In Ai training and it's dependency on real world data for replication by repetition what happens when why an X is being done, radically changes and isn't known any more? Design thinking to the rescue ? Possibly.
Where the argument has always been that ( via one of the cornerstones of the design thinking toolset ) the black diamond method ( Discover, Define, Develop, Deliver ) is where abductive reasoning mentalities via contextual and holistic investigation strategies is the basis for articulating curiosity. This is the understanding that a " just the way it is " analysis process, is separate from engaging an engineering and science based thinking which is associated with deductive reasoning. Where in the past, the idea of curiosity being the differentiator for understanding what potential and yet unknown customers do and experience in their use of tools / completion of work, this can be seen ( as due to the over whelming adoption of Ai ) possibly a fundamental flaw in a new world and environment where software development and deployment plus knowledge work itself is being eroded further.
Throughout the uncoupling of the creative process: Novelty, Functionality, and Elaboration from the artistic form itself and as described in Effects Of Design Thinking On Artificial Intelligence Learning And Creativity we see the stark capability of " the state of the art " not in terms of creativity for creativity sake and thus when applied to unrestricted innovation efforts allows for a future training data set that could allow Ai systems to act in a way that anything is for the taking. This rather stark attitude that Ai cannot ascertain what can be done from what cannot be done, it is where international firms that are able to leverage Ai for suggesting and analyzing alternatives not only previously assumed generative ( as in next word suggestion and next act descriptions ) become almost artistic and therefore more valuable than ever.
Where the idea of " uncovering " via an iterative process to deliver user and customer-oriented results ( and where that process is based on rules that have already been defined ) is where active learning, constructivist learning, and where some firms define this as creativity is where the idea of iterative perspectives, problem-solving abilities, multidisciplinary learning, and complex background scanning can drive new product development. Firms that understand where what could be is as simple as what is to be invested in then realize Ai can sometimes ( in non-scientific domains ) at the least drive not only the uptake of some new product development efforts.
Where firms that have previously used design thinking models that include: inspiration, ideation, and implementation ( essentially a clear nod to the artistic mentality ) and the more recently business suited: empathy, define, ideate, prototype, and test is where these all require a consistent attitude that to understand, explore, prototype, and evaluate as well as empathy, ideation, and experimentation mindset can easily be seen an " as it is " and slightly non-visionary process. And when Ai datasets that use these types of input can only see so far that has not been seen. But as technology progresses, and more importantly the use of swarm processing and swarm datasets can Ai further expand into " why is someone doing " and where creativity is coming closer to reality. This is where the tables can truly be changed. And it's coming faster than even the most cutting edge firms can expect.
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