This presentation was delivered by Abhishek Ratna, Head of AI & ML Developer Marketing & Olivia Burgess, Product Marketing Leader, Google Workplace Security at Google, at the Product Marketing Summit, Seattle 2022. Catch up with a variety of talks with our OnDemand service.
Abhishek Ratna: Unless you've been living under a rock, you probably know what AI is, so I won't belabor the point. We’re living in very exciting times, and Olivia and I have been lucky enough to have front-row seats to see how AI is being used in enterprises.
We’ve also had the privilege of working with product marketing, product, and engineering teams as they bring machine learning products to market.
We’d like to share some of our insights into why AI matters, why you should care, and how you should think about bringing AI-based products to market.
Olivia Burgess: If you're thinking, “but I market dog food, or shampoo,” don’t worry. We're also going to share some general principles that you can apply to your marketing and that will help you articulate value back to your broader business. No matter what type of product you market, this article will be helpful.
A new world of possibilities
Abhishek Ratna: Lost Tapes of the 27 Club is an album developed by a UK-based organization that focuses on mental health issues amongst the musician community.
They wanted to raise awareness of these issues, so they partnered with a marketing agency that trained an AI on thousands of hours of tracks by musicians who lost their lives to mental health issues at the age of 27 – people like Jimi Hendrix, Kurt Cobain, Amy Winehouse, and Jim Morrison.
That AI essentially spun out a brand new album that was picked up by 50-plus publications in 190 countries, including Rolling Stone magazine. At the Drum Awards, the album won the Grand Prix for its innovative use of technology, as well as the Ad Tech for Good prize. More importantly, it helped a lot of musicians around the world find resources for mental wellness.
What stands out to me, besides the feel-good factor, is the generative power of AI. It can create original music using just a thought. Other AI technologies can do the same with images. If you type in “avocado chair” as a prompt, you can get a dozen renderings in a few seconds.
You can tell language-based models to create poetry in the style of William Shakespeare, and they’ll automatically do that for you. Gong.io is another great example; it uses advances in natural language processing to understand human conversation in real-time and take action on that.
There aren’t only tremendous creative possibilities for AI; there are also significant business implications. If your product doesn’t already incorporate AI in some way, shape, or form, you might be in trouble.
AI has become table stakes. Most products today innovate and compete on the basis of their AI superpowers. AI has gone from being a nice cherry on top to the next competitive differentiator. Boards around the world are talking about how to better incorporate AI.
As Deloitte puts it, we’re in the age of “with”. We’ve moved from the time when machines were used for carrying out our day-to-day tasks to being in a space where our capability is augmented with smart machines.
Olivia Burgess: There’s been a kind of hockey stick of innovation in how AI and machine learning have developed over time. Where before there were just a handful of niche solutions for specific utilizations, today we’re seeing more and more enterprise-ready AI tools. That means more organizations feel ready to use AI in their general operations.
There are also more companies – key players like Microsoft, Databricks, Google, and Amazon – bringing products to market that can be used very easily by data scientists and machine learning engineers within their organizations.
How to approach AI projects incrementally
Abhishek Ratna: Before we dive into the specifics of using AI, there are some principles we’d like to share. We’ve found these helpful as we’ve rolled out AI projects.
Rule number one is don't be afraid to not use AI. It's treated as this magic cure-all, but if you can get good results without it, I’d recommend taking that route.
Rule number two is to embrace data and measurement. Marketers who understand their customer data, segmentation data, and metrics very well are the ones who typically drive the most value, and best articulate the value of their projects.
Rule number three is to grow incrementally. As someone who's worked on deploying AI-based marketing projects at Facebook and Databricks, I can share that the big bang approach rarely works well. It's better to start small. Start by automating one task or doing some sort of bespoke analysis of your customer interactions. That will start giving you some value.
If you want to do something as powerful as generating product recommendations or building real-time personalization, it's wise to take a successive path and graduate to that level of maturity.
The user-first domino effect
Olivia Burgess: To differentiate the AI or ML product you're bringing to market, there are two key personas you need to speak to, and there needs to be a symbiosis between both.
The first key persona is the end user. That might be the data scientist who’s building the algorithm. It could be the machine learning engineer, who's responsible for implementing that algorithm into the business and showing value from the predictions it makes.