The Opportunities Businesses Are Missing With AI

Key Concepts

• Large Language Models (LLMs)
• Probabilistic Word Generation
• Cognitive Decision-Making Process (Human)
• Organizational Information
• Decision Source of Record Database
• Simulated Decision-Making
• Past Behaviors

Current Capabilities of Large Language Models (LLMs) Large Language Models are highly proficient at generating text based on the "probabilities of the best next word." This core capability enables them to excel in several areas, including:

• Content Summarization: Effectively condensing information from various sources.
• Narrative Creation: Constructing coherent and engaging stories or reports.
• Information Generation: Producing "solid information" in response to specific prompts.

Limitations in Human Cognitive Mimicry Despite their advanced text generation abilities, a significant challenge for LLMs is their "inability to really mimic the cognitive decision-making process of a human." This limitation manifests when attempting to query an LLM about "his or her personal preferences or personal experiences." In such cases, an LLM will typically respond with a statement like, "I don't have personal preferences or personal experiences." The fundamental reason for this inability is that the model "can't reference against past behaviors" in a human-like, experiential manner.

Proposed Solution: Leveraging Organizational Data An identified opportunity to overcome this limitation is to "provide large language models with access to organizational information that's associated with decisions and experiences." This external data would serve as a proxy for the "past behaviors" that LLMs currently lack.

Mechanism of Enhanced Decision-Making By accessing this organizational data, LLMs could "reference those decisions and experiences as part of their... mimicking of a cognitive decision-making process." Specifically, when an LLM is prompted to make a decision based on what would be considered "personal preferences or experiences" in a human context, it would instead be "referencing a decision source of record database." This database would contain documented organizational decisions and the experiences that informed them.

Conclusion and Future Implications This approach would allow LLMs to "simulate how... a human or a person would actually make a decision." By providing a structured, external reference point for "past behaviors" in the form of organizational data, LLMs could move beyond purely probabilistic text generation to a more sophisticated, context-aware form of decision simulation, thereby bridging the gap in mimicking human cognitive processes.