Differential (Aspirational) AI
We’re on to what I think popular culture would consider the “fun” bit, or at least the bit that dominates the imagination when it comes to artificial intelligence’s supposedly boundless possibilities. Whereas Incremental AI includes the scenarios that improve upon solo human performance of activities that would have been performed anyway, “Differential AI” broadly encapsulates workloads that would not have likely been performed by humans alone, scenarios that are valuable to the organization because they allow you to jump out ahead of your competition, to offer your customers something that you’d not have otherwise been able to provide. I toyed with the idea of calling this “Secret Sauce AI” or “Moonshot AI” to underscore the point.
Hallmarks of these differentiating, accelerative workloads are that they require a degree of creative thinking to dream up, can be challenging to implement, often involve deriving insights by mixing data that you already own but never had the ability to co-mingle, operate along a time dimension—that is to say, involve some sort of computation or connection that must be completed within a window of time that makes human intervention more challenging—and will require a degree of flexibility on your part at least in the early days as you figure out exactly how to harness the power of this newfangled thing you’ve built.
Join me—Andrew Welch—with HSO’s "Dynamics Matters” podcast host Michael Lonnon for part three of our AI strategy miniseries as we crack open the pretty mind-blowing world of Differential or “Aspirational” AI, chat about ownership and leadership vision for AI initiatives, and share guidance as to how organizations can balance risk across their portfolio of AI-driven workloads.
