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 hebdominal podcast. I want to apologize again for last week’s audio. I know that was a tough show to hear. I thank you for being with us, uh, through it. Uh, I certainly hope you didn’t unsubscribe from the show cause then you’re not hearing my, my apology this week cause you already left. No. Uh, thank you for listening through that. Also, this is show number 799, which makes next week’s show, the 800th Jubilee anniversary celebration. Oh, I’m glad you’re with us. I’d be forced to endure the pain of hosis if I had to say the words, you missed this week’s show. Here’s our associate producer Kate with what’s up this week. Hey Tony, we’ve got a wide ranging AI conversation with 3 experts. The company Accenture published an AI report, Learning Reinvented, about the connection between humans and artificial intelligence. Our smart panel discusses it and takes on broader related subjects, including leadership, equity, trust, values, space to adapt, and training. They are Afua Bruce, CEO of ANB Advisory Group, Amy Sample Ward, CEO of N10, and Laurel Michalowski, research specialist with Accenture. On Tony’s take too. What do I own? We are sponsored by the Bridge Conference. Tony will be with more than 2400 nonprofit professionals at Bridge, July 29 to 31 in National Harbor, Maryland. Info and registration at bridgecf.org. Here is a wide-ranging AI conversation with 3 experts. It is a genuine pleasure to welcome our panel today. Beginning with Afua Bruce, a leading voice at the intersection of technology, data, and social impact, she has held senior science and technology roles at the White House, the FBI, and IBM. As founder and CEO of ANB Advisory Group, she helps organizations across sectors strengthen their technology and data strategies. Her newest book, The Tech That Comes Next, explores how communities and technologists can collaborate to build a more equitable future. Her company is at ANB advisory.com and Afua is on LinkedIn. Amy Sample Ward is nonprofit radio’s technology contributor and CEO of N10. They were awarded a 2023 Bosch Foundation fellowship, and their most recent book is The Tech That Comes Next. So Afua and Amy both wrote books titled The Tech That Comes Next. No, actually that’s not true. There’s only one book, and they are the co-authors of that book, The Tech That Comes Next. Amy’s on Blue Sky as Amy Sample Ward. Laurel Michalowski is a research specialist on the talent and organization research team in Accenture Research. That’s a lot of research. At Accenture, she focuses on continuous learning and emerging technologies in organizations. Laurel holds an MS in industrial Organizational Psychology and has published in Psychology of Women Quarterly. You’ll find Laurel on LinkedIn. Afua, Amy, and Laurel, welcome, individually and collectively, jointly and severally. Welcome to nonprofit Radio. Thanks, Tony. Thanks for having us. It’s a genuine pleasure to have all of you. We’re starting with, uh, we’re talking about. An Extenure report, uh, it’s called Learning Reinvented, Accelerating Collaboration Between Humans and AI. Uh, as a renowned, uh, lackluster host of this show, I’m actually gonna read from the conclusion to begin. The conclusion says we are witnessing one of the biggest shifts in technology in our lifetime. We now need to reinvent how we learn and work with intelligent agents. Unlocking human potential in this way presents an incredibly exciting opportunity that co-learning can capture. The future of work will continue to be shaped by rapidly shifting and often unpredictable headwinds. The reinvention of learning represents the rise of a new capability that can help turn disruption into an advantage. I’d like to start with our researcher, uh, on the panel, Laurel, uh, why don’t you talk about the, the research? Give us a little overview of the, the methodology. Uh, let, let’s start with, let’s start there, Laurel. Yeah. So, the foundation of this report would be our two surveys. We surveyed both workers across the globe as well as individuals in the C-suite. Um, we had about 14,000 workers across, I believe 12 countries. And all of the industries that Accenture services, which is about 20 industries, um, and then we had about 1100 executives on top of that. So, in a lot of cases, um, we’ll talk about, we ask similar questions of both leaders and their workers, so we get to see that, uh, disconnect and gap. And beyond survey, we, uh, interviewed. 40 external experts with um their expertise ranging from both the talent and people side and the kind of emerging technology G AI side. We tried to find as many as we could who held expertise in both but as this panel could understand, that’s not always a capability that um That every organization has, but those were the key components of what made up the research as well as case studies gathered from, from our client work. And you looked at, um, different outcomes. You looked at, of course, financial and productivity because this is a corporate, this is a corporate study and, uh, you know, we, we’ll we’ll get to that, we’ll get to that part of it in a minute, but financial and productivity, but also non-financial outcomes like trust and skill development. Say, say something about some of the, the outcomes. Yeah. So, for most of our reports, we like to look at not only the benefits, let’s say, to the organization and the companies, those kind of bottom line business case type outcomes like you mentioned, profitability, uh, year over year growth. But we also like to look at, you know, what are the benefits for the individuals. Uh, Organizations are comprised of people, of workers. So, how can we improve those experiences, you know, what good outcomes can we find? Within them. Um, so, like you mentioned, we, we focus on, you know, their trust within the organization, how quickly they’re able to develop their skills, their engagement, their satisfaction, uh, things like that. So, we’d like to, you know, evaluate on both sides so that, you know, we get the the CFO who might be a little bit skeptical of all these soft people things, but we’re also looking in and focusing on the experience of the worker as well. How do you measure something like trust as an outcome? In this case, we asked workers directly how much their trust in their organization has changed both within the past 12 months and over how much they anticipate it changing over the next, uh, I believe it was 2 years. So, um, in light of, you know, Gen AI transformations within their organization, I should note all the workers and executives, um, that we surveyed. They were screened for, you know, whether or not they use Gen AI at work, recognizing that not every uh industry or company has has taken this on. Um, but we specifically were looking at those folks who are using it in the context of work. Um, so, yeah, to answer your question in brief, we asked them directly, how is your trust changed both either positively or negatively. OK. Thanks, Laurel. Um, so this is, you know, this is clearly a corporate report. Um, by no means do I believe or have I ever espoused on nonprofit radio that there’s millions of, you know, hundreds of lessons that the nonprofit community can learn. Uh, but there is, there is crossover and we’ve had guests, uh, uh, from the corporate side who have said that they learn and wish their colleagues would learn more from. The nonprofit community in terms of like mission and, and trust, valuing individuals, uh, you know, values. So it, it, it, it goes both ways. So, you know, we want to see what we can learn, take away. Uh, hopefully there’s, there’s something, otherwise, we’ll wrap this up in the next like 2 minutes. If there’s nothing, if there’s nothing we believe we can, uh, let’s crossover, everybody gets their time back, including 13,000 listeners. Imagine, imagine how we can move the needle on productivity just, just by wrapping up this show in the next 2 minutes. Uh, 13,000 work hours saved. Tony, maybe your, your spinoff podcast is an hour long, but it’s just 2 minutes at the beginning and then carefully curated consultation music. So people can say they’re listening to this industry podcast, but they’re just getting to work finally, you know. Yeah, yeah, that’s a, that’s a podcast possibility. Look, the way this show struggles, uh, you know, I’m open, I’m open to ideas. Um. Actually, I’d like to start with Afua. A, what, what are you, you know, like top level, top level takeaways? We’re we’re gonna have a chance to go into more detail, but you know, top level, what did you think when you read this? Absolutely. Thanks so much. And Laurel, thanks for walking us through the methodology, um, and, and the research behind this report. I found it really interesting and always appreciate going direct to the source when it comes to talking about research. Um, Tony, you mentioned about, you know, the hopes and dreams of Uh, nonprofits, being able to learn from corporate and vice versa. I started my career in corporate, work with corporations. Now, and I think, um, what’s important to remember is that all of these entities are filled with people. Um, and people are the ones doing the work. People are the ones creating community, creating culture, um, making decisions as to when and how to adopt tools, when and how to, um, embrace each other and to interact with their customers, um, and, and the clients that they serve. And I think that’s what it comes down to. I think some of the findings in the report, um, I think especially around embracing, uh, learning and changing the way people work and how that changes the way people interact with each other are really important. Uh, Tony, you, I’ll just wrap up with this. Tony, you’d also mentioned The tech that comes next being a title of a book that both Amy and I wrote and wrote together. In that we talked a lot about values and what you value is what you build. So it’s really exciting to see in this report, um, the importance of values, uh, being called out. Um, but one of the values we talked about is that things change over time and we have to build space into who we are. People into what our organizations look like to allow for the development of new skills and the application of those new skills. And so I think we’re really in a moment now where we have to be really intentional about building that value and not just to how we think about technology, but how we think about um corporations, nonprofit organizations and spaces in general. All right, thanks. Uh, so we’re not, it sounds like we’re not gonna be wrapping up in the next 2 minutes. Uh, so that’s, I’m sorry, you don’t get your hour back. Uh, no, so, Amy, what did, uh, what did you think? High level as, uh, when you read this report? Yeah, high level, I mean. I Appreciate Laurel, you sharing because my understanding as I read it and it was helpful to validate before we got into conversation that this was a survey of folks who are already using AI and um It’s helpful just to be transparent about that. I think part of the challenge that we have around talking about anything that’s declarative about whether these technologies are helpful or not or useful or not or valued or not or wanted to be, you know, is that It isn’t just people that are already using them. There is a significant or important component of every industry or organization or community where, um, folks for many different reasons that can be considered in different ways, um, are not using these tools. And so just naming those folks, uh, before we inevitably start talking about that side of things in this conversation, you know, and I think that Um, you know, in addition to my regular work, I’m this year helping independent sector with a project to gather a panel and think about AI governance from the perspective of independent sectors, 33 good governance principles, and what are changing technologies. Maybe introducing to governance conversations and the two primary lenses for that work and the conversations that that kind of community panel will have, one is trust. Trust is certainly named in this report, you know, um, how, how does the way that organizations are using these tools. Build trust, maintain trust, uh, contribute to trust, either, you know, between colleagues, between workers and execs, as Laura was saying, um, But also outward to community. Um, so that’s a big piece I think we’ll likely get into more in a minute, just naming that that door is in our hallway to walk through and then go talk about it. And then also the trust, images and also the trust between the humans and the agents that they’re totally collaborating with. Totally, yeah. Can, can the tools be trusted. Um, but I also want to name what Afua left off on, and I had Maybe a different impression, um, in, in the report, or maybe this is just what I get for reading it after my kiddo goes to bed and only part of my brain was still awake, but you know, who’s to say. But I read the report as really not addressing values at all. Um, there was the value of the tool, meaning as it serves. Those other metrics that Laura was talking about, you know, productivity, um, year over year growth, supporting efficiency and learning, all of those components. But I think not in the spirit of what we named in the book or that we talk about a lot with nonprofits, which is, what are your values? What are you going to value when you make this decision and how are you going to come back to that when you Of course, inevitably have to make this decision again because the technology has changed or opportunity has changed or your needs have changed. And, um, that values piece, like I said before, is the other piece of the lens for the independent sector work, but I also think is core to any conversation that starts, as everyone here has done with this is about people. If it is about people, then we have to talk about what we’re valuing and it can’t be the technology over those people. OK. Um, well, thank you for, uh, for calling out that we are talking about a, I mean, I in a biased sample because these are all folks using AI, but I mean, that’s the purpose of, the purpose of the report is to learn how to do it, not to decide whether to adopt. So yes, we’re, you’re, I actually have a kind of a behind the scenes note, um, Amy, to your point. We actually did when we were screening out folks for their Gen AI Gen AI use at work. Um, for those who said they don’t, we had a small sample, I think it was about 300, and before they got screened out, we asked them why not. Um, and the most popular reason of that group was that it just wasn’t allowed at work. Uh, that’s not to say that I totally agree that there is a healthy amount of skepticism in the workforce, be it, I don’t want to be automated out of my job. I don’t feel comfortable using it for creative ethical reasons, for sustainability reasons, um. Obviously, it wasn’t in, as Tony said, not really in scope, but that is something that we we assessed behind the scenes. Um, and I think frankly, not a lot of organizations that want to implement Gen AI tools into their workforce and in how they do work really have a good answer of of how to address those fears and that skepticism. Some of them just introduce mandatory we’re tracking this, you have to use it. I think leading organizations would do kind of a listening tour with their employees and say, hey, how, how would this fit in in our processes? You are the folks who are actually executing the work we do. Um, but yeah, presently, I think the arms race to be Gen AI enabled and and be seen as a Gen AI enabled company, at least for the organizations that we work with at Accenture. It’s kind of leaving, leaving that population behind, but, um, but yeah, yeah, I mean, I do agree that in the nonprofit sector. The primary driver is to be seen as an AI adopted organization. It is not to have the benefits of these tools. Um, different audience, of course, right? In the nonprofit sector, funders, philanthropy is talking a lot about AI and so those organizations who want to be grantees or are grantees and hope to stay grantees, really, we are hearing that as a primary driver for why folks want to adopt the technologies, not. Not because they have any, you know, other, I’m sure they’ll find maybe some other uses, but really. To be seen that way by their funders is, is a big piece, and I’m speaking to, um, just to counter your research with more research, um, and I’m not, it’s not just to add to this research conversation with more research, N10 and Bridgespan have an open survey right now on AI adoption and governance in the nonprofit sector, um. I don’t know when this will air. So you can go to the M10 publications page and either see the survey or see the report, whichever stage it’s at, but go to the publications page and it’s on top. Um, but something I wanted to reference about that and then maybe you could talk a little bit more. Um, I’m sorry to now have taken over Tony’s job, but I, you know, well, you’ll, you, you’ll, you’ll be better than lackluster. Go ahead. Um, but something that we have in that survey is a similar structure. Um, if folks say they’re in an executive role, they, you know, their path splits, um, and they get, get different questions than staff in an organization. And we similarly to what I, I think is in your report, see a delta between what execs think is happening in the organization and what staff feel is happening in the organization. Um, and I wonder if maybe you could talk a little bit about that delta from what you’ve seen and, and how you think, you know, having looked at the data more, and then I think Afu and I both probably have a lot of examples we could share from that area. Yeah. Um, let me just share one where I know there’s a a pretty stark difference. It’s around some of what we we’ve already touched on, which is when we ask both executives and workers how clear they feel on their organization’s kind of strategic plan or like process for implementing GN AI at work. So, do you know where your organization is headed and Oh yeah, like I think it was 80 or 90% agreement that they have clearly communicated this to the workforce, no issues, no problem. Laurel, you, you cut out for a sec, so I just want to make clear that’s the, that’s the executive perception. Yes, OK, yeah. Uh, when we talk to workers, they are far less clear on where their organization is headed and why. So, I think when it comes down and often our clients are quite large organizations, so, you have many layers from, you know, where, where the decisions are made versus where they’re implemented and we find that those workers just aren’t quite seeing the vision as clearly as the executives think they’ve communicated it. Amy, do you have any early cuts at your research yet, or, or no, it’s to, to see if you have the same disparities. I’ll say what we see now, of course, the survey is still open, you know, so please, if you, if your experience is different, go put it into the surveys. You know, yeah, now we biased the survey. Now, now we’ve ruined the survey. No, the survey is worthless. No, it’s fine. No, it’s totally biased. I mean, one thing that we do ask about, and obviously this is different because you had screened folks for usage, but we do ask both, you know, to use this language, execs and workers as the two groups, um. If shadow use is going on, tools that aren’t on the approved list, um, and you know, execs are saying no and staff are saying yes. Um, but also a lot of folks are saying that there isn’t an approved list. So it means that any use is already shadow use because it’s not, you know, um, Authorized, what you know what I’m trying to say. Um, I think another piece that we’re seeing in that, um, gap is what kinds of resources folks actually want as a support for learning or deciding what policies they should have or how they could use, you know, how like getting towards the content of, of your report as well about learning like. What those different groups want, um, is different, which makes sense. They have different jobs, but, um, I think that’s an interesting thing to remember that not everyone is going to need the same resource or template or example or course, you know, um, to suss out for themselves what makes sense. So those are two places that, um, There’s some differences, but there’s lots we could, lots we could talk about. Yeah, it, you want to weigh in on the, the, these disparities or anything else that we’re talking about? Absolutely. And on, um, the disparities between what executive management, might see versus the rest of employees at an organization might report as reality is not surprising. Um, I think what is, um, interesting and important to note as we talk about. About this is that AI is a type of technology, just like many other technologies. And so whenever you have, people have led technology transformations inside organizations or tried to roll out a new tech process, there is always a discrepancy between how clear the project leaders or executive sponsors think a plan is and what the reality is on the ground, what that change management process looks like. Um, and so, if anything, I think even as we talk about, um, AI adoption, what is important to remember is that a lot of this is about leadership, right? It’s about leadership and strong management principles and putting them into action. Humans make the decision about when to have layoffs and how many layoffs to have, um, what to attribute those layoffs to. Um, if AI, if something else. Uh, I think maybe about a year ago, 1 year and a half ago, uh, the Nvidia CEO was in an interview and someone said, oh, well, you know, well, now with AI and the chips and your chips and everything that is enabling. Um, there’s going to be mass layoffs. And he said, Well, I don’t know that we need to lay off anyone. He’s like, we should actually just be doing more work. We should be able to do more interesting things. I think maybe perspectives have changed in the past year or so. Yeah, well, plus that’s fine for, that’s fine for his company. It’s not the center of the universe, right, right. Exactly. But my, my broader point is that again, it’s the humans who are making these decisions. And so, Um, you know, there’s a lot of talk of human in the loop. I often like to say AI in the decision-making loop to remind us, uh, we are the ones making decisions. We can decide to use AI when and where. And so, I think some of these discrepancies that, um, are highlighted, uh, in the report or even thinking about how we articulate what value is, what productivity is. Really does require us to be clear as to what our leadership principles are and what it looks like to manage organizations well. And that’s right in line with, uh, Amy’s point, uh, their point about, um, uh, about values. You know, what do we value and, and in terms of, you know, deciding whether we’re going to minimize staff, lay off staff, fire, forget the euphemisms, fire staff, uh, in the, in the face of, you know, work being able to be done more efficiently. Uh, and, you know, this, and these are concerns that are, that are real. I mean, look at just a few headlines. Fortune magazine, AI is cutting 16,000 jobs a month. Bloomberg, heavy job losses in roles exposed to AI, and those roles are typically, uh, white-collar, customer service, administrative, um, New York Times, the growing anxiety over AI and jobs. So, um, I mean, the, Yeah, the concerns are real and We, we look to leadership to, to center what’s, what we stand for. Well, and I want to get back to something you said, Laurel, um, or multiple things and tie them together, but you know, the piece about, um, Staff saying they’re, they don’t really understand or feel that there is an adequate plan and Um, to validate that with N10 data. Um, so we have for two decades seen that play out that folks say, you know, they have tools, but there’s not, they don’t have a plan and that organizations that can put technology in their strategic plan are more effective in general because they can communicate. Why are we doing this? Why are we using these tools, right? So, um, in case Accenture was waiting for the data validation from N10, there you go. Um, let the record reflect, um, that’s why we’re here. We are here to explore the crossover. Yeah, exactly, exactly. Thank you. Um, but I think, I think a piece of that strong plan, whether it’s in your strategic plan or there’s a, you know, implementation plan for the project, is this piece about accountability and what happens if something goes wrong, or it just isn’t used the way we thought it was going to be used, or it doesn’t work the way we wanted it to work, you know, like something going wrong doesn’t have to just mean Material harm to someone, though that’s included, right? But I’m trying to make it also just be like our idea of what this was for didn’t work. And I, I had written down when I read it last night that 56% of workers said they didn’t know. There wasn’t any clarity about if something goes wrong, like what do I, am I at fault? You know, what do we do? And that is a huge piece of building trust and maintaining trust. That’s also To me, a reflection of like what was valued. Our community and those externalities was not considered when we made this plan, because if we had valued it, we would have made a plan for that accountability. And I’m curious, like, especially from any interviews with participants that you did or, you know, more comments versus, um, survey answers that maybe you gathered around that. What folks If if folks maybe even said what they wish that accountability plan was, or, you know, what the gap was for people. Mhm. I don’t know, again, if any organization has a clear answer. I think the leading ones are putting more thoughtful guardrails, but, your point really makes me think of the importance of experimentation and letting, you know, your workforce have the opportunity to play with these things in a sandbox before it becomes really consequential. Fortunately, with the speed at which the technology is changing and emerging and as we’ve discussed, organizations desire to be seen as being on the cutting edge, um, it gets hard to allow for that time to innovate, test, experiment before, you know, it becomes to the end user, uh, you know, important decisions. We’ve all heard the horror stories of AI hallucinated case law for a trial or, you know, falsified references or all of all of those horror stories. Obviously, you know, every organization hopes to avoid that and not be the next headline. Um, but I think allowing for that that culture of experimentation is kind of the best buffer from that, but I think a lot of organizations as well are still trying to evaluate where AI can either layer into their existing processes or where they have to totally reinvent the wheel and and totally change their ways of working. While keeping the ship running, of course. Um, but I think what, you know, this is just anecdotal, um, and something we’ve been talking about on our research team is that I think over the next few years, we might see the pendulum swing back to really having that that human in the lead and being organizations being very thoughtful and kind of differentiating themselves with competitors of, no, you will have a uh a human pick up. The phone if you call, you know, you as an employee will have an actual person as your HR rep and not just an agent. So, I think that, you know, while I totally agree that fears of job layoffs, um, are very real and have happened, the extent to which, you know, they’re being blamed on AI versus actually due to AI, I think we could, we could all argue both sides, but I really foresee in the next few years that the human being kind of the differentiator for for companies. That’s very interesting. A, what do you think about the, the, the pendulum, the pendulum swinging? Back. I, I mean, I think the pendulum is actively swinging now, sort of from side to side to side, right? We will see, I think over the past 3 years, past 3 years, right? We’ve seen headlines that everyone is all in on AI adoption. And then 6 months later, OK, well, sure, I, you know, and a company executive saying, OK, we did go all in. We did lay off a bunch of staff, but it turns out we need humans. So now we’re hiring back. Or, OK, now, you know, we’re all in on the AI adoption again. The technology has, uh, leapfrogged once again in the past 6 months, there’s a new version. We’ve got whatever coding we’re using. So, you know, now we’re tracking tokens and uh token usage, and we’re going all in. And then, oh, actually, paying for tokens is expensive. It’s cheaper actually to have a human. Uh, work on this code. And so now we’re going to figure, you know, so I, I think the pendulum is actively swinging right now. Um, I think what is true, and I, you know, I say this, like I am, you know, I’m an engineer by training. I’ve been doing AI things for 10 years. So I believe in AI and that it has its place and it is useful and it will continue to be here. But I think sort of in, as we think about wide scale adoption, adoption in all roles, not just engineering roles. I think we are very much still figuring out what that means and what that looks like. And so, I think we will continue to see the, um, the pendulum swing back and forth. Um, I also just wanted to touch on something else about how some of these themes that we’re talking about really do intersect. There’s a quote in the report that said leadership must drive a mindset shift so people see Gen AI as an empowering partner rather than a threat. That means actively communicating their vision, rallying their team around it, and fostering a culture of trust because without genuine buy-in, even the best intentions go nowhere. Um, so getting back to our themes of leadership that we’ve been talking about and trust, but, you know, what I think is not mentioned here and often isn’t mentioned in these conversations is the inherent tension and the same people who are saying, use AI to Be an empowering partner, empowering thought partner are also the same ones that are then looking at earnings numbers or looking at um a funding pipeline and saying, well, actually, now that we’ve used AI um now we have a need for fewer people. And so some of that inherent, um, tension, if your, your values aren’t clear, if those leadership principles aren’t clear, I think we’ll continue to make it, uh, challenging to get true buy-in and true adoption. Um, of, of some of these Gen AI and agentic AI tools. It’s time for a break. We are sponsored by the Bridge Conference produced by AFPDC and DMAW July 29 to 31 at the Gaylord National Resort and Convention Center in National Harbor, Maryland. More than 2400 professionals will gather at bridge. Tony will be with them. The question is, will you? Thought leaders from nonprofits, associations, foundations, hospitals, higher ed, faith-based, and mission-driven causes across the country come to Bridge to discover new ideas, solve real challenges, and connect with smart people shaping the future of our industry. From 125+ educational sessions and hands-on pre-conference workshops to Bridge tech, the faith in fundraising forum, and inspiring keynote speakers, Bridge offers something for every mission and every role. The conversations happening at Bridge will shape strategies, careers, and organizations long after the conference ends. Don’t hear about it afterward. Be in the room. Register at bridge.org. It’s time for Tony’s take 2. Thank you, Kate. What do I own? I’ve been thinking about this since I got a new iPhone. And some things did not transfer over from the old phone to the new phone. I did it all at an Apple store. Like songs, there’s a lot of music that I added by my own CDs back when I had a laptop with a CD drive in it. Remember those days? You, they used to make them with the CD drive in it. And I had a lot of CDs and I added them myself to my music app on the iPhone. Which means I didn’t buy them from the Apple Music store, because I had the CDs. I put them on. The songs that you do that with. Don’t get transferred. If you didn’t buy them from the Apple store. They don’t get transferred to the new phone. The only way to make that transfer is if in the store or wherever you’re buying your phone, if they manually. Uh, duplicate the music app from your old phone to your new phone. But if, if you just do the automated process where the, the two phones just sit next to each other and the new phone gets synced with the old phone. Songs that you didn’t pay for, don’t get transferred to the new phone. I lost a lot of music that way. And of course, I’ve, I’ve long since given away the CDs because, because I thought I owned those songs. I didn’t own them. Like I had a right to use them. Only until I got a new phone. And then They disappeared. Uh, same thing with an app. There was an, uh, I had an app on the old phone that I was using. It didn’t make it over to the new phone because that app is no longer in the app store, so I would have thought I owned that app. No. You only use it as long as Apple supports it in the. Uh, in the App Store. When, or if they stop supporting it, they don’t offer it in the store, it’s not gonna make it to a new phone. So I lost the app and all the data that was in that app. Uh, also, and also, YouTube. I had an old YouTube identity. I, it was an old Yahoo email address that I was signed in with. When you, uh, when you change apps, when you change phones, you, uh, are logged out of all your non-Apple based apps. YouTube, obviously owned by Google, not an Apple app. I got logged out and that That Yahoo address, I, I couldn’t recover it. So, now this is not, that’s not Apple’s fault. Uh, if you wanna place blame. This was Yahoo. It, it’s such an old address. I had it from 2003. I couldn’t recover it. So all the playlists that I had under that. Email address that I was signed into YouTube on, that I’ve curated for years. I had, I had science, I had music, I had politics playlists. All they, they were all private, just for me. They’re all gone. Although those years of curating those lists, they’re lost because I can’t sign in with that old Yahoo email. So, you know, you think of these things. Another one, another example, uh, off the phone now, streaming movies. When you pay for, you pay to buy a movie from Amazon, not just watch it for 48 hours, buy it. You don’t own that movie. You only have the right to watch it. As long as you have your Amazon subscription. The day your Amazon subscription ends. So does your access to that movie. So you don’t own that movie. You’ve bought it, like they’ll say, buy the movie or rent it. And you click buy and you pay 14.99 to buy it, but you haven’t really bought it because you don’t own it. You’re buying a license to use it as long as they allow you to, and they’ll stop allowing you. When you stop your subscription to, to Amazon. So you don’t own that movie, even though they say buy the movie. You know, that’s why I have a, a DVD collection of, I don’t know, 200, 250 DVDs. I own them. Nobody can take them away. As long as I have a DVD Blu-ray player, I can watch the movies that I own. They’re in my DVD cabinet closet, and I can watch them anytime I want. Those, I own. The streaming, like bullshit stuff, you don’t own it. They’re just giving you a right to borrow it or watch it as long as the, as long as you keep up the subscription. Cloud storage, same thing. You end, uh, when I end my iCloud storage account, if I should. I’m gonna lose access to, to whatever is in the cloud, uh, all that backup. It’s lost. So, this all has me thinking about. For me, you know, what do I own? And You might, you might think about that as well, because I found out, uh, I don’t own as much as I thought. And that is Tony’s take 2. Kate, oh, my voice just broke like I’m 14. Kate, Kate. Um, Yeah, you got me thinking now, cause I mean, we’re, we’re, our life is surrounded by a little rectangle, like our, our photos, our movies, how to contact each other. We’re all living through this little brick. I mean, let’s say it got smashed or you said the cloud, you can’t recover your cloud for some reason, then you gotta re-get all your contacts, reget all your pho, well, now they’re gone. Your photos are gone. It’s like opening up a scrapbook that you, you have, that you bought, that you like made little pictures and you printed stuff and you posted and you can go to your library and look at the little photo book. No, it’s gone. Exactly right. It’s, it’s a difference between physical, tangible. I can, well, everybody knows what tangible is, and, uh, digital. That’s the difference. We’ve got Beu butt loads more time. Here’s the rest of a wide ranging AI conversation with 3 experts. Part of the report sort of addresses what Afu is talking about. And, and, and, and core to the report really is this co-learning. Where the, the agent and the human, I, I actually, I’d rather put the human first, where the human and the agent are learning from each other, iterating, you, you, it’s, it’s central to, to your, to, to the, to the outcomes of the report. Can you talk about co-learning? Yeah, definitely. Um, so I think one thing that we have to distinguish about Agentic AI and generative AI is that unlike uh maybe technologies that have preceded it like Excel or what have you, it does improve as more people use it, right? Like it becomes this flywheel effect where the AI learns, learns, um, adapts to um what what the input is and, and, and how the organization is using it. Um, so when we talk about co-learning, we’re not just talking about, you know, uh, I’m in the flow of work and I can get an answer to a question I have faster. It’s also learning to work with the AI like it’s a new co-worker. So, you know, maybe me and Tony work on a project together and I learn. Through our our collaboration that he prefers text over email or something like that or I don’t know how he manages meetings, so I know when to chime in and when to pull back. Similarly, the AI agents, the collaborators have to learn how to work with the people and the humans likewise. This is an instance where you would probably would prefer the tech over over Tony. Well, but also these technologies aren’t people. So I always get bristle at the comparison to them being a coworker or an intern or whatever, because they are not people, they do not have feelings. It is a tool. And I think it’s important for us if we are even trying to make the point that this tool will change and improve based on our inputs. That we maintain it as a tool and that we are changing our inputs or intentionally putting certain inputs in for that end because it is a tool, um, just naming that for all of us in this. And I do also Think about um from Afua’s many stories, um, in her storied career of trying to be sometimes the only person trying to move a technology project forward, whether it was You know, a model or um implementation of, of a new tool, whatever. And I just think about how much, how many resources of all different types go into successful projects and I am curious if there were questions in the survey, Laurel, about What resources, you know, they could sure be budget, but they could also be what staff teams or what outside expertise or you know, what types of resources um had been Leveraged or or contributed to a project to make it successful, especially as I then think about the audience of Tony’s podcast and the, the community that I work with all the time, which are smaller and medium sized nonprofits who do not have a legal department, let alone a staff council. They do not have um flexible innovation budget or what, you know, whatever it might be. They don’t have ready access to. Maybe experts who’ve done 15 versions of this already, you know, to like try it with them. Um, there are 6 people already completely overworked on a mission that is crucial to their community, you know, and so the context, um, in some ways feels like two different planets, you know, but I wonder if we could talk about or if you heard surfaced anything like what types of resources folks did find useful. Yeah, um. I mean, I think a lot of what we’ve talked about today gets back to just classic change management. It’s always hard. It always requires so many resources. It’s always slower than you hope, requires a ton of communication with the people it affects, etc. So, I’ll start with unsurprisingly, uh, Nearly all, I want to say actually 99% of the executives that we surveyed said that they were planning to increase spending on Gen AI their Gen AI strategy in the next year. So, obviously, more dollars are being allocated to this that is a surprise to no one. Um, but when we talk to workers on, you know, where, where are your challenges with using these tools at work and what would help you use them more, um, training came out consistently as the number one. Now, That is obviously a large bucket, right? Like, what does training, good training mean to an employee versus what does it mean to, you know, the executives who we’re talking to. They, the C-suite may think, oh, well, we We ordered this one module for our workforce and that’s it. We should be. There was a brown bag that they could have come in. Yeah, yeah. I think we’re getting to the, uh, employee, the, uh, executive, uh, employee worker, uh, disparity. We, we hosted a brown bag and we put it, we, we put $2.5 million toward it, check the box, right, but Um, often, those trainings are kind of widespread organizational initiatives are not contextualized to that person’s role and how AI tools are actually going to show up in their work process, right? So, as much support as they can get in not only learning, you know, how it would be implemented specific to what I do day to day is, you know, kind of the gold standard and what most organizations. would hope to achieve. I think as well, um, again, kind of a classic change management thing, but as much as um workers can see role models, maybe their leaders, how they’re using it, where they’re struggling, where they’re succeeding, you know, sharing amongst the team, um, folks, they collaborate, lessons learned, what’s working, what’s not, I think, um, can be things that can can still happen on that small scale. Either Amy Afua, you want to talk about what, what, what you think, we think valuable training, uh, looks like in our, in our community? I will, uh, jump in first, but I know Amy has so much to say on the importance of training. I’ve gotten to collaborate with N10 also on, uh, their digital equity guide, which also talks about the importance of training there. So suggest folks, um, give that a read if you haven’t already. Um, but yeah, that’s a great resource. But Amy, could you just give us the, where we could find the, the equity guide? We, you and I have talked about it before, but I’d like to remind folks. Home page. It’s on the publications page. It’s probably in the top math, but yes, the equity guide for nonprofit technology talks about using technology, investing in it, including your own budget, and creating technology, whether you’re a nonprofit or a technology provider. OK, the N10 equity guide. Thank you. All right, I’m sorry, Afu, I just wanted to make sure people can find it. Absolutely, I, I do too. Download it today. Um, but I, I think on, on the training piece, what’s important to remember is that you have to meet people where they are, not where you want them to be, not where you hope they should be, not where they said they were sometimes, but where they actually are and meet people there. Um, I think Laurel, some of what you mentioned about making sure that the training is contextualized. Um, I think, you know, train early, train often is also a good philosophy to have. And also, uh, making a part of that training, especially if it’s done over time, adaptive. Make it learn as well. So, a way to learn best practices, learn how people are actually using whatever you’ve rolled out, and use that to sort of continually, uh, re-educate and upskill, um, staff is really important. Amy, Yeah, I mean, I think we’ve said this a little bit earlier, but I always think that when we think of training, even if it was for that 6 person organization or a 6000 employee organization, there’s just, once you have 2 people, you now are going to have different ways that people learn or, you know, to Laura’s point before, want to communicate or have different interests that guide their learning for their work, you know, and, and so that means whether, I mean, we all just made fun of a brown bag, but even if it’s not that, even if it was an all day thoughtfully planned and well facilitated event, it’s still not going to be the way that everybody could learn that information, right? There, it, it is going to have to be diverse in how people can come in and the way that they learn, but it also has to be continuous, and I think that was a point made in the report as well too, that The technologies constantly change, so our education and our trial and our error and our understanding and exploration of technology has to continue to change. Um. That doesn’t mean though that our jobs as humans alive on a flying rock in 2026, our jobs are now to just chase technology and to be like, oh my gosh, new thing released. My job today is to test it out. No, you already have a job and a life and whatever, right? Like I, I Regularly ask organizations when I’m doing a training, you know, how many of you have a mission that’s really important? Everyone raises their hand, right? How many of you have a mission that is to adopt technology? No one raises their hand. That’s not our mission, right? So, Um, keeping that in mind, these are tools that can help us if maybe, you know, we’re talking in super general language here about thousands of different technologies actually. So, uh, there, there could be tools that help us, but your, your job. Um, isn’t to do that. Just as, um, a construction crew’s job is to build a house. It wasn’t to find a use for all of their tools, right? It was to build the house and they found in certain moments different tools were useful for that. And, and then they built the house. And I want us, whether we’re talking about giant companies or small nonprofits and everybody in between. Nobody’s job is to just use these tools. And how do we, as we started this conversation, if we’re, if we’re really talking about people, how do we center people in that process, um, both through learning and through remem, OK, this isn’t at the sacrifice of delivering your great mission or your great programs and services, right? Um, this is here insofar as it aids that, but if it’s not aiding it, What was the point, you know? I’m, I’m, I’m glad you, you brought out the, the, the threshold questions, which, again, you know, that it’s beyond the scope of the, the, the report comes after that, after those threshold questions, but it’s enormously. Uh, important and, and consequential is the early discussions, whether we should, and then if so, everything else we’ve, we’ve talked about. But yeah, the, the threshold questions. Um, Let’s see. So there’s, there’s 4 conditions that, that the report sets out for, for getting the most out of the co-learning that, uh, that Laurel was describing. Um, Laurel, you’ll do it more articulately than I will, uh, as, as most guests, uh, as all, as all guests do. But could you lay out the, you know, just give like, tick off the four conditions and then maybe we’ll have some, we, we’ll have some time still left to maybe talk about just one of them or so. But, you know, these, these, uh, sort of prerequisites for, you know, maximizing your, your, your outcomes using the, uh, these agents. Yeah. Um, so, the first one is something that I think we’ve touched on a lot already in our discussion is, is leading with curiosity and creativity. So, again, our organizations creating that clarity to their employees on where we’re going? Do employees feel like they are genuinely supported by the organization to use these tools and implement them in their work? Um, the next one is incorporating learning as part of the job, which is again, we just, we just were talking about, I think. It’s incredibly difficult for, as you’ve you’ve mentioned, Amy, you know, people to stop what they’re actually doing to to start using these tools and implementing them, but the exciting thing about generative AI specifically is that it is so much easier for organizations now to to create specific contextualized, personalized, adaptive um training at scale. Uh, the third is hardwiring trusts. So, making sure again, like we’ve talked about, employees know who’s accountable when something goes wrong, that they feel like they have a voice within their organization of how AI gets used as well as they have the autonomy to decide when to use it in in their daily work. And then, finally, the fourth condition is making Gen AI work the way people work. So, similar to kind of learning being in the flow of work, it’s incredibly difficult or adds a lot of friction when these Gen AI tools live in a separate whole system that you then have to wait to load and it’s not really integrated well. At the end of the day, your employees are still users, right? So, we want to make sure that things are designed thoughtfully for employees so that it makes the tools um easy to integrate into their flow of work, as well as that they have that technical support when things go wrong. So those, that’s like a quick fly through of of the four conditions. Yeah, perfect. And we have, I think we’ve we’ve touched on elements of all of all four. A, what, what, what interests you, uh, I guess, further in our conversation or maybe specific to one of these. Conditions, what’s on your mind? Yes, I agree. We’ve, we’ve already sort of organically touched on all four of these points because they are really crucial as we think about adopting Gen AI. Um, but I think even more broadly as we think about what it’s like to adopt any new technology and to some of what Amy mentioned as to how we make sure that we are giving people the best tools they need to do their jobs. Um, if you are to extend, uh, Amy’s metaphor of building a house, if you’re building a house, You just want to build the best house with the best tools, you’re looking around saying, what is available to me and you pick that. In organizations and companies and nonprofits, as people are serving their clients, serving their communities, you want to do so with uh the best tools. You want to deliver the highest quality service to them, usually the most amount of people that you can. We are in a resource constrained times, financially. Um, time and attention-wise. And so, the question is, how do I, how can I best equip myself to execute on this very important mission that I have? And if that is your stance as a leader, as an employee in one, in an organization, you should want to use the best. Tools. Sometimes that will be a Gen AI tool to help you, in which case, you wanna make sure that you can get it. You wanna make sure that you’re trained on it, you wanna make, you wanna make sure that your organization is learning from it, that you, um, are contributing back to the best practices and everyone is growing together for that co uh learning aspect as well. And so I think, um, What to just reiterate, which I think. The, the four points, um, do support is that it’s about doing good work. It’s about doing good work in the most um efficient, effective, and I’d argue, most compassionate, uh, manner, um, especially in the nonprofit sector. Oh, compassion. No, we haven’t mentioned that word. Remember. That’s a good word. That’s a good. I thought about mentioning empathy to continue with the ease. I was like, whoa, whoa, whoa. Let’s start with compassion. Uh, I do love alliteration, but compassion is, compassion is a good, that’s a good value. Well, it’s not really about, it’s, it’s, it’s, it’s just a, it’s just something we should honor. Go ahead, Amy, you’ll be more articulate. It’s a great lens again, just to like continue on this piece if, if we’re. Going to center this on people. Compassion is such a great Word to remind us of considerations there because I think a place where we’re hearing from nonprofit teams, you know, people in all positions and all different size nonprofits is this pressure in making an AI policy and an AI plan and like the approved usage list and all of that stuff, um. That that there’s so much pressure to get it right. And, you know, just as a reminder, perfect doesn’t exist in anything. And I think having compassion for people, centering people, is also a reminder that there’s no way you could make an AI policy once because the technology changes. And if we’re going to center continuous learning, And value experimentation, you know, things that we’ve all talked about on this call, we need to remember that the maybe most important part of our AI policy, or however you’re naming that document, is when will we revisit this? How will we redecide what we decided here? Who will be part of that process? That I think is the most important part of a, of a plan, not whatever your answers were today or which tools you put on that approved list today. Because, um, People will continue to learn. People will continue to find new things. The internet just keeps releasing more, you know, it’s like, God, just could we, could we just pause so I could clear my inbox? Like why? Right? Why? I understand that AI has helped you write a lot more emails, stop sending them to me, right? So Really having compassion for ourselves, having compassion for the number of Beyonce hours any of us might have in a day, right? Is to say our plan isn’t perfect, but it does say how we’re going to reconvene and continue learning together. Our plan doesn’t already know magically everything that’s going to happen, but it does say what will happen if something goes bad and that we will deal with it together, right? Or, or whatever. So, I think for me, compassion helps root to some of these important but actually more open-ended, more flexible parts of how we could create a plan for an organization. I’m gonna let Laurel in because, uh, all I ever asked her to do was, uh, talk about the report. Talk us through the ports. Talk us through the points. What’s the reason? So, Laurel, what, what’s on your mind about this? Well, I, I, I think, um, Amy, when you were talking about, you know, when are we going to revisit this plan at Accenture, something we talked about a lot is just the expectation that organizations are continuously reinventing. We have a research team, some of my colleagues do a pulse of change survey about 2 or 3 times a year. It goes out to a couple 100 executives and We’ve been tracking over I think 17 or 18 waves now and the level of change that, you know, we asked executives, how much do you think, you know, everything is changing. It only continues to go up and up and up and so, there’s a lot of overwhelm and similarly, you know, the the noise, the the kind of Floodgates of all of the change with Gen AI alone, it, it’s, it’s untenable. So, I think that goes back to organizations needing a clear communication strategy with, with their workers. Um, I think where transparency is able to be shared, it makes it better because then you can go back and say, hey, we realized this wasn’t working and here’s how we’re going to change moving forward. I will say one of the questions that came to mind in our executive survey for Learning Reinvented, we asked um executives whether the maturity of their strategy to skill their employees for the integration of of AI. And only 60% of them said that it was developed to support their long term business strategy. So, not everyone is even thinking as big of a picture to kind of where their business is going. Now, 60% over a majority. But I think, you know, it has to the trajectory of the strategy has to follow where where the business is going, where the mission is going maybe for nonprofits, but it can’t just exist in a vacuum, otherwise, yeah, we’re going to be spinning our wheels, um, giving chat GPT enterprise seats to to our people and letting them loose. A lot of, uh, what you just said makes me think of sustainability, and I, I question how much of the, the, the tumult that you described, uh, makes it, uh, uh, makes so much of our, our, uh, I don’t know, our economy, uh, unsustainable. I wonder, I wonder. All right. That was Laurel Mikhalowski. We gotta leave it there. Uh, we could go further, but, uh, we’re over time and, uh, we gotta honor our guests. So, that was Laurel Michalowski, a research specialist on the talent and organization research team in Accenture Research. You can find Laurel and all three of the, the panelists, uh, all of, all of, uh, my guests on LinkedIn. Um, actually, that’s not true. Amy Sample Ward is more active on Blue Sky as Amy Sample Ward. Amy Sample Ward is our, uh, technology contributor and the CEO of N10. You’re more likely to find her, to find them on Blue Sky than you are on LinkedIn. And Afua Bruce, a leading voice at the intersection of technology, data and social impact, founder and CEO of ANB Advisory Group at ANBAdvisory.com, and Afua is on LinkedIn. That was outstanding. Thank you. Thanks to all three of you. Afua, Amy Laurel. Delightful panel. Thank you. Very provocative. Valuable, valuable for our community. Thanks so much. Thank you so much. Thanks, Tony. Next week, it’s our 800th show. It’s the 800th show, the 16th anniversary jubilee. The whole team will be together. If you missed any part of this week’s show, I beseech you, find it at Tony Martignetti.com. We are sponsored by the Bridge Conference. Tony will be with more than 2400 nonprofit professionals at Bridge, July 29 to 31 in National Harbor, Maryland. Info and registration at bridge.org. 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.