Almost Timely News: 🗞️ Cultivating an AI Mindset, Part 1 (2025-11-09)

How you think about AI leads to your AI results

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What’s On My Mind: Cultivating an AI Mindset

This week, let’s talk about mindset. No, this isn’t some esoteric deep dive into mysticism or such, but a concrete talk about mindset - your mindset - as you approach the use of AI.

Lots of people and lots of studies show conflicting information about the value we derive from AI. Some studies show no benefit. Some studies show massive benefit. Some studies show no ROI. Some studies show bountiful ROI. What are we supposed to believe?

Ethan Mollick of Wharton Business School likes to call this the jagged edge - the technology is so new that it is unevenly skilled in what it can do. I extend that to us, to the people using it. A skillful worker using AI can punch well above their weight, becoming that mythical 10x worker. Remember a couple years ago when everyone was talking about the 10X marketer and the 10X coder? Well, today with generative AI, that’s actually achievable. An unskilled worker using AI will create more work and save neither time nor money, and generate bad results.

Here’s the disappointing news about this newsletter: I don’t have an answer about how to uniformly get an unskilled worker to be a skilled one. I can help people individually, but every person brings their own strengths and weaknesses to AI, and as I say ad nauseam, AI takes the good and makes it better, and takes the bad and makes it worse.

A highly motivated, highly creative person will become even more productive, motivated, and creative with AI.

A lazy, checked out person will become even more lazy and checked out with AI.

So with those warnings dispensed, let’s dig into what I can share: how I think about AI broadly, and the tactical lessons you can borrow from for your own uses. I can’t promise you success if you adopt the different aspects of my AI mindset, but I can promise you that it will at least broaden your thinking and hopefully help you generate better results.

Part 1: Fundamentals

To understand generative AI, you need a fundamental understanding of how the technology itself works. Most AI incorporates in part or in whole a predictive technology called transformers, no relation to the awesome 1980s toys. The transformers architecture is notable for many neat tricks that overcome previous predictive algorithms problems, but we can boil it down to something really simple:

Everything helps predict the next thing.

In my keynotes and workshops, I explain it like this: imagine you’re texting with a friend. A normal friend responds to your previous text, and you go back and forth, texting as one does.

Now imagine you have a really weird friend. That really weird friend, instead of just replying to your message, copies and pastes THE ENTIRE CHAT HISTORY in every message. They copy everything you’ve ever said in that chat. They’re really weird. But in doing so, both you and they get to see the entire conversation history in every message. We could debate how convenient that is, but at the very least it does show you everything that’s happened up till that point.

That’s what’s happening when you prompt AI. It’s not responding to the previous message. Every word in the conversation, from both sides, is part of what it uses to respond, which is why it’s so capable. When we prompt AI, it is leveraging all of the knowledge from within that chat to predict its next response.

Knowing this fundamental fact means you know how it can go wrong. Say you’re having a conversation about Dorset apple cake and how to make it. Then you switch topics and start talking, in the same chat, about SEO. Guess what’s happening. Has the AI forgotten about apple cake? Nope. That conversation piece is still influencing the overall conversation, and your results will be skewed. You will get different answers about SEO than you would have if the apple cake conversation had not been there.

Say a conversation goes on too long. Can you imagine if YOU had to copy and paste the entire conversation over and over again? Eventually you’d get tired. You might make mistakes. You might forget to copy parts of the conversation. The same thing happens to AI - a conversation that goes on too long exceeds its short term memory, and it loses coherence. It forgets, or worse, mangles words and concepts.

Part of the AI mindset is knowing what the tools can and can’t do, understanding their limits so you work with them, not against them.

Here’s another basic: all AI is based on probability, on generating the most probable responses. I talk about this at length in my book, Almost Timeless: 48 Foundation Principles of Generative AI but here’s a simple trick to use. Avoid asking AI for a single answer. Instead, ask it for several, and require them to be different in some tangible way.

For example, instead of saying “give me a title for this video based on the transcript and my audience’s needs (combined with information about who your audience is)”, you would say something like, “give me 3 highly appealing title candidates based on the transcript and my audience’s needs, plus 3 wildly incorrect titles that would annoy my audience”. Why does this work? Because it forces the AI model to generate a wider set of probabilities, a wider range of options.

What you’re doing is skewing the probabilities by forcing it to do something almost counterintuitive and that helps it be more creative.

Part 2: It’s All About The Salad

One of the ways I explain how AI works, conceptually, is like cooking. I do love cooking analogies, mainly because everyone eats; there are few other activities so universal.

Suppose we’re making a salad. Maybe we’re making a chicken Caesar salad. What goes into making a salad? You should probably have a recipe or at least an idea of what to make it with. You should probably have ingredients - hard to eat without them. You should know techniques like chopping, mixing, etc. And be able to make simple dressings from things like egg yolks and vinegar. You should know how to grate cheese, and in what order to introduce ingredients. You’ll need a salad bowl for mixing, plates for serving, tongs or utensils for tossing, probably some way to grill chicken, if you’re making a grilled chicken Caesar salad.

All of that is pretty straightforward. Yet people try to dramatically overcomplicate AI. Here’s the breakdown:

• Recipe = Prompt

• Ingredients = Data

• Technique = Process

• Tools = Model

• Chef = Operator or Agent

What should be immediately obvious from this grand analogy is that AI is far more than just prompting. Prompting is important, yes. Making a dish without a recipe, especially a dish you’ve never made before, is not going to go well. But equally important are things like good ingredients. No amount of skill or great recipes can overcome a bowl of rotten, moldy lettuce.

This is critical to understand because if you’re wondering why your AI efforts aren’t yielding results and you’re stuck obsessing over prompts, the problem might be elsewhere - bad ingredients, no skills, bad tools, incompetent chef. Fixating on the prompt blinds you to all the other things that could be going wrong.

Part 3: The Algorithmic Mindset

The algorithmic mindset is something I learned from the culture at Google. I never worked there, but I’ve studied a bit how they do things, in particular how their engineers do things. There’s a cardinal rule which is probably apocryphal: if you do anything more than twice, write code so you never do it again.

This extends to our use of AI. When I’m confronted with a problem, my first line of inquiry is whether the problem is a one-off or is going to be repeated in the future. If it’s likely to be repeated, part of me knows beyond a shadow of a doubt to reach for code, tools, etc. That enable automation, That enable me to generate the same result again quickly by not having to reinvent the wheel.

For example, whenever I get a request from a client for an ad hoc report, and it’s something that’s not super specific, there’s a good chance they’ll ask for that report in the future. When that happens, if I approached the problem algorithmically, I can re-run my code or process and generate a new version of the report in a few moments. If I didn’t approach the problem that way and I did something ad hoc, say in a spreadsheet or something, it might take the same hour or two as the first time.

With tools like Google Opal (now available broadly), n8n, Make, Zapier, etc. Even non-coders can build relatively sophisticated automations, which means changing how you think about solving problems. Even a basic Gem/GPT/etc. Can be re-used over and over again to solve a specific problem rather than writing net new prompts each time or copying and pasting from a prompt library.

If you do it more than twice, build some kind of automation to never do it again. That is the algorithmic mindset and it’s a key part of the AI mindset.

Part 4: Lateral Thinking

One of the most powerful thinking tools I’ve learned from the martial arts is the concept of lateral thinking, taking a solution from one space and applying it to a different space. In the martial arts, we have a three step learning process, in Japanese called shu-ha-ri - learn the basics, vary the basics, transcend the basics.

For example, if someone’s trying to punch you in the face, you can learn ways to deal with that. But biology and physiology are relatively limiting - there’s only so many ways you can successfully punch someone in the face, so you learn those and you solve that problem for the most part. Now suppose someone comes at you with a knife. It’s not like they suddenly grew a third arm - there are still only so many ways to deal with that kind of problem, and many of the core concepts for someone trying to punch you in the face apply to someone trying to stick you with a knife.

That’s lateral thinking, The ability to take what works in one context and move it to a different, similar context. And the people who develop this skill are insanely successful in general, but in AI specifically.

What works in one domain or context often works in another, and AI models that succeed in one domain can usually succeed in another. For example, if you write a prompt that generates really good marketing copy, there’s a pretty good chance you can write a similar prompt that generates really good fiction copy. Yes, there are stylistic differences and nuances, but if you can make the tool do one, you can make the tool do the other.

Where many, MANY people fall into a trap is getting stuck on things like “the perfect prompt”. There’s an entire cottage industry of people out there who want you to believe that they have the perfect prompt for this or that, and if only you fork over 99 Euros or whatever, everything will be better.

You avoid that trap by understanding lateral thinking, by transferring success in one domain to another. Suppose you’re at a workshop or event and the speaker is showcasing a prompt for, say, an oatmeal company. Someone without lateral thinking skills might despair - “I don’t know how this applies to me, I don’t work at an oatmeal company” is the common refrain. And those people are typically very unhappy at the end of that workshop because they’re like, “There was nothing for me to learn here. I didn’t learn anything because I couldn’t see how it applied to my situation”.

Someone with lateral thinking skills can look at a prompt for generating video of oatmeal and say, “Okay, I can see the underlying structure of the prompt, the core principles. I can apply those to my industry/company. Even though I don’t work at an oatmeal company, I understand the core lesson being taught and I can extract the successful ideas and translate them to what I do.”

Here’s where generative AI plays a key role: even if you’re not good at lateral thinking, AI is. You can feed it a prompt from a domain you don’t know or isn’t relevant and ask it to extract the key principles that make it successful, then apply it to your specific use case.

Here’s an example starting prompt you could use:

Lateral thinking is the art and science of transferring key principles that enable success from one domain to the next. Once we understand what leads to success or failure, we can abstract that away from a specific situation to apply it to another situation. Here is a prompt that is successful in a domain. {prompt} Convert this prompt to apply to my specific situation. {put in your specific situation as well as context about you and your company/job/whatever}. Show me 3 different candidate prompts that leverage the key principles which make the original prompt effective.

You might not be good at lateral thinking, but generative AI can say, “okay, I understand what you’re trying to do here. I understand the original”. I can see what made the original successful, and here’s three ideas for applying it to your specific situation. That gives you lateral thinking capabilities, even if it’s not your strong suit.

Part 5: Ingredients

Remember the salad example, where the ingredients equal data? This is where you succeed or fail an awful lot with AI. The more relevant, specific data you bring to the party, the better AI performs. Here’s what separates the best from the rest: knowing where the data is and how to work with it.

Let’s say you want to do some content creation. You could prompt the AI tool of your choice for what it knows about a specific topic and use that as the basis for your content. Lots of people do this. The problem with this specific method is that AI models are trained on a lot of information and not all of it is current or correct. Even for models and tools that invoke web search, there’s no guarantee the information it searches for is necessarily correct.

Imagine you go searching for something about a health situation and it brings up Aunt Esther’s Healing Crystal blog. If you prefer something that was, a little more scientifically rigorous, Aunt Esther’s Healing Crystal blog probably isn’t going to help.

The next level up is to know how to use Deep Research tools really well, how to have them synthesize data for you from specific sources or places. If you’re skilled at Deep Research tools, you can assemble some very valuable data very, very quickly and have it be more correct, on average, than what basic searches will obtain.

But the pinnacle, the top skill, is in curating the raw data itself. With tools like NotebookLM and the sources (not the synthesis) that Deep Research tools provide, you can hand-craft and hand-curate the very best data to produce the very best results.

Here’s an example: lots of people can ask AI about how to interpret a medical test result from your physician.

Wait, obligatory disclaimer: I am not a doctor. I cannot give medical advice, and neither can AI. Always consult your qualified healthcare practitioner for advice specific to your situation.

The basic level of skill would be to put in a test result (deidentified, please - strip away all identifying info like your name) and ask a tool like ChatGPT what it means. That might get you a pretty good answer.

The intermediate level of skill would be to put the deidentified test result into Deep Research tools and have them explain in detail what the test result means, based on the research it can do for the you know relevant papers about whatever the test result is.

The advanced level of skill would be to extract all the relevant sources from the Deep Research tools and put just those sources plus the test result into NotebookLM and then ask for an explanation, because NotebookLM is much less likely to hallucinate from the various papers you give it.

And then once you have the results, you go back to your human healthcare provider and ask followup questions. AI enables you to ask better questions by helping you do the research and understand what the test result means in a bigger picture.

But that’s not the extent of the data part of the AI mindset. Another key part, one VERY few people know, is knowing what data is available to you. Here’s the thing: governments around the world have published open data sets for just about everything under the sun. No matter where you live, there’s a good chance that a data set has been published by a government that can offer at least supplementary help for any type of AI work you are doing. Very often those data sets are also free.

The more data sets you know off the top of your head in terms of where they live, the more capable you are at using AI because you can provide real world data as part of your prompts. You are providing more and better ingredients than AI could provide by itself.

For example, maybe you’re doing some research on uh careers, and you know that there are lots of data sets available, say from the St. Louis Federal Reserve Bank, about hiring demand for specific industries. If you know that, you can go and get that data and make it part of your prompt and part of your content creation, and it will be better than someone who’s writing a piece of content with AI that didn’t do that research, that did not get that data.

No surprise, you can also prompt Generative AI tools to locate specific data sets about the topic of your choice. Here’s an example starting prompt:

Locate 3-5 datasets for publicly available data about {topic} from official government websites around the world. Data should be available in {format of your choice} and be published on or after {date of your choice}. Restrict your search to {sources of your choice}. Show your results in descending order by dataset size.

This prompt allows you to have the tools help you research what data is even available that you could then go and use, download, extract, and ultimately understand how that data will inform the content you’re creating.

Part 6: Wrapping Up

This is part 1 of what’s likely to be at least a 2 part series. Next time, we need to walk through task decomposition, tooling, APIs, and infrastructure as part of the AI mindset.

The critical thing to keep in mind about AI mindset is that we’re trying to get the most out of AI through how we think about using the tools. In the same way that you get the most out of cooking by thinking about the ways that you can cook rather than focusing too much on any one given component of cooking. My goal with this is to help you cultivate an AI mindset that lets you be more effective as a human with AI tools.

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ICYMI: In Case You Missed It

Here’s content from the last week in case things fell through the cracks:

• Why Generative AI Struggles with Math (And How to Fix It)

• Is “Human in the Loop” AI’s Biggest Flaw?

• H1B Visa Fee Surge: Is Big Tech Secretly Pushing for AI to Replace Workers?

• How AI Is Generating the Equivalent of 3,846 New Windows 10 Versions Every Week

• Are You Using the Dumbest AI Model Without Realizing It?

• Almost Timely News: 🗞️ How to Improve Sales Skills with Generative AI (2025-11-02)

• In-Ear Insights: Account Management in the Age of AI

• INBOX INSIGHTS, November 5, 2025: 7 Ways to Get Started with AI, Deterministic vs. Probabilistic

On The Tubes

Here’s what debuted on my YouTube channel this week:

• So What? Data Horror Stories!

• You Ask, I Answer: Is Chat History A Fatal Flaw?

• You Ask, I Answer: How To Use AI Note Takers Safely?

• You Ask, I Answer: How To Prioritize AI Use Cases?

• Almost Timely News: 🗞️ Why Deep Fakes Are So Dangerous (2025-10-26)

Skill Up With Classes

These are just a few of the classes I have available over at the Trust Insights website that you can take.

Premium

• 🧭 New! The AI-Ready Strategist

• 💥 Generative AI Use Cases for Marketers

• 👉 Mastering Prompt Engineering for Marketers

• 🦾 Generative AI for Marketers

• 👉 Google Analytics 4 for Marketers

• 👉 Google Search Console for Marketers (🚨 just updated with AI SEO stuff! 🚨)

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• 👉 New! From Text to Video in Seconds, a session on AI video generation!

• Never Think Alone: How AI Has Changed Marketing Forever (AMA 2025)

• Generative AI for Tourism and Destination Marketing

• Powering Up Your LinkedIn Profile (For Job Hunters) 2023 Edition

• Building the Data-Driven, AI-Powered Customer Journey for Retail and Ecommerce, 2024 Edition

• The Marketing Singularity: How Generative AI Means the End of Marketing As We Knew It

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Get Back to Work

Folks who post jobs in the free Analytics for Marketers Slack community may have those jobs shared here, too. If you’re looking for work, check out these recent open positions, and check out the Slack group for the comprehensive list.

• Chief Marketing Officer at The Eighth Day Club

• Director Of Marketing – Demand Generation (B2b Saas) at Journyx, Inc

• Director, Product Marketing at Savant Labs

• Director, Seo Data Analytics & Insights at Raptive

• Growth Marketing Lead at CherryPitch

• Head Of Marketing - Agentic Ai Data Agent Start-Up at Zearch

• Head Of Marketing at Kismet Search

• Head Of Product Growth at kadence

• Marketing Expert at asobbi

• Marketing Operations Manager at ERA Group

• Marketing Science Vp, Advanced Analytics & Data Strategy at CMB

• Vice President Of Growth at GolfForever

Advertisement: New AI Strategy Course

Almost every AI course is the same, conceptually. They show you how to prompt, how to set things up - the cooking equivalents of how to use a blender or how to cook a dish. These are foundation skills, and while they’re good and important, you know what’s missing from all of them? How to run a restaurant successfully. That’s the big miss. We’re so focused on the how that we completely lose sight of the why and the what.

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How to Stay in Touch

Let’s make sure we’re connected in the places it suits you best. Here’s where you can find different content:

• My blog - daily videos, blog posts, and podcast episodes

• My YouTube channel - daily videos, conference talks, and all things video

• My company, Trust Insights - marketing analytics help

• My podcast, Marketing over Coffee - weekly episodes of what’s worth noting in marketing

• My second podcast, In-Ear Insights - the Trust Insights weekly podcast focused on data and analytics

• On Bluesky - random personal stuff and chaos

• On LinkedIn - daily videos and news

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• My free Slack discussion forum, Analytics for Marketers - open conversations about marketing and analytics

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Events I’ll Be At

Here are the public events where I’m speaking and attending. Say hi if you’re at an event also:

• MarketingProfs B2B Forum, Boston, November 2025

• MASFAA, Southbridge, November 2025

• Social Media Marketing World, Anaheim, April 2026

There are also private events that aren’t open to the public.

If you’re an event organizer, let me help your event shine. Visit my speaking page for more details.

Can’t be at an event? Stop by my private Slack group instead, Analytics for Marketers.

Required Disclosures

Events with links have purchased sponsorships in this newsletter and as a result, I receive direct financial compensation for promoting them.

Advertisements in this newsletter have paid to be promoted, and as a result, I receive direct financial compensation for promoting them.

My company, Trust Insights, maintains business partnerships with companies including, but not limited to, IBM, Cisco Systems, Amazon, Talkwalker, MarketingProfs, MarketMuse, Agorapulse, Hubspot, Informa, Demandbase, The Marketing AI Institute, and others. While links shared from partners are not explicit endorsements, nor do they directly financially benefit Trust Insights, a commercial relationship exists for which Trust Insights may receive indirect financial benefit, and thus I may receive indirect financial benefit from them as well.

Thank You

Thanks for subscribing and reading this far. I appreciate it. As always, thank you for your support, your attention, and your kindness.

See you next week,

Christopher S. Penn