On Episode 21 of BRITE Ideas, JP and Matt interview Joel Beckerman, Founder and Chief Creative Officer of Made Music Studio. As Joel notes early in the episode, humans are hard wired to connect to music and it “imparts emotion to all [our] other senses.” Joel shares his insights on how he works with his clients to create sonic elements that align with the brand and have the power to craft a distinctive connection – “sonic semiotics” -- so there is recognition of the brand, the story behind it, and it's vision. This is all fleshed out by Joel taking us through examples of successful branded sounds. In addition, Joel touches on how music and sound can impact human behavior and shares some of his hopes and concerns of AI and its capacity to create content. Finally, he details his BRITE idea of “sonic humanism” in which the emotional power of sound, music, and silence, can be used in ways to aid society. Enjoy!

On Episode 20 of BRITE Ideas, JP and Matt interview Kabir Ahuja '03SEAS '08BUS (Senior Partner, McKinsey and Co.) and Eric Sherman '20SPS (Knowledge Expert, McKinsey and Co.). The conversation delved into a wide range of areas from the role and relationship of the CMO within the C-Suite, leading brand and growth strategies, how AI is affecting organizations and society, marketing's unique potential to harness genAI, and the talent management issues that come with a rapidly changing world landscape. Enjoy!

On Episode 19 of the BRITE Ideas podcast, Matt speaks with Eric Siegel '93SEAS '95GSAS '95SEAS '98GSAS '98SEAS (ah, the multiple degrees that accumulate during a custom-designed PhD effort), former faculty at Columbia University, and author of Predictive Analytics and, his latest, The AI Playbook.

At the core of the conversation is Eric's reminder that traditional AI/ML efforts -- "predicting who will click, buy, lie or die," etc. -- are highly effective and still underutilized AI tools within organizations. The hype of generative AI is certainly warranted, but Eric is eager to help the data scientists and managers of organizations learn together a business framework and paradigm so more established predictive AI models move beyond testing and into production. Common misunderstandings about the true predictive accuracy of AI are discussed as well as the vital role of ethical reviews of model use. In particular, Eric highlights how important it is to avoid using models that create higher false positive rates and thus deny or penalize groups without just cause.