In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss CEO AI strategy and whether businesses should add AI to everything just because it’s trendy. They explain why following directives to “AI-ify everything” can backfire, and share a smarter approach using processes like creating user stories and the Trust Insights 5P Framework.

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In this week’s In-Ear Insights, is AI, like Nutella doesn’t go on everything.

Should it be in everything.

And there’s two different perspectives on this.

Katie, you saw something on? I think it was threads.

Right? The which is it? Which is a replacement for the long more on Twitter? What do you want to set the stage as to what it is that people are discussing about AI?

Yeah, absolutely.

So over the weekend, I was just sort of scrolling through.

And, you know, what’s interesting about threads is it’s a blend of personal and professional conversation, at least for me, I usually try to keep all my social platforms separate.

And this one, this one thread came up where the poster said, two of my friends at different companies reported leadership literally saying, quote, put AI in everything was the new mission, not based on what, on what it’s good for, what their products need, just AI a phi, everything because investors like it.

And I grabbed it, because, you know, we, Chris, you and I often talk about, you know, the prop, like the five P’s.

And, you know, I’ve mentioned that AI isn’t the solution to everything.

And so it’s a little concerning to me now that, you know, in very broad strokes, leaders are saying, without doing like a five p or something like that, they’re just saying, Put AI in everything.

And to me, that’s very much a panic move.

It’s a short sighted move.

And it’s also an ignorant move, because they don’t know what that means.

So where do you want to start with that, Chris?

So I sort of agree with that.

It here’s why not because investors like

In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how to better manage people and marketing through frameworks, predictive analytics, and anticipation. We talk about how anticipating questions, needs, and potential issues can help smooth communications between managers and employees. Chris shares how predictive analytics and AI assistants can help managers plan for situations and employee interactions. Katie suggests using frameworks to set context and clarify purpose when starting potentially tough conversations. We explore emotional barriers to anticipation and how to create psychological safety. Tune in to learn communication tips for improving management and marketing outcomes through anticipating needs.

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In this week’s In-Ear Insights, one of the most popular sayings when flying a plane is a three word catchphrase.

aviate, navigate, communicate, fly the plane, keep falling out the sky, navigate, make sure you go and we’re supposed to go and then communicate.

However, there are all these types of frameworks for other kinds of work management, SEO, content, marketing, etc.

So, Katie, when you think about the kinds of frameworks that are helpful to you, and particularly for managing people and for marketing, what is what are some that come to mind for you that would help you as a stakeholder, manage someone better?

You know, it’s funny, I, I don’t have one.

And that’s, that’s part of the problem.

So there’s, you know, there’s frameworks around, you know, communication in terms of is it helpful? Is it necessary? I don’t remember exactly what that one is.

But, uh, there’s a framework.

And, you know, it’s interesting, because it’s actually taught like, all the way down to like preschool and kindergarten, in terms of communication.

And it’s the idea is, if you have something to say, you know, is it relevant? Is it helpful? Is it necessary? Like, is it timely, and I forget what the acronym is.

But that’s a framework that has always stuck with me as an adult, that even down to, you know, kids were five years old need to learn or need to understand how to participate helpfully in a conv

In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the peculiar budget cuts that CMOs are making for 2024 marketing budgets and how it will impact marketing teams. We talk about the surprising reductions in spending for CRM, customer experience, and brand building. We analyze the disconnect between using AI to improve productivity while severely cutting staff. Katie and Chris examine the different types of creative thinking needed on marketing teams and the risks of letting go of divergent thinkers. We explain why AI alone can’t magically fix poor data tracking or replace most marketing roles. Katie and Chris provide helpful perspective on AI’s capabilities and limitations that decision makers should understand before slashing budgets. Tune in to gain insight into crafting budgets and strategies that balance AI and human skills.

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In this week’s In-Ear Insights, let’s talk about marketing, budgeting 2024.

And some very peculiar decisions that CMOs are making around their spending.

So Katie, the new cmo survey just came out not too long ago, according to what you were just saying.

And there’s a whole bunch of really interesting things.

In this biannual presentation, one of the ones that really stood out to me that I was just absolutely shocked at was the demos decided, in terms of where they’re going to be changing, they’re spending in 9%, reduction, CRM, okay, I can understand that a 90% reduction in customer experience spending, which seems counterintuitive since customer experiences while the less differentiators you have.

And then a whopping 43% reduction in spending on brand building, which to me, is quite possibly the stupidest thing you could do in 24.

So Katie, what were your takeaways? As you were looking through this deck, particularly on where it’s where people are spending their budgets for 2024? Well,

there’s a couple of things.

So it looks like budgets are budgets are basically still going down, but it’s dropped near pre COVID levels.

And this is what we’re talking three, four years ago now.

Budgets are getting slashed left and right.

I mean, we’re seeing it in the job market, we’re seeing people get laid off.

In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss changing your MarTech stack and how to thoughtfully add new marketing technology to your existing tech stack. They talk through the importance of gathering requirements upfront to ensure you select tools that solve your specific business needs. Katie asks questions to understand the impact of adding a new email marketing platform on their overall data strategy. Chris stresses taking a methodical approach to integrating new systems to avoid creating data silos. They explain the value of mapping where all your data resides to enable accessing the right information. Katie and Chris urge doing the planning work before implementation to prevent buyer’s remorse down the road. They emphasize considering the people and process changes required, not just the technology itself.

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In this week’s In-Ear Insights, let’s talk about adding to your marketing tech stack.

So your Mar tech stack is the collection of tools, software, services, things like that, that power, your marketing.

And lots of companies have lots of technology.

But not lots of companies have good governance around it.

In fact, even though the CMO spends more on technology services than the CIO does, the marketing function when it comes to technology tends to be a little disorganized.

And as a result, companies can end up spending a lot more and having lesser results from their marketing tech than they do the regular it.

In fact, a few years ago, Katie, you and I were working on a client, where they had, we had purchased three different instances of Salesforce that were not connected together, and were conflicting with each other.

And they lost like the a billion and a half dollars of pipeline was missing.

So we’re talking about changing up our own tech stack here at Trust Insights.

So Katie, you said you have some questions? What kinds of questions have you got?

Well, before I get into the specifics, you know, I think that one of the things that a lot of companies, you know, and you know I include us in this conversation can potentially do wrong, is just stacking, literally stacking more tools on top of the stack.

And so you get all of these layers of tools.

A

In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss key takeaways from the recent MarketingProfs B2B Forum conference. We cover topics like AI, digital marketing tactics, and generative content. Katie shares insights on non-AI focused sessions and the importance of remembering that technology is just a tool. Chris discusses legal implications around AI and copyright. We talk about the risks of over relying on AI, forgetting basic marketing skills, and striking a balance between technology and fundamentals. Overall, we provide an informative recap of B2B Forum while emphasizing the need to use AI wisely and maintain core competencies.

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In this week’s In-Ear Insights, we are back from a whirlwind tour at the MarketingProfs B2B forum and 2023.

Katie, you spoke, I spoke, and we had a bunch of really great sessions.

So let’s start off Katie with what are some of the things from the sessions and things you went to that you that you’ve learned some lessons learned, that you think are worth sharing?

I think the big thing, I mean, obviously, everyone is obsessed with generative AI right now, that was probably one of the larger themes that I noticed at the event, including with the opening remarks, and then the two follow up sessions to that we’re also focused on AI.

But the focus was more so how to make AI work for you not to be afraid of it and not to let it sort of take over everything.

As in, it’s just going to do your job.

No, it’s just going to do this.

But it was more around really understanding it.

That being said, because it was the thing that everybody was focused on.

The sessions that didn’t folk didn’t feature anything AI tended to be less attended.

Because everybody wanted to know the tools.

Everybody wants to know all the shiny objects and gadgets.

And so if I look objectively from the outside, I think that that is a risk.

Because the tools change so fast.

And it means that you’re not focusing on all of the other skills and things that go along with generative AI.

What was your takeaway?

In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how to vet and hire the right analytics expert or analytics agency for your business. We cover the importance of having your processes and goals clearly defined before bringing on help, so you can assess if a potential hire or agency is truly a good fit. We explain the types of strategic questions an analytics expert should ask in the interview process to demonstrate their experience. We also compare the pros and cons of hiring an individual contractor versus an entire analytics agency. Finally, we offer suggestions on evaluating skills and certifications when deciding between candidates. The key takeaway is doing your homework on your business needs first allows you to hire the right level of analytics talent.

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In this week’s In-Ear Insights, let’s go back to our roots.

I know we’ve been talking a lot about artificial intelligence lately, but one of the things that is so important is things like AI feed off of the data we already have, right? We have analytics systems of all kinds.

We have Adobe analytics, Google Analytics pathoma, we have Salesforce and Hubspot, all these different marketing systems that contain tons of data.

It’s valuable data.

And very often, it’s data that is not in very good condition.

So sometimes we need to bring in outside help, we need to bring in a new team member, maybe a consultant, maybe an agency, and one of the questions that we get a lot is how do I know who to to bring on? And the sort of the unvoiced question is, how do I not know that they’re not full of as the Spanish what’s the excrement? Are they Toto?

I’ve heard that one in a while.

You know, and it’s such an interesting question.

And it it goes under, you know, why you should have a really strong hiring manager, which isn’t for this episode, but you know, for to get it to get a good understanding of what someone can and can’t do.

It’s not enough to just say, hey, so you’ve worked with data before, right? And they say, Yeah, of course I have you, you need to dig deeper.

And so the way that I like to approach that is to ask more sc

In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the impact of generative AI on businesses, especially hourly businesses like agencies. They explore the problems with time tracking and utilization rates, and how it breeds inefficiency. Katie shares her experience being forced to innovate on command. Chris talks about the importance of prompting generative AI with creative ideas. They discuss how to better align employee and employer incentives through value-based compensation. Ultimately, they conclude that the key is to leverage generative AI to automate repetitive tasks so people can focus on innovation.

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In this week’s In-Ear Insights, let’s talk about the impact that generative AI is having on businesses.

We know even just from what’s already happened in the last 18 months, that gender of AI obviously impacts, things like content creation, it impacts image creation, if you are running a clipart, or stock photography businesses is obviously a significant problem.

But over the weekend, and last week, I was chatting with some folks about hourly businesses.

And Katie, you and I, we worked at an agency for five ish years, and which is an hourly business with timesheets and time tracking and productivity and utilization and all that fun.

And I wanted to get your perspective on how you see things like generative AI impacting those businesses, because for a lot of C level folks, it’s a, it’s not just a technology thing, it is a this is going to fundamentally change the nature of your organization.

Well, let me take a step back for a second.

So you know, when we worked with the agency, we were definitely billing like that.

And then prior to that, when I worked at the other company, I was responsible for the overall product budget.

And every week when people would submit their timesheets, you know, I was me and the other product owners were chasing people around, trying to get them to reallocate their time, because when, you know, they didn’t know where to build their time, they would bill it to our products just as like, you know, general overhead, which would put all of our product budgets over.

And a lot of times, it was a software development team who were doing, you know, cleaning up technical debt,

In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the important issue of who owns your data. They talk about why companies need to be aware of who has access to their data, especially when using tools like Google Analytics or generative AI models. Katie and Chris explain how you may be giving competitors access to your data indirectly through models trained on your data. They offer suggestions for ensuring you maintain control and ownership of sensitive company data when leveraging new technologies like AI. Tune in to hear their examples and recommendations for governance, collaboration, and setting up the right data architecture.

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In this week’s In-Ear Insights, let’s talk about data specifically, who owns your data and who’s using your data.

We’ve had a couple of interesting things happen recently one, we’ve had one client who wants to do a specific kind of tracking on their website and Google Analytics 4 simply cannot do it, you would have to make some significant modifications to the code of your website to accommodate GA for as opposed to making the tool work for you.

That’s one aspect.

Second aspect ChatGPT recently announced its ChatGPT Enterprise Edition, which we’re pretty sure it’s going to be reassuringly expensive.

And the promise there is that your data will not be used by OpenAI to train their models.

This goes in tandem with a huge number of lawsuits and questions and things going on.

about who’s who owns the data that these these models are being trained on.

That also goes with questions we’ve had in our Slack groups like our analytics market, a Slack group, which you can get to at trust insights.ai/analytics, for marketers, someone asking, Hey, I have some interest in a custom model for this.

And our first question is, do you have the data because if you don’t have the data, you can’t tune a model.

And you probably don’t want to be making part of the secret sauce of your business based on data that you don’t own that you didn’t put into the model yourself.

So all of this to say that right now, companies are at an inflection point, if they if you don’t own your data, or at least a good portion of it.

You may be at risk not only from just not beyond do basic things like analytics tracking, but may not be able to take a

In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris provide a guide to generative AI for the C-Suite and discuss how CEOs should approach understanding and using large language models and AI, starting with identifying business problems first before considering technology solutions.

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In this episode of In-Ear Insights, we are talking about large language models in AI, but specific to a corner office, the big chair, the CEO, and that, of course, Katie is you.

When you look at the landscape of all the things that we talk about in AI, and you think of it with your CEO hat on, what are the things that you as a CEO need to know about? In order in order to be effective in order to understand the opportunities, the risks stuff? What is what’s on your mind, when you think about all the stuff that I foam at the mouth that regularly?

You know, the first question I always ask is, tell me when I need to pay attention? And, you know, that’s a loaded question, because it depends on what the business is about what the CEO cares about.

So for us, you know, my questions more specifically would be, well, you know, we’ll generative AI, will large language models make our business better? What does that mean? Will we have a competitive advantage? If we integrate large language models into our business? What does that mean for my bottom line? Is there going to be a lot of cost upfront to get them set up? But then in the longer term, I’m going to save much more money than I invested into this thing? What does that mean? For me needing to skill up myself? What does that mean, for me needing to skill up my team? What kinds of resources do I need to consider? Are there legal implications for using this kind of technology? That’s a question I have about any kind of tech that we bring on of what are the legal implications? What are the security considerations? Do? You know we personally at Trust Insights don’t deal with HIPAA data protected health personally identifiable? We just that’s thankfully, not the nature of our business.

But those are, that doesn’t mean that I don’t need to be aware of what that means.

So I guess so to sort of sum up, the first question I would ask is, what information do I need as a CEO? Right now, today? What do I need to be paying attention to? Do I need to be paying attention to all the little startups who have their own

In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how to mitigate gender bias in AI systems by assessing risk, implementing human review processes, and building an equitable company culture.

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In this week’s In-Ear Insights, let’s talk about gender bias and fairness, specifically, in the context of artificial intelligence, it is no secret, obviously, that large language models and diffuser models both have biases in them on things like gender, and race or ethnicity, background, religion, etc.

Because these models are cobbled together from the entirety of the public’s content on the internet.

And the reality is, these are in many ways, mirrors of humanity itself, and humanity contains biases and things to look at when we talk about gender bias and fairness, if you wouldn’t mind start off, in your mind, what is fairness? What does that mean to you, particularly since you’re of a different gender than I am? And how do you see that manifesting in the uses of artificial intelligence.

So at its core, fairness, is giving equal opportunity to any one person to any one gender to any one background, ethnicity.

So Chris, if you and I were, you know,

both presented with a speaking opportunity.

To be fair, we would both be given equal, you know, opportunity to, you know, apply for it to, you know, when the opportunity, whatever the thing is, unfortunately, what tends to happen is an unconscious bias or conscious bias of, well,

Chris is a man and Chris has been doing this longer.

And Chris has data scientist in his title, so therefore, he must be more well suited for this.

And I’ve never heard of Katie, even though she’s spoken at a bunch of events and doesn’t have scientist in her title.

So I’m going to pick Chris, because I know him versus Katie.

So that’s sort of where the unfairness comes in.

When you start to create gender bias.

That’s when you’re like, well, this audience is going to be more men.

So they probably respond better to a man speaking versus a woman.

And that’s, and so and then therefore, Chris, youR