In-Ear Insights from Trust Insights

About In-Ear Insights from Trust Insights

We help marketers get better results from their marketing data.

Publisher

Trust Insights

Category

business · business · business

Language

en

Explicit

No

First episode

30 Sept 2020

Latest episode

16 Sept 2026

Latest episodes

252 episodes in the feed.

1. 

   16 Sept 2026

   In-Ear Insights: How AI Impacts Billable Hours

   In this episode of In-Ear Insights, the Trust Insights podcast, Katie discusses modern billing challenges, and how AI impacts billable hours. Katie tackles billable hours and what happens to them once AI cuts your work time down. She makes the case for value-based pricing over clocking hours, walks through the 5P framework for keeping your process clear to clients, and explains why saving time only counts if you actually do something with it. 00:00 – Introduction 02:15 – The billable hour dilemma 06:40 – How artificial tools change the workflow 12:30 – Finding value in the process 18:15 – Call to action Watch this episode to see how you can adapt your daily routine and charge fairly for your growing skills. Can’t see anything? Watch it on YouTube here. Listen to the audio here: https://traffic.libsyn.com/inearinsights/tipodcast-ai-and-billable-hours.mp3 Download the MP3 audio here. Need help with your company’s data and analytics? Let us know! Join our free Slack group for marketers interested in analytics! [podcastsponsor] Machine-Generated Transcript What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode. Katie Robbert: In this week’s In-Ear Insights, you get just me, Katie. Chris is on the road. And so we’re going to try something a little bit different. You get me, and as always with me is my chief marketing officer, Georgia. So she may or may not pop in. This week I want to talk about billable hours. This is a hot topic in the world of consulting. It always has been. Billable hours is one of those things that people have always struggled to really define outside of legal, because that’s really where the precedent comes from, isn’t it? When I worked at the agency, we were tasked with all of our work being billable. But when I looked at what we were doing, and this isn’t a knock at agencies, this is just the way that they work, we’re looking at this client has signed up for this much retainer. And this much retainer covers this much work. And so when you start to break down budgets at a very high level, you have however many people are going to be assigned to that particular retainer. And then you have to calculate out, well, what is the bill rate for this for any individual? And then sort of back into the numbers. When I was working as a project manager, this is how we would do our budgeting. And so we would have software developers, we would have creative folks, we would have our marketing team. And every single one of those people has to have some sort of value assigned to them. And that value then translates into how many hours they can contribute to any given project or task. And so if the task was we have to create a one-page brochure, that’s likely going to involve our marketing team and our creative team. Therefore, our software development team doesn’t need hours unless it’s something being put up on the website. So then they need to have time. And that’s like roughly how you would calculate the budget. Now, when you’re talking about billable hours, you’re kind of working in the opposite. So you have this idea of how many hours any given person. So let’s say my bill rate was 100 dollars an hour just for easy math. And I had 10 hours to complete something. So of course I’m saying easy math, and I can’t calculate 10 times 100. So that’s a thousand. So let’s say I have a thousand dollars. Let’s say my bill rate is 100 dollars an hour. So I have 10 hours that I can work with. What ends up happening when you have those billable hours is you say, okay, I have 10 hours. So I have to find 10 hours worth of work to bill, even if the work is only going to take me five hours. Because then I have this five-hour bucket left that I’ve already told them I’m looking at bill for. Or if you&#82

2. 

   9 Sept 2026

   In-Ear Insights: The Non-Techie’s Journey to Local AI

   In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how you will cut energy waste by shifting to local AI. You’ll discover how to structure your planning so you track tools after defining your goals. You’ll learn how to build a self-updating memory system that keeps every project organized without extra effort. You’ll walk away with a clear checklist to audit your routine and select the exact technology you need. 00:00 – Introduction 03:15 – The trap of picking platforms first 08:42 – Building a self-updating AI memory system 14:30 – Replacing heavy tools with simple scripts 21:05 – Balancing cost and environmental impact 27:50 – Call to action Press play now to uncover the practical strategies that will transform how you handle technology. Watch the video here: Can’t see anything? Watch it on YouTube here. Listen to the audio here: https://traffic.libsyn.com/inearinsights/tipodcast-the-journey-to-local-ai.mp3 Download the MP3 audio here. Need help with your company’s data and analytics? Let us know! Join our free Slack group for marketers interested in analytics! [podcastsponsor] Machine-Generated Transcript What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode. Christopher S. Penn: In this week’s In-Ear Insights, let’s talk about local AI and the reasons and, well, the implementation of it. We’ve talked in the past, and you’ve seen in the Trust Insights newsletter and on LinkedIn and all the places we post, about how local AI is one of the ways that you can reduce the environmental impact of generative AI by doing things on computers under your control, either on your literal laptop or maybe specialized desktop devices like the Nvidia DGX Spark. But either way, you don’t need a massive hyperscaler data center that vacuums up entire rivers and electrical power grids just to write email summaries. Katie, you’ve been on this journey for a bit now. What have you found and where are things going for you? Katie Robbert: It’s been interesting so far. One of the goals that I had when I started this journey was to document everything so I could share it in the Inbox Insights newsletter, which you can subscribe to at TrustInsights.ai/newsletter. Each week I plan on going through basically the big things that I’ve learned, which to be honest, is a lot. One of the first things that became very clear to me is that I was breaking my own rules when it came to the 5P framework by Trust Insights. And so if you’re not aware, you can go to trustinsights.ai/5p-framework. The 5Ps are purpose, people, process, platform, and performance. And so in this journey to understand what it looks like to migrate to local AI, I was leading with the platform. I was doing what we highly recommend everybody not do. And I was doing it. And I want to be very clear, and I want to call myself out because it’s such an easy trap to fall into. We’re like, oh, I should move to local AI because it’s going to be greener, it’s going to save the environment, it’s going to do this or that. I already chose the platform. I already chose the solution before really exploring the other P’s. And so, acknowledging that—I didn’t realize it until I was about partway through creating the first set of requirements. When I looked at it and said, I started with the platform. What am I doing? This is ridiculous. So all to say, this is why we talk about the 5P framework the way that we do. So where I started was with a user story. As a persona, I want to say that a user story is a simple three-part sentence: As the CEO and an environmentally conscious citizen, I want to figure out where I am negatively impacting the environment with my use of AI without losing productivity, so that I can feel good about the work

3. 

   2 Sept 2026

   In-Ear Insights: How to Create New Content Ideas in a Sea of AI Sameness

   In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how you will overcome creative blocks while navigating information overload and create new content ideas. You’ll discover why AI keeps offering generic advice and how you can bypass those predictable suggestions. You’ll learn to pull unconventional strategies from unrelated fields and apply them to your marketing plans. You’ll identify two distinct thinking patterns that unlock fresh content ideas when your usual topics feel stale. 00:00 – Introduction 03:15 – The challenge of information overload 07:40 – Why AI defaults to common advice 12:20 – Deductive versus inductive thinking 16:50 – Finding psychological safety for exploration 21:10 – Cross-pollinating ideas from other industries 26:35 – A practical research workflow 31:00 – Call to action Hit play to uncover how shifting your perspective transforms stale ideas into bold marketing breakthroughs. Watch the video here: Can’t see anything? Watch it on YouTube here. Listen to the audio here: https://traffic.libsyn.com/inearinsights/tipodcast-content-marketing-inspirations.mp3 Download the MP3 audio here. Need help with your company’s data and analytics? Let us know! Join our free Slack group for marketers interested in analytics! [podcastsponsor] Machine-Generated Transcript What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode. Christopher S. Penn: In this week’s In Ear Insights. This is the age of AI, where there is more information than ever, more data than ever, more everything than ever before. And yet behind the scenes, when we were prepping for this episode today, Katie, I said, hey, what would you like to talk about today? And your response was, I have no idea. Let me ask you a couple of different questions. One, is it that the material recovering feels like it’s been done before, or is it also that there’s just so much to choose from that you end up with what Barry Schwartz calls the paralysis of choice? Katie Robbert: Yes. So let me pull those apart. So much is changing in the field of AI and where we focus down in on with analytics, marketing, operations, and helping businesses. We do such a good job of thoroughly exploring topics, and the process that we have for creating content is so efficient that we can churn out a lot of it very quickly. So I feel like personally, the things that we would cover that are relevant to our business and relevant to our audience now there’s always another stone to turn over. But then you start getting really deep into the weeds. And where I wrestle is do you start alienating parts of your audience to really focus on those very specific niche pieces? To the other part of the question? Yes, I do feel like there’s so much information. Again, this is sort of the side effect of generative AI that you sort of become numb and blind to all of the information because there’s so much. I remember when we worked at the agency and you used to produce this report, I think it was yearly, on the number of pieces of content created. And you know, this was prior to generative AI, this was just the number would go up exponentially year over year. And you know, I can’t even imagine what that number would be now. And just sort of seeing the uptick. And so I don’t, I feel like I’m deep in the weeds of stuff that I’m working on day to day in the business. Part of it is I’m like, I don’t know if anybody would even care to hear about this, if it even resonates or if it makes sense or if we’re supposed to be doing that big thinking and like introducing new concepts and you know, doing the thought leadership of what’s happening with AI and analytics. Here’s the thing about analytics. There’s only so many ways. There&#821

Reach and audience

Public platform figures. Ratings count people who left a rating, not total listeners.

YouTube views

58,054

Score snapshot 15 Sept 2025

Apple Podcasts (US)

5.0 / 5

9 ratings

Spotify

5.0 / 5

2 ratings

Podcast Authority Score: 54 / 100

A composite of feed quality, social presence, YouTube performance and engagement. Read the methodology.

Quality

41

Social presence

0

YouTube

85

Engagement

85