What can you do if you know AI could improve your marketing work, but your company is still stuck in committee on it? Start experimenting with personal projects that could also solve business problems.
AI has escaped confinement in the business world and is now everywhere. At the grocery store, my app uses AI to suggest what I should buy. My dentist even told me about a new AI-powered app designed to help people clean their teeth. AI is suddenly everywhere.
I know some people are wary of technology, and for good reason. AI is advancing faster than anything we’ve seen before. But this is an inflection point not just for email or digital marketing, but for society as a whole.
This is similar to the early days of email, when we had to learn email skills and develop our own programs because businesses didn’t believe in email yet. Once again, people have the power to create new things through new technologies.
In my latest MarTech columnI said that the first innovations in email began in super-corporations and trickled down because employees at the largest companies were the most motivated to use the new technology.
The opposite is happening with AI. Innovation and applications are bubbling, with people working alone or in mid-sized or small businesses to explore and expand the uses of AI.
Your customers are searching everywhere. Make sure your brand introduces himself.
The SEO toolkit you know, plus the AI visibility data you need.
Start free trial
Start with
I have several friends who have created apps or websites incorporating AI to solve practical problems. Instead, super-corporations and large corporations lag behind because information security and privacy restrictions, or corporate inattention, limit their creativity.
How to learn AI now
If you want to move full speed ahead with AI beyond superficial uses like copywriting, but your company is still debating it, learn for yourself. Don’t wait for your business to catch up. Instead, be ready when it finally happens.
This idea should be obvious, but it is not. I always talk to marketers who use AI as a search engine or who have limited or no understanding of its power and potential. This means looking deeply into AI and immersion.
Now is the time to explore and learn on your own, without violating company privacy or security policies. Learn about AI first, then use what you’ve learned to solve practical problems, whether at work or in your personal life. That’s what I did – and it all started in my wine cellar.
I tested my knowledge on a pressing personal problem: inventorying and managing my 300-bottle wine collection. This may seem a little frivolous, but I am a wine enthusiast with a collection built up over more than 20 years. Keeping tabs on it has been a thorny problem for me.
Knowing which AI tool can do what is a key lesson. ChatGPT, Claude, Google Gemini, Microsoft Copilot and other major language models each have their own strengths and weaknesses. For example, Claude is terrible at generating images.
By using what I learned on this personal chore, and not on a business need, I was able to explore and learn more about each tool and subsequently avoid a major pitfall in my professional life: failing because I used the wrong tool.
I needed to inventory my collection, but didn’t want to spend all the time it would take to record each bottle. So I asked myself: “How can AI help me achieve this?”
I started with ChatGPT because I had already created several dashboards with it and was already familiar with it. He suggested I take a photo of each wine label, upload the images, then use pattern recognition to build my inventory.
Failure can teach you a lot
It seemed easy enough. So I spent about half an hour filming and uploading the photos. But when I launched the app, it didn’t recognize many labels and returned incorrect information. For example, he told me I had a bottle of 1999 Screaming Eagle Cabernet Sauvignon worth $2,985. I wish. There were enough similar errors to make me doubt the accuracy of ChatGPT’s inventory.
I was surprised. ChatGPT has been very good at forecasting and business modeling for my work projects. Why did he fail here? I still don’t have an answer. My assumption, however, is that I was asking ChatGPT to do something it wasn’t designed to do.
I switched to Google’s Gemini, which I had also experimented with and generally found to be quite useful and responsive.
I gave it the same context that I gave to ChatGPT and the tag photos I had taken. The results were hit or miss. Gemini couldn’t process images, but that wasn’t the worst part. Instead of returning an uncertain answer, he guessed the labels, and he was usually wrong.
I tried to add more context and correct errors, but Gemini didn’t produce a reliable inventory either.
My wife is a marketing manager and loves working with Claude. So, I tried. This time, the results were a pleasant surprise.
It recognized most of the labels on the photos and pointed out which images I needed to retake because they were too blurry or I had cut out important information. Instead of sending back hallucinations, it sent back helpful suggestions.
Once I took the photos that Claude needed, I quickly built up an inventory of 300 bottles. It was exactly what I was looking for.
Why did Claude succeed where ChatGPT and Gemini failed?
I didn’t say anything to Claude that I hadn’t said to ChatGPT or Gemini. I used the same context, the same prompts, and the same processes. What emerges from this experience is a clear distinction between the three models, driven by their respective infrastructures.
In short, each AI model differed in what it could do well and what it needed to be pushed to do. Does this mean Claude is the platform for you? Not necessarily.
My wine inventory showed me that I needed to understand the unique capabilities and idiosyncrasies of each system. I am now using this knowledge in other projects to better understand which AI model will best suit the task at hand.
I know it would be easy to find infographics explaining the differences between the models, but until you have experienced using the models yourself, you cannot know or trust anyone else’s graphics.
Knowing how these models differ has given me an advantage that I will continue to use as I begin to look at technology platforms incorporating AI. You can achieve the same advantage when your business finally decides which direction it will take with AI if you have already tested the technology and have a good understanding of what will best meet your company’s specific needs.
Build your career with AI knowledge
The training you get from experimenting and working with the different AI platforms will pay off not only now, but also later in your career.
AI know-how is the number one skill marketers will look for when hiring their teams, according to Litmus State of Email Report 2026. The time (and money) you spend now learning about AI and experimenting with tools will make you a much more valuable prospect.
For my experimentation with AI models, I used the paid versions for my professional and personal projects. And, as with signing up for a multitude of streaming TV services, the costs add up. Remember, this is an investment in your future.
See the full picture visibility of your search.
Track, optimize and win in Google Search and AI from a single platform.
Start free trial
Start with
If you’re just starting out, a free or less expensive tier of each AI model can help you understand the limitations of each platform. Once you know this, it may make sense to upgrade to a more expensive tier, especially for the systems you find most useful.
Some AI users dive deep into AI models: coding, hosting Python servers, using GitHub, and get grounded in the process. Others simply use it as a search engine or create cute images and memes.
We marketers need to be in the middle. We need to know enough to be useful, enough to organize and complete a task with the right AI tool, and enough to recognize when a human needs to step in to correct mistakes.
The only way to get there is to start experimenting now. Try all platforms. Make mistakes. Push models to their limits because it’s important to know what those limits are.
We are at an inflection point in AI adoption, so now is a good time to start learning about it. It would be nice if you could do this at work. But sometimes the easiest way to learn is to complete a project that you can apply in your personal life.
Your business may be slow to adopt AI. But you don’t have to be.
