Editor’s Note: This post is part of a series featuring speakers from the Marketing Artificial Intelligence Conference (MAICON). For more information, visit www.MAICON.ai.
Sara Hocking, a self-dubbed “regular” marketer, was tasked with marketing artificial intelligence (AI) experimentation as part of the Marketing Experiential Learning Lab (MELL) at Grant Thornton LLP. She’s not a technologist or data scientist, but she’s made great strides in marketing AI implementation. The keys to making progress, according to Sara, include curiosity, openness and a willingness to fail.
Inspired yet? We were, so we sat down with Sara to discuss her journey to piloting AI—plus what she will dig into during her session at the Marketing Artificial Intelligence Conference (MAICON) this July 16 - 18 in Cleveland.
Read on for more on how MELL is using AI, plus tips for other marketers who might just be getting started.
Q: Can you tell us about the Marketing Experiential Learning Lab? What are the goals of the lab and why did the team decide to tackle the topic of AI?
A: Grant Thornton developed MELL to improve the skills and culture of the approximately 150 marketing team members across the country and in Bangalore, India through collaborative, hands-on learning. The Lab’s learning tenets are to provide opportunities that are trend-worthy, edifying and applicable.
MELL also aims to further Grant Thornton’s pursuit of innovation by exposing participants to creative tools and frameworks, including design thinking, collaborative brainstorming, and agile development.
We decided to tackle the topic of marketing and AI because our team voted it the topic they desired to learn about most. Everyone was hungry to know more – to know anything!
Q: How is your team using marketing AI? What has worked and what hasn’t?
A: We’ve created three AI use cases and scored them, and we’ve run two pilots. The AI use cases center on optimizing content and digital performance, competitive intelligence and sales enablement. Our pilots have focused on optimizing content and digital performance, and improving understanding of our clients on a personal level.
Our approach to AI through learning and collaboration has worked well. Teammates who participated in MELL seemed to appreciate understanding when AI makes sense and when it doesn’t, and how to create use cases versus taking a technical approach.
For our pilots, the solutions themselves seem to be really compelling – the information and possibility for improving marketing has been interesting and inspiring. The technology doesn’t seem to “not work.” What “doesn’t work” is the lack of specificity of the business problem we’re trying to solve; therefore, we haven’t established the right metrics or seen strong adoption. Also, we haven’t improved our ability to document our business processes, which has also contributed, in my opinion, to low adoption and an inability to scale the solutions. I think what this adds up to is a situation where we’re still searching for how to create the right “case for change.”
Q: Realistically, what outcomes can/should marketers expect from their first AI pilot project?
A: Realistically, marketers should probably expect a failure. That doesn’t mean the work or the solution will be a failure, but adoption of the solution might be a failure. It may take a few tries to understand how to implement an AI solution in a way that will integrate well into the work of people who use the solution.
I also think marketers can expect to be inspired. AI has the promise to help marketers achieve fundamental marketing goals: knowing who the customer is, and what they want, at the right time.
Q: What advice do you have for marketers just starting with AI?
A: Don’t be afraid to engage “experts” and ask for help. They are happy to give it! We did this – engaged experts within our organization, who offered invaluable guidance. They helped us learn some of these lessons:
Q: What are you most excited about at MAICON 2019?
A: I’m most excited to meet other people and understand how they are approaching the evaluation and prioritization of use cases and learn how and who they are collaborating with, either within their organizations or outside. I hope the connections made will allow us to continue the conversation after the conference wraps, too.