To clean up more than 200 million pounds of trash in the ocean by 2040, ocean cleanup must be intentional about which areas it is targeting to fulfill its mission.
The nonprofit has revealed that it has turned its eye to AWS to provide a system for finding ocean hotspots filled with discarded plastic using satellite data and floating trackers.
“We combine these data internally to create a platform that can direct the vessel at the highest concentration of plastic, allowing us to improve the efficiency of our business,” said Ricardo Farina, head of corporate partnerships for the nonprofit. AWS has also developed AI-powered surveillance systems to enable ocean cleanup to detect marine life and limit harm to the extent possible.
Farina and other employees of AWS customers and partners were interviewed earlier this week at AWS New York City Summit 2025.
While new collaborations with AWS are focused on AI technology, Ocean Cleanup is already familiar with using AI technology to extract the data needed for your goals. The group collected data on where water objects or debris were located in the ocean by installing AI training cameras on the ship.
“[It proves] AI can help solve environmental problems,” Farina said.
Infor and Equinox Data Foundation
It also proof of the importance of data in the development of AI-powered tools and applications.
“It's paramount to have a solid data foundation that will help you successfully implement AI,” said Jeanne Newberry, Senior Vice President of Ecosystem and Business Development at Infor, a multinational vendor that provides industry-specific enterprise tools and applications. Infor uses the Amazon Bedrock Generative AI (Genai) platform.
One company that demonstrates the importance of a robust data base for effectively using AI is Equinox, an international fitness and health chain. Equinox has been an AWS customer for over a decade. It uses machine learning, personalized models, and bedrock infrastructure and tools. In recent years, Fitness Club has used member data to create recommendation systems to match members to different classes.
“If you have thousands of classes and hundreds of thousands of consumers, it's your standard model that you're using trends to say who should take which class,” said EVP and CTO Eswar Veluri.
With genai, Equinox has moved from providing recommendations to updating conversational UI from stiffer dialog boxes to more natural language interfaces. This allows Fitness Club members to ask questions and refine them based on system responses.
“To allow backs to be entered, understand the response, assimilate it, and then provide a change in the response is the biggest advantage of genai as it becomes a bit of a two-way system for the end users or members of this particular case and feels happy with the response,” says Velui.
Equinox is currently using current data to inform AI and Genai applications, but is considering how data can inform future use cases of AI technology.
“It's what equinox is trying to do and when it comes to longevity, where longevity is having more biometric and blood data… How can we leverage AI to provide better recommendations to our members who want more premium services than we have,” Verli said.
He added that Equinox hopes to create better member data for digital twin development, identifying future members' ideas and goals, and visually show what it looks like to reach those goals. This means having a member proxy to identify fitness goals and needs.
Two types of data: Dine brand
Another company with a long history of having AWS is also the user data that directs the application of AI technology and Genai is Dine Brands Global. Company Restaurant Holding Company owns and operates approximately 3,500 restaurants and builds digital channels on AWS for chains, including IHOP and Applebee.
“As all data resides in the AWS ecosystem, it was a natural fit to tackle some of these AI and Genai use cases and workloads and use AWS,” said Jason Suarez, vice president of digital and CRM engineering at Dine Brands Global.
The Dine brand created external and internal applications using two sets of structured and unstructured data.
Unstructured data contains information from SharePoint drives and regular documents.
“It's something that can help some of our technical support agents quickly and efficiently,” Suarez said. “They have access to it, but they can't do it as quickly as they have an assistant.”
Using AWS, Dine Brands has created the Franchisee Technology Services Assistant, an AI-powered technical support system that helps to handle technical support agents with service calls from franchisees using natural language.
The second type of data it had was structured data derived from the loyalty program at IHOP.
“Of course, we have more data from guest transactions and actions, so we can provide a more personalized service,” Suarez said. “It's a natural fit that we… can provide meaning to our guests and improve the guest experience.”
Esther Shittu is an Informa TechTarget News Writer and Podcast host that covers AI software and systems.
