Episode summary

AI solutions can demo beautifully but they can also fall flat when the rubber really hits the road in an organization. James Schwartz has watched this happen across the non-profit sector for years, and he doesn’t dress it up: stop chasing the sexy features and instead start addressing your real pain points one at a time.

James is co-founder and CTO of Bursting Silver, and he leads Datascout, the AI engagement platform BSI just brought into full ownership. He calls himself a pragmatist, and he’s earned it over 16 years working only with associations, unions, and regulatory bodies. In this conversation he goes through the AI use cases that are actually producing results. Answering the flood of repetitive member inquiries, so staff can spend their time on the questions that matter. Cleaning up bad data with AI-generated forms. Auto-tagging member records so you can actually query them. Even running the numbers on whether a union will grow or shrink, based on who’s retiring versus who’s joining.

The point he keeps coming back to is one every stretched leader needs to hear. AI isn’t a multi-month transformation project you have to brace for. It’s a tool that backs up your team. Datascout clients are usually live within days and running member campaigns inside the first month, and engagement tends to jump from the usual 1–2% to somewhere around 15–20%. James is honest that it hasn’t all been smooth, and just as clear on what works: tell members when a message is AI-generated (they thank you for it), start small, and take the tedious work off your people so they can do the work only a human can do.

In this episode

• Why so much AI “demos well but doesn’t deliver”, and how to tell the difference before you buy
• The practical, unglamorous use cases that actually move the needle: member inquiry triage, data cleanup, auto-tagging and segmentation, and predictive analytics
• How Data Scout took members from a 1–2% engagement rate to 15–20%
• Why being transparent that a message is AI-generated builds member trust instead of breaking it
• How to adopt AI in bite-sized chunks instead of as a daunting transformation project, and what James would tell a stretched executive director to do first

The Modern Membership Org Podcast

Full Transcript

James (00:00)
can tell from this conversation, I’m a bit of a pragmatist, so I’m looking at things that you can do today. And you know, I’m not trying to look five years, ten years down the road. We AI is here now. Let’s see what we can do with it today.

Riley Miller (00:21)
All right, welcome back. We’ve got the Modern Membership World Podcast here. My name’s Riley. I’m your host here from Bursting Silver, and we have a very special conversation today about AI enabling nonfor profit organizations to be able to work faster with less. I’m joined today by our CTO and ⁓ leader in people and culture, co founder of Bursting Silver, James Schwartz, and he’s going to be going in on all things that he’s experienced as this.

advent of AI advancement has happened ⁓ across the nation and ⁓ giving some insights on on how we got to where we are today and what that experience looks like. So welcome to the podcast, James.

James (00:59)
Thanks, Riley. Thanks for having me.

Riley Miller (01:01)
Yeah, absolutely. I’m I’m excited to have you on here too. I know that there’s a lot of things that have been moving lately and a lot of big announcements that have been happening too. So we’re obviously gonna touch on that. But first, I think to set the the table here for those listeners that don’t know who James Schwartz is, maybe give us a little bit of your background and and who you are.

James (01:19)
Absolutely, yeah. So I got into technology, I guess, in the early 1990s as a you know pre-teenager, getting into hardware and building computers and things like that. So my technology experience goes back quite a while. I I got into software development in the early 2000s after after graduating from college. And what I found was that I wasn’t, a deep technical software developer or that type of person. I was more in between the business and technology. So I was always kind of

catering to that, the bridge between the technical team and the business team. So being that person in between and speaking technical language and also speaking business language. So my career as a consultant in the not-for-profit space started 16 years ago when we co-founded ⁓ Bursting Silver. And I’ve been working exclusively in the not-for-profit space since then. So I’ve been doing a lot of work with associations, regulatory bodies, unions.

and we also co founded another company called Data Scout two and a half years ago and I’ve been spending most of my time focusing on growing that business as well. So that’s kind of where I’m at these days. Thank you.

Riley Miller (02:19)
it’s an impressive C V.

Well it’s also nice too, like you you highlighted that yeah you got into the space ⁓ just I don’t know unpacking wires and and putting things together and solving technical problems. And so you stacked many hats but it seems like they’re all based around things that you’re interested in. you touched on obviously we’re gonna be talking about Data Scout a little bit and just

The piece that Data Scout I think fits into the whole puzzle is we live in a very unique time now with AI being readily adopted by all platforms on all shapes and sizes. And that’s being pushed out to organization teams that we work with in those different verticals that you highlighted. But in your opinion, ⁓ what do you see the gap currently between what’s associations are trying to be sold or or being pushed from this?

mass adoption or mass rollout of AI and what actually is moving the needle and what’s resonating the most with our industry leaders here.

James (03:16)
Yeah, I think AI is really cool. it sounds sexy, it’s sounds amazing, it’s r it’s also very scary. So it it’s it’s a bit of a mixed feelings between, you know, good and bad. So people are reluctant to adopt it too fast, but they’re also excited to adopt it. So there’s a bit of a mixed feelings about it. I think I think a lot of associations are seeing a lot of cool AI features that demo really well.

But they don’t actually make a significant difference or a significant impact on their business challenge. It might look cool in a demo, but it’s not actually addressing the challenges that they’re having. So I think Al talked about this when he was on the show as well. I think associations should focus less on the technology, the cool aspects of AI, and more on the business challenges. So identify a business challenge or a pain point that you’re having and look at AI and how it can be leveraged to address that pain point.

So we’ve got a few examples that I’ve seen so far of how AI can help. We have some clients are overwhelmed with member inquiries. So they’re getting a lot of emails, phone calls, et cetera. I think AI can help answer a lot of those repetitive questions, those repetitive, tedious things that staff are getting tied up with. AI can help answer those questions to free up time for staff to answer the more value-added questions. So that’s that’s one example where I think AI can help.

Another example is a lot of associations struggle with with engagement, with member engagement. And I think I really think AI can be used for member communications to get better engagement from members and understand what members are feeling, their sentiment, their areas of interest, understanding all that and analyzing it and helping to engage with those members. That helps to you know expand how much how much you can engage with your members. because a lot of staff are you know under a lot of pressure, they’re short.

Staff, the budgets are slim, and it’s very hard for them to really engage with members in a in a way that really connects with the majority of the member base. So another area that I’m seeing for AI to help is bad Data. So a lot of associations don’t have great Data, so AI can really help with Data cleanup. So we’ve leveraged AI-generated forms to create a unique form that helps to gather that Data and to fill those points, those Data entry points that are missing from the

database. So I I really think that’s a an area of opportunity. It’s not sexy, it’s not exciting. It’s just, you know, bad Data is just something that organizations deal with. And ⁓ you know it’s not an exciting use case of AI, but we are finding it really helps with with our our our clients. ⁓

Riley Miller (05:49)
not to cut you off. I I think that’s a an important note just to to highlight a little bit more of how it doesn’t have to be sexy to be practical. And that might be why we’re looking at a lot of these it it as Al said, shiny, but as you said too, that AI is being sexy and and shown off a lot and people are being ⁓ mystified by all of its capabilities, but it’s overlooking the fact that like the real work.

that needs to be done and it’s not the sexy work but it’s the thing that really helps out. So that’s I just wanted to lean into that.

James (06:17)
Exactly. Yeah. I think

Even with my experience, when we started talking about AI, we would talk about all the amazing, cool, you know, future looking, forward-thinking things that AI could do to help our clients. And you know, that was exciting ex at the time, but when we started to use AI with our clients, we we had to take a step back and figure out what can AI do in the short term just to get our clients rolling and just to help them out with those business pain points. And a pain point for a lot of clients is they have bad Data. So AI really does help a lot with that.

Riley Miller (06:49)
Yeah, and I ⁓ I think there was a a final point there you I cut you off there, but before I injected my thoughts.

James (06:56)
no, no worries. Yeah, I think the last point I was going to say was that it can help with taxonomy. So adding tag automated tags to member records to make it easier to query the Data, ⁓ segment your member records. It just AI can really help to do that kind of ⁓ analytics on the member records to be able to to segment the the records. So that’s something that we found our clients are really using extensively.

Riley Miller (07:20)
Yeah. I and so I mean, back to this sexy versus ⁓ practical sense as well. I I think that being able to adopt these tools or or what what this tool technology i is really enabling a lot of people to do is be able to assess ⁓ mass amounts of Data where we couldn’t typically or wouldn’t want to be spending the the time monotony going through and updating those manually. it’s enabling teams to be able to

work much faster as we highlighted, but in new ways that we’re just finding here. So I think that even though it’s not like the the early days of AI where we’re like how many new profile pictures can I generate here or please help me rewrite this email so it’s more professional, it’s now being able to assess large Data sets and and then integrate that with your workflows.

James (08:12)
Exactly. And it’s amazing how quickly AI can go through and do that assessment. We we have some incredible reports that we’re getting for our union clients. And one of the really interesting Data points that I found was was interesting for me was it was analyzing

Basically, how many people were retiring in the near future in the next five to ten years from the union? And how many people were joining the union, you know, people with new graduates or people who are starting out in the workforce who were joining the union. And looking at the difference between those numbers just to try to figure out is there enough people to replace the people that are retiring? Like is the union going to grow or is it going to shrink based on those numbers? So it gives you some predictive analytics for the future to see what direction the union is headed. So we we found that to be pretty insightful.

Riley Miller (08:56)
Yeah, and spotting patterns and doing all that. that’s amazing. ⁓ so for those that know you then and they they know your passion for AI solutions and non profits, that passion then obviously led to ⁓ the development of the AI engagement solution that we know now called Data Scout. Can you walk us through ⁓ a little bit about the the moments where Data Scout went from an idea to really becoming what we see it as today?

James (08:59)
Mm-hmm.

Yeah, I think in the early stages of Data Scout testing.

we were using just some, you know, sample we had clients who were kind of engaged with a pilot and we were using it ourselves to test things out. And pretty quickly we started to see six to seven time increase in engagements from the recipients of the emails. So instead of getting a one to two percent click rate or engagement rate from mass emails, most of our clients were seeing one one to two percent rates. ⁓ we were seeing you know 15%, 20% ⁓ engagement rates through these emails.

Emails and that was really exciting. That was we knew we were on to something at that point when once we saw those results.

Riley Miller (09:59)
So since its conception then, the benefits obviously immediately proof in the pudding from these real time applications, and that’s translated then. two and a half years it’s been in development and ⁓ past that time it’s been either recognized or or has won the ⁓ the iMIS app challenge of the year.

Do you have some examples of maybe the feedback from that twelve percent uptick rate to the spotting gaps in union workers that i you could attribute to how it’s being recognized so heavily as of late?

James (10:33)
Yeah, yeah, absolutely. So I think our team has puts a lot of emphasis on the user experience. We want to make sure that things are snappy, want to make sure it performs well, want to make it easy to get your data, make it easy to analyze things. So that’s I think something that has really helped us to, get out there and get some very positive feedback from our clients. But at the same time, on the other hand, we’re not perfect. We have made some mistakes and we’ve made some assumptions that didn’t land well with clients.

And over the course of the two and a half years, we got lots of great feedback. And sometimes we do get constructive feedback from our clients. And we always make an effort to quickly address those gaps. So we’re always listening, we’re always evolving the product and listening to our clients, getting feedback from them. but I think overall our clients generally love the user experience and the how smooth it is and how the clean interface and how fast and snappy it responds.

Riley Miller (11:25)
Yeah, I think because it’s such a new technology as well, it’s important to highlight that it’s not all a smash hit and it’s good to know ⁓ as we move and develop more we’re we’re learning as we go. So I think with this idea of hesitance to adopt or being pressured into having an AI strategy, a lot of what is being

The deciding point between the two is that teams want to be able to do more with less. Now, whatever that means in the future has its risks or its benefits, but what does that actually mean for organizations from your perspective who are looking to adopt this technology?

James (12:02)
⁓ yeah, I I definitely think AI can you can do more for with less with AI, of course. And ⁓ I always tell our clients that adding Data Scout to your system is like adding five to ten staff to help you facilitate personalized outreach to your members. So it’s it’s like adding those people to your team. And it does a lot of the legwork, it creates personalized communications, it can do follow-ups, it can analyze sentiment, intent.

it creates a lot of insights that staff can act on. So the intention of I think AI and Data Scout as well is not to replace people, it’s to support the existing staff and help them engage more with their members instead of them being bogged down with tedious or repetitive tasks. A lot of staff are are you know spending a lot of time doing data entry or building queries or you know, integrating

data and different systems and manual effort and I think AI can free up a lot of that manual effort to create more opportunities for the staff to engage with their with their members.

Riley Miller (13:01)
Well, in that too, it has the personalized engagement, which is why I think you were seeing a lot of that uptick is that members are not being treated like numbers anymore. they’re being reached out to authentically or at least speaking to their interests, which is I guess it a foundational enablement now where we’re able to have that personal touch, but you don’t have to have more team members to do it.

James (13:23)
Yeah, and actually one thing that’s really interesting is that, you know, we’re very transparent about the fact that we are sending a communication that is from AI. It’s been generated from AI. So we’re not trying to hide that, we’re not trying to pretend we’re a person. But people are responding really well to that. So they’re actually we’ve actually had people respond to the email saying, Hey, I know this is generated by AI, but I really appreciate you taking the time to understand my needs and and what I want.

know they gave us great feedback in in those messages. So that was also that that’s happened several times over the last six months or so and and that’s been very encouraging to show you how how a personalized AI message can show a member that you care about them and and you understand what they’re looking for and what their areas of interest are and you know what course they might need to take based on their specialties etc. You know they appreciate that personal touch even though it’s coming from AI. And then that creates an opportunity

When they respond to that email, that creates an opportunity for a staff person to engage with that member. Now you’ve got their attention, now you can a human can engage with them and take it to the next level. You’re always gaining insights into what they want, what they need, and you’re able to help them from that feedback.

Riley Miller (14:35)
Yeah, I that’s I I think something that even up until now in the conversations I’ve had with Al and and James Harrison, being transparent i is a big play that is often overlooked using this technology because you wanna work faster ⁓ and you wanna have that meaningful connection, but you wanna make sure that you’re also respecting the trust that your members have in

how you’re you’re presenting yourself as an organization.

James (15:00)
Exactly. Yeah. Trust is everything when it comes to associations, unions, regulatory bodies. You really need to have that trust from your from your members.

Riley Miller (15:07)
So now for our listeners, anybody with a background on Data Scout, they’ve heard it in passing. this episode is going to be coming out just shortly after Bursting Silver has moved into full ownership of the platform. what does consolidating Data Scout ⁓ under the BSI umbrella actually change for the platform?

James (15:27)
Yeah, so so having Bursting Silver acquire Data Scout really helps us to scale the product up and evolve the product. you know, it allows us to bring more resources into the into the product and and take it further. So for just for some background in case someone isn’t aware, Data Scout was launched two and a half years ago, as we mentioned, as a partnership between Bursting Silver and a partner called Objeto the Objeto team was absolutely instrumental in helping us get a proof of concept out.

and turning that into a viable product. And yeah, we’re incredibly thankful for the effort that that Objeto has put into building the product and getting the company to where it is today. But with the acquisition, we can allocate more resources, bring it to more markets, we can bring it to more conferences, we can help grow and scale and support the product. So I’m very excited about where we’re heading. We have some great new features in the pipeline.

That we will be releasing soon, and just very excited for what’s you know what’s going to happen next with the company. So it’s very exciting.

Riley Miller (16:25)
Hence why you’ve been so busy and it’s been hard to get you on this podcast, man.

James (16:29)
Yeah,

absolutely. Yeah. There’s a lot of moving parts.

Riley Miller (16:32)
So

so with with all those irons in the fire, ⁓ and we know there’s a roadmap ahead that whether or not we want to to highlight that now, it’s okay. But ⁓ I’m gonna come to you now with my executive director hat on. I’m I’m leading an organization and my team is stretched thin. but we’re we have a lot of pressure to come up with

a some strategy in in terms of how we’re applying this technology, either from the expectations from my members or from my staff, we have to make a decision on on what to do next or where to go with it. What’s one thing that you would share with that executive director who who’s facing this I don’t know conflict or direction need?

James (17:14)
Yeah, I I would tell you to look at AI a little bit differently. I think a lot of consulting firms are telling organizations to look at AI as a major transformation project. something that will take months and months of effort and, we’re you have to pay us a lot of money to to do this transformation. I look at it more as a tool that assists your staff. And, I would prefer to

tackle AI in bite-sized chunks, right? Take a current business challenge, a pain point, as we talked about earlier, and use AI to address that pain point. So something it could be something very small. We talked about Data cleanup. Take a certain pain point, use AI to address that pain point. I’m trying to shift the perception that a technology project needs to be a multi-month engagement. So for for Data Scout, we have most of our clients up and running within a few days.

And within the first month, they have our clients already have multiple campaigns created. They’ve launched their campaigns. They’ve sent out communications to their members to engage with them. So we’re up and running very fast. So we’re starting small and expanding from there rather than doing a major multi-month or multi-year transformation project. So yeah, take something small and look at your business. Don’t look at the technology necessarily. Look at the business requirements first, the business challenges first.

And see if AI can help you with those pain points.

Riley Miller (18:31)
Awesome, very well said. So then with with everything you’ve seen

what’s one thing that we can distill down in this episode here that every membership leader should hear about before they adopt AI into their organization?

James (18:44)
Yeah. Yeah, that’s that’s a great question. as

can tell from this conversation, I’m a bit of a pragmatist, so I’m looking at things that you can do today. And you know, I’m not trying to look five years, ten years down the road. We AI is here now. Let’s see what we can do with it today.

So one thing I would say to a membership leader would be go and talk to your team. Talk to them about some of the things that they’re struggling with, some of the things that might be repetitive or tedious.

Some the tasks that they’re doing that AI could help with. So go talk to them and look for opportunities that they can that can improve their work today. And the time that they save, what can they do with that time to engage with members more? What are some things that they would love to do to engage members that they can’t do today because of those tedious tasks or those things that are taking too long? So that’s something I would say to a membership leader.

Riley Miller (19:32)
Yeah, it absolutely. I and I think like we can start with step one at any point, but it’s a conversation a lot of teams have to take internally anyways before they go and pick up that shiny new toy or get ⁓ wooed by the new sexy features that we talked about. So very, very good advice and ⁓ excellent way to close on our talk today. Now before we roll out, we do have plenty

on the horizon for Data Scout and much more to come. If individuals were, looking to see or keep up to speed on the new features that are coming out with the platform, where should they look or where would they go to find that?

James (20:10)
Yeah, the best place to go would be datascout.ai. So we’ve got information on our website and we continuously add more details to the website. Or, you know, drop me a note if you want to chat. It’s James at datascout.ai. Send me a note and love to talk to you about how AI can help with your your business challenges.

Riley Miller (20:28)
Fantastic. Well, thanks for coming on the podcast, James. It was excellent having you on.

James (20:32)
Thanks for having me, Riley. Really appreciate it.

Riley Miller (20:34)
All right, that is a wrap. Thank you guys so much for tuning in again to the Modern Membership Org Podcast. If you have a chance and you wanna give us a little love, please feel free to give us a rating here on Spotify or wherever you consume your podcasts. And we’ll see you on next week’s episode.