You Ask, I Answer: Generative AI and Public Relations, Part 4 of 5

In this episode, this is part four of five of an interview I did with Stephanie Moore of Kent State University about generative AI in public relations. Makes sense. Kind of in that line in that vein, then what best practices do you follow to ensure the responsible and ethical use of AI? And then maybe you can talk a little bit more about how you disclose the use of AI with your clients. And I know I know you do that because I actually see in your newsletter you you have disclaimers.

So yes. So uh responsible use of AI is kind of a meaningless fluff term. Um ethical use of AI, again, that that goes to if you want AI to be used ethically, make sure that ethical people are using it. So try to avoid hiring jerks. Um but also your corporate culture has to be one in which you prioritize high quality results.

The ugly truth is most agencies do not. Most agencies prioritize meeting client deadlines, getting things done as efficiently as they can because you're working 80 hours for a 40 hour paycheck, um, and client demands are never ending, and clients have, and you know, to some degree, understandably, they don't care what your priorities are. They think that they are the number one priority all the time. And so if you operate in a my hair is always on fire uh methodology, then you will make ethically questionable choices because you feel like you have to. And so part of ethics is having leadership and client policies and things that say, we believe in ethics, we believe in doing as little harm as possible, we believe in things like the truth, and we will enforce that with our clients.

We and you know, you empower team members to say to the client, nope, you're not gonna get that today, and you're probably not gonna get tomorrow either. Sorry. Um, or you say, Well, you you can have that today, but I'm gonna add a 40,000 dollar charge on your bill. Do you want to proceed? Yes or no.

Um, and that and that typically helps to to do some of that. So that's how you ensure ethical anything is giving people the ability to say no uh to things. If people don't feel like they can say no, they're not going to make ethical choices. In terms of the disclosure of ethics of AI, so there's two things to keep in mind here. Number one, in some parts of the world, it's required now, like the EU, you must disclose the use of AI.

Number two, in a study from MIT Sloan last fall, um, they did a test, they showed content to three groups of people. Um, the first group was just told you're going to rate this content, and they were not told who made it. The second group of people was told you're going to be reading this content. Some of it was made by AI, some of it was not. You will rate it, but you know that some of it's machine-made.

The third group of people was was told you're going to rate this content, some of it's made by AI, some of it's not. Each piece of content will contain a disclosure of who made it. When consumers consumed the content unlabeled, they preferred AI content over human-led content. When they were told that humans were involved, they boosted human scores, but they did not penalize AI. And so one of the key messages from that study is it's okay to disclose that you're using AI, you will not incur reputational harm from disclosing that something was machine made.

How you do it's pretty simple. Like, hey, this was made this piece here was made by AI. And it's important to disclose not just that you use AI, but what part of the content or how it was used, because we go back to copyright. If I have an AI-generated image on my blog post, but I wrote the human blog post, and I just say I used AI, the uh logical assumption would be that the entire post is made by AI, and therefore someone else can copy it because it's in public domain, and then you have to go through a whole lawsuit of like, you know, trying to enforce your copyright. If you disclose the header image was made by AI, but I wrote this blog post.

You are reasserting your copyright over the portions that are human-led. And therefore, in that disclosure, if someone then copies your blog post, you retain your rights and you can go sue them for infringing on your copyrights. So disclosure is just as much about protecting you as it is helping the end consumer. Great. That's great advice.

Okay. All right, moving on. Um, let's talk about some pedagogical approaches in terms of teaching AI. So, how do you train your team about about how to use AI for strategies and tactics? The biggest thing we do is we have a shared prompt library.

Um, and we basically, you know, it's it's obviously very, very proprietary. Um, but we have essentially a code database because prompts are code, they're just code written in plain language, and people can pick them up, adjust them, tune them to their specific use cases, or generate new ones. We also teach people our methodologies. Um, they're on our website. You can download the PDFs for free uh of the basic methodology, and then they're advanced methodologies that we obviously keep to ourselves.

Um that say, here's how you you develop this set of outcomes based on what you want it to do. A big part of it is helping people understand the principles behind how these machines work and the structure of getting to the output you want. And my my co-founder and CEO Katie Robert talks about this a lot. Really skillful prompting and use of AI looks an awful lot like good management of people. If you are good at giving direction and delegating and giving measurable outcomes, you will be good at prompting.

You just treat it like uh a junior employee and you you you work with it in exactly the same way. If you're a bad manager of people, you're also going to be bad at AI because you're going to get frustrated. You're going to give it you know incredibly short directions. To give you a sense, a prompt in generative AI today in Google's Gemini hold it can be as long as two million tokens. Two million tokens is 1.5 million words.

That is two copies of Shakespeare. Two of these can go in a prompt. So these people, you know, sharing there, my ultimate chat GPT prompt that's a paragraph long. Like, no, that is that that's like saying my rest, you know, here's my secret cookbook, and every recipe is one paragraph. Yeah, you're not cooking anything with that except shoe leather.

Um whereas if you look at a proper recipe in a cookbook, it's at least a page, if not two pages long. That's a good benchmark for how long your prompts should be. And we tell people as part of training, if your prompts, I mean it depends on the task, but generally speaking, your prompts should be it at least a couple of pages long. Most of my day-to-day prompts that I use are four to six pages long. That's great.

So that's it reminds me of the garbage in, garbage out, right? More context the better. So any specific challenges that you have with people learning about AI use? Within our organization, it's it's helping people focus and not get overwhelmed. Um, because there's so many toys and tools and gadgets and things that you can play with and stuff like that.

Um, that's a big part in turn in our organization. Generally, generally when working and training others, particularly I do a lot of keynotes, I do a lot of workshops and things. Um, is getting people to think like programmers, getting them to think in the way that you know, how do you skillfully give instructions? Um, it can be a uh very challenging thing to do. Um also getting people to overcome their resistance to trying the technology because they have in many cases a preformed opinion of the technology, good or bad.

And the if they've had any experience, it's been novice level experience where they have not seen the use cases that make them go, holy crap, I want that. I'll give you an example. Um, I was working in the PR department of one of my clients, and they're like, 'Oh, yeah, we've used AI a little bit, but it didn't do such a great job.' I said, 'Okay, well, let me show you this.' I we took a blog post and fed it into a very long programmatic prompt, and it said, 'Okay, you're gonna come up with six pitch ideas for local news outlets, and it gave a you know, the list of the news outlets and the coverage and the types of things they wanted, and it generated some great pitch ideas.' Like, this is really incredible. Like the their eyes were open and said, I understand the technology now because I could see the benefit. I'll give you another example.

I was going to pitch this one reporter, um, this is relatively recent, and I said, I want to pitch this reporter. Um, it was to a uh uh a query, something that they won't have to work very hard with, but I want to obviously say the things I want. So I grabbed the reporter's last 20 articles they wrote about, and I told the generative model learn the supporters' writing style down to the letter. And they said, here's my here's my pitch, rewrite my pitch in the reporter's writing style as if the reporter wrote it in it, reformatted it and stuff like that. I said, Here, there you go.

And the poor is like, this is great. I don't have to do any editing. Like, no, you don't, because the model did it for you. Wow, that's brilliant. These are great ideas too for the classroom.

So thank you. All right, so um, kind of moving on then from learning, how do you assess the effectiveness of using these tools and meeting communications goals? What are the goals? That's right. That's what I'm saying.

Safe time, save money, make money. If you do one of those three things, you're doing a great job. Okay. And you kind of already shared examples of that. How about assessing your team's understanding or proficiency in using AI for strategies and tactics?

We don't have a formal process for that. We have on as overuse determined, we have a continuous learning process. You know, every time uh someone on the team is like, how do I do this? We'll talk through and say, okay, here's how you do it. And then if the person has a good prompt or they have, hey, like this prompt didn't do work to what I thought it would do, then we troubleshoot it.

We troubleshoot it, you know, live or in team meetings. Um we have multiple get togethers throughout the week with folks to say, like, how are you, you know, how are you getting on? What are you stuck on? What things do you need some additional assistance on? Um, a real good example that is we do a lot of, you know, as everyone does, proposals and scopes of work.

We have a standard proposal template, we have a standard my master services agreement. And so one of the things that I showed our sales team how to do it is like here's the call transcript from your last it for your input call. Have here's you tell the model here's the scope template, here's the MSA, here's the call transcript, right scope. Um and these and you know, there's a list of like 20 rules. These are the rules for this scope.

And proposal and scope writing went from taking six to eight hours to like 15 minutes. 15 minutes for them from the time a sales call is input call is done to the time the proposal is going out the door is so much shorter now. That's great. If you enjoyed this video, please hit the like button. Subscribe to my channel if you haven't already.

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