To better understand the use-cases and impediments of AI across different verticals we hosted a panel with three AI CEOs in healthcare, agribusiness and food manufacturing:

  • Dr. Michelle Perugini, co-founder and CEO of Presagen, a health care company building scalable AI across women’s health products
  • Fiona Turner, co-founder and CEO of Bitwise Agronomy, an agtech scaleup that’s using AI to bring insights to farmers
  • Jamila Gordon, CEO and founder of Lumachain, which uses AI to disrupt global protein and food supply chains. 

For Perugini, the greatest advancements that AI brings to healthcare is through efficiencies and standardisation, but perhaps more importantly, accessibility.

“It’s bringing global technology into clinics that wouldn’t otherwise be able to access or afford it. And it’s bringing affordable and accessible health care to patients around the world,” Perugini says.

Turner cites forecasting and predictions as some of the greatest insights that AI can bring to farmers while Gordon notes that the safety and security measures that AI brings to the food supply chain are its greatest asset in her sector.

When it comes to the impediments of AI, the three leaders, despite their contrasting industries, were in agreement of AI’s biggest impediment: having the right data sets.  Making sure the data sets are broad and deep enough to solve for a problem without needing continuous retraining and rebuilding is one of the greatest focusses in the AI space, no matter the sector.

Turner says it’s “how we curate our data sets” that matters to Bitwise Agronomy, as they work to include wide enough regions and growing types in their training of AI.

“It used to be that everyone was trying to get the largest data sets. I don’t think it’s like that anymore. 

“I think there’s a recognition that you need the right data sets. You need globally scalable data sets. Those data sets need to be representative broadly of the domain in which you’re using AI to solve a particular problem,” says Perugini.

“So everything that we do as a company is around solving that scalability challenge and getting the right data, which is globally diverse so that we can deliver these products at scale and low cost,” she says.

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