Build AI Stories with Gesalt's Rules

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Key focus for AI narratives and AI storytelling: Be strategic, have impactful narratives at its core, occasionally supported by visual evidences/ story cards. 

In this context, Gesalt's principles of visual perception management based on similarity and proximity, are relevant and well applicable. 

For example, combining multiple AI narratives in close proximities, into one high-impact AI story. This ONE-AI story can be based on proximity of enterprise targets (e.g. Strategic business goals: growth, profitability, market share), audience groups, adjacent resources and tech capabilities e.g. data-cloud-AI algorithms combined capabilities, etc. 

Gesalt's principles applied in the context of AI storytelling

Proximity: Build clusters of visual-hybrid AI stories. Clustering logic can be by same industry/ function/ context e.g. in banking- retail/ corporate/ investment banks, customer profile intelligence usecases: Lenses can be different for different usecases within the same clusters, e.g. for verifications vs. for relationship value predictions

Similarity: Personal/ role relatability e.g. same skill requirements- how did your peers/ competitors/ partner overcome it? How did they use AI for improving their talent branding, employer brand value, retention metrics, eSat and cSat?

Enclosure: Broad definitions of boundaries, e.g. scope of initiatives- key focus areas -> usecases prioritization matrix, user journey-mapping and usecases implementation plan/ road mapping

Continuity: Through sequential graphics storyboarding, e.g. pre-implementation scenario (pic and description) vs. post-implementation: Highlighting what's changed. 

Closure: Clearly define the scope of each AI story, e.g. starting with the specific strategic challenges that were targeted for resolutions, and then proving how they were resolved through the various AI interventions/ usecases briefly described, and showcasing the post value-realization changes through graphics (pics, testimonials, videos, charts)

Connection: Show the links and thread between adjacent usecases and their business impact, e.g. linking different AI usecases in a cluster, say, retail banking, showing how the KYC done from one product engagement has been leveraged in upselling/ cross-selling another product, impacting the fast 'go-live' of the new relationship channel for an existing customer

Order and Alignment: Between story threads/ narratives e.g. in cloud infra vs. data & AI infra, needs of different libraries/ APIs/ algo's/ techniques e.g. few shots/ zero shots, need for more rapid infra scales than just static big data infra, etc. 

Sequencing: Story dependency analysis e.g. without the success stories of universal truth-lakes, usecase-specific data infra e.g. AWS health lake, enterprise data lake/ pretrained models/ curated text or image corpora, rapid AI stories cannot be built. 

A combination of static and dynamic visual representations e.g. charts, pics, videos etc. with narrative storyboards, utilizing these principles, can exponentially strengthen the impact of successful enterprise AI stories.