After implementing generative AI, companies often fail to see improved efficiency due to a lack of context and standards within the organization, Kongyusida argues.
Key points
•AI implementation often fails to boost efficiency due to lack of organizational context and benchmarks
•Kongyusida recommends a 'P.O.S.T' framework for structured company operations
•Small organizations need systems to convey their work standards to AI
At a special lecture on agri-food startups organized by Tabang Ventures in September, Kongyusida analyzed that the failure to increase work efficiency after AI adoption is due to the absence of company-specific context and benchmarks. They emphasized that structured information for the AI should be prepared before using prompts.
Kongyusida likened the situation in small organizations to new employees whose job standards are concentrated in the minds of key personnel, such as founders or core practitioners. To address this, they suggested a 'P.O.S.T' framework—Project, Operation, Standard Operating Procedures, and Task—to systematically manage company operations and provide an environment for AI to work.
The lecture provided practical examples from agri-food startups, including overseas market expansion, trade fair participation, production order placement, and sample shipment standards.
