The Power of Discriminative AI for Precision in Communications - with Danny Gaynor

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Dan Nestle: Welcome, or welcome back to the trending communicator. I'm your host, Dan Nessel. You know, I think it was Brene Brown who said, if you can't measure it doesn't exist. And I think Peter Drucker said, you can't manage what you can't measure, or something like that. And I just learned today that Lord Kelvin apparently said, if you can't measure it, you can't improve it. Fine, I get it. In fact, if anyone who's been in marketing or communications for, like, more than a minute hasn't heard of some version of this or, you know, or hasn't been thinking about measurement, I'd be shocked. I'm sure everybody gets it by now. I mean, we have to, right?

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Dan Nestle: Never mind Einstein's assertion that not everything that can be counted counts, and not everything that counts can be counted, which to me is equally, if not more important for communicators to keep in mind these days. No, in our field, we are on an endless quest to quantify. We want hard data to tell us that our stories are effective and our reputation is improving and our content has an acceptable roi, et cetera. Look, we have to figure out the balance between art and science every day, especially when it comes to measurement. What to measure, how to measure, and how to interpret and analyze all of it to come up with useful and brilliant insights. Fortunately, we're getting better at it. Our knowledge is improving, and the tools we have at our disposal are leagues ahead of where they were just a few years ago.

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Dan Nestle: Add in AI, and we're looking at expanding our capabilities even further, measuring things that couldn't be measured, and even predicting what we should be communicating. If we want to build reputation, credibility, and influence. It's a lot, I know. So today we're lucky to have a guest who can help us make sense of it all. A true comms professional. He's been a politico, a policy wonk, a speech writer, a corporate and agency executive for Nike and for Weber Shanwick. In other words, he had a storybook comms career. And then he left it all behind to co found Kelp, an AI powered corporate reputation platform that was acquired by Signal AI in 2022. Now he's running kelp as part of Signal AI, and he's at the leading edge of advances in Comtech or Comstack. Depends on which way you want to say it.

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Dan Nestle: Of AI, of predictive analytics, please welcome to the trending communicator, a guy who really is trending, my friend. Danny Gaynor. Danny, hi, there I am. How are you?

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Danny Gaynor: Thrilled to be here. What an intro. I should have that on repeat in my own household over dinner every night with my wife. That is great. Thank you so much for having me here. And I'm thrilled to be on longtime listener first time guests.

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Dan Nestle: Oh, it's so cool to be with you here on the podcast. I mean, I know that we've been talking for a while and just, like, chatting about AI and chatting about measurement and all that fun stuff and just about comms in general, because you've had a life so far and hopefully a lot longer one after this. But we have a lot in common. There's a lot of, I guess, experiences that you've had in the comms profession that have shaped you and brought you to really think about measurement and analytics and ultimately sentiment analysis and things like this in a way that most people only barely scratch the surface of. And, you know, I just.

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Dan Nestle: I thought it would be really important to have you on the trending communicator because, you know, look, we're all about identifying the changes in the profession, the trends that are upon us. Nobody, you know, nobody doubts or second guesses the fact that AI and measurement are really critical to the profession, but how we do it and how we interpret our measurement platforms and which platforms to choose and are we measuring the right things? These are really big questions that everybody's struggling with, and I'm really glad to have you on to help us talk about some of that stuff and maybe just dig into AI and have a lot of fun. So if that sounds like a good.

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Danny Gaynor: Trip to you, I gotta tell you, I love the questions you started out with. I got my career as united to in politics, and I worked on a presidential campaign for a guy who ran against Bernie and Hillary for the democratic nominations. Name was Governor Martin O'Malley. And Governor Martin O'Malley may have said, I think all three of those without, you know, without proof, you got to measure it or whatever the key line was. His was a dream with, I'm sorry, a dream without a deadline. So, oh, my gosh, I'm messing it up for you. A goal without a deadline is just a dream. A goal without a deadline.

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Dan Nestle: A goal without a deadline is just a dream. Yep.

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Danny Gaynor: You can't measure it. You certainly can't improve it. And I think that very much summarizes the arc that I've been on across all those different enterprises I've had the fortune of working at. So I am thrilled to dive in with somebody who I regard as an expert in the field and really excited to roll up our sleeves and dig into the measurement side of AI. We're very familiar with the generative side of AI, but in order to be creative, you need to understand the landscape and more importantly, where to place smarter bets.

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Dan Nestle: That is. I'm glad you said that, because, of course, the idea of AI, when we talk about it, everybody's thinking chat, GPT, they're thinking generative AI. And today we will talk a little bit about that. But really, there's a different there are many different kinds of AI out there. There's all different kinds of technology. It's been with us for decades. But your particular, I guess your platform is based in discriminative or discriminatory AI. Am I getting that right?

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Danny Gaynor: Discriminative? Yes, it might need a rebrand discriminative, but nonetheless, exactly that, discriminative AI.

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Dan Nestle: So we're going to add that to our lexicon after today's podcast, discriminative AI. We'll get to that in just a moment. But how does somebody who is like a politico, who's like, running campaigns decide ultimately that, you know, measurement and AI is their thing, and, you know, what does that journey look like? How did you get there?

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Danny Gaynor: As a communications professional, I think we're all vexed by like the exact same question, which is, am I throwing the right spaghetti at the wall? And to what degree can I minimize guesswork around what's sticking? And that very much was true for me across government, working in the Obama administration as a presidential appointee and on presidential campaigns, and at Nike and at Weber Shamwick, which is the world's largest publicly traded marketing firm. My arc was very much trying to figure out what are the pillars of a breakthrough narrative? How can we consistently campaign on those pillars? And how, most importantly, can we find and leverage new proof points from within the organization to ensure that our narrative is always fresh and relevant and compelling for our key stakeholders?

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Danny Gaynor: So to take that back, I started my career in the Obama administration as an appointee working on foreign policy. I did everything from public private partnerships to use Coca Cola's supply chain to get vaccines into Africa to managing the response around the Ebola epidemic. And I assure you, if there is a pandemic even scarier than COVID, Ebola is having to write messaging that my boss took to the White House press pool to try to convince the american media and regular Americans that, listen, we have this contained was a bit of a rush and also a high wire game from there at Nike helped start up something called the corporate narrative center of excellence. So at Nike, just like many big companies, there are various business units that don't always align in terms of messaging, let alone their go to market activities.

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Danny Gaynor: At Nike, the golf team and the tennis team didn't always talk, let alone the sustainability team and the investor relations team. And so my squad put together again those fewer, deeper storylines that compose the Nike corporate narrative and then help translate it through a variety of big moments. It could be a quarterly earnings call. It could also be me standing on a racetrack in Milan at five in the morning as we attempted to run the first ever sub two hour marathon. Really fun job. And then at Weber Shandwick, I saw that story replicated over and over. So from Johnson and Johnson to IBM to Salesforce, I had the chance to work with a huge number of Fortune 500 executives through a practice I started called the narrative strategy and analytics practice.

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Danny Gaynor: And what I saw was that every single company was chasing against the same overarching storylines that were very much competitive territories across industries. We want to be regarded well on ESG and being the most sustainable company on our path to net zero. We want to be seen as an employer of choice who embraces diversity and inclusion. We want to be seen as a great growth trajectory and have a stable supply chain. You name it, everyone's trying to do it. The question is, how can you put forward your most authentic proof points and a message that truly is differentiated for you versus your competitors as well as your aspirational peers across industries? For that reason, I decided to quit my very comfortable job, asked my wife to get under healthcare, and started kelp data with my co founder, Shan Bigleone.

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Danny Gaynor: We've been on that path for a couple years now, and joining Signal AI was very much rocket fuel for our trajectory.

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Dan Nestle: That is, it makes sense, your story, especially if you've been on the side of communications where you're trying to knit together narratives that, you know, you think are gonna hit or you assume are gonna hit, you know, and a lot of, I think, corporate comms folks, especially not just corporate comms and brandcoms people, are just like you said, they're throwing spaghetti at the wall, right?

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Danny Gaynor: Yeah.

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Dan Nestle: You just mentioned some of the big buzzwords, ESG, you know, employer of choice and what is it? Supply chain, this kind of stuff. You know, how many times have I seen this rhetorical question? You know, how many times have I seen, storytelling pillars that include all of those items? You know, it's just, it is ridiculous that you go place to place, year to year, company to company, and you're seeing the same kind of requests or the same ask from your, you know, from your executive teams or from, you know, your CEO or, you know, whoever it is saying that this is our mission, this is our purpose, this is what we need to kind of focus on. But those are too broad. They really are. If you're going to talk about ESG, so is everybody else. Although right now we might be.

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Dan Nestle: It's a little bit touchy right now, I think, in the ESG space, but it's hard to figure out where within those broad buckets we should be focusing. Right. You know, we've looked at traditional kind of data. I say traditional data source. This is not traditional. It's only been around for a little while. But you look at audience research, for example, or you look at current content out there in the marketplace and you're looking at trends, but all of that is rear view. Right. All of it is looking at the past to try to see what's going to happen and it still ends up being, okay, yeah, maybe the target on the wall is a little bit smaller, but you're still throwing spaghetti on the wall, right?

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Danny Gaynor: Absolutely. So, I mean, I think about, just as a quick Nike example, I always think today is the day, we're recording this on the day of the Boston marathon. And I live down the street from the Boston marathon. And I was thinking about how when I was at Nike, every shoe was the fastest shoe yet. Right?

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Dan Nestle: Wow.

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Danny Gaynor: Every shoe was by definition the fastest shoe yet. So what could we say that was possibly differentiated? And then if you think about it, every company has that sort of issue, right? Intel has the fastest chip yet. Pfizer has the most effective vaccine yet, but Tesla has the either fastest Tesla yet or the most sustainable Tesla yet. And so we have to figure out, using data, how to differentiate storylines that are very much going to be consistent month over month, quarter over quarter year over year. And that is particularly true with cross cutting storylines that are competitive, narrative territories like purpose. You mentioned ESG or innovation products, services, quality assurance, or even performance. Right. Every company wants to get earned confidence from investors in their growth trajectory. So I think you're exactly on target.

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Dan Nestle: Well, it's interesting because you think about, we're all going to talk about sustainability, and if everybody's talking about sustainability, is anybody really interested? Like, you're talking about things because you have to, and you need to find that hook or that proof point or whatever. It is within that area of sustainability that is very specific to you. That's a differentiator that's going to appeal to the audiences that you specifically care about. And when those audiences that you specifically care about don't really care much about sustainability, or if they do, you have to tailor your stories so finely tuned to them, that's it ends up being somewhat of a crapshoot in the way that you spend your money to develop certain topic areas. Anyway, this is the way that it's always been done until you start to get much more clarity with data.

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Dan Nestle: And for the longest time, you're looking at varying data sources. As I mentioned earlier, you're looking at the advent of social listening was like, oh, now we can really talk to people and understand what they're looking for. But there are problems with social listening. It's very one dimensional. Maybe one dimensional isn't the right word, but it's. It's a bubble, right? You're looking at the entirety of pick a platform, Twitter, LinkedIn, Facebook, whatever it is. But the uniformity of the types of people that are on those platforms sometimes denigrates the data that you're looking for, unless those people happen to be your exact audience.

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Dan Nestle: It's very easy to get sort of sidetracked and tempted into data from the Twitter stream or Twixter stream or whatever you want to call it, that tells you, oh, you know, our products are trending on Twitter and we must be doing well. Well, Twitter is such a small slice of reality, if it is a slice of reality at all. So taking that with many grains of salt, it's about time that we figure out ways to really tell that data story properly and tell that and really find better ways to really get that audience research that we're looking for to understand exactly what the topics are that we need to talk about. And technology is certainly one of those levers that we can pull to make that happen. We can always do better research. But research fizzles, right?

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Dan Nestle: Research is like you get a focus group together or you do a field research and it takes months or weeks, and by the time the research is done, it's not fresh anymore. Right? So what is technology doing for us, Danny, that we can kind of start to solve these problems?

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Danny Gaynor: I'm so glad you stated that question in the context of history, because going back, I feel millennia. But certainly since the advent of PR with Edward Bernays in the early 19 hundreds, surveys and focus groups have always had a certain limitation, which is that you can't ask that many people about that many companies across that many topics that many days in a row. And from the second that you get that qualitative research back, it is increasingly outdated. From the second it is done, it is a point in time analysis.

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Danny Gaynor: So it always vexed me as a communicator working with Fortune 500s or at a Fortune 500 when we would reference a survey that was like six month old or three years old, because we spent a bunch of money and we've got to keep using this thing, even though it is increasingly a portrait of the past. What really excited me about using AI and using AI to slice through big data to craft a strategy that whiffs reputation was that you could match breadth and depth. We could look across a huge array of entities, aka companies, nonprofit organizations, C suite executives, across a huge array of topics ranging from, yes, big picture things like purpose, but then clicking deeper and deeper into those big picture storylines to have a much more specific sense of where the conversation is shifting.

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Danny Gaynor: So instead of saying you're weak or strong on purpose, it's like, well, is that about Deni or sustainability? It's within sustainability. Okay, within that, is it about renewable energy? Yes. Within that it's about geothermal power. Within that, it's about carbon capture and storage. Right? And as you know, as a communicator, the more specific you can be, the more granular your data points from within the organization are, the more authentic your proof point is, the more natural the message should feel to you. So, for example, every pharma company on the planet, in their messaging says we put patients first. And as a result, no one really believes it. But a company like Pfizer says we're using AI to model molecules faster, to develop therapies and get them to the market safer and more efficiently than ever before.

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Danny Gaynor: And that's how we developed Paxlovid, the most effective therapy to fight COVID. That is a much more authentic proof point that only Pfizer can put forward. And even though it's more technical, using AI and data, we can verify that it's the right thing to place a bet on. That's very much what our approach at signal AI is all about. We train an AI to recognize, not keywords, but literally the concepts. So a company or a topic, we then score those across a variety of media metrics, like the volume of conversation, the sentiment around it, even the salience. Are you a headline or are you a bullet point?

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Danny Gaynor: And then we index every single company across every single topic on a normalized scoring system, allowing us to demonstrate really quantifiably where you have areas of strength and where there are icebergs sitting outside the ocean, so that you can navigate around risk and towards efficiently just the strongest areas of opportunity.

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Dan Nestle: So the discriminative AI examines level after level like you feed it and train it, the information at each click down, and each click down, it's making its own. You know, it's recognizing, based on your training, what is positive, negative, neutral, just to oversimplify a little bit. And then it goes to the next click down. Or like, how does it work from there? Because this is a concept I think a lot of people aren't quite necessarily going to get on the first pass, right?

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Danny Gaynor: So we all know when we talk about AI, particularly in the comms landscape, we're really talking about generative AI because it's very much captured the public's attention. So generative AI is fundamentally predictions, right? Predicting the next pixel to create the picture, predicting the next frame to create the video, the next note to create music. And we certainly know from chat GPT predicting the next word. The problem with generative AI is that it's prone to hallucinations. So as an example, one time I asked it who was the CEO of Nike and Gemini in this case, told me that was Colin Kaepernick, the retired NFL quarterback, which is obviously not correct. Debatable, even mentioned alongside. Yeah, debatable, depending on how you look at it. So let's talk about the contrast.

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Danny Gaynor: So, discriminative AI is best thought of as a complimentary tool that can mitigate some of the downsides from generative AI, while also enhancing generative AI's creative potential. It's a branch of machine learning that is specifically designed to evaluate content, categorize new information, and recall the data that is most accurate to the question you're asking. I think together, generative and discriminative AI could really make a formidable team, because what discriminative AI excels at is differentiating ideas, topics, content sources, and entities. So it's really skilled at categorization. For example, it can discern between whether a news article is discussing Apple the fruit or Apple the computer company, Coca Cola the company versus a can of Coca Cola, Nike the company versus your cool new Nikes that you just bought. And because its power is discernment, it excels at decision making.

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Danny Gaynor: It is grabbing only the content relevant to you, and it is proving to you that it can check its sources. So basically, whereas generative AI creates something new, discriminative AI determines if something is correct. And eventually we should get to the point where we confuse the two. Generative AI, natural language interfacing to just ask questions, and discriminative AI to ensure the data you're getting back is accurate. At signal to your point, we focus on discriminative AI to ensure that we can move beyond some of the limitations of Boolean searches, like using keywords that are naturally infected with bias to evaluate the accuracy of a topic model.

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Danny Gaynor: To say, hey, 90% of the time, this is in fact getting us data on cell therapy, and most importantly, to create a sense of reputational ROI for communicators operating at some of the world's biggest and most sophisticated businesses.

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Dan Nestle: So, getting there, right?

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Danny Gaynor: Yeah.

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Dan Nestle: If I wanted to go right now and create anything, a comms plan, an approach to artificial intelligence that would work for my company, a recipe for matzah Braai. The Passover is nearly ponos. I could just go to generative AI, I could get a lot of those things. And sometimes it would be really great, and sometimes it wouldn't. It's not clear, I think, or it's not as clear how a communicator, how anybody could say, okay, I'm going to go to my discriminative AI engine, and do what? Get something? Go somewhere. I mean, clearly this isn't something that the end user is necessarily operating, right? So how does it work in terms of how it interacts with the communicator? With the user?

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Danny Gaynor: Well, first of all, you're totally right. With Passover upon us, I would not look to our system to develop a recipe on butsa bry or gefilte fish, or like whatever else is on the Passover seder. But I would say there are three core ways that you should interact with your discriminative AI system. The first is to identify areas of strength. So, as I mentioned, narrative territories today are very much the new competitive landscape. Everyone wants to be renowned innovation and be purpose driven and everything in between. And I think it's very powerful to map the topics across the competitive set that you're stacked up against to understand where you are excelling beyond the field, on what topics are your strengths, and where are you lagging. Right? And then really lean into areas of strength.

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Danny Gaynor: So instead of just creating your narrative in a vacuum, you should see your narrative as a constant source of optimization, always shifting the components of your narrative so that it remains fresh and relevant in today's discourse. The second element is to evaluate, can.

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Dan Nestle: I stop you there? With because that number one is pretty, you know, just kind of makes me think of a few things because I think one of the difficulties that a lot of, I think communicators, you see, I always use these scientific terms like a lot of communicators. Right. I clearly have done my studies, but, you know, I think that's, that from what I hear and from what I've seen, it's very difficult for a lot of folks to say, here are the three things that I need to be focusing on. Like, here are the. If you're, if I'm going to identify the areas of strength, you know, how do I know that I'm identifying the right areas of strength? It's kind of like you can go into this kind of madness.

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Dan Nestle: Loop of, okay, I'm going to rely on, this platform to help me understand the white space that I need to get into based on areas of strength. And it's going to let me understand my areas of strength. But first, I have to understand my areas of strength in order to tell it what those, you know, so, like, it's an end. I know I could drive myself crazy, as I, and I often do. It sort of makes me think of people who are trying to figure out their own purpose and their own mission and their own personal brand where, you know, you're just like, okay, what is it that I can do that's unique? And what is the offer that I can put in the world, you know, and where do those things cross?

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Dan Nestle: And then that kind of Venn diagram helps me to understand my personal brand. Now, companies, corporations often do not have a very good idea, right, of what these strengths are. They think that, they think things that aren't true or they're living in a bubble, or they're navel gazing, something like this. So how do they even get point number one done?

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Danny Gaynor: Sure. Absolutely. So there's a technical side and there's a human side. The human side is really important because any communicator who's knee deep in their business will know, in a sense, what differentiates that company. And that is important because you need to have some foundational theory that you're going to gut check against the system. But on the other side, I think the key way that you can understand how your strengths are popping out is by creating an AI framework that maps as many topics as possible. The key thing with the narrative is to not be stuck navel gazing. So as an example, you might think that innovation is your core strength. And you might come to a company like signal AI or PR agency and say, I want you to map just our innovation discussion, because we know that's a strength to our narrative.

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Danny Gaynor: If you're being treated the right way, then your AI vendor should ask the question of how are you sure that it's innovation in this context that you should only be looking at? Why not scan the entire horizon? Now, the limitation of surveys and focus groups, as I mentioned, was that if you do too many topics across too many companies, it limits your ability to go into depth. But with AI, you can go into unlimited depth across unlimited breadth. So what we do is we quite literally train topics in the system. You have a human being, an actual expert in the industry, work in the AI. The AI surfaces an article, and you say, yes or no. Is that article on topic, or is that article relevant to this entity?

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Danny Gaynor: Is it truly about Pfizer, or is it more about, like, the Travis Kelsey Pfizer commercial? And you click yes, no, yes, no, yes, no, yes, no. And eventually the AI stops surfacing content for you to grade, but without you knowing surfaces content for it to test itself against you on. And so, quite literally, are the green dots with the green dots and the red dots with the red dot. Do you agree that this article is on topic or is not on topic, and then we can have an accuracy score? Do we agree with the model? Do we agree with that topic 70% of the time? 85% of the time? 50% of the time? And so, unlike keywords, and unlike surveys, I can keep tweaking my definition of an individual topic until it reaches like 80, 9100 percent accuracy.

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Danny Gaynor: If you rinse and repeat and do that 100 times or 200 times, in our case, we have thousands of topics that we've trained over the years. Then what you're able to do is run analysis, not just against the individual narrative pillar that you care about, but instead see the market in a neutral way, to have an objective mirror how you are perceived versus your peers, regardless of what your narrative priorities are. And so when I think about the areas of strength, it is all about how broad your horizon scan is. If you can look across 100 plus topics and a dozen plus peers, it is a much sharper way of understanding your areas of strength, where your narrative is actually resonating than just testing your narrative in the marketplace because you will naturally infect it with blind spots.

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Dan Nestle: How often do your clients come to you with a completely, almost wrong idea of what their strength is?

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Danny Gaynor: I would say that 100% of the time, there needs to be tweaks to the narrative, but the key thing is that their narrative isn't specific enough. So I wouldn't claim that it's wrong. For example, every single company we work with has a commitment to be carbon neutral by 2035 or 2030.

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Dan Nestle: Sure, sure.

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Danny Gaynor: So the real question is like, well, what is the credible evidence base? What are the proof points? What are the operational investments that you have to make? This true, and if it's frankly not that robust of a plan, perhaps the way that you talk about sustainability should be through a different avenue, a different investment, a different technology, a different corporate commitment. So I wouldn't say it's wrong so much as it is unspecific. And that's what we help do.

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Dan Nestle: Drill into the details and validating. Right, so you validate folks narratives like what they think their narratives should be.

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Danny Gaynor: And, you know, to your original question, there's three key ways you should interact with discriminative AI. The first is identifying strains, like I mentioned. Map the topics across.

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Dan Nestle: Sorry, we'll move on to number two.

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Danny Gaynor: Yeah, no, I'll do it quickly. Which is the second piece is just to figure out where your messages are falling flat. In my experience, working with signal AI works with about 50% of the Fortune 500, and I mainly work with our C suite customers. And I can't tell you how many times over and over again, the issue isn't that your message is stepping in a huge red flag issue. The problem is that whatever message you're putting out to the world is falling flat.

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Dan Nestle: It's just a thud.

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Danny Gaynor: And so, understanding where you can, frankly, trim stories from the editorial calendar in Steve Jobs language, what not to do was equally important in the innovation journey. How can we focus on fewer, deeper storylines? And that's key. And then the element of really risk avoidance. Right. So the third bucket is determining whether or not a certain issue is a material threat to your reputation, whether that's an ongoing crisis or that's a perspective threat that most might be out there on the horizon, being able to say, listen, here are the 10% of issues we truly need to spin up a response around, and here are the 90% that, frankly, are going to be a little blip is a huge time savings and, in fact, cost savings with regard to focusing your attention on what truly makes a dent in your reputation.

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Dan Nestle: So you put all those things together and you've got a powerful dashboard. Powerful, almost like a research company in your pocket to help people figure out what they should be talking about.

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Danny Gaynor: Exactly right. The way we think about it is providing these insights in two key ways. The first is dashboards. As you mentioned, you've got to have a quantifiable backing, an easily accessible, user friendly, highly digestible batch of metrics that you can use to make decisions. But honestly, what we've seen is just as important as the human element. So we work hand in hand with our clients, and we have a team of analysts, kind of like the McKinseys of marketing, who dive into the data and create insight reports and strategy slides that are used to craft editorial calendars, to inform C suite communications, to pick the key storylines for an earnings call, and everything in between.

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Danny Gaynor: And we believe that the match of the data side, the automatic dashboards, and the human side, aka the strategic Insights reports, are really what allows us to make data digestible for communicators who, like me, failed algebra. And we want to make data as non intimidating as possible and as digestible as possible. So that's sort of the hand in hand machine and man together.

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Dan Nestle: That's probably where the generative AIP comes in for a lot of, or will increasingly come in for people, because you mentioned connecting these to the gen AI with discriminative AI. And I'm sure there are numerous ways or numerous things on development within your team and other companies as well. But to me, I'm like, okay, give me a report and I, I'll catch a lot of it. Cause I'm not a stupid person. I will read it. I will understand a lot of the insights based on my experience and my understanding of my industry. But I'm gonna miss a lot of nuance. I'm gonna miss things.

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Dan Nestle: So that's where I would turn to a gen AI platform with a really good prompt or a good system prompt, or, you know, a good analysis tool, and reanalyze it and look at it again and see what I'm missing. There's so much. You can just add power after power, layer after layer, using the various little buddies that we have now. I mean, I don't like to call them tools anymore. I was talking about this with somebody their days. It's not just calling AI, or especially Genai. A tool is puts it kind of squarely in the wheelhouse of the carpenter or the plumber, and nothing against plumbers, by the way. I love plumbers, but puts it squarely in the toolhouse of people who use tools for a living. You don't realize that Genai especially is nothing like that. It is wholly accessible.

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Dan Nestle: So even by naming it a tool, you're sort of, you know, you're restricting the usability of it by preventing people who would otherwise be interested in using it from jumping out and using it. Oh, I don't need another tool.

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Danny Gaynor: Right.

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Dan Nestle: I don't need another tool anyway. That's just my soapbox. But I think when you.

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Danny Gaynor: I would totally subscribe to that, by the way. I just want to say, I just think that's where we're headed, where in a few natural language prompts, you can take a generative AI platform. Obviously with, in my view, the discriminative AI credibility behind it, and you can craft that AI platform to have functional expertise, industry expertise or company expertise in a way that makes the outputs truly unique to you and therefore your own competitive ip. And I think that's an incredibly exciting area for communicators to go because they will have a role not only in activating on the insights through really sharp communication campaigns and great messaging and speeches and media buys, but earlier in the process literally designing the products that give them insights.

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Danny Gaynor: And for me, as a communicator who went and started an AI company, that has been the biggest breakthrough in terms of my understanding, because I'm not a coder, I wasn't a computer science major, I barely passed through my political science classes, let alone deep machine learning. And what I've seen in bringing more communicators into the fold from big companies and PR agencies alike is that communicators, with the advent of generative AI and in retooling the power of discriminative AI, which has been around for a long time, we can very much be product developers. And that creates a huge competitive moat for your company if you can deploy company wide. So I think you're dead on the money.

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Dan Nestle: Well, let's dig into that a little bit, because for communicators or any people who are really embracing gen AI and kind of, if you can combine it with that validity and credibility of a discriminative AI kind of baseline, right? So, you know that's not going to hallucinate. It's based on the right data set. It's based on whatever information it's going to give you, has been vetted, researched, tested, et cetera. It's coming out. I mean, I could already see very clearly if you have the direct connect, almost like the pipeline from what you're doing with discriminative AI and you have the generative AI power kind of layer over it, then you're just creating stories or content that is already within the preset guardrails of what you know is going to hit.

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Dan Nestle: And as you tweak the discriminative AI over time, and as it changes over time, those guardrails change, and so does your content.

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Danny Gaynor: Right.

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Dan Nestle: You know, and boy, that sounds a little scary. Like a little scary automation, putting some people out of, out of a job. But you know what I mean, to be able to operate that and understand what you're looking for, you need to have that kind of connected dots type of a mind to do it.

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Danny Gaynor: Well, I would agree. I also think gone are the days where communicators can operate on intuition alone. Don't get me wrong, your intuition is the most important aspect of being a communicator. To just get a data point and then take it to market would be a very silly thing to do. But without data, it's just guessing, right? And when I think about AI, and by that I mean discriminative and generative coming together, there are three big value adds. Right? I think, first, AI can validate our intuition as communicators. It can provide that quantitative rationale for our recommendations. Here's the reason why I'm proposing this strategy. It can offer a reputational ROI that elevates communications into the same respected business impact conversation that sales and marketing have long had, because we're able to offer reputational ROI.

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Danny Gaynor: And reputation, according to Harvard Business Review, composes up to 80% of a company's market value. And then the third is that AI can act as this convening force. Far from being seen as this, like, cold, faceless data world, it can actually act as a single source of truth. So that all your partners, across all the functions, from investor relations to hr to operations, and all the teams that they compose, can operate from the same set of facts and benchmark themselves on improvement over time. So I very much see the folks embracing AI and communications as being even more influential leaders at their companies, as opposed to being out of a job.

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Dan Nestle: From your lips. That's exactly what we would love to happen in that way. And I think there's a lot of opportunity for sure in the future of AI and where we're going job wise, where we're going functionally, the lines between marketing communications are already blurred, and AI is a great unifier in that way, I think also as well, and we're going to talk about that more, I think, in the future anyway. But I think you have to have the right mindset at first with AI, which is, and my listeners be like here again, Dan? Yes. This is not a zero sum game. We are not in a zero sum game situation. You cannot look at AI with anything other than an abundance mentality, vision. And if you're stuck with, oh, well, if AI takes this away, what am I going to do?

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Dan Nestle: Well, then frankly, you probably deserve not to do anything once AI takes your job away. Not that it's going to take everybody's job. You need to be at this stage of your life, and certainly at this stage of the world we're in, you have to be open to change and to upskilling and to learning new things and to just constantly evolving. I'm not saying that you'll have the same type of job. I'm not saying that you'll have a better job, just saying things are going to change. That is the fundamental kind of rule number one, that things are changing. Then when you add to that abundance, that this is an additive element.

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Dan Nestle: If you choose to make it that way, well, then you can start to think about, well, what are the new things I'll be able to do that I haven't been able to do before, and what can I stop doing that is just wasting my time, or that is just not the best use of my talents or capabilities and doesn't go for every field, but certainly for ours. I think it's 100% true.

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Danny Gaynor: I totally concur, and obviously I'm a little biased, but I would add a parenthetical to what you just said. You mentioned there's an abundance of tools out there. The way I would say it, is because they're more accessible than ever, because you don't need to know how to use Excel, let alone write programming language. Because the opportunities to experiment with these tools, given how either cheap they are or free they are, would allow any curious person to gain a relatively differentiated level of expertise right now, given how nascent the conversation is. And I truly feel that if were having this conversation two years into the future, that the conversation would really be about not how are you experimenting with AI, but in what ways are you making AI your own?

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Danny Gaynor: In my context, in what ways are you training topics specific to your company or industry in the signal AI platform, in what ways are you doing analysis that stretches beyond reputation research and more into competitive benchmarking or competitive strategy? And I think that communicators, because they are by definition responsible for having the broadest purview of anybody at the company, having the greatest awareness of the context in which their companies operate, should be not only the forerunners in adopting AI. But if they start adopting AI today, in a few years, they will be regarded as the experts by the corporate strategy team and by the C suite itself in terms of using data to drive decision making.

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Danny Gaynor: And I think we're going to see this absolute sea change in the comms guy being in the room being told, hey, write the press release or hey, script the email from the executive to them being the first person that's asked the question, what should we do, given the situation? And that to me is a really.

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Dan Nestle: Exciting strategic shift you're describing, you know, like my dream scenario and in many ways for, certainly for my own trajectory as far as I see it. But the profession as it is now, and the, I think, look, we're reputation professionals, right? We build reputations, we save reputations, we protect reputations. You know, you could change that word to brand and it would be almost the same thing. I mean, we do a lot that is unsung and unseen, but ends up, as you said, as HBR says, 80% of the company value comes out of that reputational build. And there are those times where, okay, were it not for having the comms team in the room, boy, we would have been sunk. And we end up being thanked and being regarded as that kind of emergency response team in many cases. But are we?

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Dan Nestle: But that doesn't give us the green light to start working with the strategy team on strategy, on business strategy. It doesn't give us the green light to say, you know what, we have some ideas about marketing or about sales. Even if we do, I'm not saying that we should just start sort of like break out and like jump over and say, hey, look, marketing people, ive got this AI thing and it can help you. And heres what you need to do. Because everybody loves an interloper, right? Everybody loves that. But winning the influence of others, winning influence within your company and gaining that credibility is something that I think our profession really is still at the very early stages of.

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Dan Nestle: From where you're sitting, you're looking at Fortune 500 clients who are, many of whom are doing more advanced things in AI than certainly other companies. I think, by definition, to work with what you're working with on your platform represents a certain advancement in embracing AI that many companies aren't doing. What does the profession in your mind need to do? What do we need to do to get to that stage where we are being recognized as, or for the AI expertise, the strategic input that we can have, et cetera.

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Danny Gaynor: Yeah, I would say three core elements. First, train your own AI, whether, as I mentioned, like in signal, training your own topics, constructing your own reputation index, or, for example, in a more public context, like training your own custom chat GPT. Right, which anybody can do. So I live in Newton, Massachusetts, and I created a custom chat GPT to summarize the city of Newton, Massachusetts, budgets and figure out which streets got repaved. You can absolutely do that today. You can work with companies, or you can design your own on public platforms. The second is, I think, to be the one who gets really good at socializing. If there's any trope about comms, people that I think is true, they're extroverts. They're good at making succinct presentations, they're good at socializing, data driven messaging.

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Danny Gaynor: And I think that could be really an automatic way for you to start being renowned as the AI or the data person in your organization. The third piece is the most element, which is to demonstrate how data drove impact, to tell the holistic story from insight to action. We did this research, whether it was scoring us versus competitors or it was crafting messaging options using AI, we deployed it through the channels that the data suggested would be most valuable. And then here are the results. We had 40% more mentions, we had 50% more positive sentiment than compared to this time last year or compared to this benchmark conference. Whatever the moment might be, those three are exceptionally key to me.

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Danny Gaynor: And then, like I said, having the ability to bring reputational ROI to the table to introduce a new business metric, I think is a really powerful opportunity. Right? Sales and marketing and operations and finance, they've got metrics that go back centuries, and only with the advent of these new technologies are we going to be able to create reputational ROI metrics that are as important to the company's market cap as any other. So I think that's just a really unique moment that over the next decade will get developed into something that in a century we'll see as industry standard.

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Dan Nestle: So we're sort of in this mode. It's just to kind of tie that up a little bit. It sounds like we're in this mode where if you want to end up, is the wrong way to say that. If you want to. To succeed and become more than the press release writer of the organization. Run pilots. Right? Experiment. Learn. Run pilots. Show what you've learned and present that properly as a business case. So that could be a good way to start to open minds and open doors within your organization.

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Danny Gaynor: I realize that I may not have either answered your question or more positively, you gave me an addendum idea. So let me jump in with two other ideas. There are two other ways to do research, and I'm talking more about the measurement side of things. The first is to do a benchmark analysis. So if you want to really demonstrate the capacity that AI and big data have, well, you can look across 150 topics and 50 peers, whether you're in the defense industry, the pharma industry, the entertainment industry, and just have a really nice landscape analysis that you can shop around your company because different functions are going to see different elements of it as valuable.

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Danny Gaynor: So, for example, at signal, we work with one of the world's top streaming companies, and then we have a report that because of its breadth and because of our ability to get highly specific in different areas, is literally chopped up and sent around to different functions. The public policy team cares a lot about the impact of the Hollywood strikes on their reputation and among the entertainment community. The content team is very concerned about user generated content and things like Sora as challenging the supremacy of its content library, which is by far its biggest expense. And the investor relations team is very interested in the perception of this streamer, which has recently introduced ad supported tiers, and how that's affecting its vibe as a premium provider of content. And those can all come from the exact same data source, but provide very function specific value.

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Danny Gaynor: And so I think that's a really important way to maybe use AI, like do a landscape analysis and shop it around your company. The other way is to do the opposite, which is to just do a deep dive analysis. Say you've got like that big industry conference coming up. Maybe that's your moment to say, let's deep dive just on how we can win this individual moment. So, as an example, we work with a top pharmaceutical company that wanted to win the JP Morgan healthcare conference, which is like every January in San Francisco with the Super bowl of healthcare. And they were just looking at how the m and a discussion across the biotech community shaped over the last year, and how they could really identify the most winning storylines to be seen as an m and a buyer of choice. Two very different scenarios.

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Danny Gaynor: One's landscape, one's deep dive, but nonetheless are different ways of making AI and data relevant for people who are a little bit hesitant within your company.

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Dan Nestle: Yeah, the scary thing is it just makes sense. Why aren't we doing this? What is the state of our profession that we are not doing this? More. I think that's a big question that we're trying to get at as well, is what is the state of the coms profession and how is this affecting it?

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Danny Gaynor: I have a hot take on this. I suppose this is the forum for hot takes, which is that it's not just on the communicator to embrace AI and bring it around the company. Honestly, it's on the C suites to expect that of the communicator communications team. Companies that I see who are deploying AI in the most innovative ways are the ones who, from the CEO down, have a digital transformation remit and are asking everybody from operations to IR to comms to marketing, demonstrate how you are going to be using AI to fulfill your job functions, to automate what is wrote and unstrategic, and to up level the more strategic big bets that we can make. And so I really think it works both top down and bottom up. Yes, the communicators should be experimenting and indeed first adopters.

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Danny Gaynor: But ultimately, I would challenge the C suites, listening to this podcast to issue a challenge equally to every organization and not treat comms with kids gloves as if they can't be data driven. They now can be, and I think would be very excited to embrace some of these technologies if pressed to do so.

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Dan Nestle: I'm going to add to that, because I couldn't agree more that would be a wonderful world or a wonderful corporate environment if that was the case. And I think that certain companies, and without revealing too much about my own company, there's a lot of openness to this kind of experimentation. What I would add is, I would ask the C suite to not pigeonhole AI as a tech tool. So the inclination, I think, because it comes through the pc, because it comes through the network, is that it is the domain of your CTO, is the domain of your digital team, and it's so much different than anything we've ever done before. Okay. Yes, the capability, making sure everybody has enough bandwidth and everybody has the access credentials. Yes. That belongs with it, making sure that the infrastructure is in place.

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Dan Nestle: As far as maybe developing apps that are requested, who knows? But as far as who owns enterprise wide artificial intelligence, I think we're looking at a different model. I think we have to look at a different model. I think it has to be shared everywhere. And I am a die hard capitalist. I don't like sharing. I don't like the idea of communal anything. But in this case, right? I mean, AI is not, you know, it's not. I joke, but AI is something that, you know, every team needs to have a kind of ownership of and needs to be recognized like that, recognized as such by the leadership of the organization, if the organization really wants to go places with it. Because the only thing, that's the only thing that is really limiting AI's potential.

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Dan Nestle: And, you know, I guess both Gen AI and as other forms of AI get more common or get plugged into the ecosystems. But specifically with Gen AI, the only limit is the person's natural language, ability to talk to it. It's a black box. We don't know what's going to happen with it. We don't know what it can do. And of course, assuming policy, security issues are taken care of, assuming that's first kind of, we're okay with that, assuming we're good on security and Pii. All right, then it's off to the races. It should be off to the races. You know, I know that I'm preaching to the converted here. I understand this, but I just, you know, I really want this to happen. And in case my listeners can't tell, I think this is a really important thing.

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Dan Nestle: And I love being in the middle of it. You know, I love being at the forefront of it in many ways. But I really think that we have so far to go, and the state of our profession is such that right now, we're still at the very early stage learning curve. People don't know what it can do, and I don't think they're afraid of it. But I think there's a lot of misclassification and maybe misunderstanding of the direction that you can go with it. Because the answer is, we don't know where you can go with it. You can go almost anywhere with it.

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Danny Gaynor: I would summarize what you just said, which I thought was very articulate as you can make it your own, you can make AI your own. You can make the ingredients your own, and you can make the outputs your own in a way that is more accessible than ever before. You're right. AI is not a tool. AI, as some people have said, is talking software. And what I think is so exciting for the communications profession is that if there's one fundamental remit of our jobs, whether we work in a nonprofit or a Fortune 500 company, is that it is our job to humanize, to make messaging as relatable, digestible, and emotionally resonant as anything.

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Danny Gaynor: And what we do for companies is take these inanimate objects, these corporations, and try to humanize the announcements, the products, and indeed, even the people, the C suites, leading the company in a way that will have broad resonance across many audiences. And if AI, at a bare minimum, can make you more aware of your blind spots, can challenge your inherent biases and unlock other directions for your message to be more human, more digestible, more emotional, more relatable, that alone will be a groundswell of change. Where I get really excited is when it can turn your communications activities, whether it's pitching media, doing a marketing campaign, running a Super bowl ad, launching a new product at CES, and we can prove that beyond sales and beyond impressions, there's this other territory of impact that we can develop.

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Danny Gaynor: I think it's a very rare moment in the history of commerce. You mentioned you love capitalism. It's a very rare moment in the history of capitalism where we can develop something that will be industry standard and that will be reputational ROI, that will be quantifying the impact of communications. And for me, whether you use signal AI or you hire a bunch of developers, or you're just experimenting with a custom chain chat GPT, that is something that's going to happen over the next ten years. And mark my words, when this podcast goes in the time capsule and it gets unearthed in 200 years, I think that will be the piece that turns out to be correct.

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Dan Nestle: I hope you're right. And it's interesting because I was just recently speaking to Melanie Samba, and I don't know if you know her, she's just dynamite. She's the CEO of a startup called Sproxy, Sproxxy. And Sproxy is wholly focused on conferences. So using AI and using the proprietary software, et cetera, that they've developed, massive database of every conference in the world, understanding where you need to be at a conference. So imagine you take the work that youre doing with signal AI, with discriminative AI. You understand the topics that youre supposed to be talking about. And then you go over at sproxy and you go, you know, what conferences should I be at?

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Dan Nestle: And then sproxy helps you to understand all essentially through their trained AI, helps you understand what conferences you need to be at, helps you pitch your executives, you know, and helps you kind of manage that conference team and understand your conference ROI. So there's more and more of these. I just bring that up as an example of one of the, just one of the kind of channels that we deal with in marketing and communications that is just beginning to see, I think, the connection between the benefits of the information and the quality of information we can get from AI and just the accelerated innovation and accelerated dot connecting, I suppose, that we can make as practitioners in this field. It's a brave new world in many ways. We don't have to even talk about the scary stuff.

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Dan Nestle: We know there's scary stuff, but I like talking about the hopeful stuff and what you've just said. About 200 years from now, if the time capsules opened up, you know, we're sort of on the right track. That gives me a lot of hope. So I had so many other things I want to talk to you about, Danny, but we will get there at some point. Right now, I just, you know, I know that we've just, I think, given our listeners a fire hose worth of information in a very short amount of time about, you know, different kinds of AI and certainly some of the things that are going on in our world. If you had one, I guess your final words to our listeners, little words of wisdom from the one and only Danny Gaynor. Here's your chance.

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Dan Nestle: Anything you want to say, go for it.

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Danny Gaynor: Sure. I would say this. I failed algebra three times in a row, and if I can do it, so can you. That might be one thing. But I would be serious in saying that as with all times of disruptive innovation and the Internet in the early two thousands and before that, just having computers in our home, AI will be eventually taken for granted. I think that this is a really cool moment where people like you and me, regular folks who could not program the game of Pong, let alone fully develop their own LLM, are able to be the experts in adapting AI to new and unforeseen avenues. You mentioned sproxy picking out conferences. I would have never thought of that. But now they're going to be able to own a channel.

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Danny Gaynor: And I would ask the folks, particularly those in the less sexy industries, to experiment with this. We work with a ton of companies who are not necessarily household names in the life sciences space or the aerospace arena or the manufacturing sector, but indeed, they are just as vulnerable to reputational shifts as well as successful in capitalizing on reputational opportunities. And it's very rare that a tool comes out that. That you can truly make your own without any technical capabilities. So to the greek folks. Want to chat with me about that? I'm Dan Gaynorignal Dash, AI.com. This guy Dan Nestle's got my info. I got all cell, email and everything in between. Be more than happy to have those conversations.

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Dan Nestle: Thanks, Danny. I was just going to say, anybody is interested, you can find Danny on LinkedIn at Daniel M. Gaynor and just, you know, I keep calling Danny because that's what he insisted on being called. I was Danny when I was a kid, and went back and forth about the Danny thing, but Danny likes Danny. But Dan Yolgaynor on LinkedIn and Signal AI.com is the name of the company. There is also signal AI HQ on Twixter if you're looking for that, but I don't know how active the company is on those platforms. I think if you just deal with LinkedIn and signal AI, you'll get everything you need. And do yourself a favor and do a little bit of a before Google goes out of fashion and is wholly replaced by perplexity.

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Dan Nestle: Check out Danny on just look for some stuff and you'll find some information about the Newton, Mass. Story, that local boy saves town type of a thing. Look, Danny, it's been a pleasure to talk to you. I'm so glad you're here, and you got to come back again sometime.

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Danny Gaynor: Well, I look forward to making this my own mini series within the trending communicator ecosystem. And thank you so much for taking the chance to chat. It's really exciting to talk with somebody who's, I think, at the forefront of adoption. And your level of experimentation in many forms of AI that I'm barely agile in is really fun to see. So perhaps one of these you should do like a show and tell video demonstration of just all the cool stuff you've shown me.

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Dan Nestle: Be careful what you volunteer me for there, Danny. But it's, you know, there are some things happening. I'll be at, you know, I'll be at the page society in a couple weeks. We'll talk about it then with you, by the way. Yes, and, well, by the time this airs, that will have already happened. So anyway, on that note, thanks again, Danny Gaynor, for stopping by the trending communicator. I really appreciate it.

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Danny Gaynor: I'm saluting. See ya.

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Dan Nestle: Thanks for taking the time to listen in on today's conversation. If you enjoyed it, please be sure to subscribe through the podcast player of your choice. Share with your friends and colleagues, and leave me a review. Five stars would be preferred, but it's up to you. You do you have ideas for future guests or you want to be on the show? Let me know@danarendingcommunicator.com. Thanks again for listening to the trending communicator.