When it comes to AI initiatives for your enterprise and business leader decision-making, in the words of Stephen Covey, "First Thing's First."
As our Advisory experts Dave Horstein and Greg Gant unpack prioritization for AI initiatives, Gant puts it this way, "Center your prioritization around 'find my things, fix my things, and update my things,' which means look at areas where you're already working to improve your efficiencies.
You're not solving new issues, it's about solving existing and even age-old issues more efficiently and realize value more quickly. Start by identifying high-priority use cases.
Learn more in this episode of Exchanges with Hitachi Solutions, and don't miss our upcoming webinar on "Finding Your AI Onramp," which further explores this topic to help business decision makers map their way forward.
global.hitachi-solutions.com
When it comes to AI initiatives for your enterprise and business leader decision-making, in the words of Stephen Covey, "First Thing's First."
As our Advisory experts Dave Horstein and Greg Gant unpack prioritization for AI initiatives, Gant puts it this way, "Center your prioritization around 'find my things, fix my things, and update my things,' which means look at areas where you're already working to improve your efficiencies.
You're not solving new issues, it's about solving existing and even age-old issues more efficiently and realize value more quickly. Start by identifying high-priority use cases.
Learn more in this episode of Exchanges with Hitachi Solutions, and don't miss our upcoming webinar on "Finding Your AI Onramp," which further explores this topic to help business decision makers map their way forward.
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 exchanges with Hitachi Solutions, where we are going to continue our exciting conversation in season four around aligning with and realizing actionable AI for our customers.
Dave Horstein:
With me today is a special guest and a dear friend, our VP of Corporate Strategy and Advisory, Greg Gant. Greg and I have been working together for, I think it's been six, maybe seven years now. Um, I, I can't even tell you how many different engagements, uh, workshops, implementations, you name it. Uh, we've been in the trenches together for quite some time.
So I'm very excited to welcome you, Greg, to this show. How are you doing? Dave, I'm doing amazing. Thanks for having me on. One of my favorite topics today. Can't wait to chat. Love it, Greg. One of the reasons we wanted to have you on, you have been really leading and spearheading the effort around AI for our company for at least the past maybe year now, a lot of conversations with executives from different industries and organizations.
And we really wanted to tap into some of the conversations that you've been having to figure out how we can make AI more practical as we go into 2024. Um, what we've seen in 2023 was a lot of the experimentation of, of AI trying to figure out use cases that can truly drive value in an organization. A lot of the conversations we were having were really focused on trying to figure out where there might be value in sifting through what is an AI use case and what is not.
Where would you say we're at today with, with all of that?
Greg Gant:
Yeah, I mean, things are things are moving very quickly. Um, we're seeing, I think we've seen from our customers. We're seeing this internally in our own use cases. Um, the curve is moving or we're moving down the curve very quickly. We've gone from awareness.
We've gone into usage. We've gone, uh, through the exploration side. And now the big question that we're getting, and I think you've seen it to Dave is, Everyone wants to know what are others up to? What are, what are others doing? What are other customers using? How are they using generative AI? You know, the famous question, and I kind of chuckle because now it's all over the internet.
You can even go into chat GPT and say. What are other customers that look just like me doing? What are, what are their use cases? Give me your use cases. So we're kind of in this age of the use case is I've been, Um, talking to customers about actually was on a call this morning where. Um, we had a great conversation about doing some exploration around some data set mining for use cases.
And then the call ended, almost wrapped on a weird tone of like, well, this has been really good and really helpful, Greg. Thanks. And we had a great team on there, really good collaborative session. And then they said, cool. Okay, good. Well, we look forward to the next steps. Hey, just as we wrap, what are, what are other folks doing?
Tell us what others are doing. Give, give us the cheat codes. And the reality is Dave, and I think you'd agree. There is no cheat code. No organization is identical to another organization. Some of them do look the same, no doubt. And there are use cases that are usable in similar other organizations. But the reality is the way people work, the processes, the data, the business applications, there are no identical footprints.
So how we bring generative AI might look similar. But the use cases are rarely identical, and we have to have a, uh, an approach, and that's where we are today. How do we approach identifying and discussing and applying real use case identification within our customers and internally? I mean, we are going through this today.
Uh, no different than a lot of our customers are doing as well. That makes sense. And, uh, as a big Simon Sinek fan, I'm, I'm all about the long game. And when I get asked that question, what are other, other organizations doing? My, my caution there is when you start trying to emulate your competition, you yourself.
Then have no competitive advantage. And in 2024, innovation and competitive advantage is how you survive, not just how you thrive. And so my, that's what I caution everyone with is just don't just look at what other organizations are doing. I think it's okay from an inspiration perspective, but if you're just copy pasting these use cases, it's not giving you a leg up on, on your competition.
And so it goes back to my big argument that you need to start with your business strategy and not just start. Kicking the tire, so to speak of a I, Um, but that's, that brings me to a question for you, Greg. What, what I hear a lot is, you know, it's difficult to even figure out how to, how to figure out our use cases or find our use cases where, why is it so hard and where do we tend to start with, with our customers?
Yeah. It's so easy to overcomplicate this, which is kind of a funny sentence, right? We've got easy and overcomplicate in the same sentence. And when we go talk to customers, we say, let's think about what we're really trying to do and it's let's simplify it and it's funny your intro. I laugh because we have been up to this for 8 years.
And, you know, I think about some of our early projects, uh, going back to, uh, you know, I think. I'll leave, I'll leave it anonymous to protect the innocent. Right. But we, you and I go back to a CRM implementation back in Vancouver, Canada. And we were building a call note application where after a customer service agent wrapped a call.
They would leave notes and those notes would get filtered down to interested parties, et cetera. And you could say part of their role, part of the business value we were creating for that organization was helping find stuff, helping surface stuff for other members of the organization and provide a good user experience for.
Um, the member, the patient who, you know, whoever was calling in, et cetera, that was a, you know, year and a half long project. It was a complex implementation, et cetera, that function of finding my stuff, updating my stuff, fixing my stuff. Those things we built inside of CRM, Dave, those are the same things that we're doing with gen AI today.
The same things, the difference is. With generative AI, we can do those things in weeks and not years, but I don't want to stop there. I don't want to put a period and move to the next question because it's easy to just skim over that and leave it overly complicated because let's take one step back and think about those three things for one second because if we keep it that Simple for one second and just leave it there.
Like that is how we need to think about solutioning within generative AI Finding my stuff. Generative AI makes it very simple to take large amounts of things and find stuff in large amounts of things. That's let's leave it at that, right? Simple. It also can make it very simple to update my things.
That's a business function. We have been trying to build a complex business applications, custom applications, workflows, power apps, you name it to update my stuff. Let's make it as easy as possible for a salesperson, a customer service agent. Uh, an operations analyst, you name it, to update their things, generative AI, we can do that very quickly, sometimes even automatically.
And then lastly, we can use generative AI to fix my things. Fixing and updating are two very different things, right? Generative AI, we can almost sometimes. Fix my stuff automatically, and we can make that process very easy. So if we take those three business functions, finding, updating, and fixing my stuff, and we start categorizing use cases into those three buckets.
What have we been able to do if we do that? We're well, we now can start thinking about real business value creation and realization in three categories, because next we start talking about actual use case identification in three categories, moving it into that layer, right? So it starts getting kind of cool.
So now we can put the period on those three categories. And let's talk about, well, how do you then start identifying things there? Right. One thing on that, Greg, before we move on, what I like about the fine update and fix messed up concept is it doesn't just apply to internal use cases as we think about enhancing customer experience through AI, gen AI.
These three concepts apply in the same way. If I'm looking to find my stuff, find my order, find my last interaction, update my information, my contact information, or fix address a customer service issue that I'm having with my product, I can still apply this concept to customer experience. So it really is a universal approach.
I like it. And just to pile on that, Dave, I was with a insurer in Florida a couple of months ago, and we were doing a big gen AI summit with them. And we had the chief sales officer in there and he goes, he stood up. Why? I think he wanted to break the meeting a little bit. Right. And he goes, Greg, this is all cool, but.
I'm a sales guy, man. I'm just trying to close deals out here, trying to win business. And I love all this. This is a relationship game. And I said, well, what if, what if I told you, you know, he's running a sales team of a thousand sellers. I said, what if, what if I told you your sellers didn't have to use CRM anymore?
Because I could go update their opportunity records with Gen AI. He goes, wait, what? I said, yeah, I'm talking about updating their stuff. You start making non human gen AI human. And that's when you really start talking about value. And so it gets interesting. So I'm with you, Dave, totally agree. Yeah, I yeah, I love it.
And I was interrupting you. I think we're transitioning now. How do these three find update and fix my stuff get broken down to actual use case identification? Yeah. Yeah. So this is where it gets fun. And we use a workshop process to start kind of thinking about these three things. Because those 3 that, you know, find fix and update.
Those are really like business functions. They can sit anywhere and you can find those 3 things really in any. Nook and cranny of a business brick and mortar. Anyway, you can literally walk into any business function and say, Hey, are you finding, fixing and updating? Yes. Right. So, okay, great. Where do we go?
Look, there are really three areas that you can start to, Um, think about, or, or really start to dig in. I would call them excavation areas, right? These are the three things you want to look for the first one, which is kind of the, the one I would say people know to start here. Where do you have a lot of data?
If you have a lot of data, there's probably some gen AI use cases there. And Microsoft is really big into this one. We have some open source Um, gen AI, open AI code snippets already out there that are kind of for the public that can be used. And they're amazing. A lot of folks, a lot of customers have started doing some things around this called knowledge search.
Let me make that one real for a second, right? And how we see customers using this. Let's go back to that call center example, Dave, because this one will really resonate with you. And I'm sure a lot of listeners have been a part of projects that have done this. Call center. We think about when we think about business value on a call center.
What's one of the biggest KPIs we get into first call resolution or call resolution time. How long do we have someone on the phone or that FCR? How can we resolve the call with one person or do we have to escalate, move them on, et cetera. Well, typically for those two big KPIs, the magic way to handle those is giving the agent everything they need at their fingertips as quickly as possible, right?
What's the barrier for that on the desk? The barrier is the data for a call center agent is typically in 24 different systems. It's 18 clicks away. Uh, there's security barriers. There's data integration barriers. There's common system of record barriers. You name it. We've seen them, right? We've implemented dynamics in these worlds, and it's really difficult.
Gen AI allows us to get around some of that, right? And there's some really cool stuff, uh, where you can go in and, you know, sort of. Without getting too technical here on a quick podcast, you can do some amazing things where I can do knowledge search and quickly find data that I need to, right? Find my stuff.
And you can really find value in Gen AI use cases on where do I have a lot of data. And that business function to find my stuff, that is huge. And you just pile up the use cases around your business. Uh, we'll talk about prioritization here at the end of this. Uh, but that's, that's a really cool number one.
Number two, which starts to get a little bit, this is where it gets a little more fun, right? Where do you have complex process? So in process is interesting, right? Because it's not two dimensional process looks two dimensional at times. We put them on big vizios and we say, yep, this one's kind of complex.
Cause look how long it is or, or look how tall it is, but really complex process means cross business, cross tiered, multidimensional across region or business function or leadership tiers, you name it. But when we see something around, Um, a complex business process, it's almost always an area that we say, how can we use.
Generative AI, typically in combination of other great technologies, whether it's more traditional AI with some machine learning, with some business application front ends, et cetera. But Gen AI is a great place, whether it's copilot, whether it's looking at some of the open AI services, we're even seeing some things with Dali there that we can use.
To help complex business process, get more efficient and that could be done. Um, you know, we've seen a number of ways to do that. Come with me a little bit. Imagine when we think about making complex business process more efficient, how do we typically do that? Finding data faster, updating data faster, right?
So we're really starting to tap on those things above because now. If I'm able to update data faster, well, typically one of the big bottlenecks in a business process is waiting for someone to have what they need to update a screen to move the business flow to the next person. So what if we were able to shorten how long it takes them to get what they need?
That's gen AI. We're able to do that. That's use cases. The third one, which to me is where I have the most fun and we have seen. Incredible value here, gang, like this one for the listeners out there. And this is where we've seen some aha moments, uh, meeting with our business, business partners within customers, where do we have?
Gaps between top performing teams or individuals and maybe the next tier down or the middle of the pack. Right. And this one is always interesting because think about your sales teams and this is a, this is kind of an easy one, but it's also one of the more challenging use cases because typically you say, Hey, our top sales guys, gals, they may not be tech embracers, right?
They're out there jamming. They've got their process. They know what they need. They're not going to be adopters of the latest tech. That's okay. How can we use Gen AI to look at what maybe some of those folks do and bring it to the level below or the middle of the pack to raise them up? And I'll give you a couple of real examples.
We had a customer that brought us in to say, Hey, can you help us look at implementing some Gen AI tech to bring up that middle of the pack? And what we realized is we could. But we actually wanted to go look at what the top tier was doing and saying, could we bring what the top tier is doing down to the middle of pack?
And you know what? It wasn't that the top tier was using CRM better or that they were taking better notes. Actually, what we found top tier was taking worse notes. Dave, the top tier was taking almost no notes, which was really funny. What we found, and this was fun. And I would be really amazed if this resonated or curious if it resonated with the listeners.
The top tier knew how to navigate the organization better than the middle of the pack pricing strategy, technical pricing expertise in the organization. They knew how to navigate the legal and S. O. W. and the contract approval process better than the middle of the pack. They knew where to go faster and when and how, and they knew how to navigate their technical partners better.
And they're, they just were faster at navigating and spending their time where they needed to spend their time to close deals faster than the middle of the pack. Okay. Well, sure. They've been here longer. They were, they were maybe more seasoned. You talk about Gen AI use cases. How could we use Gen AI to go mirror those behaviors and guide our sales teams in that middle of the pack to go spend their time doing those things?
So we did. We used a combination of Copilot. From Microsoft for sales, which is doing a lot of those things already. But then we also use some open AI sort of behaviors to build in these co pilot guided sales team behaviors to say, Hey, let's, let's help you navigate pricing and sort of help you find your stuff.
And man, it started to make a difference. It really did. And cool things. I know I'm probably hitting time Dave. So I'll pause there. Anything you chime in on some of those, cause you can tell I get a little excited when you talk about finding real value with this. Techie stuff. I think it's I think it's amazing.
But that's kind of where I start. Yeah, I think what's interesting. So there is a lot of research that came out in the past year around this, Um, the use of gen AI and bringing the organization towards a common middle ground. Um, and it is about trying to figure out how, how can, how can we elevate earlier career, uh, employees to be closer to the performance of more seasoned, Um, individuals.
But what you're talking about, so naturally you would think, well, is we have to service surface information more easily and help. You know, early career in the case you're talking about, uh, something like a next best product capability could help early career salespeople figure out what they should be pitching next.
But what you're actually getting at is how to navigate the organization. And it would be very interesting to, to understand. Uh, there's a lot of information within like Microsoft's own graph on an organization about how work gets done. So I could see a lot of practical application of a generative AI, either a chat bot or some sort of process navigator that helps employees figure out how to maneuver through the organization more efficiently.
I really like that approach. It's, it's innovative and it's not the first place you would look. Yeah, yeah, it isn't. You're, you hit it. It's not the first place you would look. And I mean, a real example, we had sellers that would say they'd be new and they would say, is this. Work that we've ever done before.
And we had a, uh, basically a UI prompt that would say, you could ask the business, I can't share too much, but you would ask and say, have we ever done this and this and this? And you can be very specific and it would find proposals and winning quotes, pricing the person you should go talk to opportunity records in CRM, the leader of the delivery business that actually went and owned the business post sale and basically gave you a war room to go win the next deal.
And fantastic. I mean, literal impact on that middle pack sales team, whereas those top performers, they would have just kept doing what they were doing. They knew how to navigate those over and over and over and over again. The middle pack, if you gave it to them once. Every new deal that came in like that, they knew how to do it from that time on.
And it made a real impact very quickly on the business. Very cool stuff. Very, very cool stuff. That's awesome, Greg. I'm, I'm excited. I see there's a ton of value. In both co pilot and custom AI applications and coming in 2024 and beyond, I think as we try to round out the episode here, do you want to spend a couple of minutes just talking a little bit about how we, how we experiment and validate the value of these use cases?
Yeah, yeah, I'll just hit it very quickly because sometimes, you know, that's a lot, right? You have the three business functions. And again, you always want to stay true to the, the fine fix and update. That's how you categorize. You sort of think about it in a two by two, Um, and really stick into that. Okay, great.
That helps me think about categorization. Then you get into the, the areas to start to look at, okay, cool. Next thing you know, you're, you're in a list of like 30 different potential use cases. Typically what we like to do is start thinking about prioritization and we like to use Dave, uh, a design thinking style workshop.
I know you've participated in a lot of those, so. Let's quickly use some, some proto and preto stop typing style exercises to visualize a solution, visualize business value and impact. Let's work with our business partners to understand and envision what good looks like. And let's really get some investment on their side.
I'm not talking about financial investment, but buy in from the business before we start to build anything on, will this serve you? Is this what you want? Matter of fact, I'll leave the team here in the listeners with an exercise. We do that. I do think is not something everyone always thinks about. I've heard this from our customers that, Hey, this is cool.
We we've never done this before, right? A press release. With your business partners go, Um, for example, that sales team that we did the, the war room with, uh, we worked with that, that sales leader and said, Hey, let's, let's sit down with you and write a press release that six months from now, when we roll this out to your organization.
What will be your, your speech up on stage when you rolled us out to them to say, why are we doing it? How cool will this be? Why will it change your business? And then we got up in front of our room of about 25 different leaders, tech and business blend and read the press release and sort of at the end of all these different press releases that are different leaders did for our AI use cases.
Kind of shark tanked it and said, Hey, what's our top four, top five that we think will make a real difference on this business as a whole. You talk about buy in and people getting aligned on where we're going to spend our generative AI investment and put some real effort behind. That's how you do that.
And that's how you get prioritized on your tier one. Eisenhower style priority versus effort, uh, works out really well. So think about that press release. It's not new. You can Google, uh, design thinking. There's your free tip of the day, Google design thinking, and it is. A very, very parallel way to get into Gen AI, Um, sort of where to start exercises.
Very good stuff. Love it, Greg. I can't thank you enough for, for joining me today and walking through this. There's a couple of things I want to make sure our listeners take away. One was the concept around thinking of use cases in terms of fine fix and update my stuff. It's a great innovative way to, to.
Identify use cases that can really drive the difference within your organization and any role in any level. The next was Greg's outline of excavation areas where we're really looking at areas across the business where we can identify high priority use cases. 1 was looking at where you might have a lot of data.
Where in the organization are we housing? A lot of data that we can sift and make sense of. The 2nd is. Complex processes. Let's identify ways we can reduce the friction in our process using a I in the third is performance variance. How can we help bring the organization to a common performance level where those who might be lower tier and performance are elevated in getting closer to the higher performers?
Lot of value in A. I. And we're very excited about things to come to get another level down. We want to make sure that you have the opportunity to register for a webinar that we have coming up on February 15th. We're gonna have specifically live demos and customer stories to share with you all. So get out there on our website and register today.
Again, if you like what you heard, and you'd like to check out the webinar, check us out at global. hitachi solutions. com. Thank you, Greg, for joining me. Talk to you all next time.
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