How AI Can Help the Social Sector Make a Greater Impact

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Clare Golla: [00:00:00] Generative AI is a hot topic among boards these days.

It's a powerful tool that can help social sector organizations streamline their operations, optimize fundraising strategies, and create compelling content.

And as investment committees reflect on a strong first half of the year driven largely by a handful of companies at the forefront of artificial intelligence, exposure here can certainly also add to needed growth in foundation and nonprofit investment portfolios.

But there are serious risks to privacy, accuracy, and bias that need to be considered. And for fiduciaries, as with investments in any sector, arming yourself with information and proceeding with prudence is key.

So today we're going to tackle the pros and cons of using AI in the social sector while exploring how it can be used to enhance, not replace human staff.

So dive in with us and learn how AI can help your organization make a greater impact.[00:01:00]

Hi everyone. Welcome to Inspired Investing. I'm your host, Clare Golla, Head of Philanthropic Services at Bernstein.

This podcast is where we connect and share insights with listeners like you who are engaged in the nonprofit, philanthropy, and broader social sector. A hot topic at board meetings these days is generative AI.

How is our investment portfolio exposed? Should we have more? There's this blend of FOMO, the fear of missing out and just the fear part, right?

This language processing tool, chat GPT attracted over a hundred million users in its first two months.

So committees are asking. Are we behind the curve as we so often are with technology in the social sector?

And what about the fact that the social sector, human services, the arts, education and discourse, for example, is all about people and humanity?

Are we running the risk of replacing humans for operational efficiencies and ultimately delivering subpar services altogether? Bernstein, what guidance can you give us?[00:02:00]

Well, I've got a two part response on today's episode. The second part is actually from a true expert, Lei Q.

She's an SVP and portfolio manager of AB's International Technology Fund and the AB Disruptors ETF. My colleague Stacie Jacobsen actually interviewed her recently, so rather than bugging Lei again, we've pulled relevant content for you.

First though, for the next few minutes, you're stuck with me for a handful of concerns and opportunities related to AI.

Specific to the social sector that we've gathered from professional contacts and recent philanthropic media coverage, especially Chronicle of Philanthropy that's done a really great job with this.

So, let's start with the threats.

Okay, number one, serious risk to privacy.

Jeff Tenenbaum, who's a non profit lawyer based in DC. We love Jeff.

We rely on him quite a bit. Um, he states, the terms of use of many of these generative AI platforms make clear that once you upload something into [00:03:00] it, you're giving essentially a broad license for them to do anything they want with it.

And he continues, It's also going to be captured and used by that AI platform to generate responses to other people's questions in the future.

So essentially you lose any exclusive rights to the content once you put it in there. Number two, and this is a big one I've seen, AI may be wrong. ChatGPT, for example, pulls from a pool of information that hasn't been updated since 2021.

So it can save you a ton of time, but you need to read and edit the responses carefully. Uh, there's a lot. It doesn't understand your relationships with donors, your interactions with people, right?

And so it's going to fill in the gaps with the next best response, even if it doesn't have the answer, quite the answer to what you're asking.

Number three, legal implications of AI being biased. Back to Jeff Tenenbaum.

He states that one of the biggest legal risks to most nonprofits is in the employment setting So if you're using AI tools to help select job candidates, for [00:04:00] example, and those tools are biased you're running the risk, right?

You're opening yourself up to potential discrimination claims.

So just be careful about that for questions on disclosure policies. These are really early innings for AI and how much disclosure should be included if and when I was utilized in any given process.

This is yet to be determined. So more to come on that.

And then fifth, a big one for nonprofits is, is this truly a threat to the humanity of how we do business?

We are absolutely seeing a potential creative threat in the arts and education, for example.

And there are tons of issues to work through there. But by and large, this is more of a cultural change than a replacement of humanity.

ChatGPT, for example, isn't a perfect tool, but its knowledge and technology are here to stay.

So the Chronicle of Philanthropy quoted John Biderman of the fundraising technology platform GiveSmart in likening this technological leap to the development of the smartphone.

While smartphones didn't immediately gain total market share, [00:05:00] They're now ubiquitous with few people seeking to go back to the era of the flip phone.

Can you imagine if we all went back to the flip phone? It's crazy.

And early adopters say chat GPT will help people get more done, not replace human workers. Okay, we've gone through some threats.

Now let's walk through some of the opportunities.

The real win is efficiency. Using AI supported content drafts, ideas, and even some mechanized processes, in effect, add bandwidth to stretched teams.

Getting past that blank screen by asking first for a draft of a donor engagement letter, or ideas for events or social posts.

Even your annual appeal could be a huge time saver. So that's number one. Number two, content ideation.

Quick brainstorming on content such as social media posts, event themes, and blog articles.

You can request into the chat GPT app to generate blog posts, promotional materials.

It really does save time and resources. The third is donor facing content and templates.

So look, you can start [00:06:00] drafts with AI general content for your donor outreach letters, foundation funding, LOIs, and thank you notes even.

The fourth, I think a really interesting one that we haven't dug into too much yet is data analysis.

Using AI to analyze donor data and then provide insights that can help optimize fundraising strategies, for example. Target specific donor segments effectively and really shape our programs.

This design software and tools can craft a compelling narrative for those segments.

And I'll just wrap up with streamlining fundraising operations. Seamless donation processing, integration with existing CRMs, we haven't even really begun to dig into how much time can be saved here by already stretched staff.

Now let's learn a little bit more about the current and future state of AI from an investor's perspective.

Stacie Jacobsen: I'm here with Lei Chu, Portfolio Manager for the Alliance Bernstein International Technology Fund and AB Disruptors ETF. Lei, thanks so much for joining us today.

Lei Qiu: Oh, thank you [00:07:00] for having me. Glad to be here.

Stacie Jacobsen: Now, look, we are still very early in the product cycle for AI, but it is moving rapidly. Can you talk to us about how it's disrupted the companies you cover thus far?

I

Lei Qiu: think to your point, we are still in the very early stage of AI.

I do think it's a transformational change that we're seeing in innovation in the marketplace that we see today.

And then many people compare it to sort of the iPhone moment that we're seeing in terms of productivity. And I actually wholeheartedly agree with that.

But I also would acknowledge that we are still in the very early stage of that.

A lot of people making a lot of predictions.

And I would say with many of the big technological innovation that we have seen, you tend to overestimate it in terms of the impact in the short term but underestimate the impact in the longterm.

So I would say as we stand today, I think every company is.

It's evaluating its business model and how they can deploy AI, whether AI is a friend or foe, but it's still [00:08:00] too early to tell in terms of the ultimate impact that AI will have.

Stacie Jacobsen: Got it. So let's talk about some of those short term and longer term impacts.

So if you were to look out over the next call it 12 to 18 months, what do you think some of those shorter term impacts of AI will be?

Lei Qiu: In my mind, AI poses the ultimate question for every company, which is what is your competitive mode and will you survive AI?

Because AI is such a transformational change that is such a productivity gain and knock down the entry barriers.

So companies certainly cannot ignore it.

And so what we have seen is a lot of companies are rushing to adopt AI. We certainly are seeing a lot of large platform companies are spending tremendous amount of Uh, time, effort and money on it.

So when we think about AI, it is something that's extremely compute intensive.

That we know for a fact.

So it's Tremendous amount of data that's being generated. They need to be processed. And then we are [00:09:00] going to feed it into the training model and train the supercomputers.

So what is happening is that we have seen the rush to spend. So when we think about the innovation S curve, if you will, and then we look through the past cycles where we are in AI is sort of Early stage in terms of the build out of the networks.

So when I say networks, we're thinking about the hyperscale data centers, the spend on the servers and then where the training is happening.

And just generally speaking, in terms of increasing compute intensity.

So we're seeing is I expect to see greater demand enablers, whether it be the semiconductor company, the equipment companies, whether it be the networking memory.

So a lot of it. In the hardware area that's happening today.

I expect that change to continue. And if anything will ramp rapidly in the next 12 to 18 months, we're also seeing some companies are starting to roll out some applications, but it's still early.

But ultimately, what shape or form in terms of business [00:10:00] model it takes, it remains

Stacie Jacobsen: to be seen.

Got it. So it sounds like the more immediate beneficiaries of AI are the hardware companies.

You know, if we start to look out even further than that 18 months and longer terms, you mentioned the iPhone and I try and tell my kids what life was like before we had that device in our hands at all times, and it's very hard to actually comprehend.

So where do you think that AI might take us? And I admit this is very speculative at this point in time, but over the longer term.

Lei Qiu: So AI itself has been around for quite some time, and this current iteration of AI, the generative AI, is probably the most powerful, and it has reached an inflection point in terms of adoption, in terms of what it could do for us.

I've been around, you've been around, and then just think about the time before iPhone.

So it took a while for iPhone, even if you had the iPhone, if you didn't have the network, and the broad built out network, it wouldn't work, and it would not build out a whole ecosystem after that.

So what we have seen is You know, since the pandemic, and it's been going on for [00:11:00] some time now, but the pandemic itself certainly has accelerated the digitization of the society and the amount of data that we generated so that, you know, so much data is generated.

We can feed it into the model.

And then actually. Get output from it. And the other thing is you kind of have to use your imagination, right?

So we had a network built out. Then we had the devices. Then ultimately, we have an entire mobile ecosystem that was built on top of that.

So in seven to 10 years, I could imagine there will be a whole ecosystem that built on AI.

And it doesn't even have to be seven to 10 years, frankly.

And the history has shown that it could just be in the next three to five years. You can see an entire ecosystem built on AI.

But as we stand today, I would think a lot of it could be efficiency tools that enterprise can embrace, and then some of the mundane labor can be replaced by machines.

And then there are also other creative changes that we don't see today, and it will come with time once. You know, [00:12:00] the whole infrastructure layer is built out.

Stacie Jacobsen: All right, that's helpful.

You know, we talked a little bit about some of the short term impacts, but you know, specifically, what are some of the responses that you're seeing from companies in the tech sector?

Really just more across the industries

Lei Qiu: broadly. So we definitely have seen, you know, on the consumer side, you know, people are talking about the new way of interacting with machines, right?

So we can, instead of getting sort of set response fit to us in the machine language, we can actually have something that's a lot more interactive.

So on the consumer side, people talk about search, you know, will be different. And, uh, the way that we think about travel or just in general, the way that we plan our life will be different.

And that's one aspect to it. I think on the consumer side, you can also see, you know, in terms of how we can come up with new mode of entertainment and using AI as a tool that will be different as well.

But I think the bigger impact we expect to see would be the efficiency gain.

So we're seeing a lot [00:13:00] more companies today that actually is embedding a I in the tools that they're offering to enterprises that can actually made companies far more efficient.

One of the greatest challenge that we have faced in recent years, frankly, is the labor shortage, the rising inflation as a result of that.

And I think I will be a solution to that because it actually makes some of these Really mundane task obsolete because the machine can do it in a far efficient manner, and we're seeing company use that.

So we're seeing it in the early stage as part of the tool set that's being offered to enterprises.

We're also seeing companies, larger companies today certainly, are embedding AI in the offering that they're providing and making their tool more effective or powerful. So

Stacie Jacobsen: chat GPT is what we hear about the most.

How is that really driving innovation?

Lei Qiu: So chat GPT is certainly it's really exciting.

It's one of the app. That was the fastest app to reach 100 million users.

And that also gives you a sign of give you a sign of the time we live in, which is we [00:14:00] all live in a connected world. So, you know, it may take a little while for.

Another app or, you know, a channel to get to that many listeners, but we certainly got to 100 million users really quickly.

So that does show you that we reached a point where people are very excited about it. We've always talked to machine, but the machine talked back to us and we're always reminded that we are simply.

Talking to a machine, but Chachi BT is one where we could actually imagine that the machines interacting with us in a human like form to open AI and generative AI.

So it's very exciting, but I would also say, though, it could be so much more. So it's not really lost on some of us that, you know, it just.

It's still in its very primitive form.

It's still pretty standard. And it's not as personalized as we would like it to be. And then in the future, it could be so much more. But that's my point, which is we're still in such early stage of adoption.

You know, it's interesting. The feedback from the machine is [00:15:00] only as good.

As how much data you feed into it.

So the more data you give it, the more people use it, the smarter it gets, right? It's an iterative model.

So, you know, it's not dissimilar from, you know, the early stage of cell phone when it was as. I remember in Pretty Woman, Richard Gere had that big cell phone. It's as big as a brick.

And then, you know, 20 years later, look what we have and all the changes that it has made in our lives.

So I would say ChatGPT is a starting point. It got a lot of excitement, but it marks an inflection point of what AI could be.

But ultimately what you know, what is going to be something that adds a lot of productivity and then make our life easier?

It remains to be seen.

So yes, it's a good starting point. It's a very exciting starting point in terms of how big AI could be, but it's definitely far from what it could be.

Stacie Jacobsen: Yeah. And sometimes the answers that you get from chat GPT are very convincing, but when you read through them, there's actually that concept of [00:16:00] hallucinations, right?

That that actually is not the right answer. So can you tell us a little bit about that? Why does that actually happen?

Lei Qiu: It's sort of like I said, you know, it's like things go through iterations, right? It's just so early still, and then it has to personalize it.

It sounds convincing because it's giving you a set of answers that it knows.

It's the reason that we're excited about this generation of AI is because it is dynamic. And that's why it's so compute intensive. It's taking all these different data and then constantly iterating.

And then that's what we call like. You know, the computer is being trained to give you a set of answers, but then ultimately there's the inferencing part, and then it's going to give you things that's actually tailored to you, and that could take time for it to learn and then to actually adapt.

And so. It looks very convincing today, but it may not be the right answer, actually.

So it's going to learn. It's not that different from, you know, in the early days of search, right? Search got better and better [00:17:00] because it learned.

So it takes time for that knowledge base to build. And the more people use it, the more intelligent it gets.

So that's why everything takes time.

Stacie Jacobsen: Yeah, for now, you still definitely have to have that human touch to make sure that the responses or the summary that it's giving, uh, makes sense.

So, caution to all those college kids who are using it to write their essays, right?

Lei Qiu: Yes, definitely.

Stacie Jacobsen: All right, I'm going to make a comparison to the ESG world and greenwashing, which occurs when companies try to project an image of being environmentally responsible, but yet they're not.

I'm wondering if you see the same thing happening with AI companies really just paying lip service to the concept, but they're not really positioning their business to take advantage of AI in a profitable

Lei Qiu: way. Thank you. I think this is actually a very interesting question. I think AI is probably the most mentioned word on any quarterly earnings call in the most recent quarter.

And every company feels like if they mention the word ai, then they are ai. [00:18:00]

Uh, but that's actually definitely not true, and I would caution all.

Investors or listeners to be careful because as I mentioned at the very beginning, this is the big question that it's posing to everyone is AI going to change your competitive mode because it knocks down entry barriers.

So just because you mentioned AI does not mean that you can actually survive AI. Your business model is being challenged and it depends on how you embrace it.

Clare Golla: So a few key takeaways here.

First, as for your investment portfolio. If you are invested in the US and global stocks broadly, so for instance, exposure to the s and p 500 or the acqui I m I, which is the all country world investible market index.

You have exposure to AI and you'll continue to do so.

Second, while AI can never replace the human touch, it can certainly help organizations make a greater impact.

By embracing AI and using it [00:19:00] wisely, nonprofits and foundations can achieve their goals more effectively and efficiently.

And then third, it's important to note that AI is here to stay, and it's up to leaders in the social sector to use it in a way that benefits their institutions and those they serve.

By staying informed, being responsible, and using AI to enhance, not replace, human staff, decision makers can make the most of this powerful technology and create a better world for all of us.

And on that note, thank you so much for joining us today. If you'd like to learn more, please see the link in this episode's description.

And if you enjoyed this episode and haven't subscribed to our podcast yet, please go to Spotify or wherever you go to listen to podcasts and subscribe and rate us.

Also, please email us with your thoughts and questions and feedback to insights at Bernstein. com and be sure to find us on Twitter at Bernstein PWM.[00:20:00]