Last year I was fortunate enough to visit the New York Stock Exchange and take part of the closing bell ceremony with Container Store CEO Kip Trimball. Earlier this year, I was also lucky enough to stand next to Tony Robbins as he rang the opening bell on NASDAQ.

About a decade ago, the actual floors of the NYSE and NASDAQ represented significant trading volume for their respective exchanges. Today, it's more "theater" than reality. That is, both exchanges focus on the PR-worthy events around their respective opening and closing bell ceremonies that help them stay relevant to the major television financial news networks.

Today, the bulk of stock market trading volume is managed by algorithms. There were just too many important variables for the average stock market trader to stay on top of as trades were happening. A well-programmed algorithm could spot trends and opportunities faster than a human and execute trades in a nano-second.

Marketing Is Following The Pattern of Wall Street Trading

After being in the digital marketing business for more than 23 years, it's clear to me that marketing is following the Wall Street disruption pattern.

It started with programmatic media. With too many real-time variables for a human to manage, it wasn't surprising when all of these demand and supply-side algorithms cropped up. Despite massive bot fraud and other significant brand challenges, programmatic media has taken center stage for buying digital media.

Next came artificial intelligence (AI) based assistants. Companies such as Conversica discovered that human sales teams were much better at closing deals when an AI sales agent was used to set appointments and follow-up with prospects after the initial meeting.

Now we turn our attention to MarTech. With more than 5,300 marketing technology platforms, the average business must deploy at least 16 (and as many as 30) separate platforms in order to keep up with their sales and marketing efforts. That's crazy ... and unsustainable. When businesses must deploy more than a dozen sales and marketing platforms just to manage their prospects and customer interactions, you know we're ripe for another disruption - just like the one Wall Street stockbrokers experienced.

Machine Learning Will Trump Multi-Platform Solutions

I had a chance to speak with the former CMO of Eloqua, Brian Kardon, who is now the CMO of Fuze; a company that delivers unified communications in the cloud. What impressed me the most about Brian is how down to earth and practical he is. Here's a guy who helped grow Eloqua from $10MM to over $100MM before being bought by Oracle for $1 billion. And yet, Brian is very humble and willing to share his insights on the future of marketing.

When looking at the future of MarTech, Brian understands the challenges that so many of his CMO colleagues face. "When it comes to MarTech, the barriers to entry are very low these days. When you combine open source with AWS [cloud hosting], it's pretty simple to build software," says Kardon. "I believe that's what's accounting for the incredible growth with over 5,000 different marketing technology software solutions out there today."

He went on to explain the series of choices each CMO must make such as choosing a big three marketing cloud provider such as Adobe, Oracle or Salesforce, or multiple best of breed solutions. Either way, a lot of time is spent plugging multiple platforms together using application programming interfaces (APIs) and cross-training on each platform's offering.

"It's not that easy to bring all of these marketing technology platforms together," says Kardon. "Each system has it's own data and it's own dashboards. And, even after all of these platforms are linked together, there's still not a unified set of data for all of them. There are still blind spots in your data."

AI Still Falls Short in Some Areas of MarTech

"AI won't write a headline or produce a quality video," says Kardon. "but it will tell you which campaign works best with which audience segment in real-time. There are thousands of variables changing every second and humans can't process all that information. That's what's happening in marketing today. There are just too many data points to process by a human. The data is overwhelming and no human being can be looking at everything."

So, Kardon recommends that AI be used to analyze the data in real-time while humans focus on the strategy, creative and continued refinements. I liken having AI in your MarTech platform(s) to having access to a data scientist. A data scientist won't tell you what creative to use or which strategy will work best, but rather provide analysis around which of your target audiences responded to which creative campaigns and marketing strategies.

Focusing on Being Even More Relevant

As AI become more prevalent in MarTech, we're going to spend a lot less time focused on API integration between our technology platforms and a lot more time doing what we do best - capitalizing on the insights that AI provides us based on how well our last campaign did.

As we extract more insights from our marketing campaigns, we're going to be in a better position to be confident in where we spend our media, which audiences are most likely to respond and what campaigns will be most appealing to which audience we're looking to attract.

In Wall Street terms, that means a lot less time on the trading floor buying low and selling high and instead focusing on long-term strategies that deliver the desired results for our company and the customers we serve.