In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how to build an AI strategy. Discover why focusing on AI as a strategy is misguided and what you should do instead to achieve meaningful business outcomes. You’ll learn how to identify the actual problems you need to solve and how AI tools like generative AI can fit into your existing processes and strategies. Gain insights into the six broad use cases of generative AI and explore how to leverage these capabilities to enhance your business operations. Finally, understand how to effectively communicate and collaborate with stakeholders who may have unrealistic expectations about AI.

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In this week’s In-Ear Insights, one of the things that Katie, you and I have talked about—and you have been correctly adamant about—is that AI is a tool.

It is a set of technologies.

It is not a strategy.

It is not something you lead with.

We lead with the five P’s: purpose, people, plan, process, platform.

First, what is the thing you’re trying to do? And is AI a good fit for it? But we do have to acknowledge the reality out in the jungle that is the modern business, which is that AI is the shiny object, and everyone and their cousins are like, “What is our AI strategy? How do we build our AI strategy?” You know, marketing director, whatever, “We need an AI strategy, go get me an AI strategy.” And we’re all like, “That’s, that’s really a bad idea.” But that’s reality.

So with that in mind—this is inspired by an actual customer inquiry we got this morning—when someone says, “I have to have an AI strategy, even if there is absolutely no purpose for it, but I need to make the corner office or the board happy.

I need to come up with a strategy.” Katie, what do we, what do we do to help that person deliver something that is an AI strategy, even if it is not relevant to the situation?

So you light some candles, you lay out some, you know, your herbs, and you start to chant and say, “Please make this go away.” And you hope for a celestial event or a full moon.

In reality, you know—and you’re right, you’re absolutely right—just because we know (you and I and, you know, a handful of other people) that AI is not a strategy does not mean that that’s not what’s happening in the market.

In this week’s In-Ear Insights, Katie and Chris discuss how the software development life cycle (SDLC) applies to prompt engineering in generative AI, why the prompt development life cycle (or prompt engineering life cycle) is a good idea, and a real-life application of it.

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Christopher Penn: This week on In-Ear Insights, let’s discuss prompt engineering and software development—two seemingly disparate concepts that surprisingly converge. Here’s why: programming involves writing a sequence of text instructions for a machine to produce a repeatable, reliable result. You write code in C++, Python, or Java to perform a task and generate an output. Ideally, good code yields good results.

When you write prompts for generative AI and large language models, you’re essentially programming, just in a language like English, French, or Danish instead of C++. This means adopting good software engineering practices becomes crucial. You’re writing code, so you should protect it, audit it, and manage it effectively.

Katie, you’ve managed development teams—including me, bless your heart—when it comes to prompt engineering, development, internal sharing, and debugging. Many people, especially marketers, seem to just wing it instead of having a scalable, structured approach. So, when managing the prompt development lifecycle, what should people consider?

Katie Robbert: Requirements are paramount. Let’s take a step back. I was just checking our website for our version of the SDLC (software development lifecycle). We probably have it somewhere, maybe as an In-Ear Insight.

Chris, this topic arose last week when you drew a parallel between the SDLC and prompt engineering, highlighting the significant overlap. The question is, can we adapt the SDLC for prompt engineering? Absolutely!

The SDLC, like any project management or development lifecycle, has a few key phases: requirements gathering (business goals, development goals, needs, and approach), process definition (tools and methods), execution, testing, and iteration. It aligns surprisingly well with the five Ps framework: purpose, people, process, platform, and performance.

In this context, the purpose is to write a prompt for a specific task. People involve gathering stakeholder requirements. Process encompasses the prompt’s content, research needs, iterative testing, and validation methods. Platform seems straightforward—the environment for writing the prompt—but it might also involve data extraction from various platforms, feeding back into the process. Finally, performance measures the success of the task. While the SDLC is well-established in development communities, the five Ps framework provides a versatile lens.

There’s substantial overlap

In this week’s In-Ear Insights, Katie and Chris review the new Trust Insights PARE Framework and 4 power questions to ask generative AI language models when you’re working with them. These four questions will dramatically improve the performance of generative AI. Get the free download, no strings attached, at:

https://www.trustinsights.ai/powerquestions

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This week’s In-Ear Insights, I have something new Katie is something that I built because one of the things that we were talking about recently was making content more accessible to people, particularly useful tips and things.

Because as much as I like to geek out on the intricacies of like, the latent space and the language model, most people don’t care.

Most people like just just tell me what to do, within reason.

Is that a fair statement?

That is, I would say, that is completely a fair statement.

And that sort of, you know, that’s the challenge that I know that, you know, you run into, and I run into is that, you know, we want to make sure people understand the why.

And they want to know the why until we actually tell them, and then they’re like, great, I don’t care.

So they think they want to know, but they really just want to get to doing the thing.

And it does, it is a challenge because you and I are so like, it is so much in our DNA to really understand how the thing works.

Before we do the thing that we feel like we also then have to get everybody else on the same page as us before they can do the thing.

And they’re just like, great, what button do I push? Exactly.

So one of the most popular things that we’ve produced along those lines is our race strategy for prompting for generative AI, right, so roll action, context, execution.

And if you follow this framework for using gender of AI, it’s a good starting point.

It’s not by any means the be all end all the founding, but it’s a good place to start, especially if you’re new to language models.

In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the potential impact of AI on empathy and society. You’ll learn about the risks when AI creators lack empathy and the importance of compassion in a tech-driven world. Discover how democratizing technology and prioritizing human concerns can combat these risks. Find out how you can be part of the solution and shape the ethical use of AI.

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In this week’s In-Ear Insights, let’s talk about AI, specifically generative AI, and empathy.

And here’s, here’s the perspective that, Katie, I want your take on.

There was a statement on LinkedIn as a video of Sam Altman from OpenAI, talking about how the race is on in Silicon Valley, to who can be the first to build the first billion dollar company, with one employee, and no one else, everything else be handled by AI by machines, by AI agents, etc.

And that got me thinking, on the one hand, as a as a nerd, that’s cool, right as just just from the pure nerd perspective, that’s cool.

But on the other hand, it struck me that that was a absurdly non empathetic point of view, about how to use this technology to, to better humanity, which theoretically is open AI is mission, building a billion dollar company that employs no one, but you strikes me as I want to say selfish, but it is kind of selfish, in the sense of, like, yeah, I want all the money, and I don’t want to give it to anyone else.

And that, to me, is highlights something that we’ve seen for the last 30 years, but really badly in the last five, you look at papers and research on the topic of just the absolute decline in our overall ability to be to practice empathy, sort of an empathy deficit.

So what that taught me thing is, you have a bunch of people who are building these systems, who have no sense of empathy, and no sense of connection to like regular people.

And that does not bode well for how this technology is going to be used at a societal level.

What’s your take on this?

I mean, that’s a lot to unpack.

So, you know, I have a lot of questions, as you would imagine, number one, what kind of business? Are we talking about? You know, number two, you know, is this assuming that the one person who builds the business is the one person who’s goin

In this week’s In-Ear Insights, Chris and Katie discuss generative AI limitations in marketing and why focusing on generative AI might not be the answer to your woes. You’ll learn the best ways to analyze your data to find the true cause of the problem and get actionable steps to turn things around.

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In this week’s In-Ear Insights, a follow up from last week’s episode on predictive analytics in generative AI, we got a comment on YouTube folks who said, Hi, I thought it was an interesting topic but didn’t get anything from the episode that was actionable to go experiment with, it’d be helpful to analyze an example of how to solve data, sell funnel leakage problems with generative AI, they give an example of a family law firm for traffic’s going up, and and prospects are going up.

But sales qualified leads and retainers has stayed flat.

And obviously, they’re kind of wondering, I’m guessing they’re kind of wondering why some of the funnels moving upwards, some of it isn’t.

So Katie, when we tackle problems like this for paying clients, we obviously spent a lot more time doing requirements gathering much more than you would get in a YouTube common.

If this person were to say, onboard as a client, what would our first steps be?

I would, I would probably run them through the five P’s first and sort of understand like, what is the purpose? Actually, no, I would step back from there probably and say, you know, I would want to undo? Well, gosh, it’s hard, because there’s so many things that I want to know more about.

And so let me see if I can sort of like restructure it.

So yeah, let’s start with the five piece, because that’s always a really good place to start.

And so, you know, what is the purpose? So the purpose sounds like they are trying to understand, you know, to your point, why some of the funnel is moving, and some of the funnel is stagnant.

And so, you know, so the question they want to answer is, how do I get the rest of the funnel to start moving again? The people so you know, are there salespeople involved? Are there content marketers involved? You know, whoever’s involved? What is the process? I think this to me sounds like where we woul

In this week’s In-Ear Insights, Katie and Chris discuss predictive analytics and applying it to reducing churn throughout the customer journey. Learn what types of predictive analytics apply, how generative AI fits in the mix, and steps you can take to reduce churn.

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In this week’s in In-Ear Insights, let’s talk about reducing churn.

How do we reduce churn using things like predictive analytics and generative AI.

So to set the stage we should probably talk about what churn is churn is essentially when you lose customers faster than you can gain them.

So if you were a B2B company, you have subscribers, for example, with with Trust Insights, if you people were subscribed to our generative AI course, and it would happen if more people stopped being customers than we were able to acquire them.

If you’re a company like Agorapulse, it’s the number of monthly recurring revenues as customers you have in retail.

It’s how fast those customers churn, right? How fast do you lose customers? So Katie, when you think about churn as an executives, particularly a financial stakeholder, what is top of mind for you, obviously, you know, less is better.

But how do you think about attacking this problem? Oh,

it’s complicated, really? Well, because first, I mean, first and foremost, you have to recognize that it’s happening.

And a reduction in revenue is usually a good indicator.

But that’s not always apparent, because sometimes the churn happens in such a way that, you know, there’s like a lag between what you see in the numbers and the churn actually happening.

And so that’s where I like to pay attention to, I keep an eye on both top of funnel and bottom of funnel, because I feel like as the top of funnel, so that’s your awareness, that’s people coming to your website, for the first time, people may be checking out your product for the first time.

That’s, to me a really good indicator of if your overall churn bottom of funnel is going to go up or down.

And so if your top of funnel starts to reduce, then you can expect that trickle down effect for the bottom of the funnel.

So I like to

In this week’s In-Ear Insights, Katie and Chris reflect on lessons learned from Social Media Marketing World 2024. With generative AI taking center stage both literally and figuratively, what does that mean for social media marketers? Tune in to find out!

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In this week’s In-Ear Insights we are back from days and days and days upon the road.

Katie, you were at Social Media Marketing World 2024 with me and I just finished the world tour going all over the place.

And we wanted to take some time to revisit some of the key takeaways and things that we experienced at Social Media Marketing World, particularly as the the landscape has changed so much.

The one thing that I noted that Social Media Marketing World, this is should be is and should be no surprise to anyone was like every other session was had something to do with generative AI huge surprise, right.

Yeah,

it was.

It was interesting.

I was.

I don’t know why I was surprised by that.

I think I was surprised.

Because you and I, Chris were in what was considered the AI track.

So there were speakers brought into the event specifically to talk about AI.

And then there was supposed to be the rest of the speakers talking about, you know, all of the different aspects of social media marketing.

And what I noticed instead was that every talk was about generative AI.

So then I almost kind of felt like, well, why are we here? And the reason we were there was to go deeper into generative AI and not sort of surface level it like the other speakers were going to do, because their focus was more of the social media tools and the tactics.

But I was a little concerned because of the potential conflicting messages, because of the overlap of sessions.

And because of how overwhelming it was, as an attendee, you had no breath, no break from hearing about generative AI, you walked out the door and somebody brought up generative AI, you went to a separate off site event? And oh, my god, somebody brought up generative AI? And can we please just stop talking about generative

In this week’s In-Ear Insights, Katie and Chris discuss how to do practices like predictive analytics and classical AI/machine learning when you’re data-poor. What data is available to forecast and work with? How do you create data when you don’t have it, and what strategic advantage might this confer? Tune in to find out!

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In this In-Ear Insights, what do you do with your analytics when you are a data poor? So this is a question that came up when I was on the road recently, I was in Canada at a at a speaking gig doing a talk on predictive analytics, which was kind of fun, because I’ve done that one in a while.

And one of the folks said, I love this, this is this is great.

This makes sense.

Canada as a whole is a data poor nation.

We don’t have the same federal agencies like the US does that provides all this great data.

And it’d be nice to be able to forecast, you know, this industry stuff.

What do we do when we’re data poor, not the individual company, but like the consortiums, the public agencies, they just don’t have data.

So Katie, what do you do when your data poor?

That’s such a good question.

Because I feel like, you know, we take for granted the fact that in the United States, we are not data poor.

If anything, we are overwhelmed with the amount of data out there, our challenge is finding the good quality data that we can really trust.

But there’s no shortage of data that we can use.

So I would say to someone who feels like they are data poor, the first place to start is to try to find something similar enough.

So you know, Can Can these Canadian resources use a proxy? So a proxy being like, can they look at European data or United States data or Australian data? Is it close enough? And so there’s, there’s obviously a lot to unpack with that.

But that would sort of be my first instinct.

You know, Chris, what would you say?

So, a couple of answers I gave was, one, there are data sources that are geographically agnostic that y

In this week’s In-Ear Insights, Katie and Chris discuss generative AI impact on search engine marketing. What is​ the impact of services like ChatGPT, Google Gemini, Perplexity, and Microsoft Copilot on traditional SEO and search? Tune in to find out, and what steps you should take to prepare for the future.

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In this week’s in your insights, search is changing more people are using tools like Bing, Google Gemini or perplexity to do searches to ask language models.

Hey, what’s the best place to get a burger around here? Or how do I do this thing? And increasingly, these generative AI models powered by search databases are doing a better and better job.

This is changing the way people search.

This is changing this the SEO world in general.

And one of the questions that came up at a recent event I was at was, what do we do about this folks like random Rand Fishkin, and Andy Crestodina, and Aleta solace, are all talking about how users prefer generative AI searches over regular searches.

In many cases, because there’s less garbage, right, there’s less clicking through to 48 websites, you just get an answer, and maybe a few links to click, and it’s a better user experience plus a consent the size things that you might have to go to five or six search results to get.

And so the question to you, Katie, is, how do we think about the way search is evolving into general AI being the way people search? And what should marketers and company owners be doing to plan for a future where there’s even more intermediaries between you and the customer?

All right, so that was a lot of questions in one.

So let me take a step back for a second.

I’m not using generative AI to search.

And so when as you’re saying, like, everybody’s using it can edit this isn’t to be like a sarcastic thing.

But like, where’s the data on that? Who’s everybody? Like, is it? You know, is it you and other, you know, AI specialists, who that’s your world? So you’re feeling like everybody’s doing it? Or is there data that supports that says consumers are turning to ChatGPT and Google Gemini and Microsoft Bing? To do their sear

In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the complexities of data privacy in the era of generative AI. They offer practical tips on safeguarding personal and company data, emphasizing the importance of understanding terms of service and the implications of sharing information online. You’ll gain insights into how different AI models handle data and the significance of opting for secure, private AI solutions for sensitive information. Tune in to navigate the challenges and opportunities presented by generative AI in a data-driven world.

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In this week’s In-Ear Insights, let’s talk about data privacy and generative AI.

It is no secret that for many of the tools that are out there, those tools are looking for more data to train on to get better feedback from users to say this was a good response.

This was a bad response, sometimes with not dubious consent, but with consent that is deeply buried or implicit.

So for example, about a year ago now, x, the service formerly known as square changed his Terms of Service and said, Hey, we’re going to use your data to train our AI on and the only way to opt out is to cancel your account.

There are many, many other services out there, the golden rule that we have said for 20 years now is, if you are not paying, you are the product.

And that has never been more true in the age of generative AI.

So Katy, last week, you did a seminar with MarketingProfs.

And one of the questions folks had was like, what should I be thinking about? When it comes to data privacy and generative AI? So you want to recap sort of how things went with that in terms of your perspective on it? Yeah, absolutely.

And so it’s a really good question.

I’m glad that people are asking it.

Because if you’re not asking about your data, privacy and security, then to your point, Chris, you are the product.

So the question was around the GPT model, so ChatGPT, anthropic, Claude, so and so forth, and they were asking, you know, what do I need to know about privacy and data security? How do I protect my information.

And so it’s a layered response, because it really depends on your specific use case.

But at