The best kind of data to have is data about your audience of prospects - the more detailed and personal, the better. Marketers can use AI to analyze that data and better understand individual prospects, at scale. Therefore, collecting personally identifiable data on large audiences, and feeding that Big Data into machine-learning algorithms, offers marketers great power. Repeat after me: With Great Power, Comes Great Responsibility. Now the European Union has put a price on that cybersecurity responsibility: EUR20 million, or $23.6 million. That's the lowest possible mandatory maximum fine that can be levied against your company by the EU ...

Have you ever set out to add complexity to a problem or project? Probably not. In the history of business, the typical goal is to simplify, focus in, and define problems and solutions to be grasped and broken down into digestible pieces. However, some believe the increased pace of change is calling for deeper dives in shaping new solutions.

In late March, when the so-called "scandal" broke about how Cambridge Analytica used Facebook data, there was a story in The Wall Street Journal about how crappy most companies' data is for actually understanding their customers. To me, these two stories represented two ends of the same data+AI spectrum, and together say something important about the potential impact of artificial intelligence and machine learning algorithms on the competitive landscape of just about every business.

A story in The Wall Street Journal this week took my breath away. The implications of "Police Want to Send AI Into the Street"? Marketers will have the power to know and directly address every individual in the U.S. far sooner than they can imagine.

Seemingly no one ever reads user terms of service, which are practically impossible to get through -- typically, mini novellas in four-point type using difficult-to-understand legalese. In his book "Future Crimes," Marc Goodman quips that these contracts should be more aptly called "terms of abuse," as they specifically tell the user how their data will be owned and used in myriad ways to the benefit of the company, and sometimes the detriment of the user.

I've begun to get the sense that most marketers aren't yet taking AI seriously enough. Sure, it won't put you out of business next month. It won't make everything you know wrong, overnight. But consider the implications of Bill Gates' aphorism from "The Road Ahead": "We always overestimate the change that will occur in the next two years and underestimate the change that will occur in the next 10." No, really, stop -- and think.

I've begun to get the sense that most marketers aren't yet taking AI seriously enough. Sure, it won't put you out of business next month. It won't make everything you know wrong, overnight. But consider the implications of Bill Gates' aphorism from "The Road Ahead": "We always overestimate the change that will occur in the next two years and underestimate the change that will occur in the next 10." No, really, stop -- and think.

Once upon a time, expensive, high-value products like Electrolux vacuums and the Encyclopedia Britannica were sold "door-to-door." In the near future, an AI brain will see you coming, scan your profile, review your recent interactions, and then build a customized interaction - whether it's a banner ad, spoken conversation or a video avatar - that conveys what's most relevant to you, of all people.

It seems to me the science of AI (technology that artificially replicates the way the human brain perceives, reacts and learns) is regularly underappreciated. We naturally take for granted the human brain's ability to perform simple tasks such as listening and understanding, or seeing and identifying -- but these brain functions have evolved over millions of years to become refined and automatic. What's required for a computer to mimic these capabilities shouldn't be underestimated.

Recent research, "Reshaping Business with Artificial Intelligence," produced by MIT Sloan Management Review and The Boston Consulting Group, reveals stark differences between companies that are leading the charge to adopt AI ("Pioneers") and those that are not ("Passives").