The AI in Business Podcast

Mark Miller, Managing Director of Life Science Advertising, Marketing & Commerce at Deloitte, joins the podcast to discuss leveraging data in AI use cases in pharmaceutical sales and marketing. He emphasizes the importance of connected data and decision algorithms to engage with healthcare professionals effectively. Topics include challenges in the life sciences field, the impact of AI and data on marketing and sales, adapting strategies in life sciences, and applying Gen AI in the pharmaceutical industry.

John D’Angelo, Real Estate Solutions Leader at Deloitte, discusses challenges in real estate industry, including slow adoption of technology and resistance to change. He explores potential of AI in leasing process to streamline creation of complex leases and mitigate risks. Importance of storytelling and local knowledge in real estate emphasized. Conversation also touches on ROI in legal document creation, potential of AI in contract creation, and challenges and trends in real estate innovation.

Ciprian Porutiu, VP of Operations and Strategy at Marsh McLennan, discusses the challenges of scaling systems in the VUCA business landscape. The importance of business agility, transparency, and quick feedback cycles in customer experience development are emphasized. The application of the dynamic of sandwiching in user experience and navigating compliance governance and business agility in a volatile environment are also explored.

Sreekanth Menon, VP and Global Leader of AI/ML Services at Genpact, discusses responsible AI practices and data governance policies. He emphasizes the importance of reliability, security, autonomy, accountability, and traceability in AI models. The podcast explores the use of prompts and structured approaches in generating AI models, and highlights the significance of responsible AI in highly regulated industries like finance and healthcare.

Steve Astorino, Director of the Canada Lab and VP of Development in Data and AI at IBM, discusses the challenges and misconceptions in generative AI, addressing the skills gap in utilizing emerging technologies in enterprises, challenges and governance in enterprise data and models, and the intersection of data governance and AI ethics.

Michael Tambe, Head of Data Science for Amazon Advertising Field Sales, discusses the limitations of prescriptive analytics in sales and the importance of generative AI in retail. He emphasizes the establishment of infrastructure for generative AI and the integration of physical and digital elements in the retail industry. The podcast also explores the challenges and uncertainties of conversational AI.

Haden Kirkpatrick, VP of Innovation and VC at State Farm, discusses the use of AI in the insurance industry, gathering data from devices such as smart homes and wearables, the future impact of AI technology on insurance, providing transparency and customer satisfaction, and understanding short-term and long-term changes in technology

Harsh Kar, an enterprise leader in data and AI at Genpact, discusses the challenges and opportunities of generative AI in organizations. He explains how previously siloed enterprise functions will work in concert and offers criteria for developing data governance practices for leveraging generative AI. The podcast also explores AI's impact on the insurance industry, partnerships in gathering data, upsell opportunities, large language models, and the changing customer-business relationship.

Caitlin Hodges, Risk Manager at Amazon, discusses reducing policy fraud in eCommerce. She shares insights on tools to fight competitor fraud, detecting AI-manufactured negative reviews, and the impact of abusive behaviors on sellers and platforms. The podcast also covers fraud during peak shopping events and the use of AI in addressing fraudulent behavior in online marketplaces.

Tomas Ohlson, Founding Engineer at Einride, discusses how electric and autonomous vehicles are streamlining logistics workflows and providing cost reductions. They explore the best places for human judgment in supervising supply chains and the role of data optimization and machine learning. The chapter on the autonomy spectrum in freight workflows highlights the need for human supervision and problem-solving for efficient operation.