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430- Stop Buying AI Projects Backwards w/Michael Baillargeon · You've Been Heard

• 0:10
  Speaker A: Welcome everyone. Back to. You've been heard. So Mike, here's the thing. After talking with IT leaders, we realized nothing gets done in any company without IT touching it. And what we also realized is that whether people want to admit it or not, nothing gets done without technology touching it. And most people that are in a leadership role in technology right now, at least in the mid market space and enterprise space, didn't go to school for it, or they might have gotten an mba, they might have done something related to it, but no one grew up saying, I'm going to be a CTO or a cio. We had bag phones back then. We still had phones sitting on the wall with cords attached to it. The Internet was brand new. People were still very skeptical of it. And then here we are today with AI, which is what we're supposed to talk about today. But even till today, all the work that we've done as technology leaders, we get a seat at the Executive roundtable, but we're not always heard that well. So the mission and point of this podcast is that it's great to have a seat at the Executive roundtable. How much nicer would it be to be heard? And a lot of C level executives don't necessarily listen to their IT leadership. They have to go to places like Gartner and they have to go to places like Insight and they have to pay them $120,000 a year. And we like to say, well, we'll give you Gartner level Insight without the six figure price tag and we'll give you access to all kinds of people that will sanity check decisions and everything and make you feel comfortable about what your CTO and CIO already know. But this isn't about the IT versus the business or anything like this. This is really about bridging the gap and making sure that we translate and I guess get to a point where everyone understands the words that are coming out of the CTO and CIO's mouth. They may not know what hypervisor is or what to do with that or anything, but they kind of understand at least enough to say, I don't need to check with Gartner. Who's going to assign an entry level college student to your account? Does this sound familiar to you at all?
• 2:21
  Speaker B: Yeah. What's interesting is you hit on something. So I was an English major in college. I wanted to bring Shakespeare to the masses and ended up moving from New England out to Phoenix, Arizona. I had the thick Boston accent at the time. It's kind of dwindled Down a little bit, even though people still say I have it. And I ended up at a call center. And I was a call center agent. And I was an agent for, I don't know, maybe a year. And I realized it's a hard job. It's a challenging job. So an opening came up in the IT department to be like the junior phone system administrator. Now I could spell pbx. That was about all I could do. But I interviewed pretty well. And I tell a good story. Right. That English. Sure. You tell stories. Remember what we tell all our salespeople? And what you tell all your IT professionals is, hey, numbers tell, but stories sell. That's how to really get your point across when you're at that leadership table. Give them the story, just don't give them the numbers. So I ended up being the phone administrator on this million dollar AT&T wasn't even loosened and it wasn't even Avaya. It was AT&T PBX and 30 star codes, Unix boxes. And I learned a lot.
• 3:44
  Speaker A: What was really wild about this story. Yeah. Is that I was an English major and my first technology job started off like this. I was in a call center. And I remember the IT guys in the bullpen, you know, that were like, oh, yeah, we audited your call today. You shouldn't say. I'll never forget this coaching conversation I got from my boss. You shouldn't say, oh, my bad. I was in college and that's not professional. You shouldn't say, my bad. I got moved to Quest Premier. That was the best.
• 4:14
  Speaker B: Yeah.
• 4:15
  Speaker A: It was a LAN party because there was no security back then or anything like that. It was a daily LAN party. So we only got five calls a day because it was like you had to have a hundred cell phones or more account.
• 4:27
  Speaker B: Yeah.
• 4:27
  Speaker A: So we all had PCs networked together. We had an OC12, which was like unheard of. It was like unheard of.
• 4:33
  Speaker B: Yeah.
• 4:34
  Speaker A: Where is this bandwidth coming from? I hadn't even had DSL yet at home. Okay, so you ended up in telecom. I ended up in telecom. And of course the natural transition is to all things technology from telecom.
• 4:48
  Speaker B: So that company was kind of a bpo. And then we lost our two or three big customers. And then I went to the on prem PBX world. I worked at Intertel and Shoretel and Billy Bobtel. Right. So very natural progression. And then maybe in 2017, 2018, I had this Oprah aha moment that the cloud is worth. You mean no more hardware, no more power supplies, no more patches? All Done in the cloud. I'm like, tell me more. And I've just been cloud first last almost 10 years now and it's fantastic. But I remember the big kind of part of that was back in 2018, I call it the UC earthquake. It's when Cisco bought Broadsoft. Think of it, right? Cisco, the biggest hardware manufacturer on the planet and Broadsoft, the biggest soft switch on the planet that signaled the decline of the on prem pbs that day you can pretty much put a stake in the ground because it validated what you do every day and talk about cloud, it just validated that. And come full circle, 2018, then the lockdown, that was the biggest beta of work at home ever, right? And that seemed to work out okay. We coming off that this and now people are like Mitel, they're discontinuing everything. Avaya is just consuming everything. Cisco, they want to, but it's a little longer haul. But I mean there's this graveyard out there of on Prem PBXs that are no longer valid. It's crazy how many clients I talk to still say I have a Panasonic, I have a Toshiba, I have a Shoretel, I have an Intertel, I have
• 6:36
  Speaker A: a Toshiba was great. Toshiba was the best key system.
• 6:39
  Speaker B: Yeah.
• 6:40
  Speaker A: So how do we transition this all to AI? And I have Greg on the call because every morning I talk with him, he's like oh that's so like 15 minutes ago. Here. Here's the question, right? If your CEO came to you tomorrow and said show me an AI win in 90 days, where do you start? Your CFO doesn't want 47 Internet bills. We help IT teams consolidate multi location Internet into parent child billing with coterminous contracts while still buying wholesale from the best providers. It gets control, finance gets clarity. No disruption, no sales pitch. Just go to you've been heard.com and answer the seven questions to simplify, streamline
• 7:28
  Speaker B: and save to a few places. First of all, there's no skew for AI. AI is not on a box on a shelf somewhere. And without an AI strategy, AI is a two letter word. That's why MIT said 95% of all projects fail. And I think when CEOs, CTOs, VP of IT, VP of Sales read that, they might say why should we even try? Right? Why should we even try? If MIT say 95%, how can we possibly be the 5%? Well, this, and we'll get to this, I think during conversation about that readiness. But there's two areas I Think you can get a quick win? One is within your customer service, customer care, customer experience, whatever you call your call center team, your contact center team. Now, I know we're going to hear, but, Mike, I'm not Delta Airlines. We don't have a call center. Let's define a call center like this, Phil. It's a group of hardworking people that either gain new customers or they retain customers. And it all adds up to revenue. Okay? Gaining, retaining revenue. That's what a call center does. Now, they vary by size, scale, and some complexity, but at the end of the day, that's my job. I want to make this customer happy or I want to make this customer buy something. In that customer experience world, a quick win would look like this. Wouldn't it be great? You call into the call center and it says, this call may be recorded or transcribed for quality purposes. Phil, did you. Only 3% of those calls get recorded, transcribed, listened to by a supervisor and then feedback given. Only 3%.
• 9:17
  Speaker A: It's a legal asterisk. Checkbox that they have to check is basically when people are doing it.
• 9:22
  Speaker B: Yeah, 100%.
• 9:23
  Speaker A: We want to record them. Somebody said at some point, the CEO, I want to record everything in case we get sued or in case someone needs to return something or lie and we have to pay out money. That's why they ultimately. I'm assuming this is the reason why most people want to record calls. Yeah. T Mobile or somebody, it's probably different. They're doing a lot more Q and A and they're very satisfactory.
• 9:45
  Speaker B: We won't talk about that lawsuit, but there's a very technical term for it, and I might need Greg to help explain this. The term is cya. That's why they record those calls. Right? To cover themselves. Now, what if we could take that CYA and turn into a profit center? What if I could take 100% of those calls, have AI listen to those calls and score all my agents based upon a rubric? As a corporation, as a company, I make sure they have validate their account number. Did they validate their date of birth? Did you upsell them all that? And that way I can make my people better. Because, Phil, you know as well as I do, remote work is part of just about every company on the planet these days. So if you. In the old days, I had a supervisor who would walk past me and kind of listen. Is Mike cussing out that customer? No, he sounds like he's doing pretty good and he walks on. Right. But now I can't visit everybody's house to do that. So how do I keep that quality level so high when everyone's working remotely, we call that AI Quality Assurance or Quality Management or Quality Metrics. Score 100% of the calls and then as a human in the loop, I can still go into each of those. So if you're our best guy, you get the 90s and 95s. And then I see Mike's in the 30s. Why, why is Mike.
• 11:10
  Speaker A: Can we take this a step further? Is there a way to do this with. I mean, why wouldn't you do this with your salespeople? Why wouldn't you do this with all of your inbound, outbound calls? It would be great if we could do it with our zoom calls. So every zoom meeting that my people run, like, could we score that? That would be cool.
• 11:28
  Speaker B: That would be very cool. And then Phil, here's the other thing. If you are an organization that has recalls, so you're in manufacturing, whether it be physical devices or food manufacturing, give you a perfect example. When I was at that BPO 30 plus years ago, we had Nestle with one of our big customers and it took us about three weeks of anecdotal knowledge to see that a certain dog food had an issue. Now I can get it done in about three minutes. With AI, it can surface that you've had 10 calls in the last 10 minutes about Sick Dog. So it's just not surfacing about if I'm a poor agent and Phil's an amazing agent. But more importantly, that voice of the customer, voice of the patient, voice of the member, what are they actually talking about? Another perfect example. I worked with this company. They make cowboy boots, right? And they only made square toe ones. Now, I'm not a cowboy boot person. I lived up here in New England. Not a lot of cowboy boots up here, but anyways, they only did the pointy toe boots they put on this AIQA QM and then they learned that 30% of the call started with, I'm on your website and I'm looking for square toe boots, but I don't see them. Well, guess what? They started making square toe boots and now square toe boots are their number one seller. They would not have known that if AI didn't surface that information. That's the power of AI That's a quick win, right? Of, let's listen to the data we already have. We already have all these call recordings. Let's listen to that data and figure something out. That to me is your quickest win.
• 13:11
  Speaker A: What about the people that just don't necessarily have a big contact center. Well, logistics, if we use logistics for an example, we have a ton of inbound calls, people booking loads. We got a lot of outbound calls, doing temp checks and like checking where the loads at, quality assurance from that standpoint. So there's one example there, Healthcare, we've got patients and I'm assuming scheduling is kind of a big one. This is all kind of like telecom examples. Do we have any other examples that are just non telecom?
• 13:38
  Speaker B: Maybe, Yeah. I think one question I've been asking IT leaders quite a bit is do you think your coworkers may be talking to a public GPT? Pick your poison, right, Claude, grok Chat,
• 13:57
  Speaker A: we know they are, we know who
• 13:59
  Speaker B: they are, the usual suspects, right? And maybe putting up PII or PHI information. We'll give example. We work with a financial advisor company, right? And what they would do is they would take my portfolio with my name and everything, copy everything and put it out to ChatGPT. How should we improve this portfolio now?
• 14:24
  Speaker A: Wow, genius.
• 14:26
  Speaker B: Yeah, my portfolio went crazy. So what people don't always understand is if you go to a public GPT, you're writing that on the Internet. So here's the other thing. We work with a couple of suppliers, so then the customer's like, well I'll just buy my own private version of ChatGPT. I'm like, okay, you can do that. How about if we could do this for you? How about if we had what we call an LLM aggregator, someone who already bought a subscription to the top 5, 7, 10 LLMs out there and now when I type in 52 and if I'm the CFO and I go to ChatGPT, it said, hey, we're the Smith Company, here's our financials. Where are we at risk? What happens is that LLM aggregator will redact any of the PII or company information, will put up general numbers up there and then when the string comes back it'll repopulate with Smith company. So now. Yeah, go ahead.
• 15:26
  Speaker A: So this is good. It's like most people listening is going to know what an aggregator. So try to imagine Grok, ChatGPT, Claude, list a few more. Greg, all in one dashboard for an IT manager to see all those active users, what they're actually chatting, what they're doing, and then give that access to all those LLMs, maybe to a private drive or what do we want to call a single source of knowledge and dip from that and know that the LLMs you're using are secure and I know like Devs AI, for example, they're hosting LLMs on their private cloud or whatever. And in the terms and conditions, we're not sharing your information versus an end user, just downloading it. And then not only that, then the cost can be spread. The cost per users may be $30, $40 versus $30 for each individual thing. And then you can share the tokens across all users because not all users are doing the same. Now here's the issue with that model and I'm going to let Greg ask the issues because I was like, why don't we do the aggregator? We're paying for this, we're paying for that, we're paying for all these other ones. He's like, well, I like the interface of Claude and I can use the Claude desktop version, which I can't really necess do with an aggregator because I've got to use their interface. So I mean, I can see for maybe like DevOps guys if they're going to need maybe each individual one. But for the general user inside a company, even building agents, you can use aggregators for. But Greg, this is where you ask the hard questions.
• 16:54
  Speaker C: Yeah. Thank you, Phil. It's interesting because the way that we work together, we really witness everything. And me, I really move from the, as Mike was saying, the public chatgpt to now really using AI as a tool around all use cases. And that's where it becomes very interesting when you can start to see LLMs not just as interface, when you ask questions back and forth, but it's really a tool that you can integrate on all your different workflows. That's what we've been trying to do, you know, with agentic AI and everything. Now the main difference, and this is where that would be interesting to have your point of view, Mike, is we are small teams, so it means that we can really control what we're sending. We know the data that we share, the one that we don't. Now if you expand that on the size of an organization, this is where I really wonder how can enterprise AI adapt to how fast it moves, how fast it grows? Because we know that usually it takes a lot of time to move to the decision maker that approve maybe the use of this model. And by the time we use this model, as Phil was saying, we say, nah, this one now is already like there's already two models, you know, that came after and just blew it up. So how do you manage that?
• 18:12
  Speaker B: Yeah, so in the past, and Phil mentioned we have a DevOps team in the past you would tell your DevOps team, go and build this application for us. You have three months right now, hey, go build an LLM of billions of units of data. It's going to take a long time, right, to create your own LLM, import all your information. I think using an LLM aggregator does two things. It insulates you from the average user, like me, who I don't know, whatever I put up there is now in the public domain. I have no idea.
• 18:49
  Speaker A: Like, for the user that's using AI, that's better than a search engine, they're using it to, like, create reports or use it for Excel, or they're using it to build PowerPoint presentations, et cetera. I'm assuming we're talking about that user.
• 19:04
  Speaker B: Yeah, that. Exactly. That user. Or someone who's like, hey, help me with this portfolio, or help me with this accounting sheet, I don't understand what's going on. Or even help me with the cold call script. Right, those users. And Greg, correct me if I'm wrong, certain models are better at certain verticals than others. Like, I'm a bit of a fan of Gamma to create a PowerPoint presentation. I think it does a really good job for that versus a couple of the other tools. Is that. Is that fair to say, Greg, that some models are better either vertical or depending what you want to do?
• 19:40
  Speaker A: Yeah, they share our tech stack. Share our tech stack. What do we do without?
• 19:44
  Speaker C: Oh, yeah, usually, like now, for example, me, I'm a big user now of GPT 5.5. So if anyone has been following a bit of the drama that was around Entropic, because Entropic, I don't know, it's a really interesting use case. I could go on for forever on this. But basically they were like the top leader. Like, you remember, Phil, I was saying. No, no, ChatGPT. Forget about ChatGPT. I canceled my subscription six months ago and now I'm all about code. Why? Because KOH is just like the ultimate. Now, what happened is that Entropic, they started to restrict the subscription model. So, for example, we used to have the $200 plan, which is quite a lot of token available. And when you combine that with your agent, fantastic, because now you can get your agent to go on the web, do some research and build some stuff. So it's basically like a one tool, won't fit it all. Then after Entropy, they've decided to restrict the use of the subscription to their ecosystem. So now, I mean that. For example, me, I built everything around Claude with the subscription, and now we have a system in place and if I compare the price I would pay with the API, that will go from $200 a month to, I don't know, that could be 2,000, even more, I think. So that's a good example of building something, relying on a provider and then that provider changed a strategy and then that's it. Producing everything.
• 21:21
  Speaker A: We're producing mass amounts of content based on what we had recorded off the podcast. I mean, infographics and like, you would have to tell me how many steps it was taken to go where, but I remember literally overnight done, like back to the drawing board. Now if that happened to a manufacturing company or that happened to someone that had invest, it would be, we're a podcast, right? Like, yeah, it was painful, it was problematic. It was like, this is why we can't go all in on AI. This is why it's got to be point solutions. Because we have that argument all the time with our friends. Like, AI is going to put DevOps teams out of business. It's gone, it's taking over. And then overnight you're kind of like, oh, back to the guys, the DevOps guys that said, see, I told you.
• 22:06
  Speaker B: Ha.
• 22:07
  Speaker A: I do want to give the audience something of, like, value here. So, number one, where are you at on your actual AI journey? Where do you actually start? What are all the different points that people are at on their AI journey? If you need to share a slide or something, go ahead and do that. But if you were to, for all the people that you talk with on a daily basis, CTO, CIOs that say, hey, we need help with our AI strategy, I really don't have time for it. I know that I've got a lot of pressure to be under AI. I'm completely overwhelmed with how fast things change all the time. Every single. Anyone that does not admit that they're not overwhelmed by AI, let's help people. Where do we get started?
• 22:43
  Speaker B: Yeah. Okay. So in order to have a successful AI project, you need a successful AI practice. A successful AI practice starts with a successful AI strategy team. These should be business leaders across multiple division departments. This team comes up not with what the AI going to do. They're going to start the other end. They're going to start a business. Outcomes. I need to know my strong performer. I need to know when Salesforce or ServiceNow creates a X level ticket that kicks off X, Y and Z practice. I need a business outcome of if my revenue is dropping X percent across a day. Why is that? What are some of the key triggers. We're on the Zoom platform. So Zoom has a product AI insight, I think where as a leader of the company, why was my service level lower last Friday? Why am I doing this? So it surfaces all this information, we have to start there, then we work backwards to find the right AI tools and models to make that work.
• 24:02
  Speaker A: How do we know as IT leaders how to surface up whatever outcomes?
• 24:06
  Speaker B: Is that the team decision? Right. The IT leader is being told, hey, we need AI in our infrastructure. What does that look like? Right. The other problem you're going to have is good structured data leads to good structured AI. Bad unstructured data leads to a bad AI experience. Perfect use case. So I'm working with this large client, 800 contact center agents at a very modern platform. Yet that modern platform didn't really give some of the AI experiences that they really wanted. So they brought us in to kind of talk of the are the possible of what can be done. And then my very first call with them, I'm like, okay, what is your single source of truth? And they're like, oh, we only have like two or three. I'm like okay, what are they ended up, Phil? They had 15 sources of truth. I said okay, which were in a
• 25:02
  Speaker A: very vague, non descriptive, make believe fashion. What were these sources of truth like?
• 25:08
  Speaker B: Yeah, so you had a salesforce, you had a cuff CRM, you had an ERP system, you had two or three sales platform ordering platforms, you had a server underneath Mary's desk that had been running forever. And every time they go by what
• 25:23
  Speaker A: they were saying their sources of truth
• 25:24
  Speaker B: were, well we got that of course, of the truth. Right. Every stakeholder said, well we use this and we use that. And the head of it was like, I had no idea we're supporting all this stuff. Right. So in order to get that good AI experience, you need a collective, unified, I want to say single unified source of truth. That's where knowledge management companies really come in. And to help kind of aggregate all that data and put it all in one spot. So either I can use it as an internal chat GPT.
• 25:59
  Speaker A: Talk to me for a second. Some people might not know what that is. That this knowledge management typicon, in other words, data cleanup.
• 26:04
  Speaker B: Data cleanup, yep, yep. We used to call it a knowledge base but now marketing gets involved, Phil. So we have to up our terms now it's a knowledge management platform. So what they do is they clean up all your data that's out there. There's a term called redundant, outdated, I don't remember what the T was, I can't remember what it was. So what these platforms do is look at updated all the sources of truth, right? From simple stuff like on OneDrive and SharePoint Salesforce, your ERP on the server underneath Mary's desk, it looks for that rot. What's redundant, what's outdated. Right. The worst thing you want to do is the agent kind of goes into an FAQ and says, oh yeah, report policy is 90 days. Where that's an outdated policy. Right. You got to get rid of all that. So these knowledge management systems come in and kind of clean that up so it can be used. Yes, again, I'm a bit of a call center guy. It can be used in the call center in this way. A call comes in, I'm a remote agent. Question comes in and it's a question I wasn't trained on. I look to the right, there's the dog, I look to the left, there's the cat. How am I going to answer this? I could put the person on hold for 10 minutes. I could go into teams chat, right? In the old days when I was in the office, I had the flag I could raise. Now with AI, AI will automatically surface the right FAQ or the right document for the agent based upon what it heard the customer say. And you really need that single database for that because it reaches out for that latest one. Now once you have, by the way,
• 27:40
  Speaker A: the T stands for trivial.
• 27:42
  Speaker B: Trivial, thank you, thank you, trivial. So you have to get that data in order. And now talking non telecom, that data is also really important for those workflows. It's nothing more than if this happens, then do that. Now it reasons what those then should be. Greg, you dropped agentic on here, right? So the biggest difference between conversational and simple generative, that was if then, right? It was if they say this, then do that. If they say this, then do that. It was like 100 ifs and 100 thens. Now with Agentic it reasons, there's one if, if this comes in, here's all the thens that are possible, right? So it should do it a little bit faster, but from an internal point of view. Internal it, that data, right? The readiness of that data will help with all those internal workflows. If this thing kick this off, outcomes
• 28:43
  Speaker A: first, what are we trying to achieve then? Okay, well do we have the data that we can. That's everything but redundant, obsolete and trivial. Third thing, you have to have the
• 28:54
  Speaker B: team with the right stakeholders in place. And then from an IT perspective, you have to have People ready for this. Right. You have to have a bit of a voice prompting skill set. Right. Like I said, conversational, with all writing, hundreds of if then statements. Agentic is you have to have the right prompt. So there is some training required. Now what's nice is when you work with Phil and I and we have a bunch of suppliers, they do all the training and whatnot, but if your team's not taking basic voice prompting classes, you're going to be behind.
• 29:30
  Speaker C: Yeah, it's really interesting what you're saying because when Phil, when I was always saying like, yeah, prompt engineering. Prompt engineering. And then there's a term that came around like a few months ago and I think that's really the right term is context engineering. And you really learn by doing when you build an agent, because it's really like training an employee. And I experienced it. I just realized that my data was a mess. I was always upset against the AI agent. Stop hallucinating. I didn't say to do it like that. And going into the flow. So AI is always going to try to do its thing, figure out by itself how to come up with something. So that's when you realize, okay, now I need to have the proper explanation of how it works. You know, you do this and this and this and that. Repeat after me. Good, you get it? Okay, let's keep going. Let's keep going. That's really the context. And Mike, I think that's what you're referring to. You know, a clean data. Like there's no room for guessing.
• 30:31
  Speaker B: Yeah. 30 years ago we used to call it garbage in, garbage out. Right. So we do cobalt Fortran programming number four Quattro. Phil, everybody's favorite. Governance, compliance and security. Right.
• 30:46
  Speaker A: So kill it all. We should probably just start with that.
• 30:49
  Speaker B: Yeah, yeah, yeah. The answer is no. What's the question? So how do you get to the yes? Right? I mean, that's sales 101. I know we're not really salespeople on this call, but everyone sells in their own way. But how do you get to that? Yes. So it's incredibly important to understand if you're in healthcare, what the HIPAA rules are, if you're government cmcc, what those rules are and then where to put. So the term you hear all the time, right, Phil, is guardrails. Where are those guardrails? Most of the vendors who we work with have those guardrails in place where if we go out to our LLM, it's going to be to your point earlier, it's our private LLM. It's not going to be out on the exposed Internet website, whatever it might be. So I am not a cybersecurity expert. Phil, you have experts on your team who are. I tend to think that InfoSec is one of the first conversations you have to have with whenever you have a new project. Now, it doesn't have to include the VP of Sales or the CRO. It can be a separate team. But you got to clear infosec and governance compliance, right. And regulations up front before the project even gets halfway done. And then you realize you spent all this time and energy and money only to hit a roadblock you just can't get over.
• 32:13
  Speaker A: So if he's on that team to begin with, we gotta have the stakeholder team. You just gotta be on the stakeholder
• 32:18
  Speaker B: team, like, yeah, 100%. Yeah. You call him Dr. No or her Dr. No, because how do you get that to be Dr. Yes. You have to get them on board with everything.
• 32:28
  Speaker A: Do we have a fifth piece?
• 32:30
  Speaker B: Well, I mean, money, I guess money comes into it, right? And we create these business outcomes. Are we going to generate an roi? I mean, that's what MIT talked about. It wasn't that they failed. They just didn't generate the ROI they expected. So your next Google is to Google the Gartner hype cycle for artificial intelligence. I tell you what, whoever put some of these names together, kudos to you.
• 32:59
  Speaker A: The Hype Cycle, because they are the hype. It's like, how can we create something before someone calls us it?
• 33:07
  Speaker B: Yeah. So what happens is they got this sine wave, right? And in the middle, it's the peak of inflated expectations. Kind of like my bank account, right? And then what happens is people have all this idea, oh, AI is going to save us money. It's going to reduce headcount, going to give us a competitive advantage, it's going to be easy. And all of a sudden, they drop way down to the trough of disillusionment. I mean, it sounds like the Candyland game, right? That's what it sounds like.
• 33:37
  Speaker A: I feel like we're kind of in that trough right now.
• 33:39
  Speaker B: In that trough. Because what happens is, CEO says, go get me some AI and it's hyped up. And then we can't figure it out, like we're part of the 95%. But if you put a strategy around it, I think these five points that Phil's talking about are critical. Right? We want to get the stakeholders involved. We want to make sure we start with a business outcome. Listen, sales 101. You talk to a salesperson, they're going to start with a product and they're going to find a solution. The product is a hammer, and to a hammer everything looks like a nail. Right? So what you want to do is don't start with a vendor. Right? Start with the internal task force, strategy team, tiger team, whatever you want to call it. Have your business objectives there, look at your data. Can you aggregate the data? Can you make it safe? Can you make it one? Is there rot in your data? Infosec, I got the data. What guardrails can we put on that data? And then the last one is if we go through this project and we hit all our markers, it's safe, secure, it's going to generate the business outcome. What's the ROI on that? Microsoft Pro, Something crazy like people who added AI saw a 300% ROI. Okay, show me how they got there. Talk about that path because you need that end to end strategy even before you talk to a vendor who can do something for you. Once you have that in place, then you're ready to look at product set. And then at that point, Phil, the big question is build versus buy. As I mentioned, you buy the LLM aggregator as you're making your own LLM to kind of bridge that gap and make everybody safe. That's just one idea, that one strategy that we put together for our clients with those five points.
• 35:32
  Speaker A: Let me make sure I get this straight again. Stakeholders, outcome, data, guardrails, ROI sounds good.
• 35:37
  Speaker B: Money comes last. Like you figure out the ROI at the end. If you do everything right, the ROI should happen.
• 35:43
  Speaker A: Well, I think some of the biggest things are the outcome, which would be. I'm trying to think of other ways to say that is like what challenge do we want to solve with bull AI?
• 35:56
  Speaker B: Maybe once we, once we put AI in, this will happen, right?
• 36:01
  Speaker A: Yeah, it's also. How do you word that challenge. So if I think of some of the challenges that we have, it might be growth of the podcast, but that could be what? That could be social media, it could be growth of the podcast on YouTube or whatever. We're primarily a technology leadership focused. We're LinkedIn only audio podcast.
• 36:22
  Speaker C: Right.
• 36:23
  Speaker A: So it's kind of like is AI the solution to that or is it just by LinkedIn advertising and advertise the podcast? And how does AI fit into that? And then obviously AI can do copywriting and all kinds of other things. So it's not a simple question to answer.
• 36:38
  Speaker B: What about AI scoring of the podcast or the reviews of the podcast? See what topics really kind of generated more attention versus you and me talking about life in New England and guy in New York is like, yeah, I'm done. Click right. So that might be one area of let's go.
• 36:57
  Speaker A: Let's use me as the example. I would love some more examples. So that's a great example, Greg. How have we done that? Because we have used AI to obviously look up all of the trending topics. Let's fire away. Let's try to stump Mike.
• 37:11
  Speaker C: The research just confirmed what Mike was saying. Because first thing was yeah, I like this one. Most AI road maps are built backwards. Organizations buy tools first, then they search for use cases.
• 37:23
  Speaker B: You buy a hammer, you look for a nail, right? Start at the outcome, start at want to happen and work backwards.
• 37:29
  Speaker A: And a lot of IT guys are going to agree with that. We don't use it as the. It's not the hammer again. Okay, I guess summary theme of this entire thing. You're an IT leader, be an IT leader. You first have to have control and the ear of your stakeholders. Otherwise they're running rogue and wild on you. So first have your stakeholders, AI roundtable, steering committee, whatever you want to call it inside your organization, maybe make it unique. That would be awesome. And maybe do your initial lunch and learn burrito, specialty day, whatever it is and brainstorm what your number one challenges are. Outcome solutions that you want AI to help solve. So now you've got your stakeholders, you've got your solutions or your challenges that need to be solved and then we can help you take care of the rest. We can help you clean up the data, we can help you put in the security guardrails and we can help you find and implement the right products from a vendor neutral perspective without the six figure Gartner magical price tag from a vendor neutral standpoint. Sound about right Mike?
• 38:49
  Speaker B: Sounds great.
• 38:51
  Speaker A: It has been a pleasure having you on the show. Any final words of wisdom or anything?
• 38:56
  Speaker B: Do not let AI drag you down. It's a journey, it's a marathon and not a sprint.
• 39:03
  Speaker A: I have another question. I forgot to ask it 18 months from now, what is your prediction? And anything a technology, I mean actually could just be anything.
• 39:14
  Speaker B: I think the number of self driving cars will increase. I don't know about you but when I was 16 the first thing I wanted to do, jump in the car and drive it. Now all these self driving cars, I do think we'll see EVs kind of making a bigger comeback especially with gas pricing. And I do see more autonomous vehicles on the road.
• 39:37
  Speaker A: Those are great predictions, though. Okay, Mikey B. You've been heard.
• 39:43
  Speaker B: Thank you.