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 Visual-Hybrid AI story-clusters by industry verticals/ knowledge domains/ functions. 

• That means the story-clustering logic can be by same industry/ function/ context e.g. in banking- retail/ corporate/ investment banks, customer profile intelligence usecases etc. 
• The viewpoints, value articulation, i.e. the lenses can be different for different usecases within the same clusters, e.g. for verifications vs. for relationship value predictions, in the context of customer intelligence and relationship management. 

Similarity: Personal/ role relatability 

For example, in usecases regarding specific skill requirements, the story-cards may include:

• How did your peers/ competitors/ partners 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

For example, the storyboards should define a clear scope of initiatives in terms of the key focus areas and consequent usecases prioritization matrix, user journey-mapping and usecases implementation plan/ road mapping. 

Continuity: Sequential graphics storyboarding

Graphics/ visual modelling for user journey-mapping is highly useful as a storyboarding practice. For example, a pre-implementation scenario (with a sample user-system interaction picture and description, with dialog boxes) vs. a visual post-implementation scenario with user-system interactions and outcomes-  Highlighting what's changed. 

Closure: Clearly define the scope of each AI story

Start with the specific strategic challenges that were targeted for resolutions. Then prove how they were resolved through the various AI interventions/ usecases briefly described. Showcase the post value-realization changes through graphics (pics, testimonials, videos, charts)

Connection: Show the links and thread between adjacent usecases and their business impact

For example,  linking different AI usecases in a cluster. Say, in retail banking, show how the CRM analytics usecases on CLV (Customer Lifetime Value) and churn prediction in one product engagement and usage pattern analysis, has been leveraged in upselling/ cross-selling another product. Then showcase how this has impacted the cost of customer acquisition/ retention or speed of TTM/ TTValue with faster 'go-live' of the new relationship channel or the new product, for existing customers.

Order and Alignment: Between story threads/ narratives

For example, at different layers of AI solution architecture, showcase the linkages and dependencies between usecases to infra. This can span from Chip to API, e.g. from cloud infra, data & AI infra, to the needs of different libraries/ APIs/ algo's/ techniques e.g. few shots/ zero shots, etc.

Sequencing: Story dependency analysis 

Credible AI value/ impact story-boards can not be built 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. 

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.