In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss critical questions about integrating AI into marketing. You will learn how to prepare your data for AI to avoid costly errors. You will discover strategies to communicate the strategic importance of AI to your executive team. You will understand which AI tools are best for specific data analysis tasks. You will gain insights into managing ethical considerations and resource limitations when adopting AI. Watch now to future-proof your marketing approach!

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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 – 00:00
In this week’s In Ear Insights, boy, have we got a whole bunch of mail. We’ve obviously been on the road a lot doing events. A lot. Katie, you did the AI for B2B summit with the Marketing AI Institute not too long ago, and we have piles of questions—there’s never enough time.

Let’s tackle this first one from Anthony, which is an interesting question. It’s a long one.

He said in Katie’s presentation about making sure marketing data is ready to work in AI: “We know AI sometimes gives confident but incorrect results, especially with large data sets.” He goes with this long example about the Oscars. How can marketers make sure their data processes catch small but important AI-generated errors like that? And how mistake-proof is the 6C framework that you presented in the talk?

Katie Robbert – 00:48
The 6C framework is only as error-proof as you are prepared, is maybe the best way to put it. Unsurprisingly, I’m going to pull up the five P’s to start with: Purpose, People, Process, Platform, Performance.

This is where we suggest people start with getting ready before you start using the 6 Cs because first you want to understand what it is that I’m trying to do. The crappy answer is nothing is ever fully error-proof, but things are going to get you pretty close.

When we talk about marketing data, we always talk about it as directional versus exact because there are things out of your control in terms of how it’s collected, or what people think or their perceptions of what the responses should be, whatever the situation is.

Katie Robbert – 01:49
If it’s never going to be 100% perfect, but it’s going to be directional and give you the guidance you need to answer the question being asked.

Which brings us back to the five Ps: What is the question being asked? Why are we doing this? Who’s involved?

This is where you put down who are the people contributing the data, but also who are the people owning the data, cleaning the data, maintaining the data, accessing