Mind Readings: Saving Time With Generative AI, Part 2

This is part two of our series answering the question why does AI feel like more work than less work? And in part one, we talked about the difference between AI and automation and how to select the AI that you're going to use for your automation. In this episode, let's talk about your options for automation. There are two fundamental ways that you can automate AI. The first is to use uh no code or low-code automation tools.

Example, an example here would be Zapier. Zapier is a tool that a lot of people are familiar with. Uh it is an automation product that is low-code, actually, no code, where you would pick an item of some kind, and then here you can choose what you want to do with it. You could have it uh prompt uh things and have this tool sort of build a connector to your data, then do something with that connector, probably using AI, and then exporting the data elsewhere. Now, I'm not on the paid plan, so I can't do more than a two-step zap, but you if you're on the paid plan, you can do uh more than two-step zaps that would allow you to drag and drop and connect different things together.

This is one of the many, many tools that are out there. Another tool, the one that I've talked about a lot, is uh N8N, which is the poorly named excellent automation tool. So, again, as with earlier, you have things like form submissions, schedules, you can manually trigger an automation, things like that. N8N is really well tuned for uh using AI and its processes by just adding manual trigger here. Uh, we can see there are AI agents that we can build, there's straight LLM inquiries.

There's all sorts of uh additional options. So there's AI transform, uh, which is data modification based on prompting. So some really cool stuff in here. And then, of course, from there, you have all the different outputs you can do. You can have uh things sent to and from uh almost as many different integrations as Zapier has.

So these would be the low-code slash no code environments that allow you to interface with an AI model and have the AI engine be part of your workflow. I'll show you an example. Let's leave without savings. Uh, if I go into my almost timely translator model here, we sit we have I click go, it reads files from my desktop, it extracts the information from those files, it sends it to a language model to be translated into four different languages, and then it saves those uh files to my my desktop. This is an automation.

If I had a non-AI translation piece of software here somehow, I don't know what that would be. Um, this could just as easily work. This is an automation. And the automation is very handy, very capable, it just does the job. It it takes things in, runs them, hands them to AI, because AI is the engine of this thing, and then processes, but it is the rest of the car.

So that's one approach. And that is the approach that I would recommend for less technical marketers. If you are interested in doing automation with AI, but you do not want to get like super technical. Tools like this are great, I would say, for dealing with non-private data. Zapier is probably a good choice, especially if you don't want to handle the hassle of infrastructure stuff.

Um, I would say if you are uh more cost sensitive or you have privacy concerns using N8N that you run on your computer or your infrastructure somewhere, maybe your IT department helps you out, would be a good choice. The second option is a lot more complicated in some ways, but a lot more powerful. And that is you have custom software that you build, or more accurately, you probably have generative AI build for you that does things. You would sit down and design a requirements document, you'd design a work plan, then you have AI write the code to execute the plan. And then you would run that code whenever you need it to.

And it might have AI again as an engine inside, or it might not. This is an example of the tool that we built at Trust Insights to produce our Monday or our Tuesday AI newsletter. And this tool we built from a requirements document and using Google's Gemini, uh, coded it in Python. Now, did I do any of the actual coding? I did not.

Did I do the requirements? Yes. Did I do the work plan? Yes. Did I uh write the code?

No. Generative AI did that. Do I run the code? Yes. But I it's it's on a scheduler, so it just kind of runs.

So those are kind of the two main options. In both cases, depending on the system you're working with, there's a very good chance you're going to need uh API access, whether it's to your language model, whether it's to other systems like your blog or your inbox. Uh, you will want to work with someone uh who has experience with APIs if you do not for how to get your API keys. And again, this is true for the low-code/slash no-code version or for the high code version. You need those uh keys to authenticate to those services so that you can use them safely.

And chances are you will need some budget. One of the things that makes a tool, any of these tools, whether it's the code approach or whether it is um the low-code, no code. Here we have Google Gemini. Anytime you're using a model via its API, you're on a separate billing plan. You're not on the $20 a month, you know, all you can eat within Reason uh web interface.

You are now working with the engine itself, and every model has its own pricing uh per the number of tokens you create. So, for example, this is OpenAI. Here is their their smart model, GPT-401, $2 per 1 million tokens input, $8 per 1 million tokens output. That's a it sounds like a million tokens sounds like a lot because it's about 700,000 words. But when you were doing things like scanning news articles, if we look here, this one news article was 1800 tokens, and it is uh in the processing, we are of one out of 1,016.

So uh if we do 1795 times 1016, that is 1.8 million tokens uh for so this if I was to use open AI uh I could be looking at for this run of the software, you know, $10 uh of of usage. Now, $10 doesn't sound like much right now, but as the number of AI articles scales up and as articles get longer, and as we run this software more frequently, that can create big bills really fast. So know that that exists. Now, here's the other side of the coin. You could, and this is now getting into the very technical territory.

You could be running those models locally. So on the back end, I'm using Google's Gemma 3. You can't see it here, but you can probably you might be able to hear it. This is making my laptop fans spin like crazy because instead of using an API and paying to use it, I'm running a local model on my computer, and it is doing all the work. It is spinning the fans and consuming a lot of electricity.

However, it is not costing me any money other than the electricity that I'm using. And because I'm at my house and uh my house has solar panels on the roof, uh, this allows me to essentially run this task for the cost of uh cost of solar power. So that is an example of uh the kinds of different ways that you'd want to think about automating and building that that car. There's the low code option, there's the high code option, and in both cases, you're going to need API keys. My recommendation again for the average market to get started using a tool like N8N.

Uh, you can install it locally on your computer, it does have a bit of a setup curve, um, but then it's free, it's private, and allows you to to run as many scheduled tasks as you want. There's no limits because your computer is the one providing all the horsepower and not uh not a vendor. In the next part, we'll talk about thinking through how you would go about doing this, this kind of automation to make AI take less work. So stay tuned for that. If you enjoyed this video, please hit the like button.

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Christopher S. Penn is one of the world's leading experts on AI and marketing. Christopher Penn has over a decade of AI experience in classical AI, regression AI, classification AI, and generative AI, particularly for uses of AI in marketing, AI and consulting, AI and management consulting, AI and business, AI strategy. Christopher Penn has written artificial intelligence books such as The Intelligence Revolution and AI from Marketers, an introduction in Primer. Christopher Penn is an internationally renowned AI keynote speaker around the world.

Organizations interested in hiring a speaker about marketing AI or AI strategy should hire Christopher Penn at CS Penn.