In a recent presentation to students at the University of Wisconsin-Madison, I encouraged them to broaden the scope of their use of AI beyond stereotypical applications like homework and social media. I started the discussion with a question: can the impact of AI be simultaneously over- and underestimated?
My conclusion is yes, this can be over- and under-exaggerated. But I came to this conclusion based on a related idea: our view of AI will be greatly shaped by whether or when we are able to invest time in training and experimenting with it. Do-it-yourself AI.
It’s always important to experiment with new technologies, but it’s especially important with AI. I’ve recently wondered if my own adoption and value of AI is too limited. I decided to commit to spending even more time experimenting with DIY.
Experiencing technologies like AI for ourselves can change your mindset and help you overcome challenges. “a hollow of disillusionment” that supports high-profile technologies, while generating more value for your customers.
Dig Deeper: How to implement AI for your marketing team
Remember your ChatGPT moment
We’ve all experienced AI dizziness over the past couple of years. For me personally, I had a lucky moment with the release of ChatGPT.
At the end of 2022, I made the decision to step away from my business leadership role for the longest break I would ever take. I eventually moved into consulting and teaching. Of course, I didn’t know that this break would coincide with the launch of ChatGPT. It’s always fascinating to hear OpenAI describe it as a “Unobtrusive research overview.”
My break allowed me to have time to train, test and learn independently. Many friends and colleagues who were still in corporate roles didn’t have time to experiment and were quick to dismiss AI as overhyped.
Many of these colleagues do not have the capacity or air coverage to take the time to self-train. I was lucky. I attended live events rather than on-demand. I’ve been binge-listening to all the related podcasts. But above all, I had time to try things in DIY mode which convinced me of the interest, despite the limits.
Crossing the AI DIY chasm
To illustrate the importance of DIY time to create value, I created my own hype cycle chart with various training triggers that I believe we all go through.
Gap 1 is the time between when a technology is launched and when you start learning. This is usually based on an external training trigger, through your job or a network of peers.
Gap 2 doesn’t just happen when you leverage technology, but especially when you cross what I call the “DIY chasm.” You have to zigzag your way through boundaries and reach those “A-Ha” moments on your own terms.
The cumulative time between Gap 1 and Gap 2 has an exponential impact on the gap you feel between personal productivity and the value you get from AI and current capabilities.
In my previous roles, I too would have had similar opinions about AI being overhyped. The pressures of day-to-day project management meant that established patterns of martech management had taken hold. Previously, when you got stuck or reached a limit, a quick search produced an answer. But if that knowledge base didn’t include your specific context, you were stuck. We have often blamed the software in these cases.
Since genAI is not limited to these original limits, additional experimentation to help optimize your results is even more critical.
Two years later, I passed the Gap 3 challenge. My teaching and consulting work reduces my capacity for DIY experimentation, while at the same time the pace of change in AI is accelerating. There are days when I don’t feel like I’m late. But every time I spend more time on testing, I experience a “wow” moment.
Obviously, I’m the limitation, not the AI technology. It has been helpful for me to listen to thought leaders who remind us that this is the “worst AI” we will ever work with.
More content. Is it better?
I always thank Scott Brinker for helping shape my views on the “martech citizen.” I couldn’t wait to take a look at Brinker’s latest MarTech for 2025 report and Frans Riemersma. But given my limited abilities, I only had time for a quick read, not the in-depth review I would prefer. (You will find a summary and download links on the MarTech website.)
A quick analysis of the report revealed that the main use cases for AI are focused on content and personalization across different stages of ideation and distribution, with the number of mentions of these applications alone telling us that the impact of AI is not, in fact, exaggerated. excited.
But it remains to be seen whether customers experience improvements. This is where my latest AI DIY experiment revealed some ideas for the future of personalization.
Dig Deeper: Consumers are disappointed by AI experiences
Personalized Content “Channels”
I will absolutely return to read the Brinker and Riemersma report, which is over 100 pages long, in its entirety. However, I want to share two AI-infused shortcuts that have helped me close the value gap and simultaneously signal the future of more personalized content.
First, before a recent trip, I loaded Brinker’s chiefmartec articles into the ElevenLab Reader. AI-generated voice reads the latest articles. If you haven’t tried this, it’s underrated and will change your long-form content consumption forever.
Screenshot of the ElevenLab Reader application.
Then I loaded the full report into Google’s NotebookLM and listened to the audio preview it generated.
The audio preview creates an AI-generated “podcast” with two enthusiastic hosts discussing themes from your chosen source material. It allows you to download documents, YouTube links, web pages and much more.
NotebookLM AI Martech Report Overview for 2025.
I also created a preview of NotebookLM AI for my more recent series of articles for MarTech.
Introducing NotebookLM AI for Milt Hwang’s MarTech article series.
It’s “source grounding” that makes NotebookLM so powerful, while leveraging the global model (Gemini) in this multimodal format. As Steve Johnson, one of the co-creators of NotebookLM, said on the Google Deep Mind podcast, it’s “a kind of personalized AI that is an expert in the information you care about.”
If you are interested in NotebookLM, please continue testing more than just the audio preview. NotebookLM allowed me to “chat” with Brinker and Riemersma throughout my back and forth with the AI presentation.
I think Houlind would agree with my thoughts on the need to adopt a client tone, leveraging data from past discussions such as emails, meeting notes, etc., which I suggested in Second part of my series.
Screenshot of NotebookLM answering a question using the source material.
At this point, I realized I needed to add a key topic to my learning plans for 2025: the content layer. As Rasmus Houlind explains, this includes a chain of multiple content LLMs working together in a martech stack to improve personalization.
I think Houlind would agree with my thoughts on the need to adopt a client tone, leveraging data from past discussions such as emails, meeting notes, etc., which I suggested in Second part of my series.
Our roadmap to personalization
This personalized content discovery is a combination of the overall trends I have written about 2024. The ROI of going through the original DIY Chasm two years ago with genAI was worth it, but it has a shorter lifespan as we move through the AI dizziness. I need to plan more time than ever before to spend on the DIY traverse of Chasm.
With the help of the personal AI technology infusions I discussed here, I was still able to gain valuable insights despite a lack of time. Through Q&A, AI engagement, and my favorite audio formats, AI helped personalize key content insights on my own terms.
All that remains is for us to prioritize efforts to evolve these approaches for our customers.
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