The AI in Business Podcast

Matt Madrigal, VP and GM of Merchant Shopping at Google, discusses the challenges retail SMBs face in AI adoption. He highlights the need to address product imagery issues, create engaging digital experiences, and provide free insights. The importance of responsible AI and countering biases is emphasized, along with the workflow and flexibility of Google's product studio tool. Trends in generative AI, bespoke models, and commodification of capabilities are also discussed.

Edosa Odaro, Chief of Data Analytics and Privacy Officer at Tawuniya, discusses the concept of Value Driven Data and the challenges of digital transformations. He emphasizes the importance of aligning organizational objectives, finding the balance between speed and sustainability in business processes, embracing frustration and failure, and connecting silos for transformation.

Shahar Chen, CEO and Co-founder of Aquant, discusses the role of generative AI-enhanced co-pilot platforms in improving B2B customer experiences in field service across various industrial sectors. They explore the talent shortage in manufacturing and the potential of AI adoption for driving innovation. They also highlight the challenges in B2B customer experiences, the impact of increasing customer expectations on AI adoption, and the importance of AI fluency in the manufacturing sector.

Dr. Yossi Sheffi, Professor of Engineering Systems and Director of the Center for Transportation and Logistics at MIT, discusses the integration of AI in logistics workflows, emphasizing the value of cross-divisional experience and soft skills. The conversation explores the current capabilities and limitations of AI, as well as the impact of automation on management and labor in logistics. Dr. Sheffi reflects on the importance of human judgment and skills in building trust in AI workflows.

Andrea Haskell and Val Srinivas from Deloitte discuss the impact of generative AI on investment banking, including automating tasks like news analysis and M&A. They explore the implications on talent, risk management, and technology adoption, emphasizing the importance of control points for reliable output and the potential for augmented technologies to elevate workers' focus on higher-value work.

Jason Wells, CTO at Pattern, talks about integrating data science into dev team workflows, the importance of collaboration and continual development, data scientists' access and freedom in AI-enhanced development, and hiring passionate problem solvers.

Srihari Govindarajan, Senior Director of Finance Transformation at PayPal, discusses how PayPal approaches fraud and policy abuse using generative AI. The episode explores the end-to-end process of a transaction in eCommerce, challenges of international fraud prevention, and the practical impact of leveraging generative AI. It also emphasizes the significance of data quality, governance, bias, fairness, trust, and transparency in AI initiatives.

Dan Buckland, Medical Director of Duke University Health System and assistant professor at Duke University, discusses Duke University's unique approach to AI in healthcare, using tested and proven models. He emphasizes the importance of virtual care as a last resort and the role of technology in the patient experience. The podcast also explores generative AI for interoperability in healthcare IT systems and advancements in AI for patient communication.

Ramesh Durvasula, Senior VP of R&D IT at Lilly, discusses AI's impact on drug targeting. AI predicts potential drugs, drives clinical trials as alternative healthcare, and enhances the work of researchers. Generative AI invents new drugs and evaluates chemists' solutions. AI analyzes scientific journals, provides deeper insights, and prompts experimentation for uncertain futures. Caution is advised in enterprise-level AI adoption.

Marco Argenti, CIO at Goldman Sachs, discusses the importance of safe experimentation and caution in adopting AI in financial services. They explore the emerging abilities of AI in reasoning and workflow and highlight the potential of generative AI to solve complex problems in the industry.