How AI Companies Can Find Their Voice In Their Content Marketing

I have a little bit of a rant for you today.

I have been doing a lot of work with Artificial Intelligence and Machine Learning, as I am sure you are all aware of.

If not here’s a link to my Medium Page

Anyway one of the things that is really interesting about the marketing in the AI world is that IT ALL SOUNDS THE SAME!

This is something I have noticed in very technical or academic types of businesses. These sorts of businesses get very concerned with sounding scholarly or academic and end up sounding out of touch.

One of the big reasons for this is something called “The Curse Of Knowledge.”

The curse of knowledge is a cognitive bias which states that the more you know about a subject the more likely you are to explain it in a way that assumes a high level understanding in the listener.

So for example an AI company might do text analysis and text analysis is kind of like the swiss army knife of Artificial Intelligence it works for a variety of industries from law to human resources which means there are a lot of companies that this AI firm could do business with.

Not only that, but the guy(or girl though it’s rare) who founded the company did his/her thesis paper at some super fancy tech school on text analysis. They have been working on this stuff since he/she was literally a teenager. We’re talking 15-20 years of daily obsessive focus on text analysis. So when he’s thinking about marketing, he REALLY wants to show off all of the high level cutting edge functions of this technology.

And he/she forgets that their audience is not nearly as advanced.

If you look at the AI marketing that is currently being done, the voice is academic, and technical.

It’s writing that is designed for academia not sales.

So, how can AI companies find their voice?

I have a few suggestions:

  1. Don’t let your engineers or founders create marketing materials. One of the most frustrating things about working with AI companies in 2018 is that they have marketing departments that are staffed with engineers and technical writers not marketers. There is a strange distrust and quite frankly and obvious lack of respect for the marketing profession at most AI companies as if somehow marketing is a “soft” science as opposed to the more rigourous computer and data science needed for AI. Right now having technical content is not hurting these companies but it will VERY soon. 85% of executives surveyed by the Harvard Business Review predicted that AI is going to change their companies by 2021. This means that in order to grow AI companies are going to need to stop focusing on Fortune 1000 companies and start focusing on SMBs who are not going to have anyone capable of doing some of the equations in these white papers.
  2. Find the fun. One of the best examples of AI marketing done right is Soundhound which showcases it’s unique Houndify voice enabled platform in everything from cars to break dancing robots at CES in 2018. Instead of focusing on the science behind their platform, Soundhound put the focus on all of the fun and entertaining ways their platform could be used. People gravitate to content that is enjoyable and fun. As Bomani Jones once said ” No one can regulate how much fun you have at work.” AI companies should find the fun both in the office and in their marketing.
  3. Identify and Write To Your Evangelists. One of the interesting things about technology and media is that they need evangelists to spread. I personally learned about Spotify, Uber, S-Town, and Serial from friends and media personalities I trust. When it comes to technology because the learning curve is usually so steep the early focus has to be on finding and creating evangelists both in the press and in the business world. Quick question, right now who is the most trusted writer in artificial intelligence? Chances are you couldn’t think of one because there really isn’t one. Furthermore there isn’t even a group of gurus who you can trust like there is in marketing or other industries. The group of experts are a bunch of data science PHDs who have trouble explaining these concepts without using words that the audience has to look up. Right now when you google AI talks or videos there’s an 89.9% chance you’re going to get a PHD up on stage. And who do academics talk speak to? Other academics. At some point the cycle has to be broken and companies have to identify who the AI evangelists are in the media and who knows how to market AI to SMBs and other companies that didn’t even realize they were the ideal audience.
  4. Pick a niche and produce the best AI content in it. Right now another problem for AI companies is that their products can be used in too many different ways which means that the same company might have a white paper on using AI in sports and a video on using AI in medicine. Right now there is an incredible opportunity to own parts of the AI world. Companies need to focus their content and the voice in their writing and videos on being the best company at a certain niche whether that’s being the best drug discovery AI firm or being the AI firm that handles legal briefs. Once you define this niche it helps you to define what your content is actually about and frame things appropriately.

I’ve tried really hard to avoid saying HIRE CONTENT PROFESSIONALS because I understand that right now the online sales market for AI is not really there and all sales are made at the C level face to face or through long arduous government contracts. But the point stands. Before these companies know it, the time to sell to SMBs and focus on widespread adoption will be here and the companies that take their time in finding their voice, dominating a niche, and continue to let academics create marketing materials the further behind they will fall.

New Post On AI

Hey there,

Happy Tuesday, that’s a set of words you don’t hear too often.

I am enjoying the beautiful 91 degree weather out here in Las Vegas while trying to decide what I want to eat for lunch.

I spend a lot of time thinking about what I want to eat, but unfortunately I am not able to become one of those decision fatigue avoiding robots like Alabama Football Coach Nick Saban who eat the exact same thing everyday.

Just in case you were wondering, Nick Saban (who I call my illegitimate father) eats iceberg lettuce, tomatoes and turkey with I think ranch dressing for lunch each and every day. And he’s rich! Imagine choosing to eat that when you make 9 million dollars a year. But I digress…

In other news, I’m thinking of doing a whole series of beginner’s type posts on A.I and Machine Learning.

I think a lot of the more advanced concepts go over people’s heads and they want to be able to understand and define these terms easier.

The beginner’s series would be aimed at businesspeople who understand that AI and ML are important and that they are going to affect the way they do business in the near future but don’t really have a firm understanding of AI or ML.

These are the executives and small business owners who KNOW they should have a better knowledge of AI and ML but either don’t have the time, or are too embarrassed to admit that they don’t understand terms like Predictive Analysis.

If you know AI and ML are important but would freeze if I asked you to define either term then these blog posts are for you!

Or so I think.

Let me know what you think in the comments below.

And while you’re here be sure to take a look at my latest blog over on medium:

The Beginner’s Guide To Predictive Analysis

TL:DR

Predictive analysis is when computers use machine learning to design a model which will make predictions about the future.

 

All the AI and Machine Learning Writing I’ve been doing lately.

Lately I have been focused on carving out a space as a writer in the Artificial intelligence and Machine Learning space.

So I’ve been doing a ton of research and writing a bunch of articles on Medium and other places.

Here’s some links to all the AI and ML writing I have been doing lately so that’s its easy to find in one handy dandy blog post.

I’ll also give some Te-nahisi Coates style post breakdowns on what I could have done better or liked about each post.

  1. What is Deep Learning?

This is the most recent thing I wrote on AI and goes into the process of deep learning using Artificial Neural Networks (ANN). This piece is pretty good but I could have included other examples of deep learning besides the 4 I put in there.

2. 9 Ways Intelligent Automation Can Grow Your Business!

This post was good for beginners in AI and business development. I think looking back on the piece I could have positioned it more to be specific to small and medium sized businesses as larger businesses have been doing this stuff.

3. Separating the steak from the sizzle when it comes to AI and ML

This post is good at outlining what is available right now, but I probably could have done a better job at explaining how close some these breakthroughs are to being reality.

4. How Machine Learning and AI are Influencing Data Driven Marketing

This post would be perfectly at home here on the Content Marketing King blog. If anything this posts shows how much data is being collected and how data focused the next generation of content creators will have to be to survive.

5. Millennials, This how AI and ML will affect your job search

I used to write a lot about the job search process because I find the process to be inherently unfair. Employers have all the power and often lie in job applications about the job or compensation. This post accurately lays out how the age of intelligent machines is going to affect finding a job over the next 20 years or so.

6. 7 Ways That Machine Learning is Affecting the World

This is a pretty basic list of applications of machine learning. It essentially lays out the areas that I would be writing about in later pieces. It is interesting to see how some of these areas like autonomous driving have been in the news lately.

7. How Predictive Analysis is predicting Earthquakes, Court Decisions and Everything in between

In the first piece I wrote around AI and ML I looked at the idea of predictive analysis and how AI and ML are being used to make predictions about everything from court cases to earthquakes with various levels of success. At this point I was still figuring out the differences between ML and AI.

Ok that’s most of what I have written about AI and ML that is on Medium I’ll have to track down some of the links in INC and Fast Company as they tend to change fast.

I’ll be sure to post more about AI and ML in the future as I continue to find a place for my writing on the subject.

 

 

7 Random Thoughts On AI and Machine Learning

This week I have interviewed the founders or CEOs of 4 different AI and Machine Learning companies for upcoming articles on Forbes and INC.com.

In this interviews I learned a lot of interesting things that might not necessarily fit in the pieces I am writing but I figured I would share here as a sort of “Behind The Scenes” look .

And because I love list posts and I know you guys love to read them I put together my list of

7 Random Thoughts on AI and Machine Learning:

  1. What proprietary data do sports teams have that they aren’t telling us about? We already know a fair bit about Machine learning with sports data but everyone I talked to who worked in sports analytics confirmed that the teams that are using Machine Learning have WAY more data than we have access to on the outside. And I want to know what they have.
  2. How are we going to eliminate bias from legal data when the legal system itself is bias? The legal system in America is biased. One need only google the incarceration rate for African Americans vs White Americans to see that. But there’s also documentaries like 13th By Ava Duaverney which explain it way better than I ever could. To quote the great philospher Kanye West “Face it Jerome get more time than Brandon.” So another challenge arises in the legal field where machine learning data will need to be scrubbed of bias before it can give sentencing recommendations or offer rulings.
  3. What can’t they put a brain in? After talking to the folks over at BrainOS I am convinced that a big trend is going to be installing autonomous brains into everyday items essentially transforming any task that manual from floor cleaning to window washing into a task a robot can do.
  4. AI is going to revolutionize the drug discovery pipeline. The way drugs are discovered and brought to market now is horribly inefficient. It’s almost as inefficient as the Cleveland Browns search for a quarterback (AY-YO). It takes on average around 10 years and a billion dollars of investment to bring a drug to market for a rare disease. Companies like Recursion Pharma which are mapping both drug compounds and human biology at a rate of 7 terrabytes a week are going to completely change how fast drugs are brought to market and disrupt the entire drug discovery pipeline by providing a better service faster.
  5. Why couldn’t the 76ers fix Markelle Fultz’s shot with biometrics? This is a question that kept coming to my mind as I spoke with sports analytics people. The technology exists to bio-map Markelle Fultz’s broken shot but no one I talked to seemed to have an answer as to why the 76ers hadn’t used that technology or why it hadn’t worked.

6. Open Platforms are where the big breakthroughs are going to come from. Similarly to how smaller companies tend to innovate more effective content marketing strategies the companies that are creating open source platforms like Soundhound with Houndify and BrainOS with their brains are going to be poised to let “hackers” and DIYers make the major breakthroughs and then come in early and either copy or buy up those innovations to take to market.

7. Humans have ALWAYS feared Robots becoming intelligent and taking over. The first mention I could find of robots attacking humans goes back to the early 19th century and that doesn’t count the idea of “bronze” or “gold” men who weren’t quite human or friendly attacking all the way back to 600 BC. To me it seems like a guilty conscience. After all if we really believed we were living the right way wouldn’t we think smarter robots would agree? What does it say about us that we believe the first thing the robots will do once they attain sentience is get rid of us?