Welcome to Tony Martignetti Nonprofit Radio, big nonprofit ideas for the other 95%. I’m your aptly named host and the pod father of your favorite Hebdonadal podcast. Oh, I’m glad you’re with us. I’d suffer the effects of gingival hyperplasia if I had to chew on the idea that you missed this week’s show. Here’s our associate producer, Kate, to tell us what’s going on. Hey Tony, we’ve got AI for the rest of us. Our coverage of the 2026 nonprofit Technology conference continues with Allison McMillan’s survey of these AI tools, Claude, Gemini, Perplexity, and Suno. She reviews their use cases, the differences between them, their limitations, and pitfalls. Allison is CEO of Tavelin Consulting. Then Your AI acceptable use policy. Eric Mulho, founder of Bond Partners, explains what belongs in your AI acceptable use policy. As you and your AI tools learn iteratively from each other, evolving your policy and culture, you need guardrails around data protection, transparency, accuracy, ethics, and sustainability. On Tony’s take too. My updated book title. Here is AI for the rest of us. Welcome back to Tony Martignetti nonprofit radio coverage of the 2026 nonprofit Technology Conference right here in Detroit, Michigan. My guest for this session is Alison McMillan. Alison is CEO at Tavelin Consulting. Alison McMillan, welcome to Nonprofit Radio. Hello, thanks for having me. It’s a pleasure. Your subject is AI for the rest of us, a nonprofit professional’s guide to getting started. Give us a 30,000 ft overview of the topic, please. Yeah, I think right now, I mean, obviously AI is a super hot topic. Um, there are lots and lots of sessions about AI, how to use it, um, how to adopt it, but I feel like until you get really hands-on with the tools and understand different tools, their use cases, etc. it’s really hard to get to that aha moment or to get to that moment where you’re like, oh yes, I see where and how this can be useful and so. AI for the rest of us is really an introduction into what LLMs are, what, um, what the different tools are. We do sort of a survey of a handful of different tools and then it’s really getting hands-on with them so that you have that practice. You can compare and contrast tools and you can really get deeper into which tools you might want to actually adopt, um, become a power user of or use more once they go back home. Alright, cool. Thank you. And we’ve got plenty of time to dive into that. So why don’t we begin with our survey of tools? How, how should we approach this? Do you want just one by one? Should I throw them out, chat, anthropic, Claude? What, you, you know them. Um, yeah, up to, I mean, I can go through, uh, the ones that I went over in the session. Um, so we started with Claude. Um, we just spent a couple of minutes on, on cloud. Well, I guess we started with, um, what I feel like are the best ways to even think about how to get started with using AI tools. Cause oftentimes, um, people, they see all these really interesting super complex use cases through. You know, social media or different people talking to them and they think that they should start there and that feels like a really hard place to get to quickly, um, and so what I recommend is that folks start with, uh, one of three things either what are they still Googling, um, so instead of going to a search engine, go to an AI tool. Um, where do they get analysis paralysis, right? Where is it like I have so many things to compare. I don’t have the time to go deep into each of these and I feel like I need to make a decision, but I can’t and I’ve been sitting in this like need to make a decision space for too long. Um, or third, the third is what are the places where you don’t feel like it’s A good use of your time, right? Where you’re sitting there going, why am I spending the next hour doing this? There’s so many other ways that I could and should be using my time right now. Um, so those are really the three places that people can, that I recommend that folks get started using AIs and using AI and experimenting with these tools entry points. Yes, exactly. Exactly, exactly. Um, so yeah, so then we started with Claude, um, and yeah, so, and Claude is the one that I feel like, um, most folks in this audience have probably played around with at least a little bit, um, but Claude Claude is the anthropic tool. Yes, um, and Claude is, uh, really good for sort of general questions for, you know, a, a chat tool. Um, one of the things that we talked about that I like to recommend is projects which are, um, specific spaces that you can set up that have where you can give it a knowledge base so you can give it a whole lot of background information that it can then pull into. Any answers that you’re looking for, um, so we talked a little bit about projects and how to set them up, um, they’re similar to Gemini gems or, um, custom GPTs in in chat GPT, um, and so that’s Claude and then folks got a few minutes to, um, grab an idea, work together in small groups or in pairs, and really experiment and get hands on with the with the tool. Now what are some tips about setting up Claude? That that you shared we don’t want to be shortchanging nonprofit radio listeners here since we didn’t have the value of sitting in the session. What are some of your setup tips? Yeah, um, my setup tips are cloud is actually really easy to get actually all of these tools are really easy to get started with and so we don’t actually spend a ton of time on. Set up because there are so many different tutorials and things that talk about setup that you could spend such a long time in that configuration space without actually getting your hands onto the keyboard and and typing in prompts and being like, OK, if this happens, what’s what answer do I get back? What does it look like? Um, so again, I talk a lot about uh projects as like a really good space to and you can have many projects. So there is on cloud um in the top left corner. Oh this is a loud. Yes, it’s a cloud option, yes, to choose projects and then so when you choose a project, you can create a project. So let’s say I have a project on um for social media, right? I wanted to write LinkedIn posts. What I could do is I could then give a knowledge base to that project of Um, LinkedIn posts that I like, ones that I think are well-formatted, um, maybe ideas of LinkedIn post, um, formulas, blog posts, a couple of blog posts about LinkedIn posts that went viral, right? Things like that. And then whenever I want to write a LinkedIn post, I go into that specific project and I have the chat within that project. Because it has all of that context and knowledge already built in. And so one of the things that people talk about a lot is, you know, I ask AI to write an email for me or to write a LinkedIn post and it doesn’t sound like me at all or it just it doesn’t, it doesn’t land, it doesn’t feel right. Um, and so projects are really a great way to train so that you spend significantly less time editing. One of the other tips on LinkedIn or or a blog. Exactly, exactly. Um, and one of the things that I suggest is early on as you’re training that project, as you’re doing that, let’s say that you’re writing a, writing a LinkedIn post or writing a blog post or even writing a newsletter, right? That that’s Top section of a newsletter. You can co-create so um Claude can give you something and you can say, I like the second sentence, I don’t like the opening, the whole second paragraph has to change. Give me something that’s a little bit more this flavor or that flavor. It’ll give you something new. You can go back and forth with Claude in that way and then what I usually do is at the end. I say great. What did you learn about me, my organization, my style and my tone through this co-authoring of this post? Turn that into an artifact or into a document and then you can add that document also into your project knowledge base so it will pull all that information in the future. Oh, so asking it to analyze itself. What did you what did you learn about me, and then use that. Yes, because then it says like oh this is the kind of you like you use fun to or you’re very like business or this is uh for donor communication. It always has this kind of angle or you always end with the CTA like it will come up with all of the um all of the specifics about you based on that back and forth when you said don’t like this, like this, don’t like this, like this. Alright, alright, cool. Alright, it’s Claude. Claude, let’s uh what what should we, should we talk about next? Um, yeah, so then we talked about Gemini. Um, this is the Google one. Um, yes, and so, uh, Gemini is interesting because I think that Gemini lagged behind for quite some time. It was not a go to option in terms of these AI tools. You heard a lot about Claude and Chachi BT and not so much about Gemini for a long time. Um, and then Gemini really sort of like sped ahead and and leapfrogged, I think Cha GPT, um, not quite cloud, but it actually became a very, a very good tool. Uh, the other thing is that with Gemini, so Gemini itself you use similarly to how you would use cloud. There’s that chat functionality, they have gems which are similar to projects. Um, they also So have scheduled actions which I think are the easiest sort of agent-like setup to set up where basically you give it, you say, uh, you know, I, I want to know this thing um and please run it once a week. Uh, I have an out of the box one running right now which is um everyday it sends me something interesting that happened in. on this day. Um, you know, so and that’s an out of the box one that that actually Gemini offers. Um, so it comes with, uh, scheduled actions, gems, and then Gemini which you which you use like a chat functionality. Um, but the other interesting piece is that it comes with when you pay for Gemini, you also automatically get Notebook LM and Nano Banana. So, Notebook LM is really good banana. I know you’re gonna explain what the hell that means. OK. Um, Notebook LM is really good for lots of like larger research reports or basically Notebook LM does a variety of things. One of the most common use cases for it are um when people need to summarize notes or big reports, they’ll put it into Notebook LM. Um, and they have an audio overview button and it will turn it into a podcast for you. Um, so if you enjoy podcasts and listening to conversations and it’s a little bit more engaging for you, um, it will turn into a podcast that you can then listen to. equivalent to podcast, of course. It goes without saying, but I say it anyway. It won’t be that. You’ll, you’ll, uh, you’ll suffer, you know, a, a lesser, lesser engaged, uh, well, you might, you might have a better host. You’ll have a better host, but the, the guest experience is not gonna be the same. But if you have like a 30 page report and you’re like, OK, I just like I just need a summary of this, right? Instead of reading it through, right? It makes it more, more interesting, more engaging. Right, exactly. Um, so it does that. It also, um, I know actually a lot of students that use it. There’s a flashcard option so you can turn, let’s say like a study guide or material into flashcards. Um, so I know a number of students that use it in uh in that way as well. Um, so yeah, so that is what that’s what Notebook LM is and then Nano Banana. Is, um, Google’s image generation tool, uh, and it’s sort of widely regarded as the best image generation tool out there right now. Um, so and you can enable it from the Gemini chat if you say create an image, it will automatically use nano banana as what it’s using to create that image, um, or you can select, there’s like a dropdown and you can specifically select the, the tool nano banana to to use for image generation. Um, and yeah, so and then people got hands-on and there are a lot of, uh, there are a lot of images created which are which are fun but Nano Bananas is very good at when you describe an image, um, or sometimes what I recommended also is you can put in a newsletter or a communication that you want to go out, um, and you can say create for me. Image that would go along with this email, that would go along with this newsletter, etc. Um, and it will, you know, read the information, figure out and at least give you a first draft. You can also tell its styles like in watercolor style, in a sketch style, and realistic animation, in photograph, whatever. Um, and so yeah, so those are those are some of the uses for for Gemini chat? Um, no, I actually, I did not cover Chachi PT because I don’t find that it’s, um, I don’t find it’s as good as a lot of the other tools at the moment. Um, and so, and I find that a lot of people have, uh, some experience with it and so I like to get them sort of like digging into and diving into into other tools. Um, before we go to another tool, I wanna take a little digression. Ask you what uh Tvelinn Consulting is. What is that? It’s T A V L I N Tveli, what is that? Yeah, um so my consultancy is about helping people communicate more effectively. I have a software engineering background and a non-profit background and I’ve held executive leadership positions in both worlds. And so a lot of what I do um is facilitate workshops, trainings and conversations that Sort of bridge that technical and non-technical and and help people come together. So Tevlinn is actually a Hebrew word for like a spice mixture, um, and so that’s what I’m always doing is, you know, one spice is fine, but a spice mixture together, right? All those different flavors, all those different people and things coming together, um, you know, it just elevates everything and elevates. Uh, what it can be and, and the way that it’s used in Hebrew, um, is very often there’s like different tavli, there’s, there’s different tevlinns, there’s different spice mixtures, and I like to think about that also as companies and organizations that no two are exactly the same, right? Even if it’s like a little more paprika or a little more, exactly, exactly, um, and so yeah, so, so that’s what tabine means. OK, cool, thank you. Alright, well let’s go to another tool. I like distinguishing these tools. Yes, we have 2 more tools left. Um, so next we covered perplexity. Uh, so perplexity is really used a lot for research and statistics. Um, you know, what people often find and, and something that AI often gets knocked on is these hallucinations, right? Which, which are real, which are true, that if you ask for a study or statistics, um, you know, some of these other tools will, will make things up and will hallucinate and even if you. Say, OK, where are you citing that statistic from? You know, give me a link to the study, it will, it will give you a link and if you click on it, it’s like oops, that doesn’t actually exist. Um, which is a big race cause it then creates more work to actually like get the get the statistic, chase it down, make sure it’s real, etc. So that is not so much the case with perplexity. Um, perplex. gives you the list of of sources cited sources. It also for each statistic, um, it will tell you how many sources or how many places it’s cited in, um, which really gives you as a user the chance to say, OK, that one looks really reputable, that one not so reputable, right? So you get to choose which statistics you, you pull in or maybe there’s a popular report that can that then. Gets cited in uh McKinsey and Forbes and all these different places, you can see that and so you can say OK this is like a very legitimate study, a very legitimate statistic that I wanna pull in for a donor communication for uh training that you’re doing for conference talks, you know, I often use it in in blog posts to, you know, support some of the um some of the things that I’m talking about so. Um, yeah, so perplexity is great for for those research and statistics. Statistics, it’s more trustworthy than the others. Exactly, exactly. The other big difference that I always say is, um, you know, perplexity is, uh, It’s like it’s very much like a scient, like it’s very scientific. So in Gemini or Claude, um, if you ask it a question, usually it ends with, you know, gives you information and then usually it ends with what would you, would you like me to create a slide deck for you? Would you like me to do this, right? It’ll it’ll sort of guide you into your next step. Perplexity is a little bit more like I gave you the answers. These are the answers. We’re done now. So it doesn’t so much guide you anywhere else. It’s a great tool and I also think it’s very like people use it specifically for research and statistics and so, you know, it’s like it’s it’s personality. I like to think about some AI’s as like what their what their personalities are, um, so the perplexity of personality. It is very much like I have given you the information and now we’re and I hope you’re not snarky and I like to keep that to humans but uh you have it. I just yeah it’s done. We’re done we’re done together. Exactly. Um, so yeah, and perplexity is um it’s it’s great on a free model. It also has a has a pro model and and they run. A number of different specials. Um, so you can often get a year of Perplexity Pro free through a variety of different avenues. Um, so I always recommend that folks sort of like Google around or check around to see what the offerings are, um, because it’s uh you, you can often get extended, uh, time period to use it, to use the pro version. And you got a 4th 1? Yes, the 4th 1 is the most fun. It’s called Suno. Um, S U N O and Suno is Song Generation. Uh, so I use Suno a lot for, um, background music, for like, you know, or introduction music for a virtual meeting. Um, I use it often as, you know, sort of like a, like an icebreaker or you know, it’s just it’s um it’s a platform that it just puts a smile on people’s face. If we have a Zoom together, is there gonna be welcome music? Uh, do you do that for all your Zooms? No, not all, not all of my zooms, but yeah, if I’m doing an all hands or like a larger, yeah, exactly. Exactly, exactly. Sometimes I’ll also do like a thank you song for certain cause it really and it’s very, very simple like you can just put in the example that I gave to this group um was the prompt that I put in was uh an upbeat. Um, an upbeat tempo, a song for non-profit professionals describing all of the different hats that they have to wear in an upbeat tempo. And it comes up with, you know, a multi-minute song that has lyrics that, you know, are generally sort of entertaining, um, and so it’s just a fun one to to play with and to kind of have in your back pocket. Um, because sometimes, you know, you just need that morale boost. You need a little bit of, you need a little bit of fun to come in. I’m not a software engineer and nonprofit consultant and music and lyrics you don’t claim that. Good. Alright, straightforward, honestly at uh at uh. Yeah, yeah, so those are the 4 tools that we walk through, um, and then, uh, we walk through, um, I always get asked about how, especially nonprofits, especially folks with limited budgets, how they should pick a tool, uh, because there’s so much out there again, there’s so much out there, there’s different things that can be used for different specific circumstances and situations. Um, so for that, I, I talk about it a little bit like AI tools at supermarkets, um, where you, you always have your go to supermarket, right? The one that you know how all the aisles are laid out that it basically has everything you need. I’m picturing, I know where the bananas are. Yeah, you probably have even like a specific time or times during the week that that you go, right? So you might Like know a cashier, a couple of cashiers. Right? It’s it’s the place that you sort of like have invested in that you know really really well. Exactly. Exactly. And then there are other supermarkets that you go to for specific purposes, right? You might go to um Trader Joe’s when you want to stock up on really good snacks or to uh you know, HMt when you want more variety of different sort of sauces. Noodles or etc. right? And those might be um supermarkets that you don’t go to as often, right? Maybe you go once a month, maybe you go once every couple of months and you go, you go for very specific purposes. And so I think about AI tools in the same way that think about your use cases and who’s gonna use it. Is it gonna be one staff member? Is it gonna be multiple staff members? What are they going to be using it for? What are the use cases? And then pick one tool, pay for that tool, go. I like train that to really make it sort of know your people, your business, like you know, established projects or gems, whatever you’re gonna do like spend spend the time there. And then the other ones, you know, all of these you can pay for month by month. So the other ones, you know, for the month that you need it, pay, pay for a pay for a pro version, pay for a paid version for a month and then and then discontinue it and you know, go back down to the to the free version. Um, cause those can sort of, can you can toggle them up and down easily. Uh, so I sort of think about it as like the supermarket metaphor. Um, you talk about the limitations, pitfalls around, uh. Introducing AI getting started. Yeah, um, yes, I talk about, um, a couple of things related to limitations and pitfalls. Um, one is being aware of when you’re using a free version versus a paid version. There are differences in the quality of the output, um, there are differences in if the models are training right or using the data that you’re. Putting in to to train their model more more broadly, more widely, right? What proprietary information you put into um into a free version of of an AI is definitely something to be very aware of. Um, so that’s one. The second thing that I talk about a lot is compliance. Um, so a lot of these AI tools, uh, I always recommend that folks Try to investigate if the tool that they’re using. This is especially with note-takers cause I feel like there are so many note-takers coming out. People are always asking about about note-takers particularly. Um, there is a compliance called SOC 2 Type 2, which is basically an independent audit that a company has to get that shows that they handle your data and the data in a in a specific fashion. Um, and so it just provides a little bit more security, but it can be time, like it can take a lot of time to get uh SOC2 compliance and it is expensive for companies and so some of the newer, newer, newer tools don’t necessarily have that. They don’t have a trust center, they don’t have all of that sort of thought through and figured out. So, um, I always like to say you should always be aware of what the security protocols are, um, for the tools that for the tools that you’re that you’re using. Um, the third is, uh, just to have a, well, the third is around hallucinations. They are real, so just always make sure that you’re checking your sources and making sure that you’re not pulling in information that you’re not certain is, is real and true. Um, and lastly, I always recommend that folks put some sort of AI policy in place. It might change a dozen times over the next year. Given the pace at which AI is accelerating, but just a little bit about what’s the acceptable use and understanding of, you know, risks and how you intend to manage those risks and a little bit about oversight if you intend to have any oversight. It doesn’t have to be long. You can actually have AI generate a first draft of a policy for you. Um, but I always recommend because you’ll have in uh in any Team, you’ll have this spectrum, you’ll have this range of folks that are like really really hesitant and haven’t touched it at all. And you’ll have folks that are early adopters and they’re jumping into a ton of different tools and really trying to connect everything together, etc. And you want everyone to be on the same page about what’s OK to do and what’s not OK to do in this moment for, for them and for your organization. Let’s pull a little more on the thread about um you know, your own data protection. Making sure that you’re, as, as you’re uploading content, it’s not being used for general consumption and learning of, of any of these models. How do we make sure that that’s. Not the case. Yeah, I, I think um almost the on all the paid on all the free versions, I believe it’s that’s just part of um there is no way to sort of guard against that. Um, that’s why I always recommend, you know, I know that uh companies don’t have endless dollars but it is good to sort of pick a tool and pay for it. Um, and then in all the pay. tools. Right, right, um, and then all the paid for tools there there is a way to toggle off, um, some places hide it more than others, um, but there is in settings, uh, a way to to toggle off so that that, uh, so your materials can’t be used to to train sort of larger, larger models. Um, you just have to find the setting. OK, but it’s worth looking for. Yes, because if you wanna get close to one of these models, you’re gonna be giving it some. Some proprietary data. That uh you may not want released. Yeah, yeah, exactly. I also often tell people that, um, you know, you can often get what you want out of AI without having to put as much proprietary information as you think you do into the system, right? And so it’s worth thinking about, you know, I think it’s easy to be like, OK, I have this the spreadsheet, I’m just gonna put it in and do X, Y, and Z, um, but I think it’s always worth. Thinking about, OK, for this task that I’m doing with this AI tool, what is the outcome? And I have this spreadsheet. Do I, do I need all the columns? Can the people just be numbers? Do I need first and last names? Like to, you know, it often is just a couple of extra minutes of thought and really thinking about what what’s the outcome that I’m trying to To get to and then you can often get to a place where you actually don’t have to put a lot of proprietary or sometimes any proprietary information into the system, um, in order to in order to get the result that you want. That’s good advice. It’s worth thinking. What do you need? What’s the purpose here? Right? Like example, do I need first and last name? Do I need any names at all? Alright, right, exactly. So Alison, um, I don’t know, share something else that, uh, we haven’t talked about yet that, uh, you talked about in your session or, or some questions, maybe, uh, some questions that stuck with you from your session. Yeah, um, I think the, the last thing that we talked about in the session was I gave a few, uh, sort of my top, um, tips and tricks, uh, and so. A couple of them are, um, I, when I’m learning something new when I’m trying to get ramped up on something really quickly, um, I will sort of give the A tool the concept and I’ll say, explain this to me like I’m 5 and then explain it to me like I’m 10 and explain it to me like I’m a new college grad and then explain it to me like I’m a professional with 25 years of deep experience in this area. Um, and it’s a really great way to just get really. Deep into like into a topic very quickly and understand the jargon and and at what level different people operate. Um, so that’s that’s one that I really like, um. I often will use I am as a if I’m trying to write like whether I’m a client or a consumer or whatever, I’ll sort of give AI that whatever persona I’m trying to send a message to or connect with, right? So I am, you know, a non-profit executive director um with a team that has Varied adoption uses. I really want them to adopt X tool. What plan would you recommend, uh, for us to, to go or even, you know, I’m on a team, I’m in a nonprofit. My executive director wants me to be using AI more. I don’t really want to, um, you know, what, what plan might work for for me if it’s the, you know, executive director sort of trying to put this plan together. who said, you know, he uses his prompts you are actually the Backstreet Boys. You are, you know, I don’t know the song but he does. You are does it make a difference you are where I am? OK. Um, I uh I also tell people um that sometimes you just have to close the chat and open a new one. Um, sort of like, you know, with any technology, sometimes you just have to turn it off and wait 5 seconds and turn it on again. Um, so with AI like if you sometimes it can just go in the wrong direction and then once it has that context and it’s sort of traveling in in that path, it can be really hard to do a reverse direction or to be sort of like no no no, this is like nowhere near what I what I want. Um, and so sometimes just closing that chat and starting fresh is really the the best way to go and will uh limit your frustration level. OK, OK. Yeah, so that’s a good one. these are Allison’s tips and tricks. Yeah, yeah, tips. Go ahead. Um, I’m trying to think which other ones there are, um. That Yeah, I think those are the, I think those are the, the top ones. Those are really good, um, really good go tos to to think about. So just leave us with some uh inspiration for, for, for uh for the rest of us. Yes, um, I think some inspiration is, um, AI tools are. Rapidly improving, um, month by month. I, I also say like folks that tried it 6 months ago or a year ago and said. This is, this was all hype. I don’t understand why everybody’s talking about this. Should try the tools again, um, because they are getting, getting better and better all the time. Uh, and again, like your, your adoption, your usage, you don’t have to use the most complex scenario. You don’t have to use what the AI master and whatever post has said and you know, tying these eight tools. Together and doing those sorts of things. Um, it’s really great to just start somewhere and get yourself to that first what I call either like an aha moment or even like a, oh, that’s interesting, uh, moment and then go from there. All right. Allison McMillan, CEO at Tvelen Consulting. Tavlen, the, uh, the spice, you know, the spice, the spice hybrid, the spice mixture, bringing it all together. I love seeing women in uh software engineering too or any engineering, so where’d you do your degree in software engineering? Um, I don’t have a software engineering degree. Uh, I have a political science degree and then had a start up, taught myself how to code, um, yes, yes, before, before boot camps were a thing, before, yes, uh, you know, 15 years ago, um, and then moved into, moved into software development and then. You know, software engineering leadership, uh, worked at companies like GitHub, um, so yeah, it’s my, my path has been a windy one, but no, no computer science degree here. Alright, that’s OK. I still love seeing women in engineering in any of the sciences. Alright, Alison McMillan, thank you very much. Thank you and thank you for being with Tony Martignetti nonprofit radio coverage of the 2026 nonprofit technology conference. It’s time for Tony’s take 2. Thank you, Kate. My book title is lengthy, but very, uh, it’s precise. You’re gonna, you’re gonna know exactly what the book is about and, uh, See that my Personality, uh, emerges in the book through the title that I want you to see that from the title. I had a really long title, but I ran into. A constraint at, uh, Amazon. They only allow 200 characters for the book title and the subtitle. If, and your book goes nowhere these days, if you don’t publish through print through Amazon, not publish. I’m self-publishing. They’re not a publisher, they’re a printer. If you don’t sell, that’s what I, they’re a bookseller really, and a printer, of course. If you don’t sell your book through Amazon, you don’t get very far. So, their constraint pretty much sets the tone for The, the book titles, uh, in the, in the, in the world. So here is the new book title. Planned giving accelerated. The cut through the shit, no nonsense, practical, step by step guide to start legacy giving fundraising at your small to mid-size nonprofit simply in one week with bequests. Now, that, that happens to be exactly 200 characters, including spaces. They’re very precise about that, includes, because the space is a character, but they’re, they’re, they say it explicitly. So I used to have, uh, it was finally some plan giving accelerated. Finally, someone wrote a cut through the shit, no-nonsense practical. But I had to cut out, uh, finally someone wrote A, but I like the plan giving accelerated, the cut through the shit, no nonsense practical step by step guide, etc. etc. I like the. It’s not, there’s not one among many. How many, how many cut through the shit, no nonsense practical step by step guides to start legacy giving fundraising at your small to mid-size nonprofit simply in one week with, with bequests? Could there be? There, there could only be one. That’s this one. The, the. So. Sometimes constraints lead to uh more precision, because you gotta, you gotta tighten it up, you know, you cut out the flab, so. Um, so that was, that, that actually is an advantage. I like that. The cut through the shit, no-nonsense, practical step by step guide to start legacy giving, fundraising at your small to mid-size nonprofit simply in one week with bequests. Now, I used to have, following that, a, uh, a parenthetical in the title. The title may be longer than the book. Did you need a nap? That was in the title. Now that, it can’t be in the title anymore because I, I hit this Amazon, uh, 200 character constraint. But, But It can still be on the book cover. It’s just not part of the title. You can cram and squeeze as much as you want onto a book cover. It just can’t all be part of the title. So that’s what I’m gonna do. So we’re not losing. I am not surrendering. The title may be longer than the book. Did you need a nap? I think that’s important. I think it’s, uh, it sets the tone, you know, this is, uh, this is not gonna be a, uh, an academic type text. It’s not gonna be your, uh, your average, uh, journal, journal type tech, uh, text. This is a, uh, This is a book you’re gonna have some fun with, so. Just wanted to keep you apprised of the latest developments, uh, that’s a significant one because it’s the title. Again, Planned Giving accelerated, the cut through the shit, no nonsense, practical step by step guide to start legacy giving fundraising at your small to mid-size nonprofit simply in one week with bequests. And then, of course, the title may be longer than the book. Did you need a nap? But that’s not officially in the title. But that is Tony’s take 2. Kate, I like how you said you are not surrendering to Amazon’s character count. Limit. You are not surrendering, no surrendering here as the no surrender. No reset. Absolutely. It, it came out better. It came out better. I’ll be honest, I’ll probably just type into my Amazon Planned giving accelerated by Tony Martignetti, but your title does sound very, very special. And very larger than life, like I know you are. Oh, thank you. All right. I’m glad you didn’t say sounds very interesting. I, I, I was afraid you were gonna say, oh, it sounds very interesting, which is kind of dull. No, sounds special. Thank you. Thank you very much. I think it is, and we’re sticking with it. We’ve got bou butt loads more time. Here is your AI acceptable use policy. Welcome back to Tony Martignetti nonprofit radio coverage of 26 NTC, the 2026 nonprofit Technology Conference in Detroit. My guest now is Eric Mulho. Eric is founder of Bond Partners. Eric’s topic is step by step creating your nonprofit’s AI acceptable use policy. Eric, welcome to nonprofit Radio. Thanks for having me, Tony. It’s a pleasure. Is this your first, uh, NTC together or NTC I should say, not yeah. It’s a great conference. This is our 12th coming up. Yeah, that’s exciting. It’s been wonderful so far. And we’ll be at number 13 in Portland next year. Just give us an overview of the topic before before we dive into some detail. Yeah, yeah, so. Uh, there are two things I’m trying to, uh, really address here with this topic. The first, uh, obviously is this whole notion of the acceptable use policy. So we know everybody is using AI, right? Like I was just scanning some of the, uh, you, you’ve probably been talking a lot about it every day, right? Um, but a lot of organizations haven’t yet caught up in terms of their policies and in terms of providing people with guidance and guardrails and direction about how we use this as an organization. And some organizations may not even be aware that some of their staff are using some of these free tools and uh perhaps doing some of their work leveraging AI and may or may not be doing that in the way that we want them to so um the first thing that I’m gonna be talking about is actually some of those policies that I think organizations need to be thinking about. The broader issue that I’ve that I’m hoping to talk about is just how we build the culture within an organization. Uh, around usage of AI so that we are, um, really embracing it so that we’re talking about it so that we are leveraging the expertise that we have in the organization itself as we start down this journey. Very good, thank you. Um, so let’s, uh, dive into these topics. Both the uh sorry. There we go, um, as I unclip a little microphone, so, um. Yeah, your policy and also a culture, a culture. All right, so we could take them in that order. Let’s, sure, let’s talk about what, what belongs in your policy. Yeah, yeah, what, what belong, what are the key pieces? Well, there are, there are quite a few aspects that people need to be thinking about. Um, what I’m gonna focus on today is trying to, to bring that down to a manageable number of things. The first thing that most people probably think about when they’re thinking about policy. Is protecting your data, right? We nonprofits are using all sorts of data that they need to be thoughtful of when they’re, uh, using some of these AI tools. So that may be your donor data, that may be client information, it could be grant information, whatever it is you’ve got stuff out there that you have to provide some. Guidance to your team as to what they can do and what they can’t do. In other words, AI is a fantastic tool for segmenting your donor data or taking a look at donor trends or trying to understand when and how gifts are coming in, but we can’t do that just by doing a, a download of our Salesforce information and. Chugging everything up into chat GPT and saying tell me what the answer is. So we have to have some rules in place around our data to make sure that people understand what is personally identifiable information, what our tools and processes are for gaining approval, and when we’re gonna use some of that, um, whatever that looks like, we’ve got a lot of data that we have to be thoughtful of, which is one piece of that. Uh, another piece is, uh, transparency, thinking about when are we disclosing, when are we talking about, uh, AI. And this comes about, you know, in very simple aspects, right? Many of us use, uh, AI meeting note takers or we use, um, uh, record meetings or record information and then we’re leveraging AI to help us maybe generate notes or whatever it might be. Uh, we probably as an organization need to be clear as to when we’re using this, when we aren’t, when we’re telling people we’re using it, when we aren’t telling people we’re using it. Um, another area that we, uh, uh, that, that, uh, I think is, is valuable for folks to be, to be aware of, um, is of course accuracy. So data make AI tools make mistakes. They have hallucinations. They make stuff up, um. What’s our process? What’s our, uh, approach to making sure that we aren’t feeding up wrong information, incorrect information within the organization? Many organizations probably need to have some sort of formalized two-proofing, two-person proofing process or another set of eyes taking a look at outputs and, and the results that people are generating when they’re moving forward. There are ethical and sustainability concerns, so you know I’ve worked with organizations that are environmentally conscious. I’ve worked with other organizations that are really focused on content creators. Well, those organizations may have very, uh, distinct rules and may have very distinct policies that they wanna develop that match their values and match their mission because AI has a particular. Role to play in some of those areas so lots of aspects to uh to consider. OK, so these are elements of our our acceptable use policy exactly and and the key is to start the conversation, right? The key is that we want to engage our staff and we wanna give our staff again kind of the rules of the road so they don’t get into trouble and we’re not getting into trouble as an organization. What’s your advice around the uh the. The accuracy part of that, ensuring accuracy. You mentioned, you know, to human, maybe to human review. What, what else, what else can we have in place because. That that is a risk that a huge risk. Well, these are all huge, but that’s the one that puts a lot of people, I think, off, off AI or will only, they’ll only adopt AI for the most mundane tasks, right, right. Well, we, uh, first of all, we can, we can, when we are doing our prompts and I’m thinking here, I, we should probably make a distinction here. There’s AI is a massive topic. I am talking. Generally speaking about the large language models, the generative AI that we are using through a chat GBT, a cloud, uh, a co-pilot, something along those lines. Many of us have used these tools. There are lots of different ways that they are embedded in other software and in other programs, but generally speaking, uh, I’m, I’m gonna focus on that area. One of the things we can do is, as we’re working with the AI tools and as we are prompting them. Uh, we can say show your work. Um what, what’s your source for this data? Where are you getting this information from? And actually clicking on the link that it tells us. So making sure that we are as an individual when we’re getting some quote of data or or information fed back to us, um, we can take a look at that information as an organization then we can also have policies and procedures in place in terms of how we are fact checking and how we are proofing this. This may be a new policy. Or a new way that we go about work, but before something goes out on email, before it goes out in our annual report or in our fundraising appeal, do we have somebody who hasn’t, who isn’t the writer, uh, having eyeballs on that and highlighting, hey, you mentioned this fact here, tell me where it came from, how did you come about doing that? So there are things both technologically and within our processes that we can do to make sure that people are, are protecting themselves. How about the transparency you mentioned, you know, there may be use cases where you’re not. Revealing that that you used uh that you used artificial intelligence. Well, I mean that could be something as mundane as you know, write me an email and and obviously the person reviews the email. Are there other cases where it’s not necessary or or where you where you actually. Have an obligation to disclose that you have used one of the one of the tools. I think it’s very important, uh, the example I mentioned, I do think it’s very important when we have meetings and recordings and that kind of information that we are disclosing that you mentioned meeting notes, yeah, transcribers. I do a ton of interviews in my practice and so the start of every conversation is, hey, wanted to call your attention to the fact that I’ve got my note taker in the room. Is that OK with you and make sure that we are disclosing that. Um, within organizations, I think I, I want us to disclose the use of AI, um, to build that culture piece, right? I want people, I, in my perfect world, more people within the organization would say, hey, I, I’m working with AI to help me solve this problem. Uh, what do you think about this result? Or, um, I looked at the data, then I went to AI, then I refined it. Here’s my final work product. So that we are creating a culture within the organization that says these are valuable tools that we all need to be using and we all need to be learning from. I think particularly, and, and I’m seeing less of this, but I think there is a little bit of this that’s still, uh, that was still the case when a lot of these tools were launched in 2022. A lot of fear, a lot of, of, oh my goodness, this is cheating, oh my goodness, this is, uh, gonna. Are gonna take jobs and all of that and I think we’ve got to really turn that on its head and we’ve got to, to share with one another how we can be more effective, how we can be more efficient, how we can be more productive when we’re leveraging these tools so that it becomes again a tool, not, not, not replacing somebody or not replacing the critical human element, but one of the, the tools we use in the same way that we. Might say, hey, I took your data, uh, this is the analysis I did in Excel. What do you think? Does this look accurate? We need to get to a place where we’re also saying those and disclosing that this is one of the, the valuable ways that we’re accomplishing our work and that will both send a signal that this is OK, this is how we do things, and hopefully it will help us learn. One of the things that I, one of the reasons I love doing these presentations. Is invariably at some point in the presentation it’s like well how are you using these tools and I continue to be blown out of the water like people are doing crazy wonderful things with these tools and the more we begin to be exposed to how people are leveraging them, the more we can look at our own work and say, oh, I wonder if you could help me with this. What are some crazy things? Uh, I’ve heard people talk about how they’ve leveraged AI to, um, uh, uh. With dialects of language that it is actually more effective to be able to say hey we’re working with this particular population from from Guatemala or something like that that may have uh some distinct dialects and and the AI tool is pretty darn accurate in terms of translation um I had uh one of the examples, not to steal my own thunder for tomorrow, but one of the examples I use I was nobody’s gonna hear this before tomorrow. Oh all right, all right, that’s good we’re not that fast. Um, one of the examples I use is a, uh, um. Food, uh, food shelf, and they do a lot of, uh, Meals on Wheels. It’s one of their programs. They do multiple programs. Um, they realized they’re, they were spending a ton of time. Answering questions from folks. When’s my meal gonna get here? Who’s my driver? What time should I expect them? Like a lot of manual phone calls, uh, coming in. They’re developing an app with AI now that would be like DoorDash that says to these folks, Hey, your meal left our warehouse. Julie is your driver. She should be there between 11 and 11:30. I’m like, how cool is that? Like just amazing stuff that people could come up with. OK, yeah, yep, yep. Uh, uh, so, uh, on the, let’s move from the policy to the culture, um, I mean, folks may be using this and you don’t even know, correct, um, so they’re, you know, they’re certainly not adhering to any policy because you don’t even know that they’re doing it. That, that’s the worst case, that’s probably that is worse than nobody even adopting it. I think so. I think that’s bad on both ends. Well, I’d rather, wait, I guess it’s the lesser of two evils. I’d rather if I was the CEO, I’d rather nobody be using it than people be using it rogue, and, and we don’t know and they’re not disclosing it because we don’t know and we don’t know what they’re doing with it. So I’d I’d rather the former, but they’re in today’s world, they’re both setting you behind. OK. So a culture, a culture around. The adoption of and and responsible use of these large language tools. What, what that’s a very broad. Well, that’s why, that’s why I’m consulting an expert. It’s a very broad question for somebody who thinks about these things. Yeah, I, I, a lot of it we’ve been is the theme that I’ve been mentioning, uh, moving forward is, is I think, um, it’s very valuable for senior leadership to be able to disseminate out to the organization. We’re gonna use these tools. We see value in them. Uh, we see value in you too. We’re not gonna use these tools to, to get you out of a job, but we see value in these tools and so we’re gonna use them. Uh, we need to communicate out to people and we have rules about how to use them, right? Like any tool, they can be used for good, they can be used for evil. So we’re gonna use them for good. And this is the way we’re gonna protect our client data. This is the way we’re gonna protect our, our donor data, etc. and this, these are some, this is some guidance that you can have as an employee, as somebody who’s working or a volunteer, um, in order to, in order to use the tools effectively. So first of all we have to say that we’re going to use them. I think secondly, as I alluded to earlier, I think we want to create a culture within our organizations where we’re learning iteratively, right? And that’s, that’s what this whole process has been what that’s what AI is all about, right? We’re constantly throwing prompts back and forth to the tools to say, OK, now do it this way, think about it this way, reframe it this way. I think we want that within our organization where people are, how are you using it? Oh, I’m using it for this that sounds interesting. Did you get good results? Didn’t you? I tried this prompt. I tried phrasing it this way, um, and leveraging our own learning so that we’re moving the work forward. I like your analogy of learning from each other the way, the way we want the tool to learn from us. Yeah, exactly, exactly. There are experts. I would, there are probably experts in any whether you’ve got 8 people in your organization, 80 or 800. There are already people who are pretty far down this rabbit hole who are experts in in in one of these tools, and we need to leverage that. We need to take advantage of their learning and and what they, what they’ve discovered. So we need leadership buy-in for this. AI culture creation, how do we, uh, I don’t know, do we need to make the case? I don’t know, maybe, maybe there’s some CEOs that, well. They, they may see the value, but they also may be fearful of, uh, the transparency, the accuracy, the ethics, the environmental sustainability. So how do we make the case that the, the benefits are gonna outweigh the, the, the costs and concerns of the CEO? You know, one of the things, one of the little cheats I have as a consultant is when I, when I’m in doubt as to how I answer somebody, I’m like, it always comes back to the mission, which is true. It always comes back to the mission. Nobody got into this work because they wanna run spreadsheets for their whole life. Well, maybe a few finance people did, but most of the people who are in nonprofit work, most of the people who are here at this conference today got into this work because they’re passionate about the work that they’re doing, because of the mission that they’re doing. So I would say to a CEO, to an executive director, you’ve got this amazing mission to accomplish, and you don’t have nearly enough people and you don’t have nearly enough money. To get everything done that you wanna get done and now we have this transformative technology, this amazing AI tool that we’re all exploring and we’re all on this journey that can help you to become more efficient, more effective, that can leverage your the talent that you have brought on to your, uh, organization to achieve your mission. So I would frame, you know, this conversation around the culture. I would always bring it back to the mission and say we have the capacity. Uh, to help more folks or to achieve more of whatever it is we’re trying to achieve, um, when we are leveraging these tools to create internal efficiencies to help us think differently, to help us troubleshoot in new ways, so I think it’s, it’s, uh, it, it’s that big picture that the CEO is or the executive director is really responsible for disseminating out. I think what’s also important on the culture piece, especially this intersection of acceptable use and the culture piece, um. Is that we not get lost in the lawyers and HR and IT, right? These are tools to help people get work done. And so, uh, in the organization. We want the focus to be on getting the work done, getting it done effectively, getting it done, uh, ethically, getting it done within our values. Our IT partners help us to make sure we’re, we’re keeping it safe, to help us understand that we’ve got consistent tools that people aren’t going rogue on us, our HR and our lawyer folks. Help us make sure again that we’re not violating policies that we’re not violating uh laws but the big picture mission, what are we trying to accomplish that’s what we need to keep our eyes on and the policies and the the the conversations that we’re having have to serve that and as we’re focusing on big picture and having this conversation with the leadership, you know, we, we may identify maybe small things we can begin with. Absolutely. Uh, I had a guest call them, you know, pilots. Let’s, let’s test. Let’s, let’s see, hopefully it’s gonna be successful. The test is gonna the, uh, H0 is gonna be proven that this does in fact work to the benefit of the mission. So we can start in small, the small steps to bring leadership along. Absolutely. And I think we can start in small steps, um, you know, both in, both in that literal sense of like pilot. Projects or someone, what you know what would happen if, uh, we, we rethought our email campaign or we did a deep dive in our email campaign leveraging AI and trying to make that more effective and more efficient. Great pilot project we could go and run with that, but it’s also small steps in, in terms of, you know, an executive director who’s at this conference goes to a ton of sessions. What can they do next Monday? What can they do in 3 days? Well, why don’t you gather together the folks. Who are already using the tools. Let’s hear how they’re using it. Let’s, let’s, let’s just have a lunch and learn and figure out where people are at. Let’s take some small steps. We don’t have to hire a trainer or a consultant. Let’s, let’s all gather together in the conference room and say, hey, how are you using these tools? Where are you finding value? Where are they wasting your time? Where are they adding value to what you’re trying to do? So the small steps is both, you know, the literal, uh, how do we launch a project or how do we do a pilot, but it’s also on the culture side. How do we open this conversation. Uh, to make sure that we understand where people are at, who should be jumping back to the policy because we’re talking about the team meeting with the team, who should be contributing to the policy. Yeah, I think, uh, uh, obviously there’s some subject matter experts, you know, if you have a, a larger organization where you’ve actually got maybe HR and development and IT you would probably want all those folks in there. If you’re a small shop, um, you may be able to develop and leverage a policy within. Uh, you know, by leveraging one or two people within the organization, leveraging AI as a tool to help you develop the policy, we’ll be talking about that. And one of the, uh, you know, one of the assets that most small organizations have that I don’t always think they take a great advantage of. Your board members, your, your board, every single person on your board is going through this exact same conversation in their organization, and they have expertise to share as well. Um, I, what I wouldn’t do, uh, personally, what I wouldn’t do is just call the lawyer and say we need a policy. Um, I think you wanna take a look at how. People are using this. I think you wanna take a look at the existing policies you already have in place. You probably have a data protection policy already in place. We may just need to modify it or we may need to clarify or do some training with the staff that says, OK, here’s our policy. Here’s how it applies to A to AI. Um, but I don’t think we wanna just make this, uh, uh, uh, a legalistic conversation. There’s a role to be played there. My brother’s a lawyer. I’m pro-laws, but, uh, but, but, but, but we don’t want that to be the focus. We want the focus to again come back to the work that people are doing to achieve the mission. You mentioned using AI to create the AI acceptable use policy. What are you gonna say about that? I think it’s a great starting point. I think this is. Uh, this is, you know, you think about any policy. I don’t care what it is. You wanna revamp your data use policies. A lot of executive directors might spend 4 hours searching the web, going to other like similar organizations. They might call their, uh, executive director friends, they might call a lawyer, they might, you know, gather 5 different drafts and then cobble together their own new tool. That’s not an uncommon way of developing a new policy. Well, why not craft a great prompt and start there. 30 seconds of, uh, of work and you’ve got maybe 80% of it in place. Take a look at that, circulate that with the team, and then when you’re, when you’re at a place where you’re like, yeah, I think this is it, then check it over with the lawyers and the board to make sure you’re, you’re in the right place. OK. Start with, uh, you are not a lawyer. Lawyer contributing to a an AI acceptable use policy. You’re you’re not a lawyer. You’re, you’re the marketing, you’re the, the whatever, whatever, whatever fundraising your social media, yeah, whatever. I think the other thing that, that, uh, you know, when you’re starting to develop these policies again where you where AI could be a real benefit in them is, um. The nuance of your values. So in other words, in that prompt, you know, if I were, if I were to prompt Chat GPT to develop a policy for me, I would probably give them a lot of con I would give it a lot of context. I would say I’m an eight person team. Our mission is to do this. Uh, we have really sensitive data from our donors. We have some federal grants, you know, kind of lay out that, and I would say, and here are, here’s our mission and here’s our. Values because I think it’s important and we can create policies that that reflect those values um and that’s part of why every polic every organization needs their own policy because we all uh we all have different values we all have different priorities and we need to make sure those are reflected in the policies. You wanna leave us with something else that uh we haven’t talked about yet that you are going to cover in your session or maybe we a little more detail than than you don’t hold back on nonprofit radio listeners. Yeah, yeah, yeah, yeah, yeah. um, I mean in terms of uh in terms of the content that I’m going to present, one of the uh foundational frameworks that I’m just gonna skirt by but I would recommend as a resource to Uh, to the listeners, it was created a couple of years ago by the folks at fundraising.AI. They have a comprehensive acceptable use policy. It’s got about 11 different elements to it. Um, it includes just about everything you could imagine, um, as, and, and the reason I think it’s so valuable is not that every organization needs 11 pages of content for their acceptable use policy. But it’s a great thought experiment. It kinda walks you through, have you thought about this? Have you thought about that piece? Have you thought about this particular piece? And so, um, I, I often, and it’s included in my presentation, I hold that up as sort of the, this is, this is the full. You know, Cadillac version, this is, this is the high end that you would, that you may someday achieve and then for our session we’re gonna dig into kind of the, the 1st 4 or 5 that you need to tackle. All right, all right, the fundraising. AI, that’s the, the resource, the culture and the uh and the plan. Absolutely. All right. That’s Eric, Eric Mulho, founder at Boon Partners, BON Partners. Eric, thank you very much for sharing. Thanks, Tony. Appreciate it. It’s been my pleasure. And thank you for being with Tony Martignetti nonprofit radio coverage of the 202026 nonprofit Technology conference. Next week, more from 26 NTC with cybersecurity on a shoestring, and disaster recovery and incident response for accidental techies. If you missed any part of this week’s show, I beseech you, find it at Tony Martignetti.com. Our creative producer is Claire Meyerhoff. I’m your associate producer, Kate Martinetti. The show’s social media is by Susan Chavez. Mark Silverman is our web guy, and this music is by Scott Stein. Thank you for that affirmation, Scotty. Be with us next week for nonprofit radio. Big nonprofit ideas for the other 95%. Go out and be great.