Don't worry (much), AI won't bite

Be the first to comment

Marketers are excited about the potential of machine learning, but it takes more than enthusiasm to turn the technology into a business advantage, write the CMO and content director of Ready State.

Last fall, we thought marketers weren't paying enough attention to the potential of machine learning. Six months later, it looks like we already have the opposite problem. Brands are jumping with marketing dollars, but what about human creativity? 

Brands and their partners stand to reap tremendous gains from the explosion of machine marketing. Currently, the technology is not only used in recommendation engines and programmatic ad buying, but also applied to customer segmentation, multivariate A/B testing, customization of websites and more. 

But the question that has risen alongside this explosion of usability is: What will our role really become? AI might hurt a little if we aren't prepared for it. It could impact certain jobs, and anything that affects jobs meets some amount of knee-jerk resistance. However, resistance to change didn't stop mobile, social, native, programmatic, or any of the other major advances that have encountered some initial skepticism and growing pains. And it won't save your job. This is a curve to be ahead of, not behind.

In the creative industries, it's our job to bring the human connection to the work we do for brands. But to do that we need to make sure that the technology we use solves a real audience pain point and does something valuable for the customer. Bringing this focus to AI can help turn it from a gimmick into a real business advantage.

To do so, AI needs guidance on voice and tone. This is about more than the section of key words and phrases. The best chatbots are good at helping brands serve customers and find specific information that may not be readily available or is tedious to search for themselves. The downside is that typically bot answers are pretty scripted or generic sounding, which doesn't do anything for the brand. Bots should have their own personalities, but ones still consistent with the overall brand. 

The first time I realized the emotion a bot could deliver was reading Casey Newton's fantastic story about how San Francisco entrepreneur Eugenia Kuyda built a "memorial bot" for her dearly departed friend Roman Mazurenko. Kuyda collected a trove of text messages from their mutual friends, and taught a bot to speak like Mazurenko. In one exchange, a friend asks the bot why he is feeling weird. Roman answers, "Because I don't have this connection with you guys that I have in Moscow. I feel like I'm floating here and everything becomes harder. But I need to stop whining and move on." Then in response to the friend saying, "I miss you," Roman simply says, "Me too."

Obviously, strong emotions are not suitable for all brands, and a no-nonsense, all-business voice could be exactly what's called for. Our role is to figure that out, and to combine the power of the brand voice with the mechanics of natural language processing. 

Natural language AIs have gotten pretty good at understanding what is being asked, and matching the questions with specific scenarios or intents. We need to anticipate what these intents are and craft answers that help the customer, in the voice of the brand. Then we need to help train the AI to better understand or respond to what the customers are saying. 

On the other side of the voice-and-tone coin, is look-and-feel, and there is as much visual activity in AI as in language.  Realistically, jobs based on repetitive production work are at risk because computers are rapidly delivering automation to increasingly sophisticated production tasks. But as with voice-and-tone, humans are required to make visual experiences like ads, websites, etc. to connect with consumers. With that filter in place, machine learning and AI can be useful in them optimizing these visual experiences.

Where we don't yet see machines showing a lot of capability is in delivering "surprise and delight." AI is great at finding hidden rules, but not as good at finding where you can break them. Look at the well-known example of Ling's Cars from a few years back. It's shocking visual approach intended to evoke an emotional (human) reaction.

Today, an AI designer tool like The Grid is unlikely to crank out a Ling's Cars design (unless we feed the AI a lot of data so it can figure out why Ling's Cars works). That leaves another window open where humans can deliver things machine marketing can't. 

In the end, one thing is for sure: we have the opportunity to take on the role of teacher in this space. From defining the voice for conversational UIs, teaching more sophisticated natural language skills, figuring out what visuals work best in different situations, and creating experiences purpose-designed for customers, we must educate ourselves and our clients about how to make AI a true business advantage.

—Steven Wong is CMO and Derek Slater is content director at Ready State.