How AI Is (and Will Be) Transforming Business

From ad optimization and chatbots to content strategy

In the Understanding AI series we’re exploring how AI is transforming the way we work and live — from sales and productivity, to human access and art.

Once relegated to sci-fi movies and obscure subreddits, API’s, bots, machine learning, even the freaking singularity have become the focal point of marketing brainstorms.

And for good reason, this sci-fi stuff has actually become practical for business. It’s no longer just a fluffy long-term vision, you can put it to use today, well some of it. Some of it is still, well…💩.

As the application of this new technology grows, knowing where to invest and what to stay away from, is the difference between first-movers advantage and wasting a lot of time implementing technology that just isn’t ready.

An incredible reminder of the future we’re living in.

How you already use AI

AI feels new, but it isn’t.

In fact, different forms of AI are all around us. The suggestions Netflix makes, inside your iPhone camera, even online content is increasingly being written by AI (the author is not a bot… we think).

If you’re a marketer who has owned content, social, paid media, or a combination of all three, you’re already well versed on some advanced forms of AI, machine learning, and automation.

For example:

You’ve been impacted by Facebook’s Edge-rank and machine learning algorithm.

You’ve tested Twitter and Slack bots.

You’ve automated emails and social posts to optimize engagement.

You’ve utilized Google AdWords AI-powered automated bidding strategies.

In all of these scenarios, AI is a solution that makes sense. Why? There are 3 characteristics that tie them all together.

(1.) They leverage massive amounts of data, (2.) that is generated frequently, with (3.) really powerful technology.

Here’s a simplified image to help visualize the sweet spot for AI and machine learning.

Exhibit A: Google Search

Just about all of us use one of the most powerful AI powered tools available today — Google Search.

Google has been able to take advantage of AI for search like almost no other technology company. Why? Yes, they are Google, but more importantly Google Search sits smack dab in the middle of the visual above, lots of data, at a high frequency and supported by powerful tech.

1. Lot’s of structured data:

It’s hard to use Google search wrong, there just aren’t that many abstract ways to use it. This means that every single search, out of the 3.5 Billion a day, is pretty similar, at least in how the data is structured. Machines thrive when they have a massive amount of structured data like this. Unlike you or I, their power lies in the shear magnitude of the data they can process, as long as it’s mostly uniform, and the more the better. A machine learning algorithm that is trying to serve you exactly what you are looking for gets better when it knows what others like you wanted.

2. High frequency

With 3.5 Billion Google searches a day the machine learning that powers Google Search has an almost unlimited amount of chances to test and iterate. Machine learning “learns” just like we do, they try something, and if it works they do more of it and if it doesn’t they do less. This is an oversimplification but for it to happen the machine needs ample opportunity to test and iterate. A machine learning algorithm that has 3.5 Billon chances each day is going to become finely tuned very fast.

An artificial neural network could do something similar, by gradually altering, on a guided trial-and-error basis, the numerical relationships among artificial neurons. It wouldn’t need to be preprogrammed with fixed rules. It would, instead, rewire itself to reflect patterns in the data it absorbed. — The Great A.I.Awakening

3. Powerful technology

Google has done a great job of gobbling up engineering and advanced technology PhDs talent. They also have the ability to give these very smart people the two bullet points above (frequent, structured data) to play with. This keeps Google way ahead of the curve when it comes to AI The skills needed to build powerful AI aren’t things that folks can easily learn at home with an internet connection. You need to have an advanced specialization in it and the resources to turn that knowledge into software. For at least right now, if you want to have a machine learning algorithm that can predict what people want, you need to have a powerful technology partner with the capable engineering talent.

Exhibit B: Baseball

Google search is an archetypical application for machine learning. Another example that feels a little more close to home is baseball. Just like Google search, America’s game fits neatly at the middle of our AI use case image.

1. Lots of structured data

Especially during at-bats, baseball is a very structured game that produces a simple, structured data set.

There are lots of places online to access all the structured data the MLB generates (http://www.baseball-reference.com)

2. High frequency

The over 150,000 at-bats over the course of a single MLB season creates plenty of data to learn from.

3. Powerful technology

MLB teams have the resources to invest heavily in the technology it takes to get this right. In general all sports have a strong legion of capable hobbyists and engineers investing in analytics. There is even a yearly sports conference that takes place at MIT Sloan dedicated to analytics. Guess what the big theme this year? The rise of machine learning.

Understanding the use cases for how AI is already impacting business (and baseball) is the best way to anticipate what it’s going to be disrupted next.

Proven and emerging cases for AI in business

Right now we see at least two well-proven marketing use cases for AI/Machine Learning (you’ll notice that both of these fit the three tenets we looked at above):

predictive lead scoring automated ad spend optimization

For predictive lead scoring to work well, you need to generate a lot of leads (high frequency), you need your leads to fill out as many form fields as possible (lots of structured data) and you need a good technology partner to help execute (powerful technology). If this sounds like you than, there is a good chance you can put machine learning to work pretty quickly.

Likewise, for advertising to work well you need to have a healthy budget to drive enough views, clicks and conversions (high frequency). This will lead to lots of structured data from your ad partner and as long as the ad network you choose is Google, Facebook, LinkedIn, etc. you can bet they have built in machine learning and powerful AI technology to take advantage of.

I often talk to advertisers who prefer to use manual bidding strategies over Google’s machine learning algorithms and while I’m sure there are times this pays off, over the long run you’re better off with leaning on Google or Facebook to optimize your ads. It will save you time and the machines are already good at it and they get better every day.

There are also some less proven but emerging uses cases that marketers should keep a close eye on over the coming years.

chatbots content/email strategy

Using a chatbots can offer scale, automate customer service, kick off the sales process and often help customers more than a website or a phone call with a person. However the natural language processing they need to work effectively is still a work in progress. This makes using them an uncertain experience, and even though people seem interested, some of the early use cases haven’t panned out.

Content and email strategy is begging for an injection of machine learning. These areas fit well into the first two parts of the venn diagram of AI success (lots of frequent data) and are just in need of a technology to turn that data into a formula for delivering the right message at the right time. These AI technologies are emerging, but not ready for the mainstream quite yet.

Looking into the AI horizon

We’re just scratching the surface here and it’s still early for the wider business application of AI. As AI shoots up and down Gartner’s Hype Cycle clearer use cases will start to emerge.

The examples I’ve covered focus mostly on today’s most practical branch of AI, machine learning. Use the venn diagram framework to help you find new applications of machine learning that are applicable to your business or industry.

Outside of machine learning there are several other AI technologies that don’t quite feel ready but they will be soon:

Natural language processing is clunky but close.

Virtual reality has been around for a long time but seems to be finally gaining adoption.

Conversational U.I.s seem to only make sense in pockets now but their use case will get stronger.

Monitor these technologies and what the giant’s like Google and Facebook are doing with them. You can’t just “AI” something, if you don’t have a healthy amount of reoccurring data or the right technology partner — don’t force it. But you want to be ready to take advantage of these technologies at the right time…and they’ll be ready to transform your business soon.