71: Find the top AI marketing tools and filter out the noise Humans of Martech

What’s up everyone,

If you haven’t checked out our previous 3 episodes in our AI series you might want to before this episode, we give you a lot of context around some of the events that have happened and will shape the conversation today.

So basically
How fast could AI change or replace marketing jobs?How marketers can stay informed and become AI fluentExploring new paths to future-proof your marketing career in the age of AI
Today we’re diving into specific tools… there’s a lot of noise out there right now.

What tools you should play around withIn TMW #107 | ChatGPT and the artificial marketer, Juan Mendoza explains that

“...generative AI tools are already everywhere. From text generation to video and audio production, to image creation, there’s a thriving industry of technologies taking small slices out of our creative talents, packaging them up, and selling them as a SaaS product on a recurring revenue model. If you’re wanting to stay relevant five years from now in the marketing technology industry, you’re probably going to have to learn some of these platforms. In 2010 we used to say: “there’s an app for that”. In 2023, we will be saying: “there’s an AI for that.””

Outline
Here are some of the topics for this third AI episode:
Key AI technology definitions and how to differentiate real AI tools vs all the noise out thereDeep dive into toolsContent marketing toolsEmail and marketing automation toolsPredictive analytics toolsText to presentation and pitch deck tools3D animation tools for product marketersSales and outreach toolsText to website creator toolsAd and social creative toolsAutoGPT and AI agentsAnd a bunch of other tools like conversational search engines, 1-1 convos with celebrities and an even longer list of honorable mentions Here’s today’s main takeaway:
The key to future proofing your marketing career with the ever changing AI landscape is to stay curious, get your hands dirty and experiment fearlessly: Fill out some forms, spin up free trials, get on wait lists, and give new AI tools a chance. It's only by actually getting your hands dirty that you'll discover which tools truly work for you and which are just part of the ever growing sea of gimmicky AI tools.

Definition of tech terms
I’ll be using some of these terms throughout my analysis of some of these tools so here’s a primer explaining the three most common AI technologies used for marketing applications: 
ML
Machine Learning): ML is a way to teach computers to learn by themselves, without having to be programmed for every task. They learn from examples and data patterns to make predictions or decisions. Applications include segmentation, predictive analytics and propensity models. 
NLP
Natural Language Processing: NLP is a subset of ML and focuses on enabling computers to understand, interpret, and generate human language. Includes sentiment analysis, machine translation, named entity recognition, text summarization, and more. NLP techniques usually helps computers understand and communicate with humans using everyday language. 
GNN
Graph Neural Network: GNN also a subset of ML is a type of neural network that aims to handle graph-structured data, data organized like a network or web of connected points. Applications include analyzing relationships between different things like users in a social network or users in your database or recommending additional products based on past purchase history. 

Real AI vs noise

Part of the reason AI gets a really bad rep, especially in martech, is that anything that’s built on if statements or simple Javascript logic gets called AI. There’s still plenty of AI startups that shout about their proprietary AI when it’s probably just a few decision trees and a few interns running spreadsheets.

Now though, you have an even bigger bucket of noise that’s essentially “slight tweak on Chat-GPT”. 

Developing AI that was comparable to human performance was