Video: AI in Practice for Nonprofits

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with Erik Solce and Carolyn Woodard

Have you used AI for quick tasks like drafting emails or summarizing meeting notes, but wondered if it could help with something bigger?
Do you have a project you’ve been putting off because it felt too technical or too complicated to take on alone?

Video: AI in Practice for Nonprofits – a case study in progress

Join Erik Solce and Carolyn Woodard from Community IT for a case study in using AI for a real project with moving parts, not just a one-off prompt. Community IT Innovators used AI to build a marketing data lake, a centralized system for tracking performance across seven channels. The project involved someone who knew the data and the goal, someone who could help with the technical pieces, and AI as the tool that made it possible to actually get it done.

We are calling it the three-role model: topic expert, tech coach, and AI enabler. Our case study happens to be a marketing project, but the structure can work for almost any project where your team knows what needs to happen, you just need some help figuring out how.

In this session, Carolyn Woodard, Director of Marketing at Community IT, and Erik Solce, IT Business Manager, walk you through what they built, how the collaboration worked, and what they’d do differently next time.

If you’ve been experimenting with AI and want to try something more ambitious, come learn from what we tried.

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. In this case study the AI tools used were Copilot from Microsoft, Gemini from Google, and Claude from Anthropic, but other AI tools can be used with a similar three-part model, and we can answer your questions on the tools you use.

As with all our webinars, this presentation is appropriate for an audience of varied IT experience.

Presenters:

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 loved presenting this webinar on AI in practice for nonprofits based on her experience with this practical AI project.

As an IT Business Manager (ITBM), Erik Solce guides clients through complex implementation of effective technology investments and utilizing efficient IT services in direct support of their missions. He also assists with long-term planning, budgeting, and strategic goals.

The Community IT ITBM service provides an outsourced IT manager to clients at a reduced cost to hiring and having an IT manager on staff. These managers are a resource dedicated to matching technology solutions to clients’ business needs. To do this well requires an ongoing conversation with the client to continually understand their business needs, and then effective communication with client staff and leadership about the ways specific technology solutions can meet those business needs and how to budget for technology.

The ITBM makes recommendations on IT investments, training programs, maintenance, and licenses. They help the client be forward-looking, and act as a vendor-agnostic, trusted advisor with deep knowledge of the nonprofit IT software and platforms available. Because Community IT works in partnership with clients to manage long-term IT needs, the ITBM relationship with the client makes them a true asset.

Erik has 18 years of IT experience working his way up from help desk to systems engineer. He started in IT by working for his high school during the summers preparing computers for the next school year. He interned at the US Army Corp of Engineers as help desk technician while attending MCTC in West Virginia. After working his way up to systems engineer focusing on Windows Server, Exchange, and virtualization, Erik then ran his own MSP providing support to local small businesses in Louisville before being hired as an IT Business Manager with Community IT. Erik is pursuing ITIL certification.

Resources mentioned:

Transcript

Carolyn Woodard: I want to welcome everyone to the Community IT Innovators webinar on AI in practice for nonprofits. And this is a case study in progress with Erik Solce. And we’re going to talk about a project we’ve been working on at Community IT to build a data lake for marketing, and how we used AI and a tech coach to help the subject expert, which is me, tackle this project.

We think this three-part structure worked really well, and I think it can be adapted to a lot of different use cases that we could envision at nonprofits.

If you’re joining us because you also are in marketing like me, and you’re looking for a way to get more analysis out of your channel dashboards, you’re in the right webinar. But if you’re thinking about a project you’ve had in mind at your nonprofit, a different kind of project, and wondering if an AI tool can give you the capacity to get it done, you are also in the right place. If you’ve been using AI for some productivity tools but are starting to wonder how to take the next step into doing projects or automation with it, this session should give you some ideas and some resources. And if you’re looking for advice on how to get started in AI for nonprofits, we also have a lot of resources on our website that can help you at communityit.com.

My name is Carolyn Woodard. I’m the outreach director for Community IT and the presenter today. Before we get started walking through our AI project case study, I want to go over the learning objectives.

Today we’re going to focus on these themes. How can you structure an AI project at your nonprofit if you aren’t on the IT team? What did we learn about using AI on a real messy problem? It was a data problem, and we learned a lot. How do you know if an AI-assisted workflow is working? How can you verify? Can you trust it? How do you keep the human in the loop? And what would it take to try something like this at your organization?

And now I am going to let Erik introduce himself. Erik?

Erik Solce: Hello, I’m Erik Solce. I’m an IT business manager with Community IT. I’ve been with the organization for three years. And as an IT business manager, I guide clients through complex implementation of effective technology investments and utilizing efficient IT services in direct support of their missions. I also assist with long-term planning, budgeting, and strategic goals. The Community IT ITBM service provides an outsourced IT manager to clients at a reduced cost to hiring and having an IT manager on staff. These managers are a resource dedicated to matching technology solutions to clients’ business needs.

To do this well requires ongoing conversation with the client to continually understand their business needs, and then effective communication with client staff and leadership about ways to improve specific technology solutions so that we can meet those business needs, and how to budget for technology. We make recommendations on IT investments, training programs, maintenance, and licenses. I can help you be forward-looking. I act as a vendor agnostic, trusted advisor with deep knowledge of the nonprofit IT software and platforms available. And because I work in partnership with clients to manage long-term IT needs, the relationship makes us a true asset.

Carolyn Woodard: And Erik, what is your background, your technology background, before joining Community IT?

Erik Solce: My entire professional career has been in IT, mostly focused on the technology side rather than the business relationship management side. But I did run my own one-man IT shop for a little while before coming to Community IT.

Carolyn Woodard: Exactly, which is just a great experience to help you be helpful to our clients. Really appreciate that.

Before we begin, I’m going to tell you a little bit more about Community IT if you’re not familiar with us. We’re a 100% employee-owned managed services provider. We provide outsourced IT support, and we 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 nonprofits. We serve nonprofits across the United States. We’ve been doing this for 25 years.

We are technology experts, as Erik is, and we are consistently given an MSP 501 recognition for being a top MSP, which is an honor we received again in 2026 for the 10th year. We are the only MSP on their list that serves nonprofits exclusively.

I want to remind everyone that for these presentations, Community IT is vendor agnostic. We only make recommendations to our clients, and only based on their specific business needs. And we never try to get a client into a product because we get some kind of secret incentive or benefit from that. We do consider ourselves a best of breed IT provider. It’s our job to know the landscape: what tools are available, reputable, widely used. And we make recommendations on that basis for our clients, based on their business needs, priorities, and budgets.

In this presentation, I am going to tell you I use three major generative AI tools: Copilot by Microsoft, Gemini by Google, and Claude by Anthropic. I’m not advocating for any one of these tools, but I’m going to tell you my honest experience using them. The tool that you use is going to depend on your nonprofit’s policy, your comfort level, what you’re trying to do. This presentation is just to give you an idea of a structure that we used that worked, that you can use with the tools that you have.

We’re going to leave as much time as we can for Q&A at the end. You can submit your questions through the Q&A tab or the chat feature at any time. I’ll either answer them if they’re timely or save them for the end. We did get a lot of good questions at registration. So we’re going to try and answer as many of them as we can, but we only have an hour. So anything we can’t get to, please join us today after the webinar in our community on Reddit at r/NonprofitITManagement. We’re going to continue to answer some questions over there until about 4:30 Eastern, or about 30 minutes after this webinar. And I will pop back in to answer any more that come in after that.

We are recording this presentation. I will send links to the video, podcast, and transcript within about a week. You don’t have to worry too much about taking notes. Please do stay to the end and take our survey on exiting the webinar. Your feedback really helps us improve and pick future topics that you’d like to hear about. And one lucky survey respondent chosen at random will win a $25 gift certificate, if you need another incentive.

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 to always treat people with respect and fairness, to empower our staff, clients, and sector to understand and use technology effectively, 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 a part of our lives.

And with that, I would like to do our first poll. This question is, how would you describe your AI tools experience so far? The options you could choose are: starting out, haven’t tried them very much yet; have experimented with a few tools like ChatGPT, Copilot, Gemini, Claude, Perplexity (there’s a bunch of other ones out there) for simple tasks for my own productivity. The third option is, I’m comfortable using AI regularly and starting to build it into my workflows. And your fourth option is, my organization has a structured approach to AI and policies, and I use it for complicated tasks and projects. And then the fifth option is not applicable.

If you want to get in and get your answer recorded, you can just choose one. You’re starting out, you’ve experimented with a few tools, you’re comfortable using AI tools regularly. And I think that fourth one is, your organization has a structured approach and you’re very comfortable using AI for complicated tasks. And it looks like we have pretty good participation. So I’m going to go ahead and end the poll and share those results. And Erik, can you see that?

Erik Solce: Yes, I can.

Carolyn Woodard: Tell us what happened.

Erik Solce: Okay, so it looks like just over half have experimented with a few tools for tasks for their own productivity, and then about equal amounts are just starting out, or are comfortable and are starting to build this into their workflows. And then very few people, 6%, have a structured approach within their organization. And then, of course, 2% not applicable, just one answer.

Carolyn Woodard: All right. Thank you. Thanks so much for that. And I think that tracks. Erik, did that surprise you?

Erik Solce: No, no, that seems fairly common with my clients especially.

Carolyn Woodard: And I think that tracks also with a report that came out this spring that said, don’t quote me exactly on these figures, but it’s something like 72 to 80% of nonprofits are using AI, and only 7% reported that they were using AI to do something that was effective and was changing what their nonprofit was doing.

I think a lot of people at nonprofits are using AI tools for productivity, which is a lot of what they were designed to do. So that tracks as well.

What Your Nonprofit Needs in Place Before You Start with AI

Carolyn Woodard: Let’s move on to our next slide, which is, what does my nonprofit need to start working with AI tools successfully? Before we really begin talking about what we did, we want to talk about what you should already have in place at your nonprofit around IT management and policies.

AI can seem very simple. It’s always marketed as, just ask it a question, it’s so easy. But we’re finding that nonprofits that try to layer AI on top of a broken foundation end up with a lot of risks, and maybe some messy situations with staff going off in lots of uncoordinated AI directions.

Erik, did you want to say something about your clients and how they’re using AI?

Erik Solce: I think it matches up fairly well with what we saw in the responses. For the most part, I would say the majority of my clients are using it for a few tasks. Several clients have picked a solution and decided that that’s what they’re going to be using. They’ve cemented that by establishing it in an AI acceptable use policy, and they’re moving forward cautiously, seeing how AI can help improve their workflows.

I also have several clients who don’t use AI because of ethical reasons or whatever. And with those clients, I’ve always recommended that they set up an AI acceptable use policy that actually states that AI is not to be used in our organization, so that it’s at least written down somewhere.

Carolyn Woodard: Yeah, and we have a link to a template that you can download that I’m going to share on the next slide.

But yes, I think we are saying, kind of generally and also to our clients, that starting with a good IT foundation is really critical to put these AI tools on top of. We always advocate for well-managed IT, of course. We have several resources on our website that can help you if you’re not our client. But you should have an IT acceptable use policy, one that governs what your staff do with the IT, the devices, how they do all of the things that they do with technology.

Your leadership should own IT management and strategy. You should have an IT roadmap or some kind of strategic plan for your IT. And especially when the AI tool landscape is changing very quickly, having an IT roadmap that charts where you want to go with your IT gives you a way to measure how you expect new tools to contribute to that journey. Matching the new tools against your overall goals will help you discern which are worth spending staff time and budget on. And as I said, we have a bunch of resources around building an IT roadmap if you haven’t done that yet, or need to maybe refresh it or revisit it.

And as Erik said, even if your official policy is not to use AI at all, you need to state that in writing so that your staff understand that’s pretty serious policy.

We have been helping our clients with intentional AI adoption. And you can find more about that at this link, which is a free download, which I will also put in the transcript if you’re listening to this webinar after.

And as we hear more and more about rogue AI, we want to make sure that our nonprofits are aware of new security risks that AI tools can create internally, and of course the way hackers and scam artists are using AI more and more to trick people into clicking on the wrong link or sending money to the wrong account. In October, we’re going to re-release our cybersecurity playbook for nonprofits with more information on AI. And you can come back to our webinar in October. We’re going to talk about that with the author.

If you need more resources on any of these fundamentals, as I said, we’ve got so many downloads and content on our website. It’s all free. We don’t have a paywall. We have past webinars about all of these things. I hope that that can help you if you’re thinking you need to go back and make sure that you have this foundation in shape as you’re moving forward with AI.

And then we have some more resources specifically on AI in nonprofits on our website, including our acceptable use policy, which is a template. Unfortunately, you can’t just download it and stick it in, put your organization name in, and then, voilà, it’s your policy. You do actually have to go through it and see what makes sense for your organization.

We really recommend that those be a lot of open conversations, that you’re at all staff meetings talking about how you’re using AI, because you’ll find people have really different opinions about it, or they’re using it in really different ways. So just continuing to talk about it is really important.

We do have a library of all of our resources that you can find right on our website. And then I just wrote an article on a three-filter framework covering how you feel about AI and environmental impact, electricity, water impact. Some things that you can keep in mind as a framework as you’re talking about and thinking about your AI policy.

The Challenge: Nine Marketing Channels, Nine Dashboards

Carolyn Woodard: But now we want to move on to the actual project that we did in the case study. I’m going to talk a little bit now, so get your tea if you need it.

What was the challenge I faced, and why did I think AI could be part of the solution?

I’m going to take you back in time a little bit for the backstory. In 2018… I’ve been at Community IT for 20 years or so now, first as a contractor and now as an employee. But as the marketer in that time, I have a lot of channels. We do an email newsletter, we have a website, we do webinars, we now have a podcast. And all of those channels have their own dashboard.

I was lacking the capacity to build a unified dashboard, and I was lacking those insights from being able to see those metrics across channels and in context.

For example, for the webinar, I would go to Zoom and I would see how many people had registered and attended the webinar, and then the next month I would do the same thing. But I would just kind of keep in my head if it was going a little bit up, a little bit down, which topics were popular, which were not popular, if there were certain months that people had low attendance. And I just had to kind of keep it in my mind.

And the data was really siloed. I had a lot of manual workarounds. I would download it into a spreadsheet, and then I would try to get some answers out that way, especially around the webinars. And the analysis just took a really long time. And it just wasn’t a good use of my time. I have to make new webinars, and I have to publish things to our website and share information with all of you on how we can do IT better at nonprofits.

I did work on trying to build a dashboard through Power BI, and I worked actually with a coach here at Community IT. And I quickly ran into the idea that it was going to take a really long time, even if we just work on one channel at a time: validating the data, the fields that it’s pulling from, am I entering the data correctly?

Then when I thought about doing this across nine different channels, it just really was overwhelming. And I kind of put it aside.

I had started working with Copilot, Gemini, and Claude. A lot of marketers have been using AI for quite a while, so I was aware of it, with colleagues who were working with it. I’ve done a couple of webinars here about how to use AI a couple of years ago. We started talking about frameworks and how to do AI. I started working with AI tools to help with drafting content. And I started to think about other things that it could do.

This is one of my learnings: when you start using AI, you start thinking about, well, what else could I use this for? I started asking actually all three of these tools about what I wanted to do.

I started out by asking questions. I didn’t give it a prompt of like, make me this thing. I just said, well, what do other marketers do? And how would you approach it? What are some ways that I could address this problem of trying to get better insight from the metrics from these different channels that all have their own dashboard, et cetera? And I asked questions. And as I said, a lot of marketers have been using AI for a while, so there were a lot of examples to draw on.

Even a year ago when I started looking into this… the tools are much more capable now, I will say, having used them for a year. I started out asking Copilot, and then I asked Gemini. And I want to put in a little thing here: we always recommend that you have a paid account, that you not be using the freemium tools that are just out there, and that you log in with your work email so you have that coverage as well.

I started doing that, and I got a couple of really different answers. So Copilot said one thing, and Gemini said a totally different thing, and I don’t even know if I was using Claude yet.

I realized that I didn’t know enough about the answers that these tools were coming back with to be able to assess which direction I should go in. When I got these very different answers, which is one of the things they recommend: don’t ask just one tool, ask a couple of different AI tools and see where they differ. And they really went in different directions.

I thought what I needed to do was talk to someone who knew a lot more about the technology than I did.

Also, it was very interesting at this point: the more questions I asked in this part of the journey, the more it came out. I think this actually was when I started working with Claude. Claude asked me, I asked it to ask me some questions back about why did I want a dashboard? And I will tell you, I’m not a particularly… I don’t look at a spreadsheet and just instantly know what it’s saying to me.

After that pushback, I really rethought what I was looking for, and we came up with this data lake model. I wanted to be able to question the data more than see the data. And some people can just look at the data and get their questions answered, but I am not like that. So I wanted to be able to ask the AI to look at the data and tell me what it was seeing.

Bringing In a Tech Coach: What Broke and What Worked

Carolyn Woodard: As I said, I needed someone who knew a lot more than I did about what was feasible. Luckily, we have a whole department of IT Business Management who are also working on AI with their clients, rolling out tools, learning about the tools. All these tools are so new.

I posted my job description there, and Erik responded that he would have time to meet with me. And we set up a weekly meeting so that he could coach me through these questions that I had about the tools and the basic direction that I would need to go in. So that was my journey.

And I guess I’ll say also that I did start out with Copilot. I moved to working a little bit more with Gemini. This was last fall. And then I did get a pro license for Claude, and that was really a game changer for me. This is my honest opinion. Claude was much more capable. And we kind of worked a lot with Claude once we really got into working on this project.

Erik, do you want to tell us a little bit more? As you came on as my tech coach, what were some of the challenges that you faced?

Erik Solce: Obviously, time was a challenge. We were meeting weekly. We were handing out homework assignments, like Erik will work on this and Carolyn will work on that. Expectations and divisions of labor, of course.

I had some issues with access. Toward the later end of our project, I did not have access to our YouTube channel to test setting up the API integration. I kind of had to create my own YouTube channel and just try to configure the API on my own using Claude. And we did run into a couple of problems during our meeting, because the environments were slightly different and the way we had to connect was different. We would run into problems that I had to try to fix on the fly.

Obviously in the beginning I was using Copilot, and my main goal was to try to create a Power BI dashboard that would automate this entire process, so that the only thing we were doing was looking at a dashboard once a month. This proved to be kind of a false path. And I eventually started using Claude, and we also simplified the scope. So instead of having this automated dashboard, we still have to export the data from the various data sources, import them into the correct locations, and then have Claude pull API information.

We also ran into problems where we switched course from having the data put in Google Drive, because we didn’t want to give Claude access to our SharePoint at that point. We have a Google tenant, and we set up a share drive there to have Claude access. We discovered that Claude runs much slower when it has to go back to the internet every time it needs to review data. So we ended up eventually setting it up on Carolyn’s Mac drive.

We’ve had issues with persistence of AI memory. Sometimes it will bring back the fact that we were storing it in Google Drive, even though we haven’t done that in months. Sometimes it forgets things between sessions, because we’re only running these reports once a month. So sometimes you have to remind it of information you’ve given it before.

We ran into some security issues. One of our processes for the API export created a file that contained sign-in credentials, and it was stored on Carolyn’s Mac, which is not a secure location. We had to come up with a process to save the file to Keeper so that it was not accessible on her drive.

And of course, these AI tools update constantly. Carolyn will run a report and sometimes things will change. There may be trouble, continuing troubleshooting that we’ll have to do going forward, just to make sure that it’s still working.

Carolyn Woodard: Yeah. I think that when we were meeting weekly, that was one of the good things about having that as a weekly meeting: we didn’t have to do everything in just one meeting. Like we knew we would be meeting again next Friday. So we’d say, oh, we kind of ran into this roadblock here. Let’s do a little bit more research on it or figure something out. And then next Friday we can talk about it.

And another thing that was just so interesting to me at this stage was that I really expected it to take months.

Erik Solce: Yes.

Carolyn Woodard: Because in the past, when I’d looked at it, that was the timeline I was thinking about to do it my own self. And we were both surprised at how quickly we were able to export and start this structure. And even the first month, we had four or five channels already populated with the data that I was able to download, and then Claude was able to start looking at it.

Even with some of the setbacks and some of the things taking a little bit longer, I think one of the things I’m going to get to later, though, is that it was a very doable project because I had this AI support and because I had Erik as the tech support. Chunking it up into those smaller weekly tasks, I would just work on it for like an hour in the evening, when I had done all my things for the day and I had an hour to ask it more questions and do an export and put it in the folder and see what it could see. That all made it very, very manageable.

Erik Solce: It also saves on token spend.

Carolyn Woodard: That’s true. That’s true. Yeah, I did run into that.

Somebody asked in the questions, which specific models did we use under each of the LLM tools? For example, did you use Deep Research with Gemini? I did not. I have used Deep Research on a different project, but we didn’t use it for this. But yeah, I think with Claude, it’s very interesting that you can switch between the different levels: a lighter version if you’re just doing something through chat, or if you’re really using Cowork and it’s having to code something and go check and come back and tell you something, which version you use.

I did, when I had that pro account, run into the token issue, which is something people are just talking about: how much does a token cost? And why do you run out of it? And how can you tell, and all of that? I worked with my team again, being really transparent about all of this and talking about it out loud instead of just getting myself a different license. I talked to our internal team about what would make the most sense for me given what I was using, and we figured out the economical license level that I should get.

That’s another reason to involve other people. Just don’t go off on your own and do this, I think, is my advice.

A Three-Part Structure: Instigator, Tech Coach, and AI Enabler

Carolyn Woodard: What we ended up coming out with was, as I said in the description of this webinar, a three-part structure for AI-enabled projects. And this is what I’m really excited to share in this webinar with nonprofits, because this is the model that I think could really work for other types of projects too. So hopefully you’ll find that also.

The instigator, which was me. You’ll have someone on your staff, or maybe it’s you in this webinar that is thinking about that, who is a subject matter expert. You know all of the context, all of the past reports, all of what you’re trying to do; deeply, you know what you’re trying to do. You identify the project, the problem, and the best outcome. And that may change. Like you may need to be flexible about, I don’t need a dashboard, I actually need a data lake. But the instigator is the one who has a lot of knowledge and drives the project forward, and is going to end up being the principal user, of course.

And that was something, I think, Erik, I hope it’s okay if I share this, that was something that we had to kind of work out when we were meeting. Because I think at the beginning of this project, there was some sense that sometimes you wanted to go away, create it, and come back and present me with, oh, here’s how Copilot will do this. And I really wanted to be involved. But I think also I couldn’t NOT be involved, because I was the one who was going to be using it and I needed to be really involved in building it. So that was a big piece of it.

Then we have the tech coach. The tech coach is the tech expert: listens to the problems, brainstorms solutions, tests feasibility, is a reality check on what the AI is telling you, assists with technology steps. I’m going to talk about that a little bit later on. Grounds that project in reality, a technology reality. Hopefully, this is a tech coach who really understands your setup: your nonprofit, your IT team, your operations team, and how security works. Guides that security, is there to bring up, well, saving your credential is something that you should be really careful about, because that’s your credential to get into YouTube, et cetera. So that was really, really helpful to have. And that’s the role of the tech coach: not to do it, but to assist and to coach.

And then you have your AI enabler here, on the third leg of this three-part structure. And the AI enabler, whatever tool it is that you end up using, helps analyze, research, design, iterate. And all of those are things that when you’re prompting it, you can ask it to help you think through maybe other outcomes, or what are you missing? Those sorts of things. Play devil’s advocate with you instead of just being your cheerleader. It can help perform the tasks, of course.

Now I use Claude regularly with this project with the data lake. It’s capable, it is speedy, but it is limited. It is your assistant. We’re going to talk about that a little bit more too. And while I’ve talked about security a couple of times, I’m going to drop this into the chat also. We did a webinar in February, I think, on using AI tools securely. You can go check that out as well.

Building a Data Lake for Nonprofit Marketing Analytics

Carolyn Woodard: What kind of projects are you thinking about that you could work on with an AI assistant and a tech coach? If you go ahead and put that in chat, it doesn’t have to be a long treatise, just a description of the project you’re thinking about. I’m going to continue on and talk about what we ended up with, the data lake, if this is something that you’re thinking about or something similar. I say data lake, and there’s data swamp, a data lake house, a data repository, et cetera.

Our data lake is: I mentioned I have these nine channels, so they all just live in a folder on my OneDrive. And someone asked in the registration about the KPIs. So I asked the AI what KPIs were the most important for me to track, where I would find them in these different channels, how I would do the export, how I could run the API, what the challenges were, et cetera.

Claude really helped me with designing everything. I have a monthly marketing meeting that I do. Every month I export the data for seven channels, and then I run the API for YouTube and Google Analytics. I give Claude access to this top folder in my OneDrive each session, so it’s not persistent. Then it can see into the subfolders for the rest of the day in that session.

And I have to tell you, I’m so proud of myself. After we set it up and Erik held my hand the first time, I am capable of running an API. So we can all take a moment and just pat me on the back for this. I did get an error one month, so I asked Claude to help me troubleshoot what was not working, but I run the API, not Claude. So I have not automated that step.

In fact, I haven’t automated any of these steps yet. I always go out and get the data and put it into the data lake. And I do that at the beginning of every month. So it’s just kind of built into what I do anyway.

As I said, Erik and I worked on the most secure way to run the API monthly with that credential. And so I have a whole process of how I use it, delete it, and save it where it’s more secure. Once I have the data in the data lake, I do have some automated reports that Claude can run by itself, looking in those folders. It’s only the third month that we’ve had this, so we’re still working on the most effective sequence to do that.

I have Claude create the PowerPoint for me for this meeting that I have. And I was very skeptical that it would be able to do any kind of PowerPoint. And this is again from working with Copilot and Gemini a year ago, nine months ago. It’s really just changed so quickly. I gave it our format, our template, and it just runs with it and it does a great job. And every month that it’s made the slides, I do have to go in and make them better, basically. I’m the human editor. We always talk about having the human in the loop.

For example, it was giving me just the numbers on this slide. This is kind of my cover slide of all of the channels that I’m looking at, and it would just give me the number. And I thought, well, that’s not really helpful. So now I ask it to give me the up or down. How is the data moving?

Verify What AI Tells You: The YouTube Shorts Example

Carolyn Woodard: And one of the first suggestions that came out of the first month of reports was that we had a YouTube problem with engagement. The YouTube shorts example was a concrete example of using this data lake and this analysis that Claude could do to make a decision. How do I verify and not just trust what Claude is saying?

If you use YouTube a lot, you already know this, but I’m going to give you the quick one-second explanation. Two things were pulling down our YouTube engagement. Google Podcasts switched to being hosted on YouTube a little while ago. So we had videos of our podcast, but they were just a static image with the audio from the podcast. I knew that, but I didn’t know if it was good or bad. So I just let it continue to happen.

And then I had started making shorts, and some of our shorts had a thousand plus views. So I thought, oh, this is awesome. I need to ask Claude about having a shorts strategy and how much time I should put into it.

And then Claude made this report, which made me question everything, basically. People who watch shorts were not watching our longer videos. In fact, they were clicking right off of them. Because they were seeing the short and then clicking right off, that was eating into our average watch time. So, counter-intuitively, the more views we got on shorts, the less often YouTube was suggesting our longer form videos to people who were looking for longer form content, because they thought there was lower engagement with it. And those people who want the longer, the one hour videos that I put up of our webinars, are the people we really want watching it. And we want them to be able to find it. So we’re not really interested in the shorts people.

Claude’s suggestion was not to spend any more time on shorts, to delist shorts and podcasts, and to turn off the automatic podcast publishing to YouTube, because our strength is in our one-hour videos that are of our webinars.

You need to verify what AI tells you to do. And you can see in this slide that we’re checking it every month and we’re seeing that engagement go up. So that was really exciting to see, and just an example of verify what Claude tells you to do. It should go in the direction that you expect it to.

Verify What AI Tells You: The Webinar Survey Analysis

Now, for the next example: if you’ve been in a webinar before, you know that at the end of every webinar, I tell you to fill out our survey. And again, I just kind of keep that in mind. I asked Claude to look at it in the data lake, and you can see that, well, this isn’t scientific at all. For example, we’ve done a bunch of AI webinars in the last two years. So it’s natural that people in AI webinars would ask for more AI topics in the future.

But one of the number one results under that more AI was use cases, so a case study. So here we all are! And the interesting thing was I had already planned to do this webinar, but it was so exciting that it is in fact in response to what people in the survey were telling us.

And you can also see we’ve done some of the other non-AI topics like cybersecurity, Google Workspace, change management. We have also done those over the past year. But I’m really interested in this getting started with IT. So I’m thinking now about, well, next year we need to do a webinar that’s specifically for young, growing startups, small organizations that want to go ahead with their IT. So stay tuned for that.

Verify but trust. As I said, you need to always check your AI’s work, and you are the human that’s responsible for what it’s telling you to do and the decisions that you’re making. I asked Claude, what are some ways that I can verify what you’re doing? And these were some suggestions.

Spot checking a number. Make sure that if you’re asking an AI to do some kind of analysis for you, you don’t have to check all of the numbers; that would take forever. That’s why the AI is helping you with it. But pull out a couple of metrics and go back to your actual dashboard and check that those are the same number, that it’s not making something up.

A second thing is to follow the metric. As I said with YouTube, once you make a change, is it changing in the way you expect? After you change something, keep an eye on that metric and that statistic. If it’s another different type of project, whatever analysis you’re doing, check that it moves in the way you predicted. And if it doesn’t, go back and check what’s going on.

And then a third thing you can do is cross-check the sources. Do independent sources agree? In our case, I have these nine channels. A couple of them are looking at the same things, but from the opposite side. So if those numbers are correlating, then you are getting an independent example and an independent verification that the analysis you’re doing is sound, that those channels are in fact related to each other. So that is another thing that you can do.

For example, Search Console in Google is telling you what people are looking for; Google Analytics is telling you where they’re landing on your page. Those things are in conversation with each other, so they should agree on some things and you can check. And I’m going to share an article I wrote on this that tells you more about trusting your output.

What We Learned Working with AI

Carolyn Woodard: Here are some things that I learned from working with AI. It was invaluable in helping me chunk up this project. As I said, I can do a little bit every day, I could do it once a week. And when you come back to the AI tool, it remembers what you were working on before, so you can jump right back into it. If I were paying for an expensive marketing consultant, this would take a lot of time. And they would have to have access. And I wouldn’t be able to say, I’m going to come back and talk to you about this in two months. I would still be on the clock with them. We would be paying them to help me with this project. So I really like that part of it.

Working with AI made me better at working with AI. I learned I needed a trusted human coach to help me with the technology. One big thing that Erik and I learned was that working locally was really helpful for Claude, at least. And then we make a copy and we put it into our SharePoint every month.

Claude is smart, fast, and capable. It does not learn or remember things that a human would learn and remember if you had an actual assistant. So that is just something to keep in mind as you’re working with it over time, the things you have to remind it about. Some things we asked AI to do really lingered.

And then one thing I’ve started doing, that is a little piece of advice, is to get a weekly report of what’s outstanding. I had a lot of chats going. I’m asking it about a lot of different things, I’m writing a lot of different things. So every Friday I just ask it to tell me what it thinks is outstanding. And usually half of those things I can click off and say, oh, those are completed. So I’ve also started telling it at the end of a prompt, after we’ve finished working, you can consider this completed.

And then you don’t have to use AI for everything it can do. It can make Asana tasks for you. I’ve found that it just takes me a second to go over to Asana and pop a task in, so I don’t always ask it to do that for me. So that is me, quickly.

Erik, some things you’ve learned about working with AI?

Erik Solce: Yeah, so I’ve learned that AI can help you think of solutions you may not have considered before, and it’s good at giving you a cost-benefit analysis for each option. But it is difficult for you to give it enough context to narrow it down to one choice. The decision ultimately is up to you.

AI can walk you through some complex technical processes that you’ve never done before, but you need to be familiar enough with the concept to know what a successful end result actually looks like. You need a base level of understanding of what it’s guiding you through in order to know if it’s leading you down a false path. Validating answers is as important as knowing how to ask them. After you’ve received a response, you’re probably going to want it to list its sources so that you can check them and verify.

And one of the most useful tasks that AI can perform is enabling multitasking. For example, I can format a report faster than Claude just using Excel macros. But if I task Claude with doing that, I can also work on other tasks at the same time.

Carolyn Woodard: Yeah, I found that also is really important and helpful. And I would say, on a totally different project, I got this report about our website, and I was able to work through it with Claude and ask Claude to give me some third-party sites that I could use to verify things that were in the report that this other company vendor had given me. So yes, you need to verify what you’re getting from an AI as the output, but it also can help you with verification, with suggesting other places that you can get that independent corroboration.

Other AI Projects at Nonprofits from the Chat

All right, we’re going to move into: there are so many great projects in the chat. So I’m going to put these all in the transcript, because I don’t know that I’m going to have time to go through all of them, but all of you that are in the webinar right now can see them in chat and be thinking, oh, I could do that, or maybe that’s a good thing to look at. Anything from:

Formatting messy data to migrate into a new CRM.

Personalized stewardship for individual donors based on history.

Working on a hub site with associated department sites with dashboards for all.

Automating contract workflows, that makes sense.

Dropping aggregated monthly financials into AI so I can quickly ask it questions.

Another project that someone is sharing is that they could store all of their grant agreements. So you can ask it questions about: does this grant allow food expenses? Do we have to segregate interest earned, et cetera? Great use case.

Avoiding duplication of tasks with grant applications, that’s a great one. And that’s a little bit longer, so I’ll include that in the transcript. “..ideally, identify opportunities alongside data repository that would return appropriate details in our language from our personalised data library. Essentially, as close as possible as we can be to a paid tool without the costs (apppreciate this will limit possibilities) and without compromising sensitive data.”

[Another project from chat: Creating a huge database to help support members with logistics.]

Just so many interesting projects you’ve got here. I love it. Thank you everyone for sharing these with us. And I hope this is sparking some of the things that people in the audience are thinking about, and that those of you who are reading and looking at this later on our website will be able to think about and maybe use some of these.

And there are also some websites out there that have some use cases, so I’ll share that in the transcript as well. I know the McGovern Foundation has one where people can upload what they’re using AI to do, and other people can look at it and think, oh, I could do that.

Q&A: Choosing Processes to Automate and Protecting Sensitive Documents

Carolyn Woodard: All right, so we have a little time for Q&A while we have Erik with us.

Erik, one of the questions that came in in the registration was: do you have preferred tools or methodologies you like to use when assessing the business processes to be automated that you’re going to use AI on?

Erik Solce: I think that it’s important to walk through the process with the people that are actually doing the work. You should talk to your coworkers about what processes can be improved. They’re the ones that know what takes the most time, what’s the most repetitive. You want to note the actual processes, not just what the documented processes are, because those can differ wildly depending on how good your documentation is. Try to map it all out in something like Lucidchart, or maybe even just on a whiteboard.

I would then identify items that are high volume, rule-based, or repetitive, ideally all three, because these are good targets for automation.

And then I would make sure you’re discussing these processes with others. I would definitely include IT or security teams, because we want to make sure that we are managing risk appropriately.

As far as specific methodologies, I’ve never used it before, but SIPOC looks pretty good as just a good process to improve. So maybe check that one out.

Carolyn Woodard: Yeah, no, I think that makes a lot of sense.

I think we have time for one more question, which was: how do we ensure our sensitive documents are not compromised inadvertently?

And we’ve kind of mentioned security a couple of times here. But we do have some other resources on using AI tools safely and securely. We’ve done some podcasts on checking permissions. Anything that you have permission to, your AI tool, you can give it permission to see. So that’s something else that your IT team wants to be careful about.

Or if you’re kind of running into it and thinking, I don’t know if I should have access to this, bringing it to somebody’s attention; or if you’re thinking, I’m not sure I should use AI on this.

I think the number one thing to do for AI and security is make sure you have a policy, make sure your staff know what the policy is, and do that over and over. So don’t assume that they know it, and don’t assume that the AI tool isn’t going to prompt them to do something that seems helpful, but actually is creating risk.

Make sure that your staff know they can ask questions if they’re unsure about something. They should go with that gut feeling and they should make sure that they know who to ask. In your organization, someone on an IT team who knows about security and isn’t going to make them feel stupid for asking.

I think of it kind of like a souped up anti-phishing training. You need to stop and think when you’re thinking about doing something with AI, or giving it permission, or automating it, or letting it do something for you: stop and think. Give it minimum permissions by default and check with somebody, talk with someone, if there’s something that feels like it might be a little bit suspicious or just be going a little bit too far, giving that AI permissions that you might not want it to have. It’s better to be safe than sorry.

You might run it by somebody on your team and they say, oh yeah, that’s totally fine, this is how you do it, and here’s how it’s secure. And that’s a great position to be in and a great feeling to have.

But I think AI by its nature can let people go off by themselves in different directions, using the AI as their only assistant. And so, again, just being really open about those conversations, I think is really important. Because also, like we just saw in sharing the different projects here that people are thinking about, you have people at your organization that are thinking about or doing projects, maybe doing something really similar to what you’re trying to do. So you could find them by talking openly about it and running it by them. It’s just so important to have those conversations openly.

Erik, do you have some steps you might add on security?

Erik Solce: Yes. When I saw this question, I thought about it more from the technical steps that you could take.

Obviously doing something like reviewing permissions in SharePoint or your Google Drives, especially if you’re giving the AI tool access to your document repository. You want to make sure that the people who are using the AI have the proper permissions and they don’t have access to something they shouldn’t, because if they query AI and it has access to that data, it will give them a response.

You can also add sensitivity labels. If you have an HR or a finance folder that you never want it to look in, you can apply sensitivity labels to that in SharePoint to make sure that it’s not pulling information from there.

And then if you’re using a less well-known AI tool, so it’s not like Claude or ChatGPT, definitely read the terms of service and the privacy policy to see what they’re doing with your data. And along that same line, never use free tools. If the tool is free, you are the product. So they’re using your data to train their LLM or selling it to marketers. So please pay for your AI tools.

Carolyn Woodard: Yeah, yeah, for sure. And that gives you those enterprise protections as well, such as they are. They are better than the freemium tools, for sure.

All right, so we did have someone in the Q and A saying that in recent incidents, AI tools have gone beyond what was intended, and those kind of agentic AI, which we’re going to do some more podcasts and content on as well.

I think I haven’t seen a lot of nonprofits using agents. That’s something that’s pretty tech forward. But yeah, if you’re in that situation and thinking about creating an agent, definitely you need to have more security on it. But the agents that got out at the big tech companies were pretty sophisticated and were being tested for what they could get out of, I think, in a couple of those cases. So not something that most nonprofits will be dealing with, but something that we need to be aware of.

On the smaller level, it is true that you can have an AI do something in your name or write something. The human in the loop, that’s what we keep talking about: AI is not going to get the blame for it. You are responsible for it. If you put something on your website that’s not true just because an AI wrote it, you’re going to be the one that’s responsible for it.

I want to quickly go over our learning objectives. I think we hit a lot of these, Erik. How can you structure an AI project at your nonprofit with this three part structure that really worked for us? What did we learn about using AI on a real messy data problem? Stay tuned. We’re still learning, but we learned a lot. How do you know if your AI-assisted workflow is working? How can you verify? How do you keep the human in the loop? We’ve talked a lot about that. And what would it take to try something like this at your organization? I think that’s something that all of you need to answer for yourselves.

I will invite you back next month. I’m going to be talking with Johan Hammerstrom about why IT management capacity, not just funding for IT tools, is a real lever for nonprofit resilience. And this is a project that is near and dear to my heart. IT management often becomes separated from organizational leadership. And nonprofits that struggle with the quote unquote wrong software or tech stack usually got in that situation because no one at the organization owned the strategy, governance, and decision making behind their technology. So the owner doesn’t have to be a techie, but they need to know how to manage IT.

Next month, we’re going to talk about this challenge and some ideas for funders, nonprofits, and vendors to see this capacity growing as a really good opportunity to better support the nonprofit sector. That’s at 3 p.m. Eastern, Noon Pacific, Wednesday, September 23rd. You can find the registration page on our website at communityit.com, and I’m going to share it in the chat right now so you can register right now if you want to join us again.

Please don’t forget, as you exit the webinar today, to take our short survey, because as we know now, I actually read all of those answers, but now I also will be able to make sure that those topics get covered in future months. One lucky winner chosen at random receives a $25 gift certificate for helping us learn more.

Also, we are going to hop right on over to Reddit. It’s at r/NonprofitITManagement. And I just shared that in the chat. So we’ll be answering some more Q&A over there for another 30 minutes or so. Please join us there. And you can join us anytime on Reddit and ask your question. We’ll see them and be able to answer them. You can also get in touch with us through our website.

I’m so happy that you all joined us today. Thank you so much. An hour of your time is a gift and we appreciate it. I hope that this information was helpful to everyone in the webinar today and later listening. Erik, thank you so much, both for sharing your expertise with me when I did this project and also for sharing this hour with us today, because I know you’re super busy with all of our clients, and I just really appreciate your time.

Erik Solce: Thank you for inviting me and thank you for having me. It was great. I learned more doing our project than I ever have with any other AI initiative. So it was incredibly useful to me as well.

Carolyn Woodard: Oh, thank you. I’m glad to hear that. I hope it is useful for those of you who are listening, that we walked through kind of the good, the bad, and what didn’t work and what worked. And I hope that you can use this.

If you have any questions, you can always get in touch with us. I’m on LinkedIn, we’re at our website, and of course, come back next month for our next webinar. Thank you so much for joining us.

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 Christina @ wocintechchat.com M on Unsplash