Are you ready for AI in 2024?

AI is a once-in-a-lifetime technology advancement and it's here to stay - so what does that mean for your business and how do you get started? Listen in as Vinnie chats with CapTech AI leads, Fellow Jason Snook, and CX Director LesLea Carey and we discuss:

Consumers are expecting AI, so how will you meet that need in 2024?
AI is more than ChatGPT - the endless AI use cases include dynamic pricing, personalization, automation, anomaly detection, and more.
Why you should move now and how to push past the analysis paralysis and unknowns.
Excellent AI examples from Walmart and Dominos.

Vinnie

Hello, and welcome back to CapTech Trends. I guess, not surprisingly, we have several AI podcasts coming up. Today we're talking to Jason Snook and LesLea Carey, both CapTechers. Jason is a fellow at CapTech, and he's really leading all of our strategic efforts in go-to-market as it relates to AI. Leslie is a director in our CX practice. She is working with Jason directly on some of these go-to-market solutions, including an AI chalk talk, an innovation sprint, proof of concept pilot, strategy road mapping. I promise not to make this into a commercial. So, you can go check those things out on our website. The reason I wanted to get these two in a room was to talk around why those things are interesting for our clients, now. So before we begin, Jason and Leslie, welcome.

Jason
Mm-hmm. Thanks. LesLea
Thanks, Vinnie. Vinnie

Yeah. So, what I wanted to do is, again, not get into a salesy mode with this. We can touch on these things as it relates to why the listeners may find value in these activities, even if they're doing them themselves. But, I kind of want to start with, "What you want. The, 'So, what?'" The bottom line, up front. What are you hoping to get out of this communication to our listeners, today? I'll start with you, Jason.

Jason

Yeah. We've been tracking really all trends, especially AI, for several years now. ChatGPT didn't sneak up on us, but for a lot of companies it did. So, last fall, or last spring, companies were starting to be asked by their boards and by their shareholders, "What's your plan for AI?" The reality is that most companies didn't have a plan. They've spent the summer, honestly, kind of posturing and getting their feet under them. What I think we're seeing now is that we're in the fall, people are getting into annual planning. A lot of companies have spent the summer getting a little bit smarter about what AI is, what it could be for their business, and they're baking it into their plans next year. So, the acceleration framework, as we created it, it's sprung up out of a lot of the conversations we had with clients who were at different

places in their maturity.

It asked for different things depending on, "They have no plan." Then, "We need to go ahead and workshop through what that looks like." Or maybe, they know exactly where they want to apply AI. "Okay. Let's go ahead and do the proof of concept, then. Let's jump to that." Or, they're starting from zero, "We need to do an organizational assessment." We'll probably get into that as we go. It gave us the flexibility to address kind of the conversation wherever a client was at. We thought that was valuable.

Vinnie

Yeah. It's interesting. I was at a conference recently. It was actually a panel discussion. It really wasn't a direct disagreement between two people, but they've said basically opposite things. One was saying, "We took something to market using AI a year and a half, two years ago, with earlier versions of this technology. Now, our competitors, with today's technology, can create the same solution really at a fraction of the cost, like one 10th of the cost." So, part of the advice, there, was, "Don't do too much too soon when the technology's moving so fast because if you wait six to nine months, maybe you can do the same thing for way less money." The other point of view was, "Don't do that, at all." I mean, you're already behind. So maybe, you don't have to do a full solution, but at least get in the game and understand it, prove some things, and grow with the technology. Leslie, I'll throw it to you. Between those two extremes, where's your head at?

LesLea

I think somewhere in the middle. What we're seeing, right now, is a lot of consumer expectations around AI are changing. They're aligning towards those business goals as we look to the future of, "How should AI become an integral part of our product, or our service?" Consumer expectations have changed quite a bit in the past six months, even. So, being able to meet those, but also think forward, "What may our consumers want to expect in six to 12 months?" is really going to be crucial in aligning with those business goals. Companies should definitely keep that in mind as we look towards annual planning, looking towards creating a roadmap. Not only what does the technology offer, but what are your consumers expecting? What do they want to have?

Vinnie
I'm smiling because, not surprisingly, you came at this from a very strong CX perspective. So, yeah. I

appreciate that perspective because mine was a bit different. Mine is, "Oh, you've got to move now." I was thinking more internally. I do think externally, as well. You were very well grounded in the external. I'm thinking, "How can companies use this to inform the business decisions, and their own strategic decisions, that they need to make over the next several years?" So, I'm kind of on, "You have to move now, even if it's just getting your feet wet," so as things accelerate, you're not caught learning. You can execute. So, where do you come down on this, Jason?

Jason

Yeah. I mean you got to think about the connotation of what that one side was saying. It's like, "Are you proposing not doing anything with AI, right now?" I think that it's dereliction of duty. I mean, we're just going to see how this one plays out. I mean our message, and we're usually more nuanced about technology if it's cloud computing, or something like that, very important. But, some organizations are just taking a while to get there. AI is not like that. I mean, you could pretty well assume your competitors are working on some applications of AI, whether it be internally, or what Leslie's saying. I don't feel like we're unique. Well, maybe we are unique. We are the ones thinking about how they would roll this out to their customers and going to get the data around that. In that, I think we're adding something to the conversation that I think is important. But, no. I absolutely do not think that you can just sit still, right now.

Vinnie

Yeah. And, it is one of those once in a lifetime kind of things. Maybe, you get two or three of these in a lifetime.

Jason
Couple of them, but-
Vinnie
Yeah.
Jason
... not many.
Vinnie
It affects everything. It is a part of everything that we do in technology in our daily lives, and it does play

nicely. This panel discussion kind of hit on it. What we've been saying, from a thought leadership perspective, over the past two or three years is a sharp rise in the amount of technology that people will be interacting with on a day-to-day basis. Strong personalization recommendation engines, strong wearable technology predicting falls, showing that you have Afib, driver assisted technologies. A lot of these things are rapidly increasing in your daily life, but we should and are noticing them less and less and less. So, it shouldn't be that you go to your app and hit an AI button. Right?

Jason

Mm-hmm.

Vinnie

It should be that AI is happening somewhere in the background that you're unaware of and just things are better, things are more natural, things are smoother.

Jason Yes. Vinnie

One thing I did strongly disagree with, on the talk, was that we would work less because these assistants would be doing more for us. My entire life I've heard that technology will reduce the amount of working hours and-

Jason Right. Vinnie

No. It becomes a new common ground playing field, and everyone still works hard to outpace each other. So, I don't believe that for a minute. Okay. So, if we're saying that we need to move now, why aren't some people moving now? What are some of the blockers to this, that we're addressing through the things I mentioned earlier? Why would someone not move now? Hey, Leslie. Why don't you kick us off?

LesLea
One of the things that we're seeing is analysis paralysis. Companies are saying, "What's the right next

step?" To your earlier point, Vinny. Looking at what the competitor did six months ago, I don't want to be that person. I want to be able to do something quicker, faster, better, but, "What's the right direction for me?" There's all these different angles, all these different applications. There's also ethical concerns, looking at, "How do I responsibly use this AI? How do I reduce the risk of bad actors? How do I ensure that these are secure applications of AI?"

So, a lot of these big decisions are coming into play, and it's overwhelming. It's a lot to take in as you're trying to figure out, "What's the best application of this technology? How do I create that really seamless experience for my customers, but also something that's a wow product, or service, for my company?" Those areas I think are where we're going to see a lot of churn over the next six months. At that point, AI is already going to make leaps and bounds. So, it'd be very interesting to see where it lands, but it's also at the ground floor. It's time to get in now, for all companies.

Jason

Yeah. It's kind of trying to catch a train that's already going at full speed. It can feel like that. No doubt. This is where we're trying to help our clients inform themselves. I think that a lot of them have shut down AI at their company. They don't know what it does with their data. We kind of gently are encouraging people, "Well, then, go find out."

So, I've read more terms and conditions documentation this year than ever. For whatever reason, and I won't name any particular platforms, but a lot of them, how they're handling data with these large language models is on page 47. I literally have, in my browser folder, bookmarks that go to terms and conditions and where this stuff... It's there. I don't know why they buried it. I think that, for a lot of our risk officers and data owners and different organizations, like it or not, you're going to have to go find out. I do liken it to cloud, a lot. I don't know what you observed with clients, but there was a lot of trepidation about cloud, at the time.

Vinnie
Early on, early on. Jason

Early on, we didn't understand it. It's just, "My data's just going to be on the internet somewhere." All the way to, now, the CIA is using the cloud. So, the evolution was in the understanding of the technology. If we're in a trough of disillusionment at all, I think it's the catch-up, that Leslie's saying, to

understand.

Vinnie

What's common FUD. Right? It's fear, uncertainty, and doubt.

Jason

Yeah.

Vinnie

... and that happens with every major new technology. I think the uncertainty plays a big part. "If I create content, who owns it?" Privacy concerns. Right?

Jason Yeah. Vinnie

I think those things are causing a little bit of a sluggishness in there. I also think there's some... I hate to use the word confirmation bias here, but it kind of relates to that. Look, if I go into Midjourney, and I ask them to create a picture of a cowboy playing a guitar, invariably the guitar has too many, or too few, tuners. It's like, "It doesn't know how many strings a guitar has, and I'm going to trust it to tell me if I have cancer." Right? Siri doesn't pronounce my name right, and it hasn't in over a decade. Even with me saying it, it hasn't learned that, and I'm going to trust that you're going to create a 15-page document for me? Then, you hear that this lawyer didn't check the references, and they were made up. You hear all these negative things. I think that people who are looking to be nervous about this technology get reinforced by that, as opposed to saying, "How can we use these technologies in a workflow where they are massively productive?"

One of the ones I've looked into, there's a physician study. I'm going to get some of this wrong. So, don't fact check me, but I'm directionally correct. For doing lung imaging, or mammogram imaging, artificial intelligence outperformed the physicians. However, none of the physicians would trust that without independent verification, and-

Jason Absolutely.

Vinnie

... they also couldn't identify necessarily why the model was saying this. So without understanding the path it took to get there, it leaves a confidence issue. But, the way they've kind of come around that is, they've said, "No. I'm going to go ahead and do my initial diagnosis, and I'm going to use AI as the second opinion." That's reducing workflow by 88%, or something like that. So, there's still long way around the barn here. What I'm trying to get to is, I do think that some of that fear, uncertainty, and doubt is valid, but it doesn't invalidate the technology. It challenges you to use the right workflows and use the technologies in the right way.

Jason

Absolutely. I mean, it's like everything career consultant over here. It's complicated. There's no clean answers with AI, or any technology. You have to know what it's good at and what it's not good at. The reality is, though, that these large language models, academics are still trying to figure out what the black box does. We have some general sense of what it's good at and what it's not good at. Large language models are language models. They're not good with numbers. So, you got to know how to prompt it so that it isn't doing math necessarily. You have to actually tell it to write Python code to run the math. Then give you the answer, rather than asking the language model to give you the answer.

If you ask for facts, it's biased and it hallucinates. So, you got to always double-check an actual fact. I asked it why Virginia should be a leader in AI, the other day. It gave me an amazing response. It said that we have the most data centers in the world, in Virginia, the concentration of them. I was like, "That's cool," but I wasn't going to take it for its word. I went and double-checked. Turns out we do.

Vinnie

Right.

Jason

So, you just got to know that, "Hey, you need to fact check this thing. You can't use it to do math." If you know what it's good at and what it's not good at, you can apply it effectively.

Vinnie

Right, right. How do we address these blockers? I mean, we said before about having proof of concept pilot, strategy, road mapping. To sum it up, we're saying that a person isn't wrong for having these

concerns. So, how do we address the concerns? How do we do that so they can move early? LesLea

Having a strategy, looking at the way that the business goals can align with where AI technology is
today, really lays that roadmap and provides some confidence for those baby steps to take along the way. Also, testing and learning. What we're seeing out in the market, right now, is a lot of companies are doing small pilot test using AI to solve a problem. They're starting with one store, one location, or even just a rough popup to test this out. It's low risk, low investment, but allows them to collect feedback, understand, "Is this something that customers are reacting to? Is this something that supports my business goals? Is this something that feels like it's AI just for the sake of AI, or is actually serving a purpose, making our business better?" So, I think being able to go out there, try some of these strategies out, be able to do them in a safe environment, understand the different risks there, and then come back with that really strong proof of concept and some actual data, then say, "Here's where we think this could excel," is a great advocate for continuing to integrate AI.

Jason

Yeah. I love that. Leslie, I love that point. The other side of it is governance. We call ourselves responsible optimists at CapTech, and we take the risks of AI very seriously. I'm pretty proud of the fact that we're actually dog fooding a lot of us of this on ourselves, as we go, before we go tell a client to do something.

The first thing we deployed at CapTech was our AI policy, a 1.0. We had our risk and contract team work with outside counsel. We have a policy, on the employee portal, you can get to today. We stood that up. We stood up in AI steering committee. We have gates for risk reviews as different internal, or external, projects happen, contract reviews. So, we stood a lot of that up.

Where it's kind of finding its way now, one of the offerings that we're going to start offering clients is their own internal instance of a GPT, or language model, that they can deploy internally in their enterprises, pretty straightforward, couple of weeks. It's not a big engagement, but it's got a huge upside.

As we were thinking about our approach to that, we're absolutely not going to do that without at least making recommendations around governance. We do not think that you should release a ChatGPT like product in your enterprise without certain risk controls in place. So, that's going to be part of the

engagement because it has to be part of the engagement. So, I think just being kind of clear-eyed about those kinds of things is important, too.

Vinnie
Yeah. I want to drill it down a little bit. Yes. Have a strategy test and learn, celebrate the victories. Right? Jason
Mm-hmm.
Vinnie

Those are things that we can say, "The devil's in the details." So, we talk about having a strategy. I think I'd share a couple thoughts and see how you guys react, whether you agree or disagree with these thoughts. As the models improve, I mean, I know just from going up to ChatGPT 4, now. When they started off earlier, like ChatGPT 3, you had to have a ton of data, a ton of processing. It was sort of, "Let's just prove we can do this. Let's prove it's valuable." And, they did, in an amazing way.

Then it became, "Well, how can we have even better results with far less data and far less costs and far less..." So, it's a matter of being more efficient with the models, as well as being more accurate. I bring that up to say that organizations can find value in AI without doing a year-long project. You can do small projects and have convincing wins doing that because the technology is moving to a place, now, where that's much more achievable than it was even three or four years ago. So, don't feel like it's a bigger problem than you can handle. It isn't. You can jump in and do that. So first, I'll pause there and see how you guys react to that.

Jason

Yeah. I think we've started, to your point, even in our offerings. We don't want to offer 20 proof of concepts. We're kind of zeroing in on eight, that we think are things that can be done. Our criteria for proof of concept was, "You had to be able to stand it up in four to six weeks."

Vinnie

Yeah.

Jason

And it could fail, it could succeed. That's a pilot. A lot of our clients have a lot of middle office work around document processing. That's something we've done at several clients, now. We think it's

something that you could stand up a concept in four-ish weeks. Standing up an internal, we call it Enterprise Chat, but it's an internal kind of large language model for the enterprise, like I was talking about earlier. Again, probably a three or four week engagement. Then, there are other ones. My data friends will remind me, "It depends on how your data's looking, sentiment detection." A lot of our retail clients, they have the customer feedback, and they have humans reading it. We just did this with a survey this week, where we took the qualitative feedback from the survey and used a large language model to get much more out of it than we ever would've as just us looking at it. So, those are a lot of really kind of quick win, near term things that we're starting to identify.

Vinnie

Yeah. So, I want to add one thing to that, and then we're going to talk about companies who are doing it well. So with those early wins, I like to... Maybe, I overdo this because I've been consulting for so long and I'm analytical. I like to think of buckets, or categories. Right?

Jason Mm-hmm. Vinnie

If we think of what you can do with AI, the list, you'll be over 100 and not running out of ideas. I like to summarize them up. So, I think of things like predictive modeling. That's a huge one. Dynamic pricing, personalization, automation, content generation, anomaly detection. So, anomaly detection could be fraud detection for a credit card company. It could be the cancer in a scan. It can be vibration in a machine, signaling that a part is worn and needs to be replaced. Once you break these up, then you could, again, start all your listings. So, I would challenge people listening to this to not think first of individual solutions. To think about those broader categories first and think, "What categories apply to our business, or could apply?" Could maybe challenge yourself to be creative if you don't think it does. Then, come up with a couple that you think would be easy, quick wins that could showcase that, either internally or externally. So having said that, Leslie, why don't you kick us off in terms of where we see people doing this. By the way, for those listening, if you hear us mention a company name, that is not something we did at a client. We try to not use client names during this. So if you do hear something, we're not claiming responsibility for that. It's something that's available and out there. So, let's kick this off with where we see some people doing some interesting things.

LesLea

Sure. So, one that comes to mind is Walmart. They're using predictive modeling right now to forecast customer demand and optimize their inventory levels. So, what we saw a lot of during COVID is, of course, supply chain shortage and supply chain overage. Once everything started coming out of back stock, and now they're trying to find that happy ground of, "How do we take the data that we have, knowing historical purchasing trends, and use that to optimize so we don't end up with these problems in the future? We don't have too much stock, we don't have too little stock."

Domino's is now using AI to create its new Anywhere Ordering platform that lets customers order pizza through a variety of channels. You can text Dominos. You can tweet at them. You can use Facebook Messenger. Place your order, and they'll actually use data to route you to the nearest store, place your order, and then to reach back out if you have any questions, as you're ordering your pizza and sitting there waiting for it. They're really opening up the door to make it a much more seamless experience for the user, taking down traditional barriers, roadblocks, and making it much more likely that you'll engage with their brand. A couple of places that we're seeing this really cool-

Vinnie
I'm going to jump in real quick- LesLea
Oh, yeah.
Vinnie

... on the Dominos thing. It caused a flashback. I think Jason and I had a podcast, years ago, with smart speakers that were doing the same thing in terms of ordering from fast food, or other retail, speaking with intents and all that. We said, at the time, that this is only going to be enhanced when the AI allows this to be a little bit more predictive in what people are saying. So, yeah. Is it interesting to see the evolution of that? And again, AI is everywhere. So, AI is not replacing smart speakers. Right? It's making them better. That's more technology in your life that you didn't notice, which I think is great. You had another one you were jumping into Leslie before I interrupted you.

LesLea
Oh, no. That was a great point. Two more that we're seeing in the sports space, as we're about to get big

into football and basketball season, is the NBA is using AI to help increase and enhance fan engagement. So they have a chatbot called the NBA fan assistant, which will answer any questions that you have, provide personalized recommendations during the game. You can create personalized video content and interactive experiences. So, it'll be interesting to see how that takes off during this upcoming season.

Then, the NFL is using it to improve officiating, which I know is a hot topic. So, it'll be interesting to see how that plays into these calls, as we're going into the midst of football season here. They're working to develop a system that can automatically review plays and identify penalties. So, it has-

Vinnie

I'm curious about the NBA one, how much they're reviewing that. Are they fully trusting the AI to answer those questions, or does it go to a human? Call centers, for instance, there's a lot of lift in having responses pre-generated and having the call center rep do a quick scan and say, "Yep. That looks good, go." But, they're still doing, "Yes. It looks good."

Jason Mm-hmm. Vinnie

Right? I know Amazon does this, and one of our clients who sells a lot of products, it's a retail company, is doing the same thing. Product descriptions, especially when you have a wide variety of products come in, having AI generate the blurb about that product, reading all the reviews, and coming up with a common sentiment for how people are reacting to that product. But again, in those situations, I know for our clients, there's still a review step. As we said before, that's not a bad thing. It's part of a workflow. You're still seeing dramatic reduction in the time it takes to do these things, as a result. I mean, for the Autism Society of Central Virginia, I'm on their board. We had our gala recently, and we used it to generate many of the silent auction items. And again, it wasn't perfect, but man, it cut the time down. I would say 80%, easily.

Jason
My anecdote is that AI saves me 20 minutes several times a day. Vinnie

Right?

Jason

It's just that kind of thing where it just kind of speeds me up, keeps me going. But, yeah.

Vinnie

Are you working less? No.

Jason

No.

Vinnie

Oh, no.

Jason

My dad has the same stories that, "Oh, all this technology. We'll be working three days a week." No. We just do more.

Vinnie Right. Jason

We see that with labor productivity. I won't get into too much of that. We've actually been looking at the economic data around this. You get a really big spike in the '40s, after World War II. Factories, all output, all that. But then, it kind of plateaus. Then, we see another spike in the '90s. Well, that's the internet. I was talking about the mechanization of America, the digitization of America. Now, the economic data is telling us that we could see a similar bump with AI. Call that the automation of America. So, it's absolutely going to increase our clients' productivity and generate new revenue streams that they don't have, today. Going back to your point, Vinny. Yeah. That human in the loop.

Vinnie

Mm-hmm.

Jason

Absolutely. I'm actually a little surprised that some of our clients are getting these kind of synthetic reviews out there, without that.

It's a tightrope doing that and not having somebody check it. We're kind of on the other side where, to your point, we're developing a solution, now, for call centers that will, in the context of the customer that's chatting and the data we know about them, we can reach out, grab pertinent customer information, generate a response. That agent is still responsible for every word that gets sent. I think that's going to be the case for a long time.

Vinnie

Yeah. It's early in the process, so I'm sure we'll get to a time, in our lifetimes, where those checks aren't necessary.

Jason

Maybe. Yeah. That's the idea with that solution, we're actually going to monitor how often the response is edited before they send it. So, we'll have some sense after-

Vinnie

Some data on that.

Jason

Yeah. Months, years. Well, 95% of the time they send what's generated for them. Well, then we can introduce automation knowing our level of risk.

Vinnie

Great. So, kind of wrap us up. Last topic, here. We believe this is one of those few moments in your lifetime that there's a seismic shift to see change. Move early, embrace it. It's going to be uncomfortable, jobs are going to change. Right?

Jason Mm-hmm. Vinnie

But, step into it. We haven't really talked about the AI acceleration framework that you guys have built. If anyone listening wants to go check that out, they can come to our website and check it out. Outside of that, how do you get started? So, we're going to wrap this up. The audience is listening. There's somebody out there who believes what you're saying. What are the first couple of things they need to

be doing? LesLea

I'd say start small. That's kind of what we share with a lot of our clients, too. There is something for you no matter where you're standing. We're happy to have that conversation around business goals, help identify some of those business goals, and really create that next step vision together. "What do those targets look like? Where does AI fit in and support your goals, rather than just drive them, essentially?" Also starting small and scaling, creating that proof of concept, that Jason mentioned earlier, looking for different areas where AI could integrate, support those business goals, and help you seamlessly scale up.

Jason

Yeah. I would just add, do that and move it to the top of your list. I mean, companies, again, that are in annual planning should absolutely have an AI goal company-wide. Appreciate our leadership, earlier this year. Our CEO stood in front of all our partners and said, "All right. Everybody needs to move on this." So, we didn't put AI in a particular spot at CapTech. The entire company is prioritizing this and moving. It deserves that level of prioritization and urgency, right now. So, I would just say that it's really a top down thing at this point, where organizational leaders need to make this a top priority going into next year. If it's anything less than that, I think they're going to see that competitive advantage kind of drain away. Then kind of coupled with that, just educating yourself. I know so many people that are still daily, "Oh, yeah. I need to get around to learning more about AI." That goes back to that urgency piece where I just think collectively, there's not a person I've talked to that doesn't need to know more about AI.

Vinnie

And, it's out there.

Jason

It's there. So, educate yourself and make this a priority going into next year. Absolutely.

Vinnie

I've got some quick bullets. Know your data, know your business, know your people. By that, I mean you already have experts, or at least people who want to be-

Jason Yeah.

Vinnie

... that are passionate about this. Right? Know what's possible. Know those categories we talked about earlier. Early wins and make it fun. CapTech has done tech challenges and innovation challenges through the years. We're doing similar things now-

Jason Yup. Vinnie

... where we understand the risks and issues surrounding this, but we're coming at it because we love technology. We love applying it. So there's, I think, a fun aspect to this, an interesting aspect to this. Knowing your people that you can tap into, people will put in extra hours to be a part of this. Right? So, to your point about prioritizing it, this isn't a big ask to get people involved and excited about something that is really revolutionizing not just our technology, but our society. Great. Well, thank you both for coming today. Really, really appreciate it. We're going to have a couple more AI centered podcasts coming up. The next one, I'm not going to tease too much, but it will, I believe, include a couple of our clients, as well, in terms of what they're doing, what's working for them, challenges they've had. So, be on the lookout for that.

Jason Awesome. Vinnie
Thank you guys. Jason

Thanks.
LesLea
Thanks, Vinnie.

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