When Ai design tools for physical and digital objects focus on the right phase of R&D&D - Research, Design and Development is where Ai gives it's greatest boost but how? Here's how.
In areas of investigating the influence of the design process in the shaping of not only initial conditions but edge constraints used in a new product development initiative, we see how there are specific Implications for Human-AI Design Collaboration that show more valuable and specific artifacts coming from certain phases in the design and new product development process especially when the widening of the lens takes place via the widening of the definition of the NPD ( New Product Development ) parameters to something many firms now embrace which is a NP&PD ( New Product & Process Development ) system where Ai shows considerable promise.
In the case of quickly being able to execute on code base deployment for rapid application development initiatives ( and where this is increasing in some cases means jumping to fully working incredibly complex code repositories in real time ) where AI Design Means Collaboration Convergence scenarios, we see internal to a NP&PD situation the cycle time of experimentation directly effected and again considerably cut and even when these environments are physical goods oriented and the multitude of additional complexities such undertakings entail.
More specifically is where we see that in cases of engineering design and it's convergent solution methodologies when combined with designers with design problem outlook exploration as divergent possibilities can be bridged by Ai systems that are translating the differences in language between the two groups and their basic archetypal underpinnings allowing for a stronger exchange of interpretations as to what are within bounds extrapolations of the initial data sets that are presented in Ai design and development systems. This also assumes that any and all collaborative efforts will require that Ai systems in each of the distinct NP&PN processes has access to the same data sets so that when further questions, experimentation, suggestions, etc., are queried by the Ai design system in this example, means that all quantitative data that is used is part of all future branches from the initial design and development start.
But when clients teams are everywhere, when time zones and check-ins are all over the map, when an originally 8hr work cycles meant ( possibly ) two shifts in global NP&PD efforts, now in moving to a 3 phase model of 3 shifts of 8hrs and thus 24 hour NP&PD efforts, even more, we see that the strongest part of the Ai assisted design and development cycle is in the engineering areas but where the further develop of Ai Discover and Define sections of the process require the highest sense of divergent thinking that when further along in the development process previously could not have occurred as far down the process as now with Ai allowing for redefining and in some cases re-sequencing previously almost as permanent parameters in a design, means more flexibility up until the last moments of production. In that, the further creativity needed to wrangle last minute challenges that in pre Ai-Design and Development environments might have been the death nail to projects.
In that explicit way, Ai NP&PD enhanced environments ( and even before and within the design and prototyping phases of said efforts ) is as the possibility might become, allow for long hierarchical divergent development paths to take place and all where one change to specific directional changes allows for all aspects of the chain to take place and specifically in a visual way further cultivating not yet explored design directions that can lead to specific NP&PD ( New Product & Process Development ) further embedding R&D&D ( Research & Design & Development ) Ai tools as primary protagonists between the 7 distinct phases of experimental efforts R&D&D = NP&PD where the axis of influence ( and explicitly within the development portion ) is ' D ' or development activities.
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