In this episode of Product Momentum, content strategist Brian Piper joins Dan Sharp and guest co-host Tim Snedden to describe the power of AI in content strategy—revealing how these tools act as creative partners, not just digital workhorses. What’s at stake isn’t just automation or efficiency, but a fundamental shift in how product managers, writers, and organizations approach content creation, measurement, and change management.

Here’s what we talked about:

Supporting intentional, data-driven content

Brian opens with a simple but powerful truth: a great content strategy doesn’t depend on luck or volume. Successful teams align their efforts with business goals and audience needs—avoiding wasted cycles and maximizing their return on effort.

“No more random acts of content,” he stated. “If every piece of content you create does not have a specific end goal or a specific audience you’re trying to connect with, you’re wasting your time…. Real content strategy is much more intentional, much more data inspired, and much more focused on helping you reach specific goals.”

Change management: the human side of AI

Adopting AI isn’t just about adding a new tool to your tech stack. Those of us trying to figure out how best to integrate AI into our workflows need to understand that it’s not just another technology adoption project.

“This is really change management,” Brian adds, underscoring the need for a thoughtful process – one that brings everyone along for the ride and seeks buy-in from all the right people, including ICs, leadership, IT, and legal.

The data behind great decisions

Throughout the conversation, Brian returns to the theme of responsible data practice. Safeguarding personal information is paramount, but public and low-risk data – e.g.,  content-related performance metrics – can unlock valuable insights. AI excels at finding hidden gaps and guiding content teams to lead audiences deeper into the brand journey.

“Now we can feed that information into AI,” Brian continues. “And it can look through all of those keywords, finding correlations, anomalies, and clusters of content topics where we already have search dominance. AI is great at gap analysis, as well. Humans are great at seeing what’s in front of us, but not so great at seeing what’s missing.

The Product Momentum team travels to Cleveland Sept. 9-10, where we will be recording in-person episodes with John Cutler, Michelle Parsons, Shensi Ding, Sahil Jain, and Axel Sooriah. Be sure to stop by the The Podcast Zone to say hi! Special thanks to our friends at Mind the Product and INDUSTRY for their hospitality!

Tim Snedden is a content strategist at ITX who’s responsible for delivering crystal-clear information to users right when they need it. He spoke at the 2024 Flower City AI conference on the efficacy of large language models for accelerating content creation and leads AI initiatives at ITX.

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Sean Murray [00:00:09] Hey everyone, welcome to Product Momentum. I’m Sean Murray, Director of Product Management here at ITX.

Dan Sharp [00:00:15] And I’m Dan Sharp, a Product Manager at ITX. It’s our privilege to continue the product momentum journey, a platform that celebrates the thinkers and doers who are shaping the future of software development.

Sean Murray [00:00:25] Our goal is to introduce you to industry leaders, innovators, and practitioners, and bring you their insights, advice, and innovations. We talk with experts from product strategy, UX research and design, security, engineering, leadership, and more. We’ll discuss how their approaches to problems help empower teams and build trust. And we’ll explore trends, tools, and techniques that your team can use to deliver the software solutions your users need today. So tune in, stay curious, and join the conversation.

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Sean Murray [00:01:01] And if you like what you hear, be sure to subscribe, like, comment, and share. That way, we can be sure to provide you with the best podcast experience.

Dan Sharp [00:01:13] Tim, we just had an awesome conversation with Brian Piper, really getting into how to implement AI successfully, building content strategy, what it does well, what are your takeaways?

Tim Snedden [00:01:28] AI has been top of mind for me this week, you know, there was that MIT study that came out that 95% of AI projects fail to have a productive impact on the company. But it also means that 5% of them do have an impact on productivity. And I think in the product world, those are numbers that we’re comfortable with, right? It’s about being brave and running experiments to find that 5%, because it could be huge. And because of the flexibility of AI tools, they can impact a lot of things in the content world from. Data analysis to find new ways to reach your audiences or new messages that can have an impact, a positive impact, on content creation and accelerating those processes and of course there are interesting things that content people need to know about how users are finding their messages out in the world. So it was a really great conversation with Brian and I think you’ll enjoy it.

Dan Sharp [00:02:17] Let’s not wait any longer. Let’s get to it.

Dan Sharp [00:02:28] Welcome to the pod. Today, joining me as co-host is Tim Snedden. Tim is a content strategist. He’s responsible for delivering crystal clear information to users right when they need it. He recently spoke at the 2024 Flower City AI Conference on the efficacy of large language models for accelerating content creation. And he leads AI initiatives at ITX. Super happy to have Tim here. This is gonna be fun. Our guest from Rochester. So we have a local guest really excited, Brian Piper. Brian’s an expert in digital content strategy. He’s been creating online training programs for dozens of companies, including Xerox, L3Harris, IBM, and Volvo. He’s spoken at hundreds of conferences and events. Recently, in the last five years, he’s been diving into AI, Web3, community building, and the metaverse. But that’s not all. He is also an author and podcast host. He’s written Epic Content Marking For Higher Education, which you can see behind him, and co-authored the book’s second edition of the Joe Polizzi. He’s also a host of his own pod, AI 4U podcast focusing on AI implementation and higher education. So that is awesome. Really excited to have you here, Brian. Why don’t we dive right into it?

Brian Piper [00:03:37] Sounds good.

Dan Sharp [00:03:38] So right, I think today we really want to talk about how to implement AI in terms of content strategy, right? How companies can start leveraging data in AI models. But before we get into all that. I would have noticed, like, how do you define a great content strategy?

Brian Piper [00:03:55] Yeah, I mean, I think one of the things that a lot of companies, uh, institutions, brands are always focused on is putting content out there, right? You want to get a lot, a lot content out, you want to get eyeballs on your content. The problem is a lot that content isn’t connected directly to business strategies or it’s not focused specifically on your audiences. So one of things I always say is no more random acts of content. If every piece of content you create does not have a specific end goal or a specific audience you’re trying to connect with, you’re wasting your time and you’re just, you know, throwing content out into the wilderness, hoping that it connects with someone, real content strategy, much more intentional, much more data inspired and much more focused on helping you reach your specific goals and helping your audience gain value from that content.

Dan Sharp [00:04:47] Love that, yeah, love not throwing content just out in the void and hoping for the best. All right, yeah since we have that baseline of good content strategy is tied to your business outcomes. Yeah, really curious where your experience has led in terms of just getting data and starting to bring AI into improve that strategy, improve your content delivery.

Brian Piper [00:05:10] Yeah, I mean, AI can really help at every stage of your content, you know, life cycle, whether it’s looking at your existing data to see what content is performing well, and then using AI to help you augment and amplify that content, repurpose existing content for new platforms, new audiences, new distribution channels, looking at your data to identify where you have gaps in your content. And one of the areas we’re really focusing on with content is discoverability, because we know that search is changing. SEO is going through evolution. People aren’t just going to Google anymore. They’re looking on social. They’re look in communities. They’re are looking in AI. So we have to figure out how to get our content into all of those channels. Best performing content that you can put out there, highest quality content. And to make sure that it’s connecting with those audiences on those channels. And, you know, we all tend to run marketing and run content teams from limited resources, limited head count. We’d love to just be able to add more people to increase our range and our effectiveness. We can’t always do that, but AI really gives you that superpower to be able scale and to be really intentional and smart about the content that you are creating.

Tim Snedden [00:06:35] Absolutely. That’s a huge strength that you touched on as well about leveraging your data to identify new ways to reach your audiences. I think when it comes to AI and large language models, especially people seem to zoom right into the content creation part of these tools, as opposed to looking at your data sets and identifying those ways that you can inspire your audiences in new ways.

Brian Piper [00:06:58] A lot of the consulting that I do when I go into organizations, they are very focused on the content creation side of things. We start doing pilot projects and we find that we can save anywhere from 25 to 40% time savings on the creation side. When you start looking at the data analysis side of thing, sometimes we’ll see 60-80% time savings in the data analysis and the insights gathering part of the content process. Versus just the creation process. So AI is really good with data.

Tim Snedden [00:07:33] One thing I’m curious about is change management. There’s a lot of skepticism out there in the world with regards to AI tools, especially among, you know, leadership, right? When it comes to change management and implementing AI tools what steps would you recommend for helping individual contributors and leadership become more comfortable with experimenting with AI?

Brian Piper [00:07:54] Well, that’s one of the first things that I really emphasize anytime I’m working with anyone on figuring out how to integrate AI is this is not just another technology adoption project. This is not, just adding another tool to your tech stack. This is really change management. You have to get buy-in from all the right people, whether that’s the, you know, the individual contributors who are actually doing the work or leadership or legal or IT. Have to have all those people in the conversation at the very beginning, or you’re gonna run into roadblocks. You’re gonna start implementing, you’re going to find opportunities, you’re to identify pilot projects, you’re maybe having some use cases and maybe even creating some case studies that show real improvement. But then all it takes is one person to kind of put up the red flags and stop the process. So you really need to. Right from the beginning, make sure you’re using awareness and education at every level of the adoption cycle so that everyone understands why you’re doing this, how you’re gonna integrate, why this isn’t a threat to your jobs, how this is gonna make life easier for you and your customers. And really understanding that and coming at this from a change management perspective is the only way to really have successful AI integration because we’re not just adding a new tool to make us more efficient. This is really a mindset shift of how do we collaborate with this intelligence that we’re working with to make sure that we are getting the most out of us, that we leveraging our experience and expertise to add the most value for our customers and to help us reach our business goals faster.

Dan Sharp [00:09:45] I love that. Um, I really love the fact that, you know, you’re talking about change management, not as just like, how do I get this tool in front of people, but how do you get the people involved to understand the value and buy in, um, especially other with, with pilot programs. I’m curious with that, yeah, running the pilot programs, how have you seen then you kind of folks you work with expand their rollouts of AI from pilot programs into like a more full, fully fledged solution.

Brian Piper [00:10:12] Yeah. And it really goes back to, I mean, when you’re switching from piloting mode to scaling mode, it really is important that you are strategic and intentional about where you’re going to start integrating because in AI really covers so many different roles, it taps into every facet of the work that we do, but you need to look at all of your workflows and figure out where it’s going to have the biggest impact, where you are going to see the games, the most improvements and really especially at first the measurable improvements because it’s important to be able to take this message to leadership and show here’s the time savings, here’s quality improvement that we’re seeing. And once you start getting buy-in on those areas and they start recognizing the opportunities, it’s only then that you’re really gonna start shifting that mindset for people to start thinking of different ways that we can reach our goals and changing the workflows that we’ve established. And that are now optimized, but how can we switch our entire processes to be more effective and more efficient really to help us reach our goals.

Tim Snedden [00:11:22] I’m interested, yeah, that’s great. And I’m interested to learn some more about some specific examples of this in the real world, examples of these, uh, pilot programs or examples of successful scaling, especially in the realm of content and content marketing.

Brian Piper [00:11:37] Yeah, I mean, there are so many use cases. So some of the different institutions that I work with have, so Ivy Tech Community College integrated an AI tool into their student success programs where they would monitor students and look for students that were skipping classes or a drop in grades or different indicators that would show that there was some issue that maybe uh, you know, the students weren’t performing up to full capabilities. And that would trigger a call from one of the counselors that would, you know, be able to go out and talk to the students and say, Hey, there are resources available to you. There are groups, there is mentoring. There is, you can talk with your professors. What’s the problem? Where are you seeing difficulties? Um, and they saw like a 95% conversion from students who were hailing and probably in the past. Would have failed out and withdrawn from school to passing grades and success. So just not even just thinking about how do we get more students onto campus, but how do keep the students that we have? How do we measure opportunities for engagement and where you want to add that human connection, that human element? And it’s funny that my podcast all the time, it always comes back to a good implementation of AI. Will help you create a stronger human connection with your audiences, because it takes away all that time that you have to spend answering questions. Forsyth Technical Community College integrated an AI-powered CRM that integrated chat bots onto their website and allowed them to save like hundreds of hours of time every month, not having to answer all the common questions that they were. You know, their student success teams were getting inundated with. And then they, their students success teams had all this extra time that they were able to actually spend engaging with students and talking about, you know, different problems that weren’t quick, easy FAQ answers. Um, so, you know, leveraging AI to do those monotonous tasks, those repetitive tasks, it helps create consistency in your messaging if everyone is using a and voice. GPT that will look at your content that’s going out and saying, well, these aren’t the terms that we use. I mean, you know, across an organization, you might have a dozen people. You might have 100 people who are sending out different marketing messages. If they’re all using one consistent brand voice AI tool, it can help kind of guide their messaging so that it now all seems to be coming from a consistent perspective, it’s all aligned with the brand voice. So there’s a ton of opportunities out there. It’s just trying to figure out which ones are going to have the highest return for you when you implement them.

Dan Sharp [00:14:38] And I think, so guess we got like our banner quote for this episode right there, right? That a good AI implementation is creating deeper human connections. Because yeah, because you’re not doing kind of the trivial stuff, you can really focus on like the big thinking problems. Yeah, I wanna switch gears a little bit just because earlier on, you mentioned that 60 to 80% savings with implementing AI and analyzing data. Yeah, am curious for those listening, What would you recommend in terms of just a good data practice so that they can leverage AI and actually have it see those rewards?

Brian Piper [00:15:14] Yeah, so first of all, I’ll throw out the caveat that you have to make sure that any data that you’re putting into the system does not have like PII, PHI, any personal information in there. Even with the paid versions of these tools, the models may not be training on the data that you are uploading, but that data is still in the systems. And we have seen… Instances where Google searches have started returning chat GPT props that were entered into the system. So there are ways that data can leak out. So unless you’re using either an on-site private large language model, or you are using an API version of one of these large language models where you can control where the data goes, you have to be careful about what data you use. That said… Any low risk data, like content performance data, all sorts of data that is public, that’s out there that anybody else can get access to can reveal incredible opportunities for you. So for example, when I was at University of Rochester, we had a dashboard that showed us all the different, it came directly from Google search console. So it showed us, all the terms that people were searching on and finding our content, what pages they were going to. How many times they were clicking all this great data. But even on just our news site, we had 950,000 keywords that we were ranking for. So typically, when I would do our analysis in the past, I would kind of sort a few different ways and look through the top like 50 or 100 results to figure out which ones were the ones that the low-hanging fruit we should go after and try to optimize for. Now we can feed that information into AI. And it can look through all of those keywords. It can find correlations, it can find anomalies, it can fine clusters of content topics where we already have search dominance, that then we can create more content in those areas. AI is great at gap analysis. I mean, as humans, we are great at seeing what’s in front of us, but not so great at seen what’s missing. AI is excellent at seeing what’s missing. So it can look at all the content you’re creating and saying, here’s a big gap in your content. Or if you’re looking at engagement cycle and trying to get people, your customers moving to the next step of the engagement cycle, AI can look at your content performance data at your Google Analytics and say, people are dropping off at the awareness stage and they’re not coming through and they are not getting to the consideration stage. So. You need to create some content that will move them to the next level in the cycle. So here are some ideas for that. So it’s one of the first use cases that I presented to my boss when we were trying to get them moving towards AI adoption. I did three months worth of work in a week, but it was work that we never would have done previously because it would have just taken too long and been too time intensive. But thanks to AI, we were able to get all sorts of insights out of multiple different data sets. That we never would have been able to spend our time doing before.

Tim Snedden [00:18:36] And really interesting as well about the time impact, right? The ability to produce more value, do more valuable work in a shorter time horizon, thanks to these advanced tools. But I would love to zoom in a bit into more of that individual contributor level. You had mentioned previously, custom GPTs for tone and voice analysis, for example. And I think that anyone who works in content who’s listening knows that tone and voice can be one of those really tricky things to nail down, especially when you’re just fresh to a project on a new client. And can be extremely difficult and some of these tools can really help with some of those more tricky tasks, accelerating your ability to become comfortable with a specific brand’s voice, especially. But it’s difficult to measure the impact of things like this, right? Do you have any recommendations or strategies for ways that you can measure the impact of AI implementations that veer towards sort of those softer skills?

Brian Piper [00:19:33] Yeah, and a lot of those, the impact is not directly from the original content production. So for example, I’ve worked with organizations who have put out leadership messages and it’s not always the leader that is writing those leadership messages. Someone else is crafting the message and then giving it to them for approval. So fairly easy to train a custom GPT or a model on you know, previous communications that have gone out so that it can get an idea of, you know what the tone and what the voice is and kind of how they structure their content. And so then when it’s time to create a new message you may not save time on the actual crafting of the message itself, but where you’re going to save time is on the approval process. So instead of having to go back and forth three or four times and have the you know, the president or whoever it is, like make changes or make adjustments in the message and then send it back. A lot of times having that custom GPT to pass that initial message through will help you save back and forth times. So sometimes it’s figuring out what you need to measure to show improvement. And then sometimes it really difficult to, you know specifically define where the improvement is. You just know that. You know, fundamentally, this process has gotten better. It’s gotten easier. That’s why during the pilot project phase, we really focus on projects that you can measure on where are we gonna show improvement on this. And then once you get into scaling, then it becomes easier to just identify roadblocks, identify barriers, where are you running into issues, where you’re spending time that you probably don’t need to be spending in your workflows. There’s an opportunity to create a AI solution for that.

Dan Sharp [00:21:23] Super timely that you guys are talking about custom GPTs. We just had Lena Sesardic on the pod and she was talking about just like the power behind building custom GPT’s, but also that it’s not actually as daunting as you might think it is, right? It’s actually pretty easy to do. That’s super cool.

Brian Piper [00:21:43] Yeah. I mean, the, the ability, you know, AI is really democratized code development in a lot of ways. I mean anybody can go in and vibe code. Anybody can go and make a custom GPT. I don’t know that I would use any of these for anything more than proof of concept. You know, I’m not going to launch a product out into market that I just vibe coded, you now in my basement, but having some developers on your staff who can look at the code and understand the code. I mean It all comes down to having that expertise. Expertise is still the secret sauce that makes any of these tools work better. You have to have enough knowledge about the strategy, the audience, the goals, to be able to feed the right information into the tool. And then at the end, you have to be to look at the output and know if it is accurate and if it quality and what parts and pieces you can use out of that. You know, we always tell people these tools are not. 100%. These are not copy and paste tools, at least not at this point. But if they can get you 60% of the way there, 40% of the way they are, think of all the time that you’re saving. And I’m using, I’ve got a dozen custom GPTs that I use for my personal life that saved me so much time. It’s well worth the $20 a month that I, that I spend on those. And then when you start looking at some of the ways that different businesses have implemented these. I mean, Moderna launched ChatGPT Enterprise to all 6,000 employees. And within four months, they had 750 custom GPTs that their employees had created and were sharing, and everybody was helping other people solve problems because it’s so easy to just go in and just tell this tool what you want it to do for you. It’s pretty incredible. Absolutely.

Tim Snedden [00:23:36] And I really vibe with what you’re saying about accelerating, especially that handoff, right? So many projects can encounter rough waters when something moves, for example, from content design or design to development. If you’re able to whip up a prototype, something that’s absolutely not secure or production ready, but it helps you communicate what you are striving for. I think that can really be a time accelerator. And I think it’s, we should dig into the use of custom GPTs in alternative ways as well. For example, for user testing, for example. There’s a lot of hubbub out there about things like synthetic users, for examples. What differences do you see? Well, first of all, do you use synthetic users? Do you have a strategy around that? And if so, what differences do see in the outputs of something like a synthetic user response versus what an actual human being would say in testing?

Brian Piper [00:24:29] So I love custom GPT personas. I think that is a huge opportunity. And that’s usually one of the first projects that I’ll take on when I go into an organization because it gives you a sounding board to be able to test all of your content against before you put it out into market. And I often tell people, don’t replace your traditional personas with AI created personas. Still create your traditional personas. If you have an ideal customer profile, that’s great. Create a traditional persona out of that, do interviews with your audience members, get all that data and all that information, and then dump all of that into a custom GPT. So now you have this kind of on-demand persona that you can ask questions about. You can dump in a piece of content, you can point it at an article online on your website and say, What are we missing out of this? What would be more relevant for you? What would resonate with you out of this content that we’re not including? And one of the neat things about custom GPTs is once you create one, then you can test it with that audience. You can give your custom GPT a set of questions, you can give a focus group audience that set of question and compare the answers, and then you could fine tune that model. And you can even create multiple different personas, because most businesses, you know, have more than one type of individual that they’re trying to target. So you can create multiple personas. You can pull them all into one chat room and have them talk with each other, and you can run virtual focus groups where you’re creating conversations between different personas within this simulated environment that then you can talk to and bounce ideas off of. I think there’s just a ton of opportunity. We’re going to see a lot more of these synthetic audiences being used for AB testing, um, you know, for, uh, providing feedback before you go out to market with products and tools for ideating. Um, not only are they good at, you know, reflecting back that audience, um. Thoughts and ideas, but they’re also very good at identifying gaps, finding things that you’re not doing, doing competitive analysis. Just so many opportunities with us.

Dan Sharp [00:26:51] I’m really curious as kind of the non-content expert of the three of us. Um, you know, when you’re working in an environment where you have that kind of a synthetic users, you have the, yeah, the chat bots got talking to each other. How do you avoid, you’ll maybe falling into a pitfall of relying solely on the AI generated outputs and not talking to real humans.

Brian Piper [00:27:16] Yeah. And a lot of that depends on the gravity of the content that you’re creating. So if you’re doing, if you creating content for crisis communications, you probably don’t want to be using AI a lot for that, maybe to do some gap analysis, maybe to look and see if the tone of the messaging comes across right for that audience, but I certainly wouldn’t be crafting any, you know, crisis messages using AI when we’ve seen instances of that. Not working out well for different organizations. And I think it really goes back to having that expertise at the end of the AI output. You have to be able to look at the output. You need at least a person who is an expert or who has some serious experience in that area. Ideally, a group of people from different backgrounds that can look at that content. And say, oh, well, we’re missing this, or we’re not addressing the needs of this audience. That’s where you really need the expertise, the experience. I mean, AI is really bad at taste. Is this better or is this better? You really need to human input and the experience to be able to look at that and say this is what we want. This is what’s gonna go out to our audience. And that’s one thing that AI cannot do is take responsibility for content that you put out. That always goes back to a human somewhere down the road. Absolutely.

Tim Snedden [00:28:47] So we’ve covered a lot of like different AI implementations in our field, right? From the data analysis through to the individual contributor level testing. But of course, it’s not just the people who are creating the content, planning the content that are using these tools, right. Like more web users are conducting web searches through large language models like chat GPT or perplexity. And so as more web searches go through these LLM tools, what approaches should content marketers be looking at today in order to tell your story when you know that your story is gonna be digested and transformed by a large language model.

Brian Piper [00:29:20] Yeah. And it’s critical, you know, the search landscape has, has changed dramatically. So I started doing SEO back in 96, which just means that I’m old, but it also means that I’ve seen a lot of things change in that technology. And I haven’t seen anything have the impact on search that AI has had. And, you, know, these zero click searches are really cutting down on the amount of organic traffic that websites are receiving. We’re seeing numbers. Across the board where companies are seeing less and less organic traffic. Now, a lot of that initial organic traffic that we got if you look at the intent of those searches were informational. So I think it’s fine that the AI is providing that information and answering those questions, but you wanna make sure that your content is getting into those search results. And the thing that I keep saying is that in this day and age… Distribution equals discoverability. You have to have the best content out there on your website, on YouTube, in Reddit, on your social channels, in different communities. That is where AI is going to find the information and to scrape and to collate all this data together. So you have to be in all those places. And especially as your audiences are searching on all those places, you have to have your content optimized for those channels. And the only way that we can do that is to use AI to help us repurpose, retarget and redistribute our high performing content. So you can use AI, to look at your content data, figure out what content is driving the most traffic, what questions people are asking about that content. And then figure out how you answer those questions on all the channels where your users are searching and where AI is scraping, so that when AI thinks about how it’s going to deliver that result, you’re top of mind for it. And we saw this as I work with different companies, different organizations, leaning into those featured snippets, like we used to target the featured snippet in Google, the people also ask. Those conversational questions are the way that people are searching on voice the way they’re searching in AI. And so if you put those questions and the answers in your content, you’re gonna show up better. You’re gonna be better in the AI results.

Dan Sharp [00:31:53] That was awesome. Yeah, Brian, we had, this has been an incredibly informative conversation. I feel like, yeah, as a product manager, if we just replace content strategy with product strategy, everything you said applies directly to that. So thank you for that. I have a few takeaways from our conversation. So I think, you know, the first one, right, good content strategy is not just blindly firing content out in the void and hoping for the best, right? It needs to be tied to your business outcomes, your business strategy. Love the conversation about change management, right? The change management’s not so much, hey, this tool is rolling out on Thursday. It’s about talking to the humans who are involved, promoting the value, starting small, sharing the wins, really communicating. You had the, speaking of as a human connection, right, we talked about how AI as a tool, it makes it easy to do kind of those repetitive tasks. You have the FAQ type stuff. And deepens that human connection where people can go and solve the hard problems, you know, kind of person to person, group to group. Really, really love that. I love how you’re mentioning AI is so good at finding gaps that it’s so strong at analyzing data. So if you have a thoughtful data strategy you can reap huge rewards out of your AI tools but also be careful and make sure that your private information isn’t accidentally ending out in the public domain. So this is great. Want to wrap it up just with two thoughts of you. So the first one Brian is you know, what have you got going on? What would you like our listeners to hear that you’re doing and then the second one I personally you know It’s a selfish question. But you know what are you reading? What should I be adding to my to read list?

Brian Piper [00:33:34] Yeah, so I’m doing a lot of speaking at conferences, connecting with audiences, and more and more consulting. So any group or organization business looking to figure out how to integrate AI in a responsible, ethical, and effective way, I’m glad to help out with that. And I just finished reading Empire of AI, which was a fascinating book, really goes into some different kind of history of the AI evolution. And then one of my favorite books is Ethan Molek’s Co-Intelligence. Really consumable, really gets down to like how to work and collaborate with these tools. And if you want a book that is terrifying and exciting at the same time, The Coming Wave by Mustafa Suleiman is fantastic.

Dan Sharp [00:34:32] All right, there’s some good recommendations. And don’t forget your own book, Epic Content Marketing.

Brian Piper [00:34:36] Absolutely. If you’re looking to do content marketing, that is the handbook for that.

Dan Sharp [00:34:42] All right, well, this has been an awesome conversation. I am much better informed than I was 30 minutes ago. So, Tim, Brian, really appreciate you guys coming and hanging out with me.

Brian Piper [00:34:52] Thanks so much for having me on. Really appreciate it. Great conversation.

Outro [00:35:00] Well, that’s it for today. In line with our goals of transparency and listening, we really want to hear from you. Sean and I are committed to reading every piece of feedback that we get. So please leave a comment or a rating wherever you’re listening to this podcast. Not only does it help us continue to improve, but it also helps the show climb up the rankings so that we can help other listeners move, touch, and inspire the world just like you’re doing. Thanks everyone, we’ll see you next episode.