AI reality, meet organizational enthusiasm

ABSTRACT: In 2024, AI which exceled in pilots has met the reality of organizational capabilities, interdependent systems, and scalability.  Making outcomes worse are emergent technologies, scarce skill sets, and use cases that cannot adapt to rapid cycle ecosystem requirements.  Is there another approach?

Why in 2024 do 80+% of all piloted AI initiatives quickly lose efficacy when scaled into a production setting?  Noted caveats and challenges for these “fail-to-scale” innovations include unanticipated data ingestion designs, fragmentation of data storage, non-linear data processing complexities, and an inability to anticipate the active data governance automation and rules-fueled oversight.  Not trivial and not without industry experienced skills and context.

Moreover, beyond the demand for Gen AI, beyond the efficiencies sought across the operational processes, and beyond AI ethics, bias, and hallucinations, are the scalability and performance failures linked to their architectures (e.g., centralized, distributed, decentralized), or the data utilized to train models and make intelligent decisions?  Is it a causality of AI platform assumptions, inadequate independent testing, or advanced skill sets tied to model realities? 

The lessons learned are that AI requires a holistic approach to its deployed purpose, goals, and measurements that often conflict with traditional infrastructures, organizations, and regulatory compliance.  To ensure that vertically defined AI systems participate across organizational functions—e.g., legal, audit, regulatory compliance—the cascading, adaptable prompts, inputs, and outputs must be designed and anticipated before performance and scale are achieved. 

The design assumptions that AI scale and performance are singularly about clouds, servers, chipsets, and speed fails to recognize the multimodality of demands and technologies, which must be precisely interconnected when compared to established application design ideation approaches.  To move beyond the 2023 prototypes and pilots and into robust, transparent, and adaptive AI production capabilities, a holistic approach shown below must be followed.

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