and other organizations to help them figure out how to adopt AI across their organization. That comes from leadership and governance to training and change management and then incorporating it into day-to-day workflows. That means we get to see the patterns that emerge from a lot of different organizations. And it’s those patterns that shaped today’s discussion.
We’re also really fortunate to work with partners like Community IT, which you’ll hear about a little bit more from Carolyn.
Carolyn Woodard: Before we get started with the presentation, if you’re not familiar with Community IT, I want to tell you just a little bit more about us. We are a 100% employee-owned managed services provider. We provide outsourced IT support and work exclusively with nonprofit organizations. Our mission is to help nonprofits accomplish their missions through the effective use of technology. We are big fans of what well-managed IT can do for your nonprofit.
We serve nonprofits across the United States. We’ve been doing this for 25 years. We are technology experts and are consistently given an MSP 501 recognition for being a top MSP, which is an honor we have just received again in 2026, and we are the only MSP on their list serving nonprofits exclusively.
I want to remind everyone that for these presentations, we are vendor agnostic. We only make recommendations to our clients based on their specific business needs. We never try to get a client into a product because we get an incentive or a benefit from that.
We do consider ourselves a best of breed IT provider. It’s our job to know the landscape, the tools that are available, that are reputable and widely used. And we make recommendations on that basis for our clients based on their business needs, priorities, and budget.
We did get a lot of good questions at registration. We’re going to try to answer as many of those as we can. Anything we can’t get to, you can join us and our experts today over on our community on Reddit at r/nonprofitITmanagement. I’ll share that link with you in the chat. We’re going to continue to answer some questions over there after this webinar until about 4:30 Eastern. And then from Community IT, we pop back into our Reddit community every so often, so if you have more questions, go ahead and ask them there and we’ll try to answer them.
And a little bit more about us. Our mission is to create value for the nonprofit sector through well-managed IT. We also identify four key values as employee owners that define our company: trust, knowledge, service, and balance. We seek always to treat people with respect and fairness, to empower our staff, clients, and sector to understand and use technology effectively including AI where appropriate, to be helpful with our talents, and we recognize that the health of our communities is vital to our well-being and that work is only part of our lives.
And with that, I’m going to turn it over to George and Mimi for the presentation.
George Danilovics: Thanks, Carolyn. Let’s put our whole webinar on one slide in one line: the more successful AI adoption becomes, the more boring it gets.
Think about what AI looks like in most organizations right now. It’s loud. There are pilots, there’s demos, there’s lunch and learns, there’s brown bags, there’s a champion, or maybe champions in your organization, forwarding articles around and best practices and new features. That energy is real and it matters. But it’s also a sign that AI is still special. It’s an event.
Now think about the technologies that actually run your organization: your email, your calendar, your spreadsheets in Excel. Nobody demos them, nobody champions them. They’re quiet, they’re routine, they’re load-bearing, they’re just there. That’s what mature technology looks like, and that’s the direction that AI is heading.
In today’s talk, we’re going to talk about that journey from the loud AI that we have today to the boring AI that is coming.
One caveat to keep in mind as we go through this conversation: boring doesn’t mean it’s unwatched or untethered or set to go run free. We’re not saying in any way to just close your eyes and let AI go rogue and do whatever it wants.
Carolyn Woodard: Yeah, that’s a good thing to say. Because there are some parts of AI that can in fact do things unregulated. So we always want to keep an eye on everything.
All right, so we are going to do our first poll. This poll is about where is your organization on AI now? The options are: you have no formal AI activity yet; you have a pilot or pilots underway; you have AI in core workflows and defined goals; or AI is shaping decisions and strategy at your organization. And of course you have the option that it’s not really applicable.
Mimi, can you read those results?
Mimi Yeh: Yeah, absolutely. It looks like a good majority, over half of the respondents, say that they’ve got AI pilots underway. That’s followed by about 28% of respondents saying that there’s no formal AI activity yet. That might mean that there’s some experimentation and some one-offs, but nothing that’s been structured into the organization. Then we’ve got a little bit of a tie between AI shaping decisions and strategy, which is great. That’s over on the far end of the spectrum. There’s an equal number of respondents saying not applicable.
It does seem to follow a bit of a bell curve shape that we typically see in these types of situations, followed last but not least by AI in core workflows and defined goals. A good scattering of different spaces and places within their AI journey. That is so interesting.
Carolyn Woodard: And George, is that what you expected to see? What is the state of nonprofit adoption of AI generally right now?
George Danilovics: The results we got from the folks on this webinar fit what we’re seeing others report in the state of the industry and what nonprofits and associations are doing with AI. What I recently read in the Nonprofit Resource Hub is that we’re moving beyond that experimentation phase. We had a good chunk of people already doing the pilots and thinking about what comes next, what’s beyond the pilot, which is great, you’re in the right spot.
The headline of all of this is that experimentation is now normal. A few years ago, we were having conversations about whether we should be using AI. Is AI coming for our work? Is this something we should be approaching or just say no?
That debate has largely been resolved, and the association and nonprofit space is moving forward. Your peers are experimenting, many hopefully have policies in place. The real question now isn’t whether to move forward, but how and how widely.
I mentioned the Nonprofit Resource Hub. Earlier this year, they put out a report where 80% of nonprofits are already using some form of AI. 80%. And what they’re seeing is operational staff are saving 15 to 20 hours a week on average by using AI. Donations are seeing a 20 to 30% increase when fundraisers are leveraging AI to personalize campaigns and outreach.
But on the flip side of all that great news, 90% of nonprofit professionals still feel unprepared to fully leverage AI. So why are we talking about this? This is your permission to move forward, your permission to start doing AI and learning about AI. But don’t feel the pressure to do something hastily.
If you’re still having the “I don’t know where to begin” conversation, it’s not too late. The late starters get to inherit better tools, they get guidance from folks like me who have already figured out what to do and what not to do. And you get to learn from those lessons.
Today we’re going to spend some time talking about what comes after that pilot, which is where a lot of you are today.
Mimi Yeh: There’s good guidance out there for thinking about adopting AI, starting from a foundational place, including Community IT’s own mission-aligned AI adoption model. As Carolyn mentioned, that’ll be part of the set of materials that you’ll see in your follow-up email.
Eventually, a lot of leaders ask this particular question, and most of the time there’s usually no written guidance on how to navigate it: how do we move beyond a handful of enthusiastic users? How do we move past a couple of pilots here and there to make this something that all of my staff know how to do?
For the folks who responded earlier in the poll that said they’ve embedded AI into their day-to-day operations, we’d love to see in the chat window how you’ve done that and where some of your successes have been, and maybe some of your lessons learned.
That’s really what today’s webinar is about. It’s less about getting started because, as we saw, many of you have already gotten started. It’s really about building the capacity to move past the starting line of AI.
Here’s a map for the rest of our conversation today, and it covers the five stages of AI maturity, which we’ve adapted from Cognitive Path research. It ranges from ad hoc through pioneering. I’m going to give you a quick explanation of each of those stages right now.
In the ad hoc stage, that’s when individuals are quietly experimenting on their own. The experimental stage is when the organization is catching up with pilots and policies being distributed throughout the entity, not just in little pockets here and there. Systematic is when AI is integrated into everyday workflows with real accountability. Strategic is when AI is used to help inform and make organizational decisions that matter. And then at the end, pioneering, that’s when AI is enabling work that just wasn’t possible before. We’ll spend some time in each one.
George Danilovics: Before we do that, look at those two curves at the top, because this picture is pretty important. That dashed line is novelty, it’s the buzz, it’s the excitement, it’s the demos, and it peaks very early. Many of you right now in that experimental phase are hopefully seeing all that excitement, but it eventually drains away.
The solid line is business value, and it runs in the opposite direction. It’s very low in those early stages, but it begins to compound as you move to the right. You’ve probably seen similar curves from other organizations. The Gartner hype cycle is one that folks are probably pretty familiar with: excitement goes down, value goes up.
But notice where they cross, right in the middle at systemic. This is the stage where AI starts to feel routine. It’s the stage where value begins to take off.
Let’s walk through the first two stages. As we saw in the poll, a good chunk of the folks on this webinar are already in that second stage, that experimental stage. Some of you are still in the ad hoc stage and getting ready to begin your AI journey.
Ad hoc looks like this: you’ve got a couple of people in your organization using ChatGPT, Claude, Copilot. Maybe they got a corporate account. Maybe they’re still using their personal account. We won’t tell anybody. There’s no policy, no objectives, no business cases. But some of this is actually useful. The downside is it’s invisible to leadership, there’s no structure to it, there’s a lot of risk. What if somebody pastes donor data or member data into one of those free tools? Nobody knows what happens, nobody finds out until it matters later.
The big important step to moving from ad hoc to experimental is where the organization begins to catch up. You’ve got a basic policy, some guardrails around what you can and shouldn’t use AI for. And people start to talk about it. They’re using AI, it gets talked about in staff meetings. This energy is normal.
If you’re in this stage right now, you should be encouraging it. Every organization passes through this experimental stage. The mistake is confusing this stage as the destination.
Mimi Yeh: This stage gets its own slide, because it’s one of the most important stages in the maturity model. Systematic is where organizations really start to make that transition from experimenting with AI to building a real organizational capability. It starts becoming a natural part of how the organization operates.
You begin to see AI integrated into core workflows rather than one-off uses here and there. Teams will have defined goals for how they’re using AI. Managers will have a sense of what success in using AI looks like for their particular teams or departments. And this is a really big change, because this is where best practices get documented and shared along with lessons learned, instead of just living in somebody’s notebook or somebody’s chat history.
This is also the stage where organizations should start investing in the infrastructure that supports the adoption of AI: things like training, shared prompt libraries, some governance on how you want to use AI as AI matures and as your organization’s use of AI matures. And then, who are the people responsible for helping to create that AI evolution in your organization over time?
From a change management perspective, it’s the point where AI stops being somebody else’s project or someone else’s pet effort. It really starts becoming part of the organizational DNA.
This is also the place, going back to what George mentioned in the title of our webinar, where AI starts becoming a little boring. We see that in behaviors like people stop announcing that they’re using this cool new AI tool, because now it’s just part of how people are getting their work done. It’s another tool alongside the ones that George mentioned that help people do their jobs more effectively.
George had this great analogy when we were preparing for this: this is the stage when you stop casually dating AI and you just commit to it. Being boring is actually a good sign, because it means you’ve moved beyond curiosity and experimenting. Now you’re starting that journey into sustainable adoption.
But it’s also a point where we want to be mindful that it doesn’t sit in the background so much that people stop sharing their lessons and stop talking with each other and collaborating and co-creating.
The other thing I want to mention is that organizations don’t reach the systematic stage just by buying better technology or better AI tools. You get there intentionally, by building these new habits, these new skills, and these new ways of working together. That’s why we consider systematic to be the pivot point, because that’s where AI shifts from being a technology initiative to being an organizational capability.
Carolyn Woodard: We have a quick question here that might be a good time to ask. Someone asks: can you give an example of having a specific objective versus it being part of the regular workflow?
George Danilovics: An example of a specific objective would be one use case for AI. Someone may have an objective that says, I want to learn prompts to write emails better with AI. You can test that, and you can see people sharing prompt ideas. When AI use with email becomes systemic, it becomes second nature. It becomes the button you push before you click send, just to proof your email and make sure the language is the right tone. It becomes second nature without you thinking about it. That’s systemic.
Carolyn Woodard: Yeah. And there’s another question in there that I’m going to save for the end because it’s a bigger conversation. All right, next slide.
George Danilovics: This slide is actually what the whole webinar grew out of.
Nobody in your organization has an internal email strategy committee. There isn’t a Microsoft Office champion. There’s no pilot program of “are we going to use Outlook?” There’s no lunch and learns on how to attach files to emails. Yet email is completely mission critical. If email went down for a day, your organization would grind to a halt. Email’s quiet, it’s boring, it’s everywhere, it’s essential.
But some of us remember that it always wasn’t that way. Outlook used to be a standalone CD that you would buy at Micro Center or Best Buy. You would install Microsoft Outlook as an application, and this was the first time that you could go from plain text emails to composing emails with color and adding attachments. There was a time when email was new and organizations had to send memos around about what you should or should not say in email.
All that infrastructure and novelty existed at a point in time, but it eventually went away, because Outlook and email just became part of how work is done. Look at job postings today. None of them say “email proficiency,” because it’s assumed that everybody knows how to use email and send attachments.
AI is on that same trajectory, and maybe it’s going to be moving a little faster.
So here’s a question for you, and I’d like you to think about this: what would your organization look like if AI was as unremarkable as email? Who would need to know what? What would people be able to stop doing? What would they be able to start doing? Because that picture, whatever it looks like for you and your organization, is the destination that you’re working towards.
Mimi Yeh: Just as a recap, we’ve gone through the ad hoc stage, the experimental stage, and the systematic stage. Here we are at strategic and pioneering.
This is when AI starts changing not just the way the organization works, but how it thinks. It starts showing up in how leadership makes plans for the future, how budgets get built, how the organization thinks. Notice that there are some people markers on this slide, and these are the ones that matter to me.
Leaders begin making different decisions: maybe more informed, maybe more holistic and reflective of not just internal operations, but external forces that make a difference. Hopefully, teams are working differently together and being a bit more selective in how they use their time, moving away from those big status meetings where we’re regurgitating content to each other. And then managers are hopefully thinking a little bit differently about roles and responsibilities.
Strategy is usually the one that gets the short end of the stick and the least amount of time. Hopefully, AI is going to stop being a tool question and become an organizational design question, something that enables people to make better use of the time they have to really tackle those big, needy problems that are out there.
In that strategic stage, there are also some governance markers: conversations around ethical frameworks, participation in industry conversations about AI, and it shifts from “what tool should we use” to “how should we organize ourselves to get the most value out of this tool.”
That last stage, pioneering, is definitely rarer, and that is reflected in the poll we had at the beginning of our conversation. This is when AI is leading the industry, helping to shape standards, helping to create new services and member value that might not have existed before.
A lot of organizations might never reach that pioneering stage, and most don’t need to. This is not a scenario where the goal is for everybody to be at the end stage of that maturity model. The goal is to be at the stage that works best for your organization.
Mimi Yeh: Let’s do a short reframe in order to remove some anxiety. Not only is the goal not for everybody to reach that pioneering stage, but it’s also important to consider that when people look at models like this, the first question that comes to mind is: where is my organization? Where do I sit on this maturity model? And that might not be the right or the best question to ask.
Something we really advise leaders to think about when it comes to AI is to try not to give your organization a single maturity score, because your organization is not going to use AI in the same way in all of your different departments and teams. Your finance team might be way ahead of your HR team. Marketing might be experimenting; programs might not have started yet. And that is totally normal.
This is an uneven picture of the organization’s progress, but it is not a reflection of failure. And it’s not a reflection of poor planning. That unevenness is actually quite useful. When you map each team separately, you get a diagnostic rather than just a grade. And that diagnostic helps you see exactly where your internal success stories are for AI, and which teams might be able to mentor others in their AI journey based on their experiences and lessons learned.
Your fastest-moving teams are the best trainers and advocates. They are the ones who know AI in the context of what your organization does and the constraints and opportunities facing your organization.
If you were to do a self-evaluation exercise against this maturity model, we would say: resist the urge to produce one number or one stage for the whole organization, and instead produce a map. Because that map is something you can act on with real next steps. I think it is time for another poll.
Carolyn Woodard: It’s time for our next poll. This one is multiple choice, so you can choose as many as apply. The question is: which teams at your organization are using AI on a regular basis? The options are membership, marketing, fundraising/development, finance, IT, customer service, programming, something else (please put that in the chat), and not applicable.
And just given that there were some questions in chat about health-oriented nonprofits with additional compliance rules: how can you evaluate if the AI is going to stay in compliance with the rules that govern you or with your own policy? It’s a thorny question. As you were saying, Mimi, you might have a team that is super cautious because they have more risks, and you may have a team that’s really an early adopter because what they’re doing carries lower risk.
George, can you tell us the results?
George Danilovics: There is one on here that’s surprising. Not surprising is that the leaders of this poll are IT at 45%, marketing right behind at 35%. Finance was number three. I’m curious about finance. Also not surprising to see fundraising and programming coming in at a tie. I am surprised to see membership pretty low, or maybe that’s just the nature of not having a lot of membership organizations on this call.
Customer service came in pretty low as well. Only one person out of the 40 said customer service, and I think that’s probably due to customer service being customer-facing. A lot of organizations are still hesitant about letting AI speak on behalf of them. So I’m not too surprised seeing customer service still as low as it is in the nonprofit sector.
Carolyn Woodard: This is so interesting. Thank you, everyone who filled out the poll, and thank you to people who are putting in the chat what they’re using AI for in their team.
George Danilovics: Our poll showed a pretty good spread. As enough teams reach systemic, that infrastructure becomes shared. You’ve got prompt libraries that people are referencing, and new hires, when they join the organization, learn that AI is part of the organization by default. They get AI training and AI expectations right after they get their employee handbook.
My favorite tell of when you’ve reached that critical mass is linguistic. Listen to how people talk. In the early stages, everybody announces, “I used AI to draft this,” “I used Claude to look into this research.”
People stop talking about AI. When’s the last time you heard somebody say, “I used Outlook to send this email”? I actually don’t think I’ve ever said that in my career. When AI stops being remarkable, in that literal sense of people not remarking about it, that’s when you’ve matured from initiative to infrastructure. That’s the quiet, boring, everywhere that we’ve been talking about.
Mimi Yeh: Along those lines: if AI is a required business skill in your organization, and I think the polls have proven that if it is not, it’s quickly going to be, then developing that skill is a leadership responsibility that sits across the organization.
We really can’t hold our staff accountable for building skills when we didn’t create a deliberate plan to build it with them and in them. So we want training to be more than a brown bag, a lunch and learn, a webinar like this, which is still great, but might need to be a more robust training program. Things like role-based training that actually tie to workflows and goals and challenges that organizations face today.
We would want to think about use cases and scenarios so that the finance team learns about applying AI for finance work. It’s also about making sure there’s protected time on people’s schedules to practice using AI. It’s about maintaining a prompt library, which was previously a marker of the systematic stage.
And a big part of it is regularly revisiting and refreshing, because as the tools change, and they do change constantly, that requires the skill set to change, and it requires our perspective and mindset about AI to change.
George Danilovics: The comparison I’ll draw is security awareness training. I hope it’s something you’re all already doing, and nobody treats it as optional, and nobody treats it as a one-and-done. It’s a structured training, it’s a recurring training, it might even be role-based in your organization. We accepted a long time ago that that’s the way it has to be.
AI competency deserves that same attention and architecture.
So, what happens next as we move through that systemic phase? Pilots have end dates; operations don’t. A pilot can succeed and still strand you because nobody planned for the day after the pilot. Ownership moves from those early champions to managers. Your champion got you here, and you need to acknowledge and thank them.
But one enthusiastic person can’t scale. The durable version of this is managers embedding AI into how their teams work. And then the scorecard changes. Success stops being measured in the number of tools tried, the number of pilots started, and we begin to look at how we’re measuring productivity.
If you take one thing from this slide, think of this: for each AI effort in your organization, can you name the manager who owns it after that pilot ends? If your answer is “that person who was very excited and ran the pilot,” you’re still in that early experimental stage.
Mimi Yeh: Yeah, that’s true.
So we want to help you continue your AI maturity journey and make this actionable before we go to your questions. You’ll see that this slide is very simple, and it’s that way deliberately. That’s when programs will stall. Narrower, achievable goals can be more easily accomplished, and they can create the momentum to move forward.
Organizations don’t change overnight. The ones that make the most progress are usually taking those incremental, deliberate steps.
This week, we recommend you do just two things. Map your teams against those five stages we showed earlier. And remember, we’re talking about your teams plural, not your one organization. You want the map. It won’t take you a long time, and it’ll probably give you more insight and perspective on what to do next than most assessments would.
And sometime this quarter, two more things to do. Pick one team and decide the steps you would want to take to move that team from its current stage to the next stage. Just one team at a time, one stage over 90 days. And maybe while you’re at it, a little stretch goal: replace one brown bag with one role-based training session that is tied to a team’s actual work.
When you do that slowly and incrementally, you can repeat that play a few more times, and then you’ll be a different organization within a year. This is really about changing, learning, and building gradually in order to create capability within your organization. It’s a capability that will compound over time.
Carolyn Woodard: I love that framing for sure, because I think to a lot of people, AI is like this giant tidal wave that’s washing over all of us. Finding these small ways to move that pilot or that team into more regular, quiet, boring, and everywhere, I think that’s a great way to think about it.
Here’s the contact information for Mimi and George. You can follow them on LinkedIn and download the AI journey guide and supplemental resources at the QR code.
I want to go back to a really interesting question I found in chat that we didn’t address at the time. Someone said it’s hard to say what it would look like if AI was as unremarkable as email, because right now the AI tools don’t seem as stable as email. The vendors could go out of business, the tools could change dramatically. Every time I open Claude, there’s another update. So it’s hard to consider integrating AI tools when the tool itself might not exist in a year, because we’re in this bubble and there’s lots of market consolidation going on.
So, how do you plan for that sort of thing when you’re thinking about really integrating AI into workflows, the way that Outlook is or the way that spreadsheets are?
George Danilovics: I’ll take the first step at this and then let Mimi chime in. When you’re looking at tools, you’re right. Everybody is talking about AI, and there’s a new vendor and a new tool every day. Like any technology initiative, you need to do a little due diligence. You need to look at the platform, look at the vendor, and see if that’s somebody you want to be doing business with. You’ve got to take that risk and start to engage with that vendor and see how their product fits into your organization and the work that you do.
Any vendor you work with could go out of business. It’s a risk that we deal with on a regular basis. And it’s actually something that you won’t know or won’t see until after it happens.
Here’s a personal example, and I’m even going to draw on a comment from Greg in the chat. My AI journey probably began about two years ago, and I used ChatGPT to create pictures of pandas because I couldn’t think of what to use AI for. So I just had it create pictures of pandas on an airplane, pandas playing baseball. And I was like, wow, this is really cool. AI can create pictures, that’s great.
But now I go to Claude for everything. If I’ve got a question about a technology, I ask Claude. If I want to know a recipe, I ask Claude. If I’m curious about how to build something, I ask Claude. Whereas before I used to go to Google, I used to go to a search engine, I used to go to a vendor’s page. That just happened over time, gradually, and I never noticed a behavior change until after it happened. Where I used to go to the Google search button on my phone, I now click the Claude app.
That systemic piece is hard to see as it’s happening. When you look back, you’ll realize when it happened.
Mimi Yeh: I’ll add a little bit onto this, and then I know we’ve got so many more questions and comments to parse through.
It’s absolutely true that tools change. I am old enough to remember Yahoo and Ask Jeeves and all these other engines and methods of getting information. We used tools that do not exist anymore today.
The tools change, but why you’re using the tool, and why your organization chose to apply AI to achieve a particular part of your strategy, your mission, or your goals, that didn’t really change.
So one thing I would say is: start with your strategy. Start with what is the opportunity or the challenge that you want AI to solve or assist you with. And maybe a side tip: document that journey. Because if it migrates from one AI tool to another, the logic and the thought process that you used probably will not change. That will stay sound, and having that document might make it easier to feed those prompts, or to create the Gem that is going to be customized to your organization with the proper prompts and the way that you want that AI persona to behave when it’s interacting with your staff. Document that journey and be clear about why you made the choices that you made.
Carolyn Woodard: Those are such great pieces of advice, Mimi. Thank you so much.
We’re getting close to the end. Greg asked this really great question in the Q&A that I saved to the end because I knew this could be an hour-long discussion on its own.
We’ve started this webinar asking about maturity. You’re already using AI. How do you get to that next phase where it’s really integrated? Greg asks: how do you address and demonstrate care for genuine concerns about ethics, social impact, and all of the issues that we know about AI and that nonprofits care about around AI, while you’re trying to put this formalized and mature adoption in place? He says, many of the people in my sector are opposed to AI based on economic and ethical concerns. I don’t want to ignore that. I want to ensure that we move forward at a pace that is good for our community and for your organization and for yourself as well, like what you feel comfortable with.
When you run into those sentiments at clients and when you’re working on AI with your colleagues, how do you start answering that question?
George Danilovics: I’ve heard this concern from multiple individuals and organizations as it pertains to AI, both on the social piece and the concerns over energy and what it’s doing to our environment.
I think all of those are valid concerns. It’s something that we, as users and stewards of AI, should be looking at, along with other organizations that are trying to make sure AI is used as a force for good, that as it’s using resources, we use it in a productive manner, and that it’s not used to the detriment of society.
I don’t think any one individual or one group can sway an entire technology. But each organization does have to think about how they are best going to use a technology or not. Some of those conversations may require consulting with your board of directors, particularly around concerns on the social or energy or resources piece, or the ethical side: where are those red lines where we’re not going to use AI?
To have that as a board decision, not just a staff decision. It is something we have to look at holistically and be cognizant of some of the trade-offs and consequences of the technology.
Mimi Yeh: Yeah, I think that’s right. Every major technology raises new ethical questions and dilemmas. AI is not different in that regard. It may be different in what the ethical questions are and the degree of concern, but the pattern is familiar.
The answer to whether and how to use AI should be reflective of your overall technology strategy and your technology approach, because there are lots of other considerations and ethical dilemmas with many other technical tools that are out there. There are all kinds of concerns associated with flying and using a plane instead of other modes of transportation. There are ethical concerns associated with using tools like Salesforce because of the way that their content is being used by certain actors and players.
We may not be able to eliminate all of the risk. We might have to instead manage it thoughtfully. And to what George said, be transparent about how you choose to use it, make sure the use of it still aligns back with your mission and your values, and that you’re still operating in a way that protects sensitive data, respects people’s privacy, and remains cognizant of potential bias that might exist within different AI tools and how you as an organization elect to proactively manage those.
Carolyn Woodard: I love both of those answers. Thank you for weighing in on something that is such a serious topic. That really helped.
I just want to echo what you said about transparency. We’ve been really recommending that this be an all-staff ongoing conversation, also with your executive team, also with your board, because you can find that you have staff who, even with a policy in place, aren’t okay with it. So you really need to be constantly having this evolving conversation about your values and how this relates to what your values are.
Another thing I wanted to mention is that, especially around environmental issues, looking to the big environmental organizations and what they’re doing can be really helpful. Many of them have put out guidance on how to think about AI, how to use AI, and where they’re coming to it from. They have a lot more experience than I do in terms of what it’s doing to the environment. So hooking on to people in our peer group who have more experience than we do is another way to address those fears and concerns.
So I want to move on. I think we hit the learning objectives really well. Thank you both, George and Mimi. We talked about the AI maturity model and gave you some clues about how to figure out where your nonprofit fits. We talked about how to build AI as a standard core competency across your workforce. And we talked about what strategic AI implementation looks like and best practices around training, measuring, and thinking about this maturity model.
I love the idea that you don’t have to get to the top of the model, but just figuring out where you are on it will help you figure out where you want to get to as well.
I want to tell you all about the webinar we have next month. I’m going to be leading that presentation with my colleague Eric Solce. We have an ongoing project using AI to help build a data lake for marketing, and we’re going to talk about the structure that we’ve come up with that helped me manage this giant project with a little tech coaching and a lot of AI. We’re going to share how the project evolved, what didn’t go as well as we hoped, what we learned. We think that kind of structure has a lot of potential for nonprofit AI projects.
If you’ve been putting something off because you don’t have the technical know-how to do it, or it’s just this giant thing that you don’t have time for, whether it’s building a data lake or analyzing years of program reports or whatever you’ve just been putting off and want to get started on, that’s what we’re going to talk about next month.
That is at 3 p.m. Eastern, noon Pacific, on Wednesday, August 19th. I’m going to share that link with you in the chat. It’s going to be on our website, communityit.com.
And then please join us on Reddit at r/nonprofitITmanagement for another 30 minutes or so. We’ll be answering questions that we didn’t get to from chat.
Thank you so, so much for joining us, everyone. An hour of your time is a gift and we really appreciate it.
Thank you for spending this afternoon time with us, George and Mimi. I really appreciate your time and all the thought and effort that went into this presentation. It really helped me, and I’m reframing a little bit of how I’m thinking about this too. Thank you again so much for coming.
George Danilovics: Thank you.
Mimi Yeh: Thank you all. Have a great day. Thanks.
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.
Photo by Evgeni Tcherkasski on Unsplash
Wednesday August 19th at 3pm Eastern join Erik Solce and Carolyn Woodard from Community IT for a case study on actually using AI.
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