A key application for AI is not just summarizing information but weaving isolated data points into a coherent "story." For academic advisors overwhelmed with student data, this transforms dozens of facts into an actionable narrative about the individual.
Design prototypes not just for user validation, but as internal "laboratories." By exposing system prompts and underlying data in the UI, you can demystify the AI, foster cross-functional collaboration, and accelerate internal alignment and learning.
A background in a seemingly unrelated field like music can be a unique advantage in tech. Skills honed as a conductor—systems thinking, creative empathy, and leading a group toward a unified purpose—are directly applicable to managing complex AI products.
Instead of locking prompts in code repositories managed by engineers, empower PMs to own and iterate on them. This treats prompts as a core product component, ensuring AI behavior directly serves user needs and business strategy, as practiced at Watermark.
To transition into AI within your company without prior experience, proactively seek out nascent AI initiatives. By raising your hand for the "messy middle" where no one is an expert yet, you can learn on the job and establish yourself as a key player.
While AI solves complex problems, it simultaneously creates new, subtle issues. AI product development significantly increases the number of potential edge cases and risks related to data integrity and governance, requiring deep, detail-oriented involvement from product leaders.