In this Season 4 preview of Exchanges — Advisory Expert Dave Horstein engages in a "Man on the Street" Q&A about how we're helping customers make an impact with AI.
Here are a few key takeaways:
Successful integration of AI in businesses depends on aligning it with business strategy, focusing on enhancing employees' skills, and leveraging AI to solve real business issues.
global.hitachi-solutions.com
In this Season 4 preview of Exchanges — Advisory Expert Dave Horstein engages in a "Man on the Street" Q&A about how we're helping customers make an impact with AI.
Here are a few key takeaways:
Successful integration of AI in businesses depends on aligning it with business strategy, focusing on enhancing employees' skills, and leveraging AI to solve real business issues.
global.hitachi-solutions.com
Welcome to Hitachi Solutions podcast. Join us as we talk with our skilled professionals and experts exploring how customers like you are delivering business outcomes, tackling business challenges and accelerating their business initiatives, leveraging Microsoft applications and technologies. Hello everyone and welcome to our first episode of exchanges this season. We've had a lot of fun bringing you thought leadership, technology advice and innovative ideas for the past three years and are looking forward to an even more insightful season four. I'm your host Dave Horstein and today you were Dave.
Yes. Hi, hi, Laurel. Hi. Hello everyone. I'm Laurel Gressler. I'm marketing manager at Hitachi solutions. We're going to turn Dave from an expert host. To being hosted expertly today. How does that sound Dave? Say we give it a shot. Let's see what we can do. Let's see what we can do So Dave, I am going to turn the tables on you in this season 4 preview and ask you a series of questions I'd love to delve a little bit first into let's pretend like we don't know who you are.
So Tell it tell me a little bit about yourself Dave Horstein Oh, putting me on the spot. Okay. Yeah. So Laurel listeners, I, uh, obviously Dave Horstein, I have been at Hitachi solutions. Uh, I think Feb, February 19th, I think will be 10 years for me. Oh, wow. Um, I've yeah. Uh, I've been in the Microsoft partner ecosystem for like maybe 14 years, I think.
Um, so I've been around a little bit, um, and have been in consulting for about 15, so 15, 16 years. So, uh, getting up there, uh, faster than I realized. So your youthful face is, is, uh, deceiving. That's right. Yeah, I look a lot younger than I am. Although not everyone can see that youthful face right now, but I'm looking right at it.
Yeah, I get that a lot. I, so during my time at Hitachi, I have been in quite a few of our practices. So I've, I've got a decent round roundedness to my knowledge about all things enterprise technology. But the past few years have been focused in our advisory team where we really, really drive. Drive business value through our technology solutions.
And I think we'll talk about that a little bit today because it can be really squishy when we use that word value. Um, maybe we could break that down a little bit, but that's, that's what's most important to my team is making sure our clients are getting the value from their implementations from a technology perspective.
And we started this podcast together about three years ago, exchanges by Hitachi solutions to. Really connect with customers in a conversational, easy to consume way to talk about what worries them at night, right? On an ongoing basis. So, and we're in, this might be our 60th podcast season four. Can you believe it?
That's crazy. Yeah, I think when we started on this path, a few of us got together to try to figure out all of the lessons that that we've learned over the years, all of the conversations we're having, how do we how do we scale that and help get this knowledge out to a wider audience? Now, I found myself back around the time we started this, I was having a lot of the same conversations.
I'd be talking to a COO from one organization could be a manufacturer. And Have, find, find myself again, the same conversation, maybe three more times over the next couple of weeks and bringing a lot of what I was learning from each of those, those discussions to the next one. And it just made sense to, to look at a platform like a podcast for us to start figuring out, well, how do we, how do we get this out there and elevate as many people as we can reach at the same time?
Yeah, it's been great. I mean, so we often talked about, talk about the depth and breadth. Of Hitachi solutions. And what I've discovered is not only are we reaching customers that have been around maybe with us for years and years and not realizing all of the solutions and services and all the depth of the advisory team, but also sometimes we're introducing some of our own team members because most of us are working virtually, right?
Yeah, that's right. Yeah. It's, it's been a great way for us to connect across the company. Um, so there's some, there's some selfish benefits there getting to know each other and figuring out how we can better partner, uh, to solve problems for our clients. Right. Well, okay. So as since we've laid this up, we've teed this up, we've turned the tables and I'm going to ask you a series of questions because as we launch into season four, we're, we're really focusing on the customer experience and how we bring value to transformation, whether it's AI transformation, whether it's people in process, solely, whether there's a solution end of it or not, that is to be determined between the customer and, and their needs.
Right. So I wanted to begin since we are so focused leading into 2024 on how. I guess putting AI to work, right? So how are we helping organizations realize the business value that AI brings, especially in the current era where technology is rapidly evolving? I mean, I'd like to call it tech time, Dave, because tech time seems to go faster than any other time I know.
That's right. Yeah, it sure seems that way, especially over the past year. Maybe, Laurel, it might be a good place to start with, as I alluded to, defining what does the word value actually mean. Okay. For organizations and then we could tie AI back to it. When, when we use that squishy word value, there's, we're referring to it, a number of things for any given organization, they, they might have.
Specific where they should have specific targets for the year, and that could be, uh, in, in revenue generation, it could be in cost optimization, customer satisfaction, et cetera. What we're trying to do is understand when we're working with clients, understand what, what sort of value is most important to them in their specific goals, and then align technology solutions.
That focus on satisfying those things. And so to be more specific, if we think of an organization that has a customer service call center and they're trying to look at efficiencies within the call center while also elevating the customer experience, we might. talk with their executive team early on and figure out what are those key measures that help us determine whether the call center is being efficient and it could be a first call resolution rate, uh, average call handle time number of customers per customer service F.
T. E. You know, there's a number of metrics that we would want to use to be able to quantify that value. When we look at Something like customer satisfaction. There are measures like net promoter score, but there's also less tangible measures there. Sometimes we're really just trying to understand if we follow a customer journey, where do we see friction along their experience working with the organization?
How do we optimize that? So we may not have very specific measures there, but we know there's value to the organization, their stakeholders, because we're able to improve through obvious changes to the experience and their journey improve their satisfaction. And so that's when we say value, we're talking about some sort of outcome that's beneficial to an organization and their stakeholders could be measurable, like I mentioned, or something less tangible, uh, like a, like a customer satisfaction experience.
That makes sense. As it relates. Yeah. And so we often travel through that with customers through our solution lab. Yeah. Yeah. And that's a great jumping off point, especially when we get specific with AI. We developed a method that, that we refined over the years. It solution labs, the name of it. It's really though a set of activities or a workshop where we're engaging with our client's leadership team to understand everything that I just talked about.
What is value to the organization to its stakeholders? How do we tie implementations or technology back to their business strategy. We're having these very strategic conversations up front to really understand where the organization is trying to go and how they measure the health of their business. As it relates to AI, what we're, we're trying to do is, is take that same approach.
So early on, let's come in, let's get past the whole age of experimentation, as we've mentioned with Jerry and others on previous episodes. We're, we're past that, that. Period of kicking the tires of AI. Let's get tactical. Now let's figure out how we can drive real value through AI solutions. And so what we're trying to do is coming up front, understand that what's important to the business, how we measure its health, understand their business strategy, and then figure out ways we can extract use cases from the things that they've already said are important to them.
So last year, a lot of organizations were coming to us and saying, Oh, let, can you show us some demos of what you can do with AI? They were just trying to kick the tires. As I mentioned, the problem was that all of us, our company, our, our clients were spending a lot of time and cycles demoing Potential solutions.
What we found and I see this when I'm out there researching the broader market is when you start with your business strategy, you have already have this clarity of what you're trying to accomplish as an organization. If you start there and you try to figure out where across your enterprise you have inefficiencies or you have capabilities that you need to unlock or elevate, and it could be Refining your product.
It could be upskilling your employees in certain areas to keep up with changes in consumer sentiments. We want to dig in and find those things so that we can then figure out later what potential AI solutions are out there that can solve for this. And I'll talk a little bit later about specific tactical ways that we do that.
I love that. So AI is a tool to address the business strategy already in place, . Yep. In my, in my opinion. Now you need, you need an AI strategy because if you think, if you think about AI as a tool you have to, you have to keep the frame of reference that it's complex. There is a lot, there's, there are security implications.
There are you know, potential for hallucinations and, and things like that, where we want to put some governance and controls around. And so it's a tool that we need to, Okay. properly planned for with a strategy, but we don't need to create a brand new business strategy, a secondary business strategy, just because we have this new tool, we need to integrate it.
Understood. And you just, uh, teased our podcast next week, Dave, on security with Brad Koontz and Michael Steele. Did you know that? I didn't know that. It's a pleasant surprise. Yeah. So talking about use case and focus on real time use case addressing real business issues. How is a I driving positive outcomes for organization in terms of both financial gains?
And operational efficiency. Yeah, we're, we're seeing both of those things come to fruition. I'll talk more broadly about what's being discussed at a kind of a market level. And then maybe we can get into some specific implementations that we've done. But early on last year, there was a lot of buzz.
Chet GPT was, was going public or sorry, going out for public availability. Um, people were, were getting excited about the possibilities. He had GPT 3. 5 and then four came out, which is, which was even more intelligent, more capable. And a lot of boards were starting to ask their C suite, how do we leverage this to save X amount of money?
And I've heard, I heard of a specific board ask a COO for billions in cost savings through AI to identify that. Now that's a tall order. You could theoretically come up with that, but it just illustrates The expectations a hope that came out last year, you know, um, so yeah, there was a lot of hope and expectations last year.
What we see very effectively with AI is, is productivity. Uh, and this could be, if you think about, um, knowledge workers like ourselves, uh, there was a recent study that we've referenced a couple of times, it was about BCG's consultants and they, what they found is through the use of a generative AI tool.
Their more junior level consultants were able to almost level out or to get upscale quite a bit Towards the the senior level and it's because they were able to use this sounding board tool That already had a wealth of information stored in it They were able to interact with it as if they had a mentor by their side.
That's pretty so that's actually oh, yeah. Yeah And, um, and then I know we've got some insurance clients where we've implemented a similar, similar gender of AI, um, applications. It's typically in a chat format. Same concept. So their early career producers are able to leverage the knowledge within the organization.
So you think of a tool that's connected to their policy and claim systems and can draw insights from it, right? These early career producers now have it. At their fingertips, this guide that can help elevate them to become more productive on an annual basis. I love it. And you know, communications is one of my favorite subjects.
I see it as a fast win through communications channels internally and externally for organizations. Yeah, strangely, all of our team seems to be better communicators since, since our own enterprise chat came out. You know, I've noticed that, Dave. I have. It's so formal these days. All right. So, given that, can you provide us, our listeners, with some examples where business advisory in particular has played a significant role in successfully Helping these AI technologies in an organization moving through the ideation to the actual realization.
Yeah, and I'd say so from an advisory perspective, everything we do is in partnership with our delivery teams, too. So, you can think of it as holistically, what, how are we helping our clients? There's some really exciting use cases that we've been implementing recently. I know one was One was a health insurer, um, where they had trouble, they were receiving a lot of PDF documents from their insureds that some of the data was somewhat unstructured.
So it was a PDF, but a lot of the responses were not structured. They weren't similar from one document to the next, and they had to manually enter this document into a CRM. And what we were able to do was implement some machine learning. Using Azure Open AI and extract that text from these PDF documents, use generative AI to basically structure it or make sense of the data based on the context that was provided by these insureds and then auto populate.
The records in the CRM are D365. And if you think about it, there's, there's two main outcomes there that we were able to drive. One being human error. That was a considerable pain point for, uh, the organization. In this very specific process, uh, we were able to essentially eliminate that, that problem.
At the same time, oh yeah, yeah, we were excited about that one. Is that the term docudrop I've been? Hearing, is that related to that? I don't know. I probably should know that, but I'm not sure. Yeah. Okay. All right. Just a random thought. I thought I'd throw in there, Dave. Okay. We can use it if not. All right.
Um, but in addition to human error, just the automation of the data entry can save thousands of dollars on an annual basis. And so it. These these investments in productivity become a no brainer. It only makes sense to implement these things. Yeah, I found in my own work that, uh, the grammatical and typos that happen in everybody's every day in communications today.
It is such. An awesome thing to have a buddy that is generative AI. And in my case, I use enterprise chat to check my work. And, uh, I never feel hesitant about asking, right. My buddy's always there for me, judged by enterprise chat. Yeah, the difference between, you know, uh, a subtle nuance in the English language isn't lost on, on my buddy, Enterprise Chat.
So I love that. That's perfect. Yeah. As long as, uh, you don't start having movie dates with Enterprise Chat, I won't worry about it. And I'm good. I'm good. That's right. I'll have to get back to you on that. Uh, a second use case, uh, that I was pretty excited about. So knowledge base has been a big one. Um, we, we recently implemented this knowledge based chat.
So again, Jenny, I very chat based pretty frequently, but we implemented a knowledge based search, connecting users to the data that's captured across the enterprise. And so, uh, if you think about. Having policies in HR, so they might have them on a SharePoint site. You've got each business unit that's maintaining documents in their, in either Teams folders or on SharePoint.
We were able to actually capture all of that, so integrate Again, using Azure OpenAI, integrate into those, those locations and contextualize that information so that these users could go to URL and chat with the chat bot and ask questions like you know, what, what is our 401k match or is this specific condition covered by our health plan?
So when we talk about elevating employee experience, um, these knowledge based searches are incredible. Uh, we were also, um, we've also implemented this. Uh, for a client to be able to use in a customer service environment. So a lot of product data, manuals and, uh, frequently asked questions, all of that.
Yeah. Um, there have historically been some good products even baked into D365 that can be used for a knowledge base. But when you augment that with an AI assistant that has that artificial intelligence and can provide additional context, it supercharges that. That call center or customer care agents experience similar to how I'd mentioned that BCG study where you've got these consultants upskilling.
Now your customer care team is, is in that same boat and they're elevating your customer experience, their own experience as an employee goes up because they're not having to hunt and peck through systems. It's just a win win on, on both sides. Um, so those two use cases have been. Yeah, we've all and it's just the beginning, you know, whether we're in the industry in the customer service side or on the receiving side.
I mean, we all know exactly how to relate to that scenario. Um, so that's a great use case to share. Well, so Dave, I mean, you know, it's early in the year. I'm a big wall street journal reader. So I, you know, I'm tracking the conversations on how AI is going to develop, and then that's how it seems to be echoing with our own customers.
I mean, based on what we're seeing in the economy and the market overall, what guidance can you give us? You know, or really our listeners, leaders who are planning their transformations into 2024, or actually maybe perhaps in the, in the middle of their strategy those who jumped out first with AI.
Yeah, this might end up sounding like our, our three things takeaway that we normally do. Um, what I would say, so a lot of the conversations we've been having are focusing on, uh, operational cost optimization almost, it's almost like a theme you could on a micro scale, um, On a day to day basis, if I'm interacting with maybe two or three different organizational leaders, um, it's, it's becoming a common trend.
Everyone's really looking at their books to figure out where can we become more efficient. Yeah. What I would say as you're looking at optimization is some of the more obvious things that people look at might be, you know, headcount. Um, the, the risk there is there, there's countless studies on, on the negative effects of, of headcount reduction.
Sometimes you have to, it's the only way to survive as a business. But what I would say is don't jump to those, those solutions so fast, because what we're seeing with AI is the ability to extract greater value from the people that you have. On staff and so, and you and Jerry talked about that a little bit in that, in that session with Jerry before the holidays hit people first, yeah.
Yeah, absolutely. There's there's so much that that your employees will be able to learn and do. You know, with that said, you have to consider your talent strategy and all this. So if you're planning your 2024, even 2025 transformations, something to make sure you don't forget is that talent strategy.
So as you're implementing any of these changes, you're Any if it's maybe you're doing a cloud migration, maybe it is implementing copilot across your user base. Be thinking about ways that you can elevate your teams so that they can focus on more strategic work. And I think a lot of a lot of organizations bring that case to their boards and they say, we're gonna, you know, we'll be able to focus these headcounts on more strategic work, but they don't actually define what that is.
So I would, in parallel with map mapping out implementations, you should have a talent strategy accompanying it so you can figure out where across the org you need to focus on upskilling and reskilling. Um, but again, just going back to the beginning, planning your, your, your road map for the coming years, I wouldn't take a platform based approach.
I wouldn't look at what's out in the market and figure out how you can use it. I think you need to start with. Your strategy and figure out what can I do in the next one or two years to help, help the organization achieve its goals. And then a partner like us could come in and help figure out what are the solutions to doing that.
But if you really focus on using, if you're an, if you're a CIO or director of I. T. and you're planning out your year, your roadmap, your budgets, focus on that business strategy first, figure out where you think you can make an impact and then. And then you can pull in individuals like ourselves to help make that vision a reality.
I love that. So you did. So, um, takeaways, Dave. So if I took what you just said as a close and bridge to the last conversation you had with our C. O. O. Jerry Hawk before the holiday, focusing on the age of experimentation, sorry, focusing on the age of experimentation. That's over. So now we're into strategy.
You just mentioned prioritization. And then the, the next two takeaways from this conversation are take specific use cases and use them as a model to encourage your own progress. Yeah. I, I'd say so. Uh, and With that, uh, don't be afraid of prototyping and testing and putting these prototypes in the hands of users.
Test out solutions before you invest heavily in them. Which is something we do all the time, right? Oh, absolutely. Yeah. Well, Dave, I have had so much fun turning the tables on you today and putting you in the hot seat. Thank you so much for your time. I think when our listeners come back, they'll see you back in your regular chair as the expert host.
And I look forward to that this season. And as always, thank you for listening to exchanges, a podcast by Hitachi solutions. We air regularly on wherever you get your favorite podcasts. You can check out our full library. 📍 We've referenced several episodes today. You may want to check them out at global dot Hitachi dash solutions dot com.
Thanks, Dave. Thanks, Laurel ....