A panel of industry executives discussed the accelerated growth of artificial intelligence and how one subset, Generative AI, will affect the way retail and healthcare business function in the near future.
Generative artificial intelligence generates text, images, or other media, using generative models. This type of AI models learn from their input training data and then generate new data with characteristics that are the same or similar in certain ways.
To set the tone for the day, moderator Deborah Weinswig, CEO and founder of Coresight Research, talked about the rapid rise of Generative AI and its impact on the business community: one study suggested that Generative AI will have a $9.2 Trillion impact on retail by 2029.
This panel looked at ways AI could create new content and ideas, including conversations, stories, images, videos, and music that will lead to increased sales, better margins and lower costs.
Panelists included Andre Persaud, Consultant, who most recently was an executive at Rite Aid; Lori Schafer, CEO, Digital Wave Technology; and Guy Yehiav, president, SmartSense by Digi.
Weinswig says Generative Ai is a powerful tool but warned that users should be mindful of the technology and its “hallucinations,” which is when AI makes up false information or facts that aren't based on real data or events. “This is the power of generative AI,” she said. “It fills in, and it may be false often when it fills in.”
The panelists gave their thoughts on the topic of generative AI in healthcare.
“At SmartSense by Digi “we help healthcare and pharmacy and CPG manufacturing, groceries and restaurants optimize their labor. So it is making sure that the products have the right quality while the people that work, that serve the clients and not think about all the regulations and the compliance that continue to change every day. It speaks specifically in pharmaceutical. You have that, uh, continuous changes, uh, in, in food as you know, uh, FSMA 2026 is coming with traceability that is similar to what came out last year for pharmaceutical. So, you know, one vertical is learning from the others, and sensors have been here for a while, but doing something with the data, optimizing it and telling people what they should do in order to optimize the outcome, that's what we do.”
“Yeah, so, you know, condition monitoring has been here for a while. I think the most critical is reducing those false positives. If I tell you every five minutes, ‘Hey, you left the door open’ and you look and you say, ‘Hey, the door is closed.’ Next time I'll tell you that we will not look at it. So reducing false positives is where generative AI can help because it looks at all the historical data then tells you “Hey, you should close the door’ and you can penalize or credit the model based on if you see the door close or open. And then there's a feedback loop that continues to learn and then use different words. So it's not just prescriptive action, but it's about that specific store that you are in with specific assets. It learns about the asset.”
“I think that to start, you look at the specific cases that you're trying to improve and then it's easier to build the data model for those. So you look at the specific sensors, you look at the telemetry data that you need, and you run the model. And typically when you run just a model, you'll be 50% wrong. Now you say, “Hey, humans, make less mistakes. But, that's the beginning. That's the first four or five weeks. What you then do is you start penalizing the model and credit the model based on the trueness of the result. And then when it reaches about 90%, you can now start releasing it. …And so starting those models is important to have a specific case that you're trying to capture, take the data, run the model, learn, and then start using it to improve.”