Helena Yin Koeppl:
So shall we move to the value? I mean, just this is a natural transition into, you're talking about that. Why would you want to adopt generative AI? What are the potential values it can bring to your organization?
Chris Hennesey:
No, it's funny, it blends the two as a transition because I spent a lot of time with CFOs, and for most organizations, the CFO obviously plays a very critical role in the company, but their teams are pretty small in the grand scheme of the companies, especially relative to maybe IT or product team. But I think it's this balancing act, Jake, to what you just said, which is there's continued demands on these organizations, especially shared service teams. There's not enough capacity, and this could be a way to kind of expand capacity and get more productivity gains in that regard.
But I think, Helena, to your point in the pivot to value, it's all eyes on what value it's going to deliver. And I know we're talking a lot about this from a consumer standpoint, but I've spent a lot of time this year with a lot of software companies who are also trying to think about how do they price in generative AI into their solutions because they know that value is being delivered in what the investments that they're making.
And at least for me, I'll run through the value, at least things in the top of my mind, and I'd love your input. Obviously, first and foremost, typically what comes up first is around just productivity and efficiency. So I can do things at a faster rate. That could be coding, depending on how that's being leveraged. It could be through, I know I came from financial planning and analysis, so I had to answer a lot of questions to customers all throughout the company. So if I could aggregate through a lot of financial data and varying source data and integrate this down to a narrative and a story and could help guide people into what they need to focus on, really powerful in terms of that regard.
I know we've all read a lot of use cases around legal and some other, maybe even healthcare use cases, where it's taking huge volumes of data and able to synthesize this in a powerful way to just streamline the way in which you work. So I think that's one value prop. I know a lot of companies are thinking about this from a servicing standpoint for customers. What are the ways by which they could either self-service or support individuals who are servicing customers? I know call center agents will come up pretty often. I've spent a lot of time just because I came from a bank around how do you best support call center agents in the way in which they're servicing customers? This could transform the way in which, and Amazon Connect, I know, is using this, and with gen AI, in ways that it's totally transforming the scripts and the ways in which you communicate, and the consistencies from a risk management standpoint to how you communicate to customers. So huge benefits in terms of servicing, quality of service, and the risk management side.
And I know I meet with a lot of companies, I'm sure you do, who are looking for totally new revenue streams. How can this open up new businesses, new ways of working, and even adjacent businesses inside of organizations of new product lines to open up new revenue streams and opportunities there? So those are the three big areas. I'm sure there's many more. I'd love to get your input. Those are the three I typically hear from customers.
Helena Yin Koeppl:
Yes, absolutely. I think to add to your points, and very often what we need to do is to look at our total workflow, and looking at points where there are really today that is more repetitive, mundane. As an example that I’ve seen… What I have seen actual use cases from customers and from my previous jobs, Form Autofill. It sounds very simple, but imagine doing that know tens, hundreds of times per week to get to what you want to have as the end result, the form being filled.
And while 80% of the time, it's actually very repetitive information about address, dates, and all of those things, and that can be very easily done and accelerated by generative AI. That's just one simple example.
And additional examples are really a lot of this is at professional fills, where legal, where you have to sort through a lot of information, or accounting. So all of this, again, that value added is really that human expertise which can do the analysis, and coming up with insights where when sorting through the information and summarizing them the right way, and a lot of this can be automated by generative AI, learning on basically labeled data. So that's an example.
Another example that I would say is really enhancing customer experience. Today, that customers, again, there are many, many different processes, for example, travel booking, for example, that customer is trying to find the things, reading through tons of reviews, and really trying to find the right product for you and for your preferences.
And all of this actually can be accelerated by generative AI, because again, it's sorting through a huge amount of information: the reviews, your preferences, and who you are, and what's your past history of ordering things? And really coming out with recommendations with narratives because there's also the generative function.
And not to mention, using the generative AI, especially in images or videos, it can generate further in terms of prototype, in terms of design and different versions. You can go through 500 different versions, and you can personalize them, and you can generate them and really with the image putting in front of you for you to choose. So all of this are really great ways to enhance customer experience.
Jake Burns:
Yeah. I think this is definitely the interesting part of the conversation, because it has so many layers to it, right? So I love your example of the autofill, because it's how many little paper cuts do we have in our day-to-day life of things that we have to just do these repetitive tasks over and over?
And this is an example, and it may seem small, but a lot of these small things add up in your day-to-day: how much can you free up your time to do the things that only a human can do? Or you have specific expertise that you could do? So of course there's the automation part and the efficiency gains through that.
And then I think there's a theme in what I'm hearing from what both of you are saying, and I'm also seeing the same thing, which is some of the things that just weren't possible before because there just wasn't enough human resources in order to do it, and the technology couldn't do it, and it's things like finding insights in just huge amounts of data.
And people like to push back on this because I think they don't quite understand this isn't a fully automated system, if we're talking about going through legal documents. Of course, people think, okay, the model's going to come up with the answer that you're going to just use in terms of a response to a legal proceeding.
But in reality, the way I imagine it being used as an attorney or paralegal, or someone is going in there and saying, "Come up with some ideas based on all of these documents on maybe some things that we could pursue. And then based on those ideas, we'll go do the work that we would normally do." But it came up with perhaps some directions we could go that we never would've considered because who's going to go through thousands and thousands of pages of documents and be able to read all of those things, right? So this technology could be very interesting in that regard.
And of course, and this was one of the first use cases that I think I ever heard, but it's still, I think, very valuable, which is imagine that your entire organization's knowledge base is accessible by each one of your employees. And of course, with guardrails in place, not everyone's going to have access to the same level of information, but how much more effective can you be in your day- to-day work?
Not just from removing the undifferentiated heavy-lifting of, “It took me 30 minutes to go find that document I'm looking for, or find that policy.” Or whatever the case may be, but also coming up with ideas that I never would've been able to come up with because maybe there was some pattern in all of this data that I just never would've seen before, right?
And then thirdly, I think the other category is this creativity category, right? And I'm not of the opinion that the models have creativity in and of itself, but they're drawing upon the creativity of the source material that goes into the models and kind of creating mashups, which can be very inspirational for people to use.
Now, of course, you can use those mashups yourself. People use large language models to do their homework for them or write a document for them, or the diffusion models to create images and use them as-is. But I think as time goes on, I think the more impactful way to use that is going to be as a starting point, right?
Or I'm seeing with image generation or with video generation, maybe you create a character, and it kind of shades it for you, or it creates a new background for you. Or it comes up with 20 different ones, and you get to pick the one that you like, and then you enhance it, right? So I think the theme here is it's a collaborative effort between humans and these models at this point. And that's where, in my mind, I think we're getting the most value.
Helena Yin Koeppl:
Yes, I mean, as we always say that there's human in the loop, human on the loop, and human out of the loop. And in 99% of the cases that we're seeing either human on the loop or human in the loop, because to your point, highly specialized professions, and we really need human to be the one who eventually make the final decision, right? So for example, radiologists and final decision on the diagnosis, or lawyers.
And so that is the ethical thing to do, and that is the right thing to do, and that's the right process. But their job can be made much easier if AI provides the drafts and basically the first highlighting and pointing out areas that they need to pay attention to due to a large amount of training data, so.
Chris Hennesey:
Yeah, Jake, I love what's being called out here, but when we get to brass tax specifically around the cost and value, as you know, a lot of companies will do business cases, and trying to articulate all the components of cost and all the components of value.
And having done and partnered with a lot of customers on business cases of cloud migrations, anytime you call out productivity as one of the big value props, it's very divisive in terms of how much you want to count that in quotes. Because in terms of you're making investments in generative AI, you're seeing value, but how do you get the returns for that value?
Some of this could be in the way in which you all are talking is you can be way more effective and efficient in what you're doing. You have more satisfied employees that maybe don't attrite or stick around longer, so that's value. Maybe you're able to scale your organization in a way to support so you don't have to invest more resources. You're getting more scale out of that.
But anytime you get into cost avoidance, and this is just speaking from a finance person, sometimes that feels a little squishy in a business case when you're avoiding cost. In reality, it's real, but some people are looking at, “How do I bring the water level down of existing spend, or how do I grow the existing base of revenue?” So I would be cautious as companies and organizations go through this.
As you get into productivity gains, get really clear around where those gains are being realized, what is the impact of those productivity gains? And are we making a decision to invest that productivity into more capacity, or do we want to drop some of that productivity to the bottom line? And that may mean you don't backfill a role because you have more efficiency, so you start translating that.
So as I hear both of you talk, I have my kind of finance mindset going through the business case here. Just be cautious on the productivity side that you're actually being explicit and embedding that, versus just ignoring it or putting it to the side. Because it's a real big portion of the equation here for many of the people, personas, we've talked about.
Helena Yin Koeppl:
That is an excellent point. And I've seen from actual customer examples, and one customer was telling me that basically currently he has a team of 50 people, and serving three markets, which is to identify, for example, know your customers, and sorting through companies and sorting through internet information, the company background and et cetera.
And he's saying that basically, he now can scale into much more markets, and with generative AI's help, making his existing 50-people team much more efficient. And that is absolutely a much better, to your point, value proposition as comparing to some others which purely saying, “Okay, we can cut the bottom line.”