Transcript
A (0:00)
Foreigning us now for five insightful minutes is Caitlin Allen. Caitlin is the SVP of market at Simbee. And Caitlin is here to share her thoughts on the puts and takes of deploying AI and robotic automation at scale inside retail organizations. Caitlin, let's start off with this. What, what do you think are retail executive buyers largest misconceptions when evaluating retail automation?
B (0:32)
It's no longer about if automation is driving value, but how. And so following where industry leaders invest is important. And we see the top CEOs and CFOs prioritizing three things. The first is data quality, then scalability and then store coverage.
A (0:51)
Yes.
B (0:51)
Know with the onset of AI, modern data models are needed. And yet poor data is the Achilles heel of most automation solutions. And so we see vendors, we see retailers rather working with vendors that, that really work with high data standards, defining those necessary elements in history and quality related to scalability as they're reinventing retail operations. Retail, retail's best are testing automation in areas like on shelf availability and price integrity to get started. And then they're looking at scaling other use cases be it across allocation planning, forecasting, planogram compliance or what have you. And then the final piece is store coverage. And you know, today retailers are tracking when products arrive and when they leave, but they have, they lack visibility into their store of what happens in between that that point in time. And so top CEOs and CFOs are prioritizing autom that surface the actions that matter most that they can start to understand what true execution looks like in the store. And I would say in closing that all of those three priorities really expose the misconception that leads when evaluating retail automation which is over rotating on one device type. This is really a conversation that needs to be about combining sensors for optimal coverage and data quality.
C (2:13)
Okay, well Caitlin, we know that Simbi uses computer vision AI. It's still new to some of the retailers listening to our program. So can you help us identify what differentiates good computer vision AI from bad?
B (2:28)
Sure. So computer vision is what's used by things like fixed cameras on the shelf or autonomous, autonomous mobile robots, et cetera, just to kind of ground that in something that we can all see. And I would say one factor really separates high value computer vision from the rest with two key supporting elements. So the main thing that's important is value. That's been proven at scale across multiple chain wide deployments, in multiple retail subsectors, in geographies and use cases where there's many applications. It's easy to claim that you have a product that does certain things. But then when, when vendors or when retailers dig in to verify vendor claims, they often find out that, you know, claims might be a little hand wavy and really like. The reason I start with that non technical answer is this. This is about the business outcome, right? That's how to, to really take a sense for whether computer vision is good or bad. And then the supporting points for that are really around depth perception and total cost of ownership. So depth perception is basically another way of saying that good computer vision sees in 3D mobile robots have become known as the most accurate and scalable and cost effective retail solution because they can move around and that eliminates data coverage gaps. And that also relates to the topic of total cost of ownership. When you have just fixed cameras, for instance, you have hundreds of them per store. That really drives up your costs and your maintenance as well as your risk of damage. Whereas a robot really requires minimal infrastructure and it's kind of the difference of managing just one device versus hundreds. So I would say bottom line, computer vision is really about having proven results at scale in prior applications. And that's especially the case when it is backed by a solid business case that spans depth perception and cost efficiency.