Name: From Insight to Impact: Leveraging AI to Transform Fundraising and Engagement
Uploaded: 2026-02-04T19:16:08.189Z
Duration: 1 h 12 s
Description: From Insight to Impact: Leveraging AI to Transform Fundraising and Engagement

Transcript for "From Insight to Impact: Leveraging AI to Transform Fundraising and Engagement": Hello, everyone. My name is Dory Bolton, and I am the account based marketing manager and the senior principal of account based start over. Hello, everyone. My name is Dory Bolton, and I am the senior principal of account based marketing at Blackbaud. And I want to welcome you today to Fundraising Insights to Impact, leveraging AI to transform donor engagement with revenue growth with Forrester. A few housekeeping items before we get started for our webinar. The audio is broadcast through your speakers. And if you have any technical difficulties, just refresh your browser. And if you have any questions or use the Q and A feature to submit your questions or raise your hand, and then please click on the document links for helpful links and resources. And with that, I would like to introduce our speakers for today. We have Nina Gupta Niddell, who is the Vice President of Product and Head of Donor Management at Blackbaud. And our guest speaker is Kate Leggett, who is the Vice President and Principal Analyst at Forrester. And with that, I will turn it over to Kate. Dori, thanks for the introduction and welcome all. Thank you for taking an hour out of your busy days to hear Nina and I dive into these topics. So to get started, just a little bit of like state of sorry. And just to get started with some opening statements, at last count, I counted over a million and a half non profits in The United States alone. And all these nonprofits continue to thrive because they provide a range of services to their communities. But we all know it's really hard in this economy for nonprofits to stay afloat, and that's because they depend on donor and grants for funding. And it's really hard to come by these days because of all the economic conditions that are swirling around us. And many nonprofits are really struggling to gain mindshare with the right messaging, the right targeting, to be able to attract and retain donors. They struggle to build these meaningful relationships with donors to keep them engaged and aligned to their mission over time, and that they're also struggling to evaluate their fundraising efforts to determine how effective all their initiatives were. And if you can't really understand and measure your outcomes, nonprofits can't tune their tactics to be able to drive higher donor engagement and loyalty. So today, what we'd like to do is explore three topics. First of all, AI adoption in non profits and fundraising sectors. Then, we want to dive into key challenges and opportunities in leveraging AI. And then thirdly, the trends and predictions over the next three to five years. So, I'm going to kick this off by sharing some market insights and data from Forrester. So here we go. So here are some insights that I see at Forrester. And again, we all know that nonprofits are really struggling to cultivate donors in this economy. Donors are really tired from constant requests for donations. They don't want to engage in just transactional giving. They want personalized, trust based relationships, so to be able to show how their donations really make an impact. But we know that nonprofits can't build or have a tough time building that relationship with the donors, and they often struggle to communicate how their donations are really making an impact. And it's really because organizations don't have a good record of who their donors are, they have incomplete data about them, they don't necessarily have a consolidated view of their giving history and then other engagement activities. And core to this issue, we see that many non profits are struggling with their technology foundations. In the conversations that I have with nonprofits, many have these silos of data in many different systems that aren't integrated, like marketing automation solutions, accounting systems, donor management solutions, CRMs, and some are even using spreadsheets. And these silos make it really hard to have that end to end visibility in your fundraising and donor management activities. And then the other issue that we see is that many nonprofits are using legacy CRMs that are not able to support their operations. And there's no way forward to be able to tap into the value, the efficiency, effectiveness gains that AI delivers with legacy CRMs. Because you can't tap into that holistic data about your donors, about their behaviors. You can't tap into all the efficiency gains that AI is able to deliver. After I show you some market data from Forrester, I'm going to have Nina go into a lot more detail about what she is actually seeing in Blackbaud customer base and in the greater nonprofit segment. So, on this topic of modern CRMs, you've got to adopt modern CRMs, move away from the legacy CRMs that really stifle your innovation. Modern CRMs is really powerful because it empowers organizations to be able to deliver those highly personal, targeted experiences to your donors that help strengthen relationships with them. And this ultimately has material impact because it boosts their engagement and influences fundraising efforts. And it's because that donor interactions along your journey really produce a large volume of really high quality, well governed, very structured, unstructured data. And you're getting all this data from emails with your donors, from phone calls, from website visits, from other fundraising activities. And then when you unleash AI on all this data, it really helps organizations summarize and surface insights, predict what actions, best actions to take that impact your fundraising efforts. And it ultimately makes your employee base a lot more productive and effective. And in Forrester, in all our research, we see that organizations are really leveraging and leaning into AI capabilities within CRM. And when you look at the top five priorities for CRM operations, three of them rest on AI for this year and for next year. And that's what I'm showing on the screen here. So again, if you look at top priorities for CRM operations, the top priority is to use data and analytics technologies to be able to uncover insights about your donors to drive that up leveled engagement, that more personal engagement, to build deeper connections with your donors. Another top priority, and it's labeled as priority number three on the screen here, is to use AI to offload the front office from repetitive tasks by automating value added, I'm going to take it from three out of the five top priorities. Three out of the five top priorities for CRM operations rest on AI, not only for this year but for next year. And that's what I'm showing on the screen here. So the top priority is really to use data and analytics technologies within your CRM to uncover insights about your donors to drive better engagement, and ultimately build those deeper connections with your donors. Another top priority, and it's labeled as priority number three on the screen here, is to use AI to offload the front office from repetitive tasks, by automating more tasks and even end to end processes. And what it does, it allows you to focus your employees on more of the value added work, more of the strategic work than just the repetitive work that can be easily automated. And then another top five of the top five priorities is really to use AI, and it's generative AI to create more targeted content for your communications. For example, sending new donors a welcome series, or sending lapsed donors, perhaps, impact stories, or sending, for example, high capacity donors, exclusive invites to, sorry. For example, sending new donors a welcome series or sending lapsed donors an impact story or sending high capacity donors exclusive invites. You may also want to use AI to help tailor messages to show appreciation in ways that matter to your donors, to them, instead of just generic thank yous. So let me share some data about how AI is penetrating operations. So our data is showing that nonprofits are using a range of AI within their operations. So, you've got predictive AI that's used to make forecasts and decisions about what next best action to take or conversation to have. You've got generative AI that creates new content based on existing content, and then you also have AI agents, or agentic AI, that takes actions autonomously, like executing a process, and then it's able to learn from outcomes and get better over time. And the data that I'm showing on the screen here is showing that organizations are using a mix of generative AI and predictive AI, and now AI agents to boost employee productivity and donor experiences. And that light green bar right in the center of the screen is showing that the majority of organizations worldwide have generative AI investments that are complementary to their predictive AI investments. Organizations are also planning to increase their investments with AI next year. And that's because most organizations have already been positively impacted by AI, both internally for internal operations and for their donor facing operations, external operations. Our data on the screen shows that 41% of organizations say that AI increased personal productivity. They were able to accomplish their tasks faster. 32% saw broad increases in employee productivity. 31% saw efficiency improvements from automation. Also, we see that organizations see positive impacts of AI on external donor facing operations. For example, 34% of organizations increased the experience that they deliver. Organizations also reported increases in brand equity and market reputation. But AI is really changing a lot faster than organizations can develop strategies to harness it. You have to think beyond just implementing individual AI features and use cases to thinking of your AI rollouts as a continuum, where you're developing a holistic strategy for all your AI initiatives that are then tied to actual business outcomes. And this strategy may take longer to implement, but it leads to greater gains down the road because you're not thinking of AI just feature by feature, but it's part of a broader holistic strategy. But having said that, we find that organizations are really struggling at all levels with AI success because many organizations really struggle and don't really have a concrete idea of how to connect their AI strategy to their business strategy. And then we also see companies, organizations really struggle from a core technology perspective On the screen here, we see that 28% of organizations are really struggling with the ability to trust outputs. We also see that organizations are struggling from a core technology perspective. And the data on the screen here is showing that organizations are struggling with data privacy and security concerns. 22% are struggling to trust outputs of AI. And organizations are also struggling with AI governance, quantifying risk, and even finding the right talent with the right skills to be able to support your AI projects. So one constant problem that we see at Forrester is around AI measurement because organizations aren't bringing that operational discipline in measuring AI success. And every time you roll out an AI feature, you've got to measure its impact. And again, it's not about just one AI feature, it's about a continuum of AI features like you're also going to be rolling out over time. And so this lack of discipline around these operational measurements is a reason why we're seeing disappointing outcomes of AI projects. And so here's some data that we just pulled from Forrester's State of the AI survey. For example, only half of organizations have benchmarks to assess whether AI is meeting performance expectations, And less than a third of organizations can link AI efforts directly to core business outcomes. And so with all of these challenges, Forrester's guidance on how to roll out AI within your organizations is really you need to focus on four steps. First of all is getting that foundations of your technology house in order to be ready to implement AI. That means modernizing your CRM systems, for example, breaking down your silos of data, documenting your processes, getting rid of a lot of legacy applications that incur technical debt within your organizations. All of that has to be cleaned up. And then, again, you want to make sure that you have focused on the right strategy for your AI efforts, that your AI strategy directly supports your business strategy, and that you are putting a roadmap that ties your strategy to your AI strategy, to your business strategy, and AI outcomes linked to business outcomes, and then you're putting in place KPIs and success measures that you will use to measure success. And of course, you want to use modern CRM technologies to be able to fuel your AI effort with AI directly embedded in your business applications that will allow you to scale and adopt that continuum of AI features that will impact your operations. And lastly, just before I pass it on to Nina, I want to say you need to also pay attention to your change management efforts, because every one of your employees will be directly impacted as you roll out AI within your organization and you need to communicate to your employees how to work with AI and use them to help co create a new future with AI that will make your operations more efficient and effective and then your donor engagement strategies and processes more impactful. So with that, I'm going to pass it on to Nina. Nina, you're on mute. Thank you, Kate. Your data and the Forrester Research, the industry expertise that you're bringing to our sector, it really aligns with and is validating our findings and really our product direction. I'm going to share with you, similar to the data that you had just walked us through, our work and research is also revealing that despite the numerous challenges that nonprofits face, they truly are taking purposeful steps and they need the tools that will help them build those lasting donor relationships that truly have to leave their supporters feeling appreciated and inspired to stay engaged. Technology is a great value multiplier there. And we find that when nonprofits have the right tools, they can employ better strategies. They can build those strategies to strengthen and sustain those relationships so that donors are feeling recognized. They're feeling that they understand what they are supporting in these causes and the mission, and that they're encouraged to really continue their support, which ultimately fuels the entire social impact and the ways that everyone benefits from that. So Kate, you shared some priorities for CRM operations and the value of AI in shaping the future of these operations. And one that stands out is the priority that you had called out is really revolving around adoption of real time AI driven or enhanced operational support capabilities. I believe you had mentioned that only 51% of organizations are tapping into the value of AI driven productivity or efficiency in their operations. And I am finding in everything that we are also understanding in the industry, what our teams are researching, and of course, how we're building the solutions, is that the cognitive loads and distractions that nonprofits face, the communication barriers, and really those delays and those workplace limitations, those are all disrupting that work that is so fundamental in what we're driving for social impact as an entire ecosystem. And when I, with my team, dive into what that means, I'm going to add some details to areas that you had covered for the challenges that can impede fundraising efforts for nonprofits. First, it's that cognitive load and the distractions that really are compounded by the number of unanticipated tasks, the interrupt driven operations, traditional ways perhaps that they are forced today to navigate their operations. Those are all impeding the really focused engagement with their donor base. So that, we agree, is a big challenge for us to not only understand, but to actually help solve with AI. Also, the communication barriers that they face today, those are a function of what we find inaccurate or missing donor data. That data does if it's missing, it impedes that communication. It adds to that operational overhead because then they have to fill those gaps in order to really engage effectively. And ultimately, every additional step, it increases their costs. So that is one more solution that we understand with AI can really help the ways that they are avoiding those communication barriers. But it also is the way to avoid dependencies and delays that are also causing the overhead that can impede their efforts. So with that, the prolonged processes we find poor coordination across teams or with their base ultimately delays and waste resources and risks that engagement that is so fundamental to how they are going to effectively be able to drive their mission and their cause. And then finally, you had touched on technology issues and adoption. And AI is one technology, of course, that is part of their ecosystem. But what we see there, as you highlighted in your data, Kate, that managing those actions and collaboration today, that can happen in disparate siloed tools, of course, siloed data. And again, that is impeding collaboration within their organization or with their donors. So all challenges that it is I will say our research and our product solutions are finding the same types of areas where we have to step in, but really understanding the data from your perspective is helping validate that we do have to solve this together with nonprofits. So our approach and also our product direction, I think it starts speaking to some of those areas and improvements that you had laid out. So for us, transformation is first and foremost, we see this as a system of intelligent action. Gone are the days where a CRM and operations within a nonprofit were just about a bunch of records. We see this as a true system of intelligent action, of course, takes incredible vast amounts of data, layers on the right technology, such as AI. And real transformation then provides that secure, scalable way to identify and build those personal connections with the donor base. And it's that vast amount of data that if we don't treat that effectively, if we don't treat that securely, then the value is, of course, diminished. So from our standpoint, you had called out data privacy and security challenges. That is exactly where we start first. We start with the types of security and the data privacies. And what does intelligence really mean in order for us to drive data in the right ways with these tools. We also know that our customers are really focused on achieving outcomes. It's not about the technology. There is across sectors a lot of excitement, but sometimes there's hype as well. But from our standpoint, technology such as AI, the direction that we're taking is it must be a value multiplier. And that value multiplier really relies on intelligence being able to automate workflows and really helping nonprofit organizations spend more time on their high value human engagement. So for us, when we take care of some of those tasks, those complexities that can be solved with technology like AI, that's truly where we are connecting the levels across the organization's ecosystem and with the context that their organization is driving as well. And we are big proponents of discoverability and experimentation. And what we mean about that is discoverability is about the return on investment. And whether it is predictive intelligence that is sitting on the data and the context across this vast level of data that they're operating in, or it's prospecting and intelligence with how do you actually identify your donors in better ways. All of that is a way for us to make sure that those data sets that are powering the entire end to end workflows are truly about building that context, that discoverability across the data sets and ensuring that their needs and their fundraising strategies are leveraging not just one set of data, but really data across that entire ecosystem of intelligent action that can really operate in effective ways for them. And I love that you had called out that our industry requires a continuum of solutions. It's never one solution that anyone can just put in front of a nonprofit organization and say, we really feel that this is going to be sufficient. We are thinking about it as you are, that it is truly it starts with what is available today. So from a product direction standpoint, I will say that embedded AI driven solutions, such as AI assistance, is really part of where we see stepping into helping them prospect or cultivate or even steward their engagements in better ways. So these are all about ways that we can help them identify giving propensity or how do you actually cultivate that giving propensity in a more productive, more efficient way? So it's about removing barriers, as you've highlighted, to that AI adoption. More and more, it is actually heartening to hear that organizations are recognizing the limitations that they may face today. You're giving them the understanding and the expertise of what it means to fill those gaps. So even today, we can actually bring those efficiencies with automation of those complex tasks within the product itself. So that truly gives us validation that we are on the right track for what we're delivering right now. But we also know that with AI technologies, whether it is the types of intelligence that you can put into a product or with AgenTik AI so the ability to build those autonomous workflows for autonomous agents, all of that is actually diversifying fundraising and really helping change the ways or, let's say, augment the ways that they are planning their campaigns, disseminating their campaigns, and ultimately encouraging their donor base to understand what this mission is going to be able to accomplish. So trust in those AI solutions when it is actually weaving together those complex tasks, those are all ways that we are understanding that the more that we can accomplish by really stitching together those areas of complexity and those tasks, we can ultimately help provide that level of efficiency and productivity. It also means connecting those data sets that I had mentioned. So context is increased in incredible ways when it's not just about one data, but those autonomous agents are actually understanding data across that ecosystem and really that intelligent action that has to weave together so many parts of what nonprofits are accomplishing. And ultimately, we're incredibly excited about the future because we are with organizations like yours, with organizations like our customers' organizations, we're understanding that there is a now and the next, but the future of seamless giving and being able to scale technologies, AI, or what's coming next is really going to be able to help us working with Forrester and with our customers deliver that level of giving. And what does that giving power for us holistically? We are incredibly grateful to be part of that ecosystem and serving this entire industry in great ways. But we do feel that AI, as you're saying, is omnipresent, making it more effective for organizations, really helping them power what the potential is, is that first step. But we are equally excited about what's coming next and so delighted to work with you on that as well. Oh, and likewise, happy to be able to support your efforts. Thank you. Well, you, Nina and Kate. We hope that you all received some great insights from both of our speakers and learned more about fundraising and the impact that AI will have, and you learned about what we're planning and what's coming and the impact that it can have for you and your organization. Thank you so much for joining us. Thank you, Ketan. Thank you.