Artificial intelligence and machine learning are two phrases that have become regular currency in the marketing world over the past several years. It isn’t hard to figure out why, as both AI and machine learning have the potential to help marketers make sense of the ever-increasing volume (and complexity) of raw data that’s become available in increasing amounts, thanks to the digitization of “everything.”

Some people use the two terms interchangeably, but that isn’t exactly right. According to Thorin McGee, director of content at Fast Capital 360, the distinction is subtle yet significant:

• AI is when you develop an algorithm that allows a computer to “think” for you towards achieving a goal.
• Machine learning is when you let a computer create an algorithm to find ways to meet the goals you give it, based on large pools of data.

Put the two together, and you have the ability to gain some really deep insights into what your data is actually telling you, thereby improving decision-making success.

On the data front, this great potential is tempered by some significant challenges. Christopher Penn, chief innovation officer of marketing data and analytics consulting firm Trust Insights, characterizes them as the “5 V’s” of data:

1. Volume — There’s so darned much of it.
2. Variety — More kinds of data are being churned out.
3. Velocity — Data is coming at us faster than ever.
4. Veracity — If data isn’t verified, it can do more harm than good.
5. Value — In raw form, data isn’t particularly useful. Like oil, data needs to be refined to be of value.

If getting your arms around data seems like trying to hug a stream of water, you aren’t alone in thinking that. Many companies are pretty adept at using data to identify what happened — and maybe even to diagnose problems and why they happened.  But it’s less easy to predict what will happen based on data … and even harder to use data to determine with confidence what should happen.

The biggest challenge — but also the one with the biggest potential payoff — is tapping machine learning to process and use data in forging future business as you wish it to be.

To date, very few companies have come all that close to becoming AI-powered enterprises. But it’s where we’re headed in the coming decade.  It represents one of the biggest opportunities for differentiating one company from another.  But it will require a disciplined and concerted effort:  talent acquisition (developers and data scientists), tapping outside vendors, along with taking available open-source code and building upon that to implement the appropriate marketing technologies.

Oh, and committing to a multi-year initiative and budget even after all of those other pieces are in place.

Surveying the current landscape, are there particular entities that you see as on the leading edge in applying AI and machine learning to their marketing endeavors? Please share your observations with other readers.