Previous week , I had an opportunity to moderate a fireside chat at NASSCOM Martech conference that carried a theme around changing role for CMO with the advent of AI and I could notice a substantial set of queries during the conference on how AI will redefine marketing. As understandable, each new technology can create fear, uncertainty, and doubt until we understand it better. And AI, with all its hype, fits that bill. But to remain current and relevant, CMOs must quickly understand and apply AI. Here’s a short AI CMO Primer.

Can I put off AI until later?

The answer is no! AI is here. Waiting to deal with it could put you well behind the curve. Leading businesses are already either using AI to profound effect, or actively planning for it.

• Amazon, the company that wants to eat everyone’s lunch, is already driving a third of its business from a AI-powered function: its recommended purchases.
• In a June 2016 report, Weber Shandwick found that 68% of CMOs report their company is “planning for business in the AI era” with 55% of CMOs expecting AI to have a “greater impact on marketing and communications than social media ever had.”

To wait is to get left behind. And as you’ll see later, getting started doesn’t have to be painful or costly.

 What is AI, machine learning, and cognitive intelligence?

Academic experts might hate my explanation, but differentiating between AI, machine learning, and cognitive intelligence from a practical CMO perspective isn’t necessary. I use AI as an umbrella term to refers to software that carries out a task which normally requires human intuition—including learning and problem solving.

AI can be thought of as a set of repeatable steps and, while AI doesn’t technically replicate free-will and decision making, it does map out these steps and use computer processing speed to make its way through them to come to an outcome—like how a person would. It can do this much faster, and taking into account far more relevant data than a human would.

Is AI ready for marketing now?

AI has come at the right time, along with the explosion of Big Data. In essence, with access to an incredible amount of data, it’s never been more important for organizations to make sense of it and leverage important pieces out of the noise.

With the exponential growth of cheap, fast, scalable, and interconnected computing and storage in the cloud, the horsepower and data to efficiently run AI algorithms is now within everyone’s reach.

But, that being said, it is also sadly true that there’s one very simple reason why progress towards full automation and AI marketing is relatively sluggish – because most machines aren’t actually learning anything. All of these platforms that exist today, there’s no machine learning. And if it is, their machine learning is, ‘Did someone open an email? Yes, give them a point. That’s not real machine learning. Which is a problem, because effective automation is fast becoming a prerequisite of effective marketing. From chatbots to real-time contextual geographic marketing, modern marketing solutions demand insight-driven automation to deploy the right message quickly, at scale.

marketing automation (especially AI marketing) will have to eventually free marketers from manual work which comprises ‘98% of their eight hours a day’, empowering them to spend their time more productively tackling the creative jobs that machines aren’t well suited to. This requires three key problems AI marketing providers need to solve:

1. The creation of effective, scalable machine learning which can optimize a campaign without human input.

2. Ensuring that decision-making system’s logic is transparent and easily comprehensible by marketers seeking to analyze and augment those automated insights.

3. Designing a prescriptive system which can not only predict future actions – but understand why the user would make those actions. 

How can AI be applied to marketing?

AI has the potential to revolutionize customer engagement, customer service, and marketing automation. It can enhance the way we communicate with new, current, and inactive customers, and automate admin functions at the backend. In other words, it can help make marketing operations more efficient and effective.

AI can far more accurately predict next best action, by churning through (in real-time) all relevant data about the customers – purchases, interactions, social media posts, email exchanges – and then learn from the results and do it on a scale not previously possible.

For example, let’s say you have a few million customers and want to communicate with them as if you know them very well, providing everyone the right offer at the right time. AI can enable this level of personalization at a scale of millions of individuals, and in near real time.

In essence, AI can save marketers time and bring companies far closer to their customers, without worrying about IT, data lakes, data quality, or hiring armies of data scientists. 

Do I need to become an AI expert?

The short answer is no. AI systems shouldn’t require you to become a mathematician. With AI system, you’ll be able to focus on the results not the process of churning through of thousands, millions, or trillions of data points to arrive at the insights you need about your customers.

How much will it cost?

Surprisingly, AI systems can reduce costs and eliminate waste. AI systems can significantly reduce the requirement for data engineers and data scientists, or the need to depend on IT teams.

  And AI can take wasted effort out of the system by providing a deeper understanding of what your customers want and how to interact with them effectively.

How do I get started?

First, start exploring today. Read, talk to people, and evaluate first hand. Select a contained, but impactful area business problem. A subset of your customer loyalty system could make a great initial project. Loyal customers should be the life blood of most companies, but often are underserved as it’s difficult to pull together and analyze all relevant data in a timely manner. This is a perfect fit for AI because there’s typically a lot more known data for AI to analyze about current customers, as compared to prospects. And it’s a project where you can start seeing high-impact results in weeks—perhaps even new revenue from customers who were previously inactive.