and how we shape the future of work so that it you know, it is for the better. It provides all the all the benefits that we strive for as humans. We leverage our human capabilities and are more engaged and satisfied with our work. We also create accessible job opportunities, meaningful opportunities, and at the same time create the pathways to develop the skills. And third but not least, we're also looking at how to use automation as a multi purpose tool within an organization beyond just efficiency and productivity. Think about those big challenges that the technologies can help you tackle, like climate change, sustainability, health and well being and others. And at the the next slide, I will focus more on the social components of, you know, the multi purpose tool that I was mentioning before, so we have obviously, challenges related to climate, we have challenges related to health and well being, but there are also obviously important challenges related to social impact. And one of the ways in which we have continued delivering on our automation for big promise is to partner with organizations such as the New York founding in New York founding, that's one of New York City, oldest and largest social service providers discovered that by using automation, they can you know, save a lot of hours that their staff can spend on more, let's say critical mission related activities without being overburdened and overloaded with repetitive and mundane tasks so that they can focus on their core mission. So you can see here in that in the slide that New York family managed to save almost 1000 100,000 hours per year. And if you calculate this by every week per clinician, you get 16 hours. So thinking about, you know, this this type of automation that they have developed with our support, it was around digital data transfer. So it was actually something probably easy to replicate by other members in the industry. In the nonprofit sector. You get this this huge return on time, that especially for the nonprofit sector, is very important. We're talking about capacity building, we're talking about mission, we're talking about making resources more efficient. And I think it's, it's a really, you know, for me to see that it's in to participate in this was very fulfilling, and I hope to see more and more of these stories. Now I know that time is of the essence. So I will have to wrap it up very quickly. I think I have one more slide. Yes, and I think it's a critical slide. So I was talking about your iPads commitment towards the future of work and making jobs more accessible. Your tech community is a critical pillar of this, this promise and vision. Right now. We have almost 2 million professionals and citizens developers in our community that we work every day together with and as part of our, you know, communication and support towards our community. We offer a lot of products and services to support their passion and their driving slow. Thinking about automation cloud automation cloud is our you know, license for Community Edition that nonprofits and organizations that have their turnover under specific thresholds in use for free so you can use the URI path on Blackboard at no cost. And if you are a nonprofit, obviously, it's really important that you also have the skill so in order to have access to the skills we provide a pathway towards gaining those skills through our academy, the Online Academy, again, at no cost, easy to access. They have a forum that's there to support you whenever you need with all sorts of ideas. And, you know, just just managing the whole conversation and your whole pathway towards automation. So I'm going to wrap it up. I think I talked too much so I hope it was useful. Thank you so much.
Thank you so much Margarita that's so helpful, and we really appreciate the presentation here. So next we'll move on to our fireside chats with Beth Kanter. So let's welcome Beth Kanter. Beth Kanter is an internationally recognized thought leader and trainer in digital transformation and well being and the nonprofits workplace. She's the co author of the award winning happy, healthy nonprofit, impact burnout and co author with Allison Fein of the best selling the networked nonprofit. Her newest book, The Smart nonprofit launches in 2022. named one of the most influential woman in technology by Fast Company and recipient of the n 10 Lifetime Achievement Award. She has over three decades of experience in designing and delivering training programs for nonprofits and foundations. As a sought after keynote speaker and workshop leader she has presented at nonprofit conferences around the world to 1000s of nonprofits. You can learn more about Beth at WWW dot Beth kanter.org So in this Fireside Chat Mark, would you Bobby will interview Beth Kanter about her newest book, The Smart non profit staying human centered and age of automation. co author Beth Kanter will talk about how your nonprofit nonprofit can reinvent work sponsible approach to adopting smart tech. So with that, I will hand it back off to Morgan data.
Thanks again, shout out to Beth and welcome her let's all welcome her to this this great event. Really excited to have you back. I'm looking forward to the fireside chat.
Oh, me too. I'm so pleased to be here and I really enjoyed your presentation Margarita. I think we're like say you're speaking we're speaking the same language. It's great to hear.
It's awesome. Thanks so much coming from you means a lot. So let's let's start that that chat. So I really want to learn more about this. Why did you write the book about SmarTech? What motivated you to do it?
Oh, you know, I've had a front row seat at the creation of a field, the nonprofit tech field for many decades. And I was there alongside so many of my colleagues from TechSoup. So I'd give a shout out to Susan Tim dam, Marty Webb if she's there, and I've always worked at the intersection of emerging tech and nonprofit mission driven work mostly as a trainer and facilitator, collaborating with technologists to help nonprofit leaders really understand the relevance and to help adopt the technology strategically. reflectively and ready their organizations and ready ready their own leadership. So smart tech, and I'll explain what we mean by that in a moment. I think it's at this inflection point to where it's common to technologies that reach everyday use, you know, there's this enormous increase in computing power and met with dramatic decrease in the cost of technology. So that means that it's no longer just for the NASA's and complicated Moon launches, if you will, but everyday people and nonprofits can start to use it for fundraising accounting Human Resources service delivery, and even more. So the smart nonprofit. It's my fourth book, my second one with my wonderful collaborator, Allison fine. We wrote the network nonprofit many decades. Two decades ago, actually, no, no, actually, I'm rushing a decade and a half and we wanted to write a guide that was not technical, and that was specifically aimed at senior nonprofit leaders to understand how to leverage both the benefits and also understand navigate the challenges.
Awesome. So can you give us some examples of the of the benefits of the organizations adopting smart tech and maybe a bit about what SmarTech generally includes, is, are we talking about efficiency? Are we talking about more? What is their growth?
Sure, so um, so we just had to come up with the term smart tech like smartphones, smart houses, and it's an umbrella term that we apply to describe a whole range of advanced digital technologies that basically make decisions for people instead of people. So this includes artificial intelligence, machine learning and all its various subsets and cousins, such as natural language processing, chatbots robots and other automated technologies. And the reason you know we, Allison and I have been writing a lot about artificial intelligence for social good AI for good AI for nonprofits for a couple years now. And we noticed when we use the term artificial intelligence it for at a leadership level, people would kind of lay back and say, whoops, that's not for me. That's that's a technical issue. But we really strongly believe, given the enormous benefits and given you know, where that we're at this inflection point that you know that the use of this technology is a leadership issue. It's a leadership challenge. It's not purely technical, and so that nonprofits really need to be working in collaboration with the technical experts and also make sure that they're, you know, including their end users, whether that's internal end users, such as staff, or external folks, such as their clients and donors, some of the design and delivery of these types of this tech.
Got it that makes a lot of sense. I do. feel the same way about you know, how you talked about AI and then smart tech people. People tend to think about it differently, right. They become more open to, to hearing what what do you have to show about technology, besides just AI? It's right, right. And I think there's
too much popular science fiction negative narrative around the term artificial intelligence. And, and I think, I think at least for nonprofits, the concept of automation and at the dividend of time, as you you talked about, I love how you quantified it but it's not just I think the, you know, the return on investment, which is significant, but you mentioned 16 hours of save time, what we think and one of the reasons why we wrote the book is like how are nonprofits going to reinvest that time for to improve what they're doing? To improve relationships with donors, for example, and maybe take a hit at the abysmal donor retention rate, you know, actually connecting with donors and asking how they're doing. On the other end, we all know, you know, and certainly I know from a lot of firsthand experience and why I wrote my second book on workplace well being is that in the nonprofit sector, it's a burnout boom, because people are overworked and work long hours. A lot of us are Passion Driven. There's been an increased demand for services, but yet where you're often spending that time doing low level administrative strain of work that can be exhausting. And that's been exacerbated by the pandemic. And so if we can free up this time, think about like, maybe we could do things like a four day workweek, maybe we could give SAS at rest they're needed or that sabbatical and still get things done. We want to caution people that automation isn't about just doing the same stuff more efficiently and working those long hours. And because you can get more done now that you have saved those 16 hours. And it's not to lay off staff because Oh wow. We've eliminated 16 hours because you know, we have to be human centered about it. The technology the robots are good at doing one thing, but humans are good at human centered stuff like relationships and empathy.
cannot agree more Beth. So since we're talking about work life balance and well being your last book, The Happy Healthy nonprofit was about that but given the great resignation, is there a connection to it? How do you how do you feel about these things coming together?
I'm really happy because these are two of my passion topics as I've been writing about thinking about for the last 2030 years and, and so, you know, when I started down this path, you know, it was actually been writing about this before the pandemic and saw the potential benefit from workplace but well being from the saved amount of time, but also, you know, the potential for certain apps to also help with that, mainly apps that can streamline workflow. So in my work, I facilitate a lot of wellbeing retreats for staff, or else I'm teaching workshops and the biggest complaint I hear aside from the the you know, the burnout and the overwork. A lot of it's caused by these unrealistic workloads and if you sort of peek under the hood around that a lot of it has to do with an automated on automated manual types of systems that are there supporting the work that take a lot of bandwidth. Take a lot of our brain bandwidth and make it make us physically exhausted and put this on Zoom and stack it with back to back you know, online screentime you know, we're exhausted. So I think if anything, the pandemic has really taught us really the importance for well being and the importance of digital transformation. Now it's time to marry the two.
Awesome, thanks so much. Bad for for the great insights. I have a question around. Sorry about use cases, examples, because I think when we're talking about use cases, people have this aha moment and they figure out okay, I can do it as well. So in your book, The Smart nonprofit, you share a lot of great use cases, examples for nonprofits. Can you share a few of favorites of these examples so that we can learn and take home?
I'm sure that's a great question. We spent, you know, the book aside from talking about like, what it actually means to be human centered and helping organizations prepare their data and for an ethical and responsible use, we did interview scores of nonprofits about how they were beginning to use these tools and for programs delivery, fundraising and back office. So things from like screening resumes based on criteria that organization set determining the eligibility for a host of social services, identifying prospective donors from your technology of fundraising data, delivering medicine and food to hard to reach places or directing refugees to available beds, and certainly the examples we're going to hear in the second half of the session. One of my favorite maybe more specific examples is from the Trevor Project, which provides crisis counseling to LGBTQ plus people, and they created a chat bot and by the way, chat bots seem to be the most common technology that nonprofits are using right now. We've came across many examples, but they created a chat bot named brylee. Not to replace the counselors on the front line, but to help train the counselors by providing real life simulations of conversations with potentially suicidal teens and Riley's always available for a training session with volunteers and that helps get staff scale the number of trained counselors without adding more resources. And of course, this is a big problem right now, especially given that the pandemic has activated a lot of teams in crisis. And the Trevor Project sees the role of the technology as being really human centered and really understood with the potential harm of putting an automated system on the frontline as a counselor. And I mean, there's many bad examples of that, right. I mean, one of the most famous ones is a Twitter chat bot named Tay. That was, the automation was one again like Vialli was very smart and it was self learning. And this chat bot on Twitter was intended to learn how to converse with young people. But the trolls got ahold of it in less than 24 hours and turned it into a racist, misogynist, swearing insulting, harmful bot and had to be taken down. But let's go back to Riley Riley is again one of these really smart, natural learning processing smart boss that learns from socializing or interaction, but it's never exposed to anybody. It's only exposed to very controlled environment and to learn from counseling approaches which are very sensitive. They would never let it interact with the general public. Some other examples just really quickly, some of my favorite came because of the pandemic. I think the pandemic inspired or we might say forced a lot of nonprofits to go through a decade worth of digital transformation in two years. And one of my favorite examples is, is in the food banks because there was a huge increase in demand, of course, for people with food insecurity. And one of my favorite examples is from Pittsburgh. So during the shutdown, kids were in marginalized neighborhoods were not able to get their lunches from the school because they weren't home. So they used AI machine learning to efficiently re engineer the bus routes. So that they get the buses can bring the lunches to the kids, instead of bringing the kids to lunches for schools. And I just really love that example. And there's many others. So these
are great examples. But I mean, that's that's just so you know, inspiring and it gives you have a lot to think about. But I really, I really think this angle that you you touched on earlier about the control environment for Riley to be, you know, safely used. I wanted to ask you about what are the challenges around adopting smart tech for nonprofits, especially talking about ethical and responsible use, such as the example you mentioned earlier? What could you share about that?
Thanks for that question. And I'm sure many of you that are in the chat, and of course all of you on the call here. Of course you mark understand these challenges intimately and and in the book where we are creating this really for leaders to understand these challenges. They don't need to know how to code but they need to understand how code is built and how it may be biased. So making strategic decisions about when and how to use a smart tech really is this leadership challenge, not purely a technical problems. And as we know, there's consequences to automating systems and processes that range from losing the ability to make judgment calls. You know, that human centered piece like giving the unusual job candidate a chance to introducing flat out bias against people of color that's blocking them from receiving certain program programs and services. So we include many examples of these in the book not only so that we can learn from these and maybe mitigate the potential for problems. bias can happen in different ways. First, the data that the software is trained on, can be biased, it can be incomplete. There can be issues around the how the data was labeled data hygiene. I'd like that word, you know, dirty data, data that's really incomplete or inaccurate. And the technology needs, you know, enormous datasets in order for the algorithms to work and my co author, Allison find likes to say, Library of Congress sized datasets. And, you know, bias can also happen due to the assumptions that the algorithm or the mathematical code that actually makes the decision is constructed, you know, is it there's a wonderful quote that I really like saying a lot is that algorithms are opinions encased in code. So we need to understand what assumptions did the developer make in creating this tech? Is it Are they research base? Is it just their view of the world? Did they do ethnographic research with you know, end users, you know, where does that all come from? There's also data stewardship and privacy issues that organizations see leaders misunderstand and and having protections on that data. We're seeing more kind of scary types of stories about you know, privacy being violated and I think nonprofits really need to think through, do no harm pledge in using this technology and not wait for something bad to happen at scale. But really be able to test and iterate their way and understand what the potential for harm is. And I'll talk about readiness in a moment. But things like having an advisory group with expertise and ethics and data privacy, and AI can really help guide the organization. And I think this is a really great opportunity for similar types of organizations, maybe the share and advisory group like different food banks. So I could go on and on and on about this.
No, but it is it is very interesting. And I you know, I I've noticed all these conversations and then the last few years, there's more and more thought leadership and recomendations and energy in this whole conversation around ethics and responsible use of, of technologies, emerging technologies. And since we are here also to talk about readiness, and you know, your thoughts get your thoughts on how nonprofits can can start their their journey with smart tech. I think it's really important to highlight that there is also a non technical component related to readiness as the ones you mentioned before.
I'm sure we do focus on the non technical, the human readiness, the organizational culture readiness in the book. In fact, we have a whole chapter that's called Ready, set, go and um, and really, it's sort of a blueprint for nonprofit leaders to ask the right questions and to set up the right design thinking types of methodologies so that they're really doing this in a very reflective, knowledgeable, strategic and human centered way. And and that really begins with getting feedback from end users, whether that's staff or clients or donors. And maybe if you might start with a particular idea around the use case, you might discover and doing this research. Oh, no, that's that could have potential harms, or that's not the right that's not solving the right problem. There's also really understanding and working in partnership, you know, with the particular types of tools that are out there, asking the right questions, reading white, their white papers, asking the toolmakers what their assumptions were, and, you know, understanding what's, you know, under the hood, if you will, without having to technically reconstruct it. And then finally, I think that last step is really, you know, in the nonprofit sector, we just want to get things done, right. We don't want to like test and learn if something doesn't work and have to correct it, but I really think that approach is necessary when we're talking about this technology to avoid some of the problems.
This this wonderful, bad, thank you so much. I definitely learned a lot and I hope everyone enjoyed this, this Fireside Chat. Really great energy, really great insights. So over to you Beth and Nina
Thank you. Thank you both
so much Margarita and Beth. And we will have time for further questions after our next section which will bring us to demos. So with us today, we have Simon Lau as VP of product@otter.ai and Michelle Woodrow Public Relations Manager Toshi IO REO co founder of you AI you include qualities. Alex gladly head of sales of Keila and Opa. Yummy. Atta Nero, AI evangelist and partnership manager of Madonna. So let's thank them for being here with us today. A reminder that we'll be sharing the replay from today's event slides and any links shared in an email within a couple days. And a very important note that this event is only as good as the engagement we get from all of you tuning in. So please share your reactions in chat, ask questions in the q&a section and tweet at us with the hashtags public good app house. We will have time at the end of our painless demos to answer questions. So please continue to throw those into the q&a as I mentioned. And with that, let's welcome Simon Lau otter.ai SVP of product and so as an introduction, and Simon Lau the award winning AI powered voice note taking and collaboration tool with otter.ai He is a seasoned a product leader with over 20 years experience building speech and AI power consumer and enterprise applications. He has LED product teams at nuance Oracle and 247 dot AI. Simon holds both masters and Bachelor of Science degrees in Electrical Engineering and Computer science from the Massachusetts Institute of Technology. In today's session, we'll talk about outer eyes missions to redefine communication to be more collaborative, accessible and productive for all. using artificial intelligence otter.ai makes information from voice conversations instantly accessible and actionable by generating real time meeting notes and audio that is secure, shareable and searchable. So with that, I'll hand it off to you Simon.
Thank you Mina, for the introduction and thank you Beth for your warm welcome. Hello everyone. My name is Simon Lau I'm the SVP of product at auto top ai@auto.ai. We our mission is really to redefine the future of communication to be more collaborative assessable and put up to fall and we do that by solving the meeting. Note Taking problem. So next slide please. So who who have meetings, we all have meetings. In fact right now this is also a meeting when we are in remote environment. And we are in back to back zoom meetings Beth just talked about it earlier. That's just so much cognitive load in people just having work life balance and be able to pay attention to all the important conversations that you have meetings. So whether you're in a hybrid world or remote work situation, or eventually we'd get back to in person meetings. All these valuable conversation can be recorded and transcribed so that you can go back and refer to the important information. So we solve the problem at all AI by providing you an app that will record and transcribe all your meetings in real time. So that everybody can focus on a conversation engaged in a brainstorming or talking to your customer or engaged in their sales calls so that you don't have to worry about missing out any information. And you can collect collaborate better by either reviewing the action items that are captured in your meeting or forwarding on to other team members who may have missed meetings. And especially when you have remote team members who might not be able to attend meetings in your local timezone that's super valuable. And so all the important moments can be captured. searchable, shareable meeting notes that you have everything verbatim. And not only is that a transcript, but it also synchronize with the recording so you can quickly search and drill down to the important bits. We have a web application. We have iOS and Android mobile apps that you can run on your smartphone. And it also integrates to zoom, Microsoft Teams and Google Meet so that you don't even have to worry about remembering to click the record button. So if I may just click share my screen I'll do a quick demo as well.
Yeah, do that.
Okay, let me go ahead and do that. So in fact, right now, for this session, I have been live transcribing a conversation so far. And if we scroll back up, I highlight a couple of important quotes. So Beth was talking about pandemic has really taught us really the importance of well being. And also she mentioned a quote, algorithms are opinions encasing code, right. So these are key nuggets or gems of the moment. I can also add some comments. So let's say I want to highlight something and say, Hey, Mitchell, let's let's share out this link so that everybody can collaboratively highlight. You can do that as well. If I scroll back earlier, I also add mentioned Mitchell and also share a link. And Mitchell will be sharing up this link so for anybody who wants to try out the odor business trial, we'll be happy to offer everybody a 10 day free trial of this product as well. Most importantly, so this is how you can see that in a live transcript. Yes, you can pay attention to the conversation. So everything's captured. But let's say if you just want to take some notes, it's much easier to be able to just highlight something just like that and then be able to highlight it. Or you can just click on one of these buttons to maybe add a screenshot or add some comments at some action items or highlight the important moments earlier I also just add a screenshot to not to not this one, but I can certainly add a photo. So for example, I can take a screenshot of this or I can add some slides and then that will be directly embedded in line as part of the conversation. Just like that. Alright, so what does a conversation look like after the fact? So for example, Otter will further identify the speakers so that you can see who spoke when. So in terms of inclusion and diversity and making sure that everybody has a voice you can kind of see who spoke what, for how long. So that's one feature that is very powerful to make sure that for future meetings, everybody's voices included. You can also click on summary keywords to jump to the important moments. And so that way you can skip to all the moments that are talking about parking, for example, and then you can play back important moments
apart, even though was the single biggest use of parking spaces greatly outweigh so
this is just to demonstrate that the transcript is synchronized with the recording. So, honor is really, really powerful to be able to enable you to take notes seamlessly and you can collaborate better by having searchable audio transcript of all of your meetings. So with that, I would pause right here and go back to the slides. Actually, I can continue to share right here, slideshow so we have three plans. We have four plans, we have the basic plan to get you started for free and then if you're a power user, you can subscribe to the poll plan to get more imports and custom vocabulary and advanced search. If you're a business and want to purchase order for the entire team. Then you can also get the audit system to integrate with Zoom Google Meet and Microsoft and also centralize billing and two factor authentication. And if you're a larger enterprise and we can roll out the order to your entire company as well. So in a hybrid world where 75% of the professionals are planning to continue to work remotely or at least part of the time, it's super important that you're providing tools to automate and help your your team members work more effectively. So otter can be a tool that helps you remove the burden of taking notes and making sure that everybody can share the important moments with your colleagues. And these are just some of the key companies that have adopted otter. So with otter business, then you can roll out to your team, your department or the entire organization, also higher educational institution. Or using otter for remote learning as well. So I don't think I would have time to go through the entire video so And finally, let me just emphasize that with otter assistant. In fact, otter is transcribing this session because the other system has joined the meeting as a meeting participant. So then, a link can be shared in the Zoom chat window and we click on it. Everybody can go ahead and highlight and collaboratively. So you take an action item or somebody else had a decision and you want to highlight the important moments. By the end of this meeting. Everybody has a meeting notes that can be shared instantly. You don't have to wait for a designated note taker. Everybody can focus and participate in a conversation and leave the leave the job of note taking to our system. So with that, I'll stop sharing and take any quick questions.
Thank you so much, Simon.
I think for now we will do questions at the end of our demo session. So you can start scrolling through the q&a section and see which questions otter.ai has. And with that, I'll go over to Toshi IO REO of you include Toshi is a finance and strategy professional turn social entrepreneur. Her work and passions are centered around using technology to drive equity and social good. Currently, she is the co founder of you include where she's building tech solutions that establish equity in the recruitment process and increase workplace diversity in this session, we'll talk UI include shoe include is a platform that empowers the most equitable and effective recruitment process for employers. Our inclusive writing tool uses machine learning algorithms to identify and eliminate bias language and recruitment material that deter qualified candidates from applying for roles. The software also supports employers and the development of inclusive content that is inviting and appealing for people of all backgrounds. The use of the product increases the volume, quality and demographic mix of applicants, while decreasing recruitment costs and time to hire. So with that, I'll hand it off to try Thank you, Lena. Um, hi, everyone. My name is Natasha, I am the co founder. They don't get to the thank you pages. I mean, they don't get the email I mean, works so you could be provided just ask everyone to go on mute. That would be fantastic. Thank you. Sorry, go ahead. No worries. Um, yes. Hi, everyone. My name is Sasha. I'm the co founder of Vienna clear and you include as a company that leverages scientific research and machine learning to generate the most equitable and effective hiring process for employers. Next slide please. So I do include we are solving two problems. The first is that is the fact that workplace diversity rates are severely lacking. In fact, only about 20% of people in the workforce are part of underrepresented groups, while on average, almost 80% of the US UK and Canadian populations identify with a marginalized group. Next it the second problem is that hiring processes are costly, prolonged and many times and effective. Excellent. So we're solving those two problems by addressing a major yet overlooked barrier to underrepresented candidates entering the workplace, and something that is also a major contributor to effective hiring processes. All of which happens in the job ad construction stage of the hiring lifecycle. And that issue is biased language and recruitment material, and more generally, a lack of inclusive and appealing language and recruitment material. In fact, our research shows that about 70% of job ads are actually dominated by gender bias and racially exclusive language. Next slide. So many people don't recognize just how powerful languages and influencing people's behavior This is especially true in recruiting. The language we use can perpetuate institutional inequality and deter qualified candidates from applying for roles. And I really want to emphasize here though that is often the the subtle and the inconspicuous language that has the biggest impact. And why is that it's, it's because we don't know that these words carry harmful undertones. And because of that lack of knowledge, our job ads are often filled. With off putting language. Next slide.
So to address this, we took on two approaches. The first is to identify the subtle and the pernicious language that is determined for candidates and a research paper written years ago discuss the impacts that gender bias language has on both female and male jobseekers and their decision to apply. The paper also provided an extensive list of words that are gender bias that we should avoid using in our job descriptions. But when we started you include we can find any research study that again identify the subtle language that is deterring for other underrepresented groups besides female job seekers, and that that was a huge priority for us and will always be a priority for us. And so, our team decided to launch our own scientific research study, starting with race based, inclusive and exclusive language and our research report and findings are now available on our research Insights page on our website. Next slide. The second way that we are addressing this problem is by creating our core product, which is an algorithm that has a number of functions, it scans your job ads and highlights all of the coded language that is in your job ad. And then it gives employers a breakdown of how gender bias racially exclusive and overall inclusive the job at his next slide. It also educates you on what what impact those words are, the what impact the words that are being highlighted are and how may come they may be impacting job seekers appeal and decision to apply. And then on top of that, it provides you with suggestions on words to replace problematic language with Next slide. And then finally, it gives you tips on how to infuse inclusive themes and to your job. ads. And yeah, so to wrap it up. We just launched a new version of our product a few a few weeks ago. And we are obviously very eager to get into get it into the hands of employers so that people can start benefiting from this technology. And so we have three apps. The first is that if you are in a position where you are hiring, we'd love to have a conversation with you. We want to talk about your hiring needs and hiring challenges and determine if and how we can support you in those. The second ask is that we are beta testing our products. And if you're interested in beta testing and and providing us with honest and candid feedback, we'd love to hear that because again, we really want to solve your problem. And lastly, we are piloting with a few companies and working with them closely. In order to again like understand their challenges deeply and tailor our product to better meet their specific needs. So if you're interested in any of those three things, please shoot me an email I would love to hear from you. Thank you. Thank you so much Toshi. We really appreciate your input here. And please feel free to run through the q&a section so we can start looking at what questions you can get answered from you. Next up, we have Alex gladly of Keela Alex gladly is the head of sales at Killa. Killa is a rapidly growing organization on the cutting edge of innovation and software development for the nonprofit sector. We have woven our artificial intelligence features into the software to give insights like who to call for each campaign exactly how much to ask. them for and when they will be most receptive to donation requests. Kyla is your fundraising secret weapon. The session titled AI for the little guy. AI is no longer a complicated tool built exclusively for large tech companies, data scientists and engineers Keela has built intelligent tools specifically for nonprofits in order to help them make better decisions by being able to predict a donors next move. Join on this interactive demo to see how our tools will help small and medium nonprofits by significantly improving fundraising efforts and with that, I'll hand it off to Alex.
Thank you very much. They're me, you know.
So if we could get into my slides, please exit and go straight to the next one. Excellent. So hello, everyone. My segment as we just said is AI for the little guy just by being here. Today. We all know that you're interested in how AI can help you. But many of you are probably wondering how exactly it will help. Well, we've made it really easy for our users by building it right into our nonprofit CRM. So let's see how we did that. Next slide, please. So the industry problem I've seen people talking about this in the comments. There's always been a huge gap in technology available for nonprofits compared to our friends in the for profit sector. for profit companies have fancier high priced solutions that promote things like forecasting and data analysis and ultimately influence the strategies these companies use for their own missions. Unfortunately, many of you probably had the opposite experience. How many people here have been told we don't have budget for new technology or when you finally get approval for the system, the one you get is 20 years behind the systems in the for profit industry. This is why we built the capabilities you need in tequila. Next, on the next slide, so your problem many of you here have a ton of data, whether it's contact donation revenue, or maybe even volunteer data, it's usually just sitting somewhere and it's probably gathering computer dust. If that's the thing. At clearly we say the issue isn't whether nonprofits are capable, smart enough or technically savvy enough to use AI. But instead this is an issue of access to technology. So let's solve that killer. We measure all of your data, and we bring powerful tools to organizations of all sizes. So feature one contact insights. These are all of our contact profiles in killer. These informative metrics help you make better decisions about your donors. These little nuggets of wisdom are built on the back of machine learning algorithms to help you better understand your individual donors, as well as to compare and benchmark your donor database as a whole, for example, and insight like the donor score is calculated. Oh, thank you go back one worse. The donor score is calculated based on how long and how much a donor has been giving and then comparing those numbers to other donors in your system. This will let a development director of fundraising How to Act maybe they need to reach out to someone with a high donor score to help get their campaign over the line. Or maybe they need to realize they're losing touch with someone important and then they need to let that person know what their last donation did and who it helped and that they are indeed valued by organization. For that person we may need to build up to an ask. You can see them in action here on the contact insights.
Next slide please.
So feature two fundraising and benchmarks. Many organizations spend countless hours calculating donor retention rates as part of their annual forecasts are the struggle to pull in accurate numbers sometimes because of the lack of time or even quality of data. Keeler pulls all of your key performance indicators for you you can choose KPIs such as donor lifetime value retention rate, recurring donations, growth rate, many more, then you can benchmark yourself against similar organizations in our Keila database, or even fundraising research and then set a goal to meet or beat benchmarking and Kieler ensures that the data you log in the system will provide quality and consistent reporting so you can fake focus on how to get the numbers up instead of spending your day juggling spreadsheets. And we can have a look at some of the KPIs that you can see in Keeler. Next slide, please. So feature free smart recommendations are our users favorite feature. We figured nonprofit folks love metrics, but they're typically time poor and just want to be told what to do. So we leverage all of the data you've collected to answer the question, what is going to happen or what could happen? So have you ever found yourself wondering what an appropriate donation amount is to ask a donate a donor for? What if you ask for too much or too little? It can certainly be tricky, and that's where SmartArt comes in. This insight is recalculated by our algorithms every 24 hours and provides a recommended donation amount and range to ask owners for based upon their giving behaviors. This saves our clients a lot of thinking time. But also increases the revenue they make for each campaign. Unfortunately, research shows donors will give a smaller amount than what they're able to if they're asked for that smaller amount. are smart recommendations even tell you when and how to reach out to your donors ensuring you can meet each donor at the right time and place to increase your revenue. You can see those on the next slide. I know I need to finish up now. So we'll just head to the conclusion. So killer tools are powered by machine learning algorithms that allow organizations to leverage data and make better decisions. Whilst we can tell you when people are likely to give and how much they're likely to give. Let's not forget that the human touch is always required here. You know your donors better than anyone. We're just getting started on using AI and fundraising and we're so glad you're here today showing an interest and we can't wait to build a better future of all you lovely people
thank you so much Alex we really appreciate your presentation and next move up to obey me out in Euro of I'm Donna. Obey me currently works as an AI evangelist and partnership manager with M Denna where they develop and deliver compelling presentations product demos and opportunities in the AI and machine learning space by me gained industry recognition and credibility as a regular attendee panelist and keynote speaker at codename and third party technology conferences, trade shows and press events. As the partnership manager from Donna OPI me continues to identify leads for potential partnerships and customer opportunities in the AI and machine learning space. In this session, I'm pairing engineers with social good projects. Um, gonna is a bottom of collaborative AI development platform where a global community of changemakers comes together to build ethical topologies build ethical and efficient AI solutions. Learn how you can make your nonprofits AI idea into running code. So with that, I'll hand it off to Oakley me.
Thank you very much. And thank you for giving me this opportunity. I'm happy to be here and thank you once again so briefly I'm Dana just like Oh, I'm Dana is the bottom of collaborative AI development platform. We're a global community of chimica comes together to build ethical and efficient AI solutions. So while majorly do is that we look for problems we solve which is the artificial intelligence. So I'm gonna work with leading development organizations as well as NGOs, to build real world solution and accelerate their parts becoming a data driven organization through a process what we call AI enabled NGO. Next slide please. Okay, next slide, please. I think my boss will read. Okay, so now how can we help you to become an AI enabled NGO? In Home Dina, we have four strategic steps that we have put in place to help you become an AI enabled NGO. We believe that all organization can can become an AI enabled through these four steps. Number one discover we want you to tell us your problem. Tell us what you want to do. Tell us what your NGO is all about. Secondly, we reach out to our community and look and send your problems our community where we where we collaborate and solve that problem is to share intelligence that will bring back the results to you which which we take which you will take into the third stage which is production which is production lines. So the which is a form have a proof of concepts of what we're able to build from the problem you brought to us. Then the last one would be capacity building. That is when you be able to like during the third stage, you will be able to use that product that we have helped you to build to raise funding for yourself. And also on the third stage we help you to like improve the product to to have any selected AI engineers amongst us to work with you throughout the office. Then the fourth stage will not be the capacity building where you're interested in recruiting people to work on your projects. So we believe that a we believe that all NGOs all NGOs can be AI enabled. All you need to do is just to reach out to us with what you're solving, what kind of problem you're solving in the community. We bring we take in the problem and we solve it through our AI collaborative platform. Okay, next slide please. So x y how we can help you so innovation challenge we, we we we have we raised a diverse team of 50 AI engineers talents with data collections, build a proof of concept and deliver a prototype for you. So the second the second stage is dedicated team. We we make available two to five vetted engineers do a production level solution that is followed by the next stage which is capacity building then we also provide you I top performance for my rewards talent pool. So what we do is what we do for all engineers, just like what I've explained your time, is that we make sure that we build a proof of concept that you can take out there to raise funding for yourself. We also provide to you AI engineers can work with you from from the from that can work with you at any level and you also add your capacity building stage where you want to where you want to employ more, employing more people to work with. See, we also have diverse teams of AI NGOs across the world that can help you at this stage. Okay, so next slide, please. So some of the projects we've we've worked on we've worked with World Resource Institute.
I'm Dana has worked on like five challenges and to challenge projects with World Resource Institute. We've also worked with UNDP UNDP. We are currently working with UNDP. And we also we've also worked with Save the Children. Yeah, we're able to build an online chatbot where we're able to educate children about online abuse. And these are just few of one of the projects with build and also love to hide that we also have a local, local chapters 100 local problems, and we abused a lot of effective solutions. Some includes solving artificial intelligence using Sobey and implementing artificial intelligence. We're able to also use solar energy base to also be able to use it solve energy problems using artificial intelligence. These are just one of the projects we've done in Ondina. Next slide please. So for so for you to become an AI enabled NGO. We believe that on data can help you to become an AI enabled no matter the kind of organization to run. No matter the level you are at any point at any stage in time. All you need to do is to tell us a problem. We take you to the next stage we work on the problem to collaborate to our collaborative platforms. And the next stage we deliver deliver to you a prototype that is a proof of concepts where you can take to raise funding for yourself and also where you can use at the production level stage. And the last stage is at capacity building. We believe that at the end of the day, you're able to you're able to at end of day also provide to you skilled AI engineers that can work with you at your capacity building stage. Okay, so for people interested in becoming an AI enabled, you can just visit our platform@data.com forward slash AI NGOs. Thank you very much.
Thank you so much. Oh boy. Yeah, man. We really appreciate your presentation. And with that, I want to give a big shout out and thank you to all of our brilliant speakers are reminder that we will be sharing the replay from today's event slides and any links shared in an email within a couple days. So now it's time to head to our q&a. Feel free to throw any other questions in our q&a tab to ask our panelists. Just just to start here. I was thinking we could start with you include Why is eliminating bias in job ads so important? And I know you touched on this a bit in your presentation but I thought we could start here with Toshi.
Yeah, thanks for that question. Um, essentially everything I really touched on in my presentation. The language we use is so so incredibly powerful. Um, it influences people's conscious and unconscious behaviors. And a lot of the words that we use on a regular basis a lot of the words that are in our job ads carry harmful undertones and they are ultimately leading jobseekers, prospective candidates to read the job ad and say, oh, yeah, this doesn't feel appealing to me or I don't feel like I'll find a sense of belonging in that role and ultimately, decide not to apply for the position and so companies and organizations are missing out on 1000s really qualified candidates. And this is especially pertinent to like underrepresented candidates, which is what our research was was all about. And so yeah, it's it's important because it ultimately determines who decides to apply and not apply their roles and the early on the sooner people or employers recognize that again, the language that they use is powerful they start scanning the language start integrating inclusive language and their job as I think it will definitely save them a lot of time and money in the process while also increasing the number of qualified and diverse candidates they receive in the pipeline. Thank you. So much. And for otter AI once again, I know we touched on this briefly but could you emphasize again on you know how otter dot make makes an organization more collaborative, accessible and productive
I believe we have Mitchell here
sir, as seeing Simon was still on, um, but yeah, there are a number of ways that you know, there's a number of use cases that we've seen with our customers from journalists to use otter to transcribe interviews, to you know, managers who use otter to make sure that all of their employees are getting the right voice. Right time to participate in meetings using otters transcript with speaker talk time. There's you know, with the transcript is shareable, no matter if you're at in the office or on a zoom call. Anyone can access the transcript so wherever you are, whatever kind of hybrid work environment your organization adopts. The transcript is shareable but also makes the the meetings that you have the conversations that you have more collaborative, and hopefully more productive as well. And more accessible, you know, having the transcript in front of you and captions in front of you, if employees are more introverted or extroverted or if employees have a learning disability, or are harder hearing. This makes your meetings a lot more accessible throughout the organization as well.
Thank you so much. We appreciate that. And then Adam asked this question in our chat, would Kyla replace our CRM or does it work with our current serum? like Salesforce, for example? And I know I think those answered briefly, but good to get a little bit more on that.
That'd be great. Yeah, thanks
for the question. Yeah, I would, it would tend to replace your nonprofit CRM it does. Also, I should mention that we have some partners called kit that do have an add on that you can attach to Salesforce if you like some of the tools that you saw, but for most part, most organizations would be getting rid of their CRM and going with killer and they usually with all of those tools, see some really good results. Those joining us from spreadsheets on average last year in their first year increase that their revenue by 46%. And those joining from other systems usually increase their revenue. So an average by 10% in their first year of us. So yeah, we'd like to think that you'd switch over to Keela. Thank you.
And I saw this one in the the q&a as well. How can AI help small nonprofits who might not have clearly documented processes are ready and I know throughout this presentation, we've we've hit on, obviously, some great demos, but maybe we could speak just generally if anyone wants to discuss AI solutions for smaller organizations and how that can help even at the early stages of implementing AI solutions. This is an open question for anyone on the floor.
Yeah, I think I can come in here. So I'm just like what I said, What are we doing? I'm Dina we believe that all NGOs all NGOs can be enabled. And so we believe that all you need to do is to tell us the problem. You tell us the problem. We work with Su from the problem stage to the finance solution stage. So we believe that we believe that student through our collaborative platform, we can help you gather data from important sources and also do a lot of research, develop machine learning models, or that can help us to they can bring more insight to the data. And then from day we're able to understand more about your about about what you do about your organization. And with that, you can also use that use the products use that product or the results for the upcoming for the next stage. That is you can use the results 2444 to reach out to organizations for funding and also for on also you can also step up the game by getting AI engineers two to three AI engineers within our community also can also work on that resource cutting based on the first challenge so that we can get more insight about data and which can also be used at the capacity building stage or production stage. So I believe we believe they all companies can be enabled. We get your data, get insights from it, and develop a lot of machine learning. Develop machine learning models, and then also provide that resource to you that you can use for to enlarge and also add also to requests for fundings and stuff like that. So
yeah. Thank you and just to add on to this, I know Paul also asked here, you know, are there any challenges and costs to kind of implement these solutions? Or, you know, first thing that smaller nonprofits especially should be thinking about, when they're they're taking that first step of an AI implemented solution?
Okay, the first step is at the initial stage, we made we are the initial state, we don't collect money for engineers at initial stage, if we are going to be charging at all it's going to be something very lead to that that can be affordable, foldable. So at the initial stage, we all we need for you is to give us the problem. We're going to take it down to our AI community, which is a diverse team of AI engineers across the world and we work on the project, get data for you gets develop insight from that data. And also develop models that you can use the consumer insights and tell you more about your, your your organization. So at the initial stage, we don't really charge much if we're going to charge something you can afford, if at all we are going to be charging you that stage
any other input from our other panelists? On these questions?
I just want to emphasize and build on everything that Nami just mentioned about starting with the problem. Um, excuse me if I mispronounce your name. That, you know, the right question to ask is what is the what is the pain point that you're trying to solve for? You know, what is the problem? Not like, what, what? How can I use this tool? I hear that a lot. And that can I think lead you down the wrong path. And I think the other vices kind of like slow down. We don't have much time. I know we're all pressured around time and lack of resources. And we want to immediately jump into the easy solution. But we really need to sit with a problem for a bit. And then we need to inch our way. And sometimes it may be that, you know, looking at it holistically at a large level. Maybe that's that's feels too big and too much maybe we're looking for like, is there a micro use that can save us a little bit of time, you know, and I was just thinking reflecting on like otter AI. I use that a lot to take verbatim notes for me in meetings when I don't have somebody there as a note taker. So I could just focus on listening and facilitating the meeting and maybe taking down a couple of the key points. And then even though we do you know in this age of remote work, we were working in zoom and if somebody can't come to the meeting, we record it but like going through a whole video that's like time consuming, it's much more easy just to scan the transcript, you know, to pick up on what you've missed. So I mean, that's a micro use, you know, and it can be you know, you step your way in small steps and, and to go slowly. And, and that first step is thinking about problems that you want real problems you want to solve and sometimes and this is a Toshi was talking about sometimes we don't know what they're, we have this problem, you know, if we have an unconscious bias and that's why it's really good not not just to talk internally, once you have it, but to test that out within users and talking to other people. Um, you know, to see if, you know, have we, you know, really identified this correctly before
moving forward.
But I was gonna say you nailed the talking point, my dog saw someone outside the window, so it was barking on the other side of the house. So I had to run across house to get the dog. And I didn't know what the question was, but I went back and looked at the transcript was gonna say exactly what you said. It's, you know, otters really good for one thing we've been looking at is how many people are in meetings, and if you could keep one person from having to go to a meeting that they don't need to participate in, that would save you a lot of money and save your organization a lot of money over time.
I was gonna say and building on Michals point two, according to the research that's out there, a lot of people are being invited to virtual meetings that aren't relevant to them. So they're spending their time because there's so much work they're spending their time multitasking and multitasking then contributes to stress and exhaustion. So so there's a well being piece to it too, as well as saving time and money.
Yeah, so I wanted to say something. When I hear from your iPad we have a dedicated tool that might be used for automation for good. Definitely we can do more in this area and we have no no code platform with drag and drop. For people that want to experience this tool is for free. We have community edition. So the only thing that you have to do is just log in there and you know, play with curiosity and maybe something can be suitable for your small enterprise. And what's needed inside. You know, the, the company that wants to automate Excel or Word or Outlook or so things that you use daily and that could spare time for you to focus on the valuable tasks in angels. Yeah, that should take all the repetitive and without value of work from aside from from our daily routine so I can give you the link in in the chat so you can explore more for free, the universe automation cloud, try to find out what's suitable for for your NGO.
Thank you so much. And I just wanted to make sure all of our panelists were able to get their input in on that that last question there. Toshi, I saw you come off mute for a moment if you wanted to add in anything else? Yeah, I mean, I was just gonna echo what everyone has already said. The key here. I think all the panelists have touched on this in some way, shape, or form and their presentations, but the key here is effectiveness and efficient efficiency. You don't want to get AI just for the sake of getting AI right like you want to get AI to help you make your organization processes month effective and help make the process even more efficient as well. My understanding of nonprofits is that they are often understaffed and employees are often overworked and so you want to get a product or a technology that can partner with your employees and help reduce that load right reduce that burden in the case of hiring. Leveraging AI is probably more critical than it has ever been right especially in a job market that is super competitive, not for not for the job seekers but for employers, right employers and we've heard this so many times in our conversations with companies employers are having such a hard time getting candidates in the door because candidates are, um, they just have so many options, right? It's a really, really great job market. And so you want to this is a great case of how you would leverage technology to help you kind of make sure that you are covering all your bases and attracting the candidates that you you wish to see within your organization and make sure that you are presenting your company in the best way. You have appealing recruitment material that is just like getting those candidates in the door so you and your employees don't have to do all of that, like heavy lifting. On the other side in terms of like doing all the extra work to like actively reach out to candidates or like, you know,
I'm like some
employers have told me about like how many hours they started like just LinkedIn, stalking. potential candidates and like sending out hundreds of messages a day. Again, this is a good example of how you would leverage AI to kind of just help you make your material more appealing so that those candidates are coming to you instead of you and your employees. Again, taking on that burden doing all that extra work just to get candidates in the door. So you know, just to wrap it up. The key is effectiveness and efficiency like you want to use AI or leverage AI to reduce the burden for you in your organization.
Absolutely, thank you so much. I think with that, you know we have a ton of questions that are in the chat that have been answered. And you know, once again you'll be receiving this at the end of our in the next couple of days of receiving the links and PowerPoints or presentations to review. So I think I'm going to start wrapping it up here. I just want to say again, I mean such a huge thanks to all of our panelists today. It's been so fantastic to hear from each and every one of you. One thing I want to do really quickly is if people could just share in chat, one thing that you learned today, so as we're closing out, I would love to see for those of you here and what's one thing that you know, sparks your interests. I love always saying that aha moment that clicked for you during this presentation. We would love to see that in the chat. And let's see if we can get a couple of those come through. Before we close out and then like what you saw today and want to support future demo events and reach nonprofits curious about technology. Consider becoming a sponsor for our future events. Please contact Susan Tenby for more information. You can see Susan's email here. As 10 be@techsoup.org If you would like to do so. And just quickly, thank you. Thank you. Thank you to everyone for attending. Especially everyone behind the scenes producers and staff at TechSoup that made this event possible. Please be sure to complete any post event surveys that you can find. After you close the zoom. We will be sharing the replay from today's event, the slides and any link shared and the email that will be sent to you in a couple of days. So thank you so much. Thank you panelists and we'll be in touch soon. Thank you
