There are a number of obstacles to converting manual work to automated -- mostly related to human reservations.

Data is often referred to as the "oil" for AI. Used correctly, it can help deliver the most relevant consumer experience in a nanosecond. And seconds saved can often result in significant revenues gained. The algorithms that deliver these experiences require not just data, but trained data, and refining data involves time and investment.

Given the vast volume and splintered fragmentation of video inventory, it makes sense that AI technologies will be used to sift through mountains of content to identify the appropriate contextual placements for brands, Understanding context, after all, is one of the things AI is good at. By tapping into deep learning technologies, advertisers can target the categories of video content that make sense for their brands, and then optimize toward video content that resonates with their customers.

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.