From Efficiency to Impact: How GenAI is Transforming Communications Strategy

Generative AI is evolving from a simple productivity booster to a strategic game-changer in communications. At the Page Up Annual Conference in October, member Ethan McCarty moderated a dynamic panel featuring João Belo of Takeda Pharmaceuticals, April Yue of Boston University, and Dan Gaynor of Signal AI. Together, they explored how to leverage AI to elevate communications as a strategic function, while addressing the psychological and cultural barriers to adoption. 

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Transcript

From Efficiency to Impact: How GenAI is Transforming Communications Strategy | The New CCO

[00:00:00] Eliot Mizrachi: If you're like me, and you've been obsessively following the adoption of AI in the field of communications, especially since the advent of Gen AI a couple of years ago, it's clear that how communicators are thinking about and using these tools has changed tremendously.

In October, as part of our Page Up annual conference in Boston, we convened a panel of experts to talk about latest developments and best practices with GenAI.

The Conversation, moderated by Page Up member Ethan McCarty of Integral, explored the fascinating intersection of AI and strategic communication and even got into the psychological reasons that might be hindering an organization's adoption of AI tools.

This panel included João Belo, Senior Vice President of Global Communication at Takeda Pharmaceuticals, April Yue, Assistant Professor of Public Relations at Boston University, and Dan Gaynor, Strategy and Communications Lead at Signal AI. We'll pick up on the conversation with João, sharing how at Takeda, his team moved from experimenting with generative AI as a productivity booster to a truly strategic tool, which is especially interesting in their competitive and highly regulated industry.

I'm Elliot Mizrachi , and this is the new CCO.

[00:01:19] Ethan McCarty: João, I wanna start with a question for you. Something that you brought up in our in our conversation was seeing a I over the last call it year and a half or so transform in your team's use of it from a productivity tool to more of a strategic tool.

Can you share a little bit about your experience with that? At Takeda?

[00:01:42] João Belo: thank you, Ethan. And thank you for the opportunity to be here and talk about this. It's been a little bit of a journey. about almost two years ago, really, when chat GPT started. We're lucky that our our João D.

D. &. T. Organization. So data digital and technology organization at Takeda immediately started to develop our own large language model, which we started to play with around the same time is shot. GPT came along. And what that did for us was you know, we, we immediately understood the possibilities in terms of productivity,

It was, it was a given, right? We saw the potential. We understood, all right, this is gonna be, this is gonna make a real change in terms of productivity, which then started to pop more sort of in depth questions. And, and one of the things we started to think about is, all right, we measured productivity a little bit,

We started to think, all right, so depending on the job descriptions, how much productivity savings were, were actually thinking about. And we went, numbers in the world away from more than 50 percent depending on your, if you're sort of a writer, if you produce content, perhaps productivity gains in the realm of, you know, 50, 60, even maybe 70%.

If you do have a little bit different job, perhaps a little less than that, we, we, we landed on a sort of an average of 30 percent productivity gains for our entire organization. So the question is, all right, if you have a team of 100, Now you have 130 people. Yeah. And what does that actually mean?

[00:03:11] Eliot Mizrachi: I want to pause and emphasize what João just shared. The conversation on AI has tended to be that you can do much more with much less. While that's true, João and his team, are pioneering an AI powered knowledge management system, trained on their organization's distinctive positioning.

AI writing a first draft of a press release is one thing, but building that kind of organizational consciousness shifts the conversation from how can we do the same work with fewer people to how can we maximize the value of these powerful new capabilities for competitive advantage.

[00:03:46] João Belo: Right? Where are you going to, you know, these 30 new employees that you now have? What are you? What are you going to do with them? Right? What are the things that they're going to be able to do? And when, as we were sort of going through this sort of existential questioning, one day, then and I were actually meeting and we in a meeting room, I actually have a picture in my head of that meeting where we started white boarding possibilities.

Right. And what if hypothesis generation, right? What if, if we, what if we train a large language model and we built sort of a relatively complex AI system and train it on our strategy and we are in a good position to do that because actually, thanks to the work that APCO did. I know there's a lot of people here from APCO.

We, about four years ago, we ran a lot of research understanding what our stakeholders expect from us. What do they value about Takeda and who we are, right? Our ethos. And we put it all that together into what we call the distinctive positioning, which is essentially a strategy for the differentiation against other pharma companies.

So we actually had a lot of material to train a large language model on. And the hypothesis was, you know, if we train a large language model on this, can we actually build a always on dashboard that will be looking into All our outputs as an organization you know, earned coverage social media posts Takeda.

com stories, all the outputs, and overlap that with our strategy and tell us, are we on strategy or are we not? And so we started to sort of whiteboard all of this and, and you know, well, it got into production in April this year. And so I think the point kind of getting down to your question is. There's a lot more to do than just productivity.

[00:05:35] Ethan McCarty: And I would say that the potential in terms of strategy and analytics is tremendous. Dan, you know, obviously you were involved in that project and in others. And, you know, there seems to be somewhat humble origins here. You know, you got like a whiteboard and, you know, You know, chat GPT and whatever.

But you know what you are doing at Takeda is sophisticated, well resourced. You know, I mean, that's a lot going on there. What would you say? Having now worked across many organizations is like a good on board. Like what's the ramp so that you can credibly get started? But like materially be embedding AI into a communications capability.

[00:06:15] Dan Gaynor: Yeah, sure. I mean I came to AI as a comms practitioner, right? So global comms at Nike, the golf team wouldn't get along with the tennis team and we needed to figure out what the story was there at Weber Shanwick. Many alumni here in the audience today, working with many companies from IBM to J and J who are all trying to figure out in this sea of sameness, how they can differentiate with a corporate narrative that feels truly ownable and authentic to them.

And one of the things that I've seen companies do well when it comes to AI, well there's a few key points. The first is standard setting. Right? Which is to say we are now a data driven organization. We see this all the time in different organizations where it's like, well I've got my media monitoring tool and I've got like a volume metric or.

We hate to say it, but impressions metric or, you know, all those other old school things. But I'm pretty much just going to do my job as I see fit. I'm going to pitch the media that I think is best. I'm going to put out the messages that I think with my gut instinct sounds best. And what I think from a standard setting perspective is so important is being able to use data to make decisions that we can all rally behind as objective proof of the bets we should make and then proof that the bets are paying off.

The second thing to João's point is to talk about strategy in a way that is very, very specific, right? I mean, I think we all know as communicators, everybody here is campaigning on being perceived as an innovative company. Everyone here is being perceived as a purposeful company. And I think we've all seen, whether it's your commitment to net zero, or your embrace of R& D across your organization, need to figure out what are the most authentic proof points, what are those core values, those distinctive positioning themes, that truly feel like you can own, because then you can use those to train the AI system.

And then, you know, of course, the, the final bit of this is, It's, it's synthesizing, right? It's thinking about the data points that you need to pull in. This could be global media data. This could be elite paywall data, like the Financial Times or Wall Street Journal. Could be regulatory, legislative litigation data.

And thinking about the data sources you need is really important because, as you all know within LLM, it's all based upon what's in the model. And so no matter how precisely you comb that model, the power is in the raw material that you're sourcing insight from. And so if you can be really specific in terms of setting a standard that your organization is now data driven and making decisions with the objective proof.

Second, making sure that you have a very specific strategy that you're using to shape the AI system to sharpen your messages and improve your storytelling and quantify your editorial calendar and most importantly, figuring out the data sources that you're going to pull from in order to make sure that the information you're getting is in fact accurate

[00:08:50] Ethan McCarty: at the end of the day, we're talking about getting a bunch of humans to behave.

Differently, which is, of course you know, that's part of the that's part of the job, right? My team at integral has for the last few years. I'm looking over you, Rob. You call it over there from Harris. We've been doing research every year with the Harris poll on employee perceptions and experience and so on.

And one of the things that we've asked about is the adoption of various digital tools, AI being among them. One of the questions that we've asked is, do you think the adoption of AI is going to be good for your company? And do you think it's gonna be good for you? We'll make you more productive. We'll make your company more successful.

And one of the big surprises for us was the same number. 79 percent of people who were in our 2000 plus person sample said, yeah, it's gonna be, it's gonna make my company more effective and it's gonna make me more productive and yet also fully a third. Said this is going to threaten my job. And probably, you know, and I think the timeframe was like rather immediately, like in the next six to 12 months.

April, you look all the time at the research, you conduct the research on change in organizations, internal communications. What are you seeing in terms of the research, the trends of adoption openness to some of the things that João and Dan were just discussing?

[00:10:12] April Yue: I think there is an overestimation on how enthusiastic, open and receptive that come professionals are about adopting a I in their work flow.

Many of the research out there, including some of my own, showed that the fear of being replaced by a I and the self esteem concerns are some of the biggest hurdles to a I adoption. A. I. Does not affect all employees, and The same way, right? So employees with low job skills and domain knowledge, they tend to be more resistant to AI adoption.

And this is because now with AI being able to perform some of the repetitive low level work, this employees are feeling more and more pressure to engage in higher level strategic creative thinking, which they may not yet be equipped To have the skill and knowledge to do so yet. So this employees are more worried about being replaced by AI.

And also research shows that, you know, individuals, personality traits do affect how they react to technology adoption. And sure. A lot of us here know the big five personality traits on some of the recent recent research shows that employees ranking high on openness personality trait are more likely to view a I as a helpful tool and assistance.

But on the flip side, employees who rank high on conscientiousness are less likely to be open to adopting a I in the workplace. Because, you know, I am. Conscientiousness is a good thing, and it's usually considered you know, employees who perform well and perform, the best in the company are usually those who are considered to have high conscientiousness, but in that particular study it shows that employees who are high on conscientiousness are less likely to adopt AI because those are the employees who prefer to take control over their own workflow rather than just giving it to AI or co working with AI.

With a I I think just to overcome the general psychological resistance to adopting I work. There are two things that organizations can prioritize. One of that is to focus on rescaling upscaling their workforce. So that, you know, employees and A. I. Can truly work in the complimentary way.

And the second part is to really identify what is the root cause of employees? Psychological resistance, whether it is because they are fearful of being replaced by A. I. Or they They just don't trust AI in performing some of the strategic high level creative work. And a recent research also shows that AI adoption can trigger employees to Self esteem concerns, which actually discourage employees from adopting AI.

And

[00:13:18] Ethan McCarty: by that you mean so like if I lean on AI, it could be a signal to others that I'm less competent or capable or what have you.

[00:13:26] April Yue: Exactly. And, and the research also suggests, you know, one thing to overcome this is that managers should encourage employees to engage in some of the positive self affirmations and also avoid overselling the promise of intelligent machines.

[00:13:40] Ethan McCarty: Yeah, that's so helpful. And, you know, I couldn't help but notice, João, when you were talking about that extra 30%. I mean first of all, I admire your humanistic view on that because that is, I mean, that's what we keep on telling folks. Like, look, if, if you don't adopt AI, you're probably not going to be replaced by a robot.

You're going to be replaced by a Somebody who is. And that's a very humanistic view. Frankly, the more sort of mechanical, financial or even cynical view would be great. You got that extra 30%. You could cut a third of those people. How is that hearing what April just said about the resistance that people had and your apparent optimism and humanism in rolling out a I.

How is your team responding to this? What is no longer a pilot, but like actually a program?

[00:14:30] João Belo: I think we're making progress. There's two dimensions to this. one is easier than the other. The first one I say is, is understanding A. I. Particularly Gen A. I. And it's potential to just, you know, make your life easier.

And I think a lot of people get that. The majority, I think, are of our teams use Gen A. I. Tools. Depending on exactly what they're doing to to accelerate their work and accelerate their productivity I think there's a different dimension which is a little bit more complicated that gets into change management more profound change management Which is if you don't have to spend a lot of your time Making progress with your tactical work What does that say about your job your job description?

Where do you actually going to spend most of your time on? Right. And what we've been trying, the transition we've been trying to make in our team is again, strategy, analytics, understand analytics much better because we now have this great tool that we call sentinel where people can go in and understand every day, right?

24 7. Are we on strategy or are we not? So that requires two things. One is just their willingness to to digest analytics, right? On a daily basis. The same way is, you know, 20 years ago used to come in the office and read the newspapers. Now it's they have to go in the dashboard and understand the analytics of the day.

So that's not easy because a lot of people, let's face it. I mean, a lot of folks in comms don't. They're not very close to analytics, right? It's not the skill that we see in all our teams members. So that's that's a part of it. And then I think the other pieces.translating insights from the analytics into strategy, which I think is another skill that not a whole lot of people have.

So we're, we're talking about a huge transition going from, you know, taking advantage of productivity,making progress, not fully there, but, but, but kind of a given. Much more complicated is, How do I think strategy and analytics on a daily basis much more than before, right?

Avoiding the proverbial jumping into tactics that our teams oftentimes do, and that is a lot more complicated. And I wish I would have a really strong answer for you right now.

[00:16:42] Ethan McCarty: Somebody

in this room has that for you. So if you have that answer for a job, please find him after the session. Dan, you've encountered many teams who are you've encountered many teams who are Trying to pull that, use of A.

I. And tools into that strategic level. What? What would be some examples that you would offer?

[00:16:59] Dan Gaynor: Well, I think it's important to recognize that a I can prove the impact of communications like never before. A big reason that I moved from being a practitioner to a founder. I had this idea that wouldn't it be amazing in that every quarterly earnings script, you'd have a reputation score.

Because reputation is 85 percent of a company's value. You've all seen that HBR study. So wouldn't it be amazing if we could quantify the impact? And for the longest time, Communications teams have walked into a room with sales and marketing and IR and they haven't necessarily been able to quantify their impact.

Sales has the revenue numbers, marketing has all their metrics about, you know, the amount of people that saw the billboard. IR can say the stock price is good and the comms person says, look at this great piece in the Financial Times. And what I think is, is really encouraging about what Joe I was talking about is being able to quantify the impact of a strategy vis a vis your competitive set and being able to prove impact over time.

Now, of course, there's static metrics like surveys that continue to be valuable, particularly on audience analysis that's hard to get to. But for the first time, we're able to show the aggregate impact of everything we're doing from CSR commitments to messaging to executive moments to earnings calls. So a few examples that I've seen go quite well.

I'm from Boston, born and raised. So if you guys need recommendations on restaurants or rotaries, like I'm happy to make advice, but a great company here in Boston is called Easy Cater. Easy Cater started out as a catering company, literally started by a grandmother, and it's close to being one of the hot tech IPOs of the next year.

Easy Cater is trying to make a transition from being a catering platform that your mom and pop sandwich shop would use. to being seen as this entire workday like platform for large enterprises. And so they're using AI to figure out, okay, what does our competitive set look like now? It's no longer like DoorDash, maybe it's more like Salesforce.

And so we need to map certain topics like AI agents and Gen AI interfaces for our customers to understand the best way to position our new innovative offerings to be perceived as a tech company so that their IPO pops off. Another example would be a big consulting firm.

They want to be able to look through a financial statement or an operational footprint and understand to what degree there are, you know, labor concerns, sourcing concerns sustainability concerns.

So that they can have, aside from financial metrics, a very precise understanding of how trustworthy that company is. So there's a bunch of different examples out there, but fundamentally it starts with the communications team, to João's point, embracing measurement as a tool for action.

[00:19:35] Ethan McCarty: And so few of us have struggled with measuring communications um, yeah, no, that's uh, that is really um, that is really, that is really powerful.

And. And even if that were the one way, that could be a very powerful way to talk about getting to a more strategic use of, of AI. April, you've seen the adoption of AI rollout. I pose a similar question to you. What are you, are you seeing some examples where AI is being used in novel, interesting, more strategic ways?

[00:20:13] April Yue: Absolutely. I think we need to Remember, one of our core functions is managed relationship and reputation. And to think about how generative AI can help us deliver on that, right? So many research right now have been done, especially in the marketing field on the impact of AI driven chat bots and how that can be used as a tool to minimize relationship risks when interacting with consumers and customers.

One piece of research that just. published, showed that in certain circumstances, a I powered chatbots function better than human agents. Sofor example customers react better to a I powered chatbots when giving a less than expected marketing deal. Because they don't view a eyes behavior as being particularly selfish.

On the flip side,

[00:21:11] Ethan McCarty: I'm sorry, could you, I'm not sure if I quite followed that. I mean, I think I did, but I want to make sure that everybody gets that. Yeah. So like, if there is an offer made, that is not a super compelling offer, and it's offered by a human, it might be received worse, or you know, Exactly, that's exactly.

than if it were offered by a chatbot. That's

[00:21:29] April Yue: exactly what the study showed. You know, this is because, Human attribute less intentionality on A. I. S. Behavior, so they don't view the offer. The worst offer being made by A. I. S. being particularly selfish. But also, if an A. I. presents a better than expected offer, they also don't view it as being particularly kind.

So when you have a better than expected offer, it's more beneficial to Convey that through a human agent than a I agent. And another thing I would like to highlight from research is that how generative AI is impacting creativity. And, you know, a recent research published in one of the sciences journals showed that writers who use generative AI with fiction writing are perceived to be more creative in their storytelling than writers who did not use generative AI to generate ideas for fiction writing.

However, the same study also showed, and this is the caveat, that in, even though generative AI boosts the individual writer's creativity, they found that the collective created, creativity decreased. So in other words the stories look very similar to each other than the stories generated by human.

So I think this has huge implications to the creative field as a whole, because even though you see increasing individuals creativity, maybe teams creativity, But it might be damaging to the collective creativity as a feud you know, leading to less diversity in thinking and storytelling. Yeah,

[00:23:06] Ethan McCarty: I see you nodding your head.

Was there something that you wanted to add to that?

[00:23:09] Dan Gaynor: Well, I think it's so interesting how the AI discussion is, is, like, living in a parallel world, I might say, between the creative and the strategic use cases. You know, so many of us in the room, we interact with AI systems in a creative context. We interact with Gen AI, which is good at predicting the next pixel, and the next word, and the next note, or the next frame of video.

And that's because it's vectorizing, it's, it's figuring out through data points where that word is closest to the next word, right? And I, I think it's so interesting where We, in my world, I think a lot about discriminative AI, which is all about tagging and labeling. Is it Amazon the company or Amazon the rainforest, Nike the company or Nike the shoes, Starbucks the company, Starbucks that you're ordering.

And what I think is going to be so important. Is using the combination of those two to figure out where the human voice continues to persist as we adopt AI in this discussion. So using discriminative AI, we might be able to determine whether or not that idea truly did come from a human being. You know, just like we can assess whether or not a college essay was written by a robot or a student at this point.

I

[00:24:15] Ethan McCarty: was just about to say to April, as somebody who I know grades many papers are you finding That you're getting submissions that kind of, you know, are clearly produced by.

[00:24:28] April Yue: Yeah, it was pretty easy to detect who was using it. In conclusion,

[00:24:32] Ethan McCarty: it's like, okay. You know, the references are

[00:24:34] April Yue: totally wrong and it's not factual at all.

[00:24:37] Ethan McCarty: Yeah.

[00:24:37] Dan Gaynor: But I think this has huge implications for corporate communications because we exist in the sea of sameness, right? What company out there hasn't committed to going net zero by 2035 or 2050? The key thing is to be highly specific. With an authentic, ownable proof point, João, you certainly talked to me about this a lot with how we are pursuing that widely aspired to goal, and only human beings can make a decision in terms of how to do that.

I wouldn't trust a I at this point to differentiate me. It would just give me options to differentiate, and it's so important with what you're saying to understand that maybe you're getting more creative output. But you also have to have that a spider sense in the back of your head that maybe I'm just being pushed in the same direction as everyone else.

[00:25:19] Ethan McCarty: Yeah, I do want to talk about so we started to raise the specter of some risks for a moment there. I want to share a couple stats. This is from some research that we'll be publishing with the Harris poll in the next week or so. Organizations you probably don't actually know how your employees Are using a I.

And what we found was that 69 percent of those whose companies use or license a I tools have used them for work on their personal computers, which may or may not be compliant with guidelines.

And then, 64 percent use both personal and work computers, 22 percent use it only on their work computers, and 14 percent only on their personal computers. So what we're finding, essentially, is that many people don't know whether or not their organization has a policy.

We're seeing people in our research who are saying I'm basically doing work on open a eyes. So I'm like going to chat GPT even if I don't have even if my company doesn't allow it or license it or what have you in order to get my work done. So this is happening now rather significantly, that is just one of the risks.

Joelle, how are you thinking about Contending with some of the known risks, like out of policy use, leaking confidential information hallucinations and so on.

[00:26:42] João Belo: Well, it's work in progress, right? And I think we've we've we're trying to uncover two things. One is is Is Jenny I misinformation risk just another manifestation of the risk that we already have with social media.

So any given day, right? There's a lot of falsehoods written about any of our companies. You know, if the company is large enough, you're gonna go on Twitter, you're gonna see all sorts of things written about your company. What we have seen is that usually, at least from our experience, is that usually, stakeholders don't kind of pay a lot of attention to that, right?

There's not a whole lot of engagement with some of the most absurd falsehoods. So is, is GNI misinformation just another manifestation of that? Or is this, is it's gonna be bigger? I don't have an answer just yet. Actually, we've been trying to do a lot of research. Our cyber security team is on this on and on.

We're obviously monitoring this and trying to see And we've actually had a risk meeting this week, and we talked about the risk of of Jenny. I, we're flagging. We're monitoring. We're trying to gather the data. my personal opinion.

I think there's a there's a potential, of course, for once deep fakes get good enough. You might actually have a big issue.

[00:27:57] Ethan McCarty: Yeah,

[00:27:58] João Belo: So I think, I think what we haven't seen yet is a deliberate attack on corporations, one that is strong enough to get us all in our heels and say, whoops, Yeah, this is this is concerning.

But I think we might. We just might. And one of these days it's we're probably gonna have to to change our issues, management, frameworks and communications, crisis, communications and what not to deal a lot more profoundly with us than we have been with social media so far. Yeah,

[00:28:27] Ethan McCarty: Yeah. What are you, when you think about the that trade off, that risk early adoption versus being late to the show and you know, trying to navigate that.

Is there a framework that you think through, that you talk with your your teams about or that you would recommend to us? I,

[00:28:45] Dan Gaynor: rather than a framework, I think about it as an opportunity. I mean, for the, Put it this way, I'm a practitioner in comms who failed algebra, like many of you in this room. Unlike many of you in this room, I failed it like four times in a row, and then I went on to a D minus in geometry.

So, if I can work in this space, I'm quite confident almost anybody could. I think what's very exciting for me is for the longest time, communications hasn't necessarily been seen as the vanguard of innovation, let alone the most data driven function, and with AI, because of how customizable it is, I would argue, and I've seen, that communications teams are the leading function when it comes to adopting the tangible, end to end business uses of AI.

Whether that is training the key, specific topics of your corporate narrative, figuring out what data sources you can use to measure your core values on a day to day basis, not an individual point in time, and having real time feedback, both before you put a message out into the world and afterwards, To understand the impact that you're making.

Where I see hesitancy is that for the longest time communications folks have been afraid of being graded, right? Our metric of success is like land that piece, get a quote in the above the fold sort of rank. And, and we now have an opportunity to go after what we all are hoping to do as communicators, which is fewer, deeper storylines, higher quality, higher impact, and less about the volume bazooka out there.

When it comes to concerns, there are many. I mean, it's the first time that I trained an AI system was only a few years ago. And I saw firsthand the bias when you click yes, no, yes, no. And you're training a topic, whether it's recognizing a press release or thought leadership. Or figuring out whether or not this is actually about vaccine giveaways or cell therapy in the biotech world.

That can really give folks an opportunity to bias the data. That's why it's so important to have a back end system where you're seeing every individual data point. Like literally articles, social media posts, regulatory documents. So you can be sure that the insights that you're getting are grounded in real hard truths and not hallucinations.

[00:30:47] Ethan McCarty: I can I want to quickly say something about how this is manifesting at my, you know, at my firm, with my team. And we have very enthusiastic adopters of AI across the board. Some of that is, you know, pretty tactical. Like, we're able to do creative briefs in visual language in a way that we couldn't before.

You know, we were typing these. Detailed creative briefs and then asking a designer to read them. Now we type a Detailed creative brief and it's accompanied by a really good version You know like that approximates what we want and we can talk in the visual language to our designers Because we've created a rendering of the thing we want but they're going to go do it for real and do it better we have added it to our workflows, like with transcripts and summarization and these kind of things.

And yet also it's a huge, there's a huge pressure on the business model of agencies. I remember Evan Krauss standing up. I want to say it was at last year's page up annual conference saying like, Well, wait, if this is going to accelerate how quickly we can work and we're, you know time and materials, wait, shouldn't, shouldn't that go up rather than down in terms of the value we're creating?

So there's, there's fundamental questions about the business models of communications that, you know, Are also in play. We've got time for a super speedy lightning round in one or two sentences, starting with you, Joe. How do you envision the role of data in a I evolving the responsibility of communications leaders?

[00:32:18] João Belo: Yeah, I have a fundamental issue with communications being a supporting function, so called. I think communications is a business function. I don't see myself supporting anyone. I see myself as a business leader in the room, the same as anyone else. I try to tell my team to do exactly that. I think what what they I can do a systems as a whole because of all the potential in helping us to be more strategic measuring analytics.

They can be competitive. They can bring us that competitive advantage to be there, right? And to show the impact that we drive as a

[00:32:50] Ethan McCarty: leader. Fabulous. April, same question. How will AI and data transform communications leadership?

[00:32:56] April Yue: Yeah, I'd say change is really happening at lightning speed. No matter you're for or against gen AI, it is important for leaders to engage in continuous learning.

Be always open minded and curious in this process, experimenting things with their team listening to your employees and addressing their needs and concerns so that you can foster a culture of just openness to technology and as well as learning.

[00:33:24] Ethan McCarty: Yeah, thank you, Dan. You get the last word.

[00:33:26] Dan Gaynor: Proving your impact helping your teams operate more efficiently is absolutely critical, but I think even better what I've seen is data and AI deployed correctly can be a convening force, can put comms at the center of the table, and whether it's marketing, investor relations, operations, or sales, Give you a leadership role with objective proof for the why behind the what that you're recommending.

[00:33:49] Ethan McCarty: João, April, Dan, thank you for sharing your expertise and your experience with us. I invite everybody to give them a round of applause. Thank you so much.

[00:34:00] Eliot Mizrachi: We hope you enjoyed this highly rated session and came away from it thinking about adoption of AI, not as an onboarding of a new tool, but a transformation of your comms teams, their mindset, and their ways of working.

João's experience revealed the potential for AI to elevate communications as a strategic function. But as April highlighted, successful AI adoption isn't just about having the technology. It requires change management, addressing the psychology, from building confidence to easing concerns about job security.

I'm Elliot Mizrachi , and we'll see you next time on the new CCO.