The dawn of Cognitive Computing has brought a revolutionary shift in the way we approach decision-making. As AI takes centre stage, it’s crucial to understand how these systems mimic human cognitive processes to make sense of complex data and generate insights. In this conversational blog, we’ll delve into the decision-making process of a new AI platform, drawing parallels to how humans observe, interpret, and determine optimal outcomes.

Imagine you’re a seasoned detective, tasked with solving a complex case. As you gather evidence and examine visible phenomena, you’re essentially observing the data available to you. This is the initial stage of the decision-making process for both humans and AI. Just like you would collect clues, AI systems ingest and process massive volumes of data to gain a comprehensive understanding of the situation.

Once you’ve collected the evidence, the detective work truly begins. You need to interpret the evidence, connecting the dots to generate hypotheses about what might have transpired. Similarly, AI systems analyse the data they’ve observed, generating predictions and insights. However, this is where AI has a distinct advantage. Its ability to process vast amounts of data allows it to unveil patterns and relationships that might be imperceptible to human analysis.

Now comes the intriguing part: evaluating hypotheses. In the case of human decision-making, you need to determine which hypotheses are valid and which are not. AI faces a similar challenge. It generates multiple potential outcomes and predictions, but how does it decide which one is optimal? This is where the concept of mathematical optimisation comes into play.

Just as you, the detective, weigh the evidence against your experience and intuition to determine the most likely scenario, AI employs mathematical optimisation techniques to determine the best course of action. These prescriptive models are designed to find the optimal solution among multiple potential outcomes. It’s similar to selecting the hypothesis that aligns best with the data and context.

Imagine you’ve narrowed down your hypotheses to two likely scenarios. Now, you must decide which one is the best fit for the evidence and circumstances. This is akin to AI determining the most optimal prediction among its generated insights. Mathematical optimisation helps AI evaluate ‌potential outcomes against defined criteria, just as you evaluate hypotheses based on your expertise.

This convergence of AI’s decision-making and human cognitive processes showcases the remarkable similarities between the two. Both rely on data observation, interpretation, and the evaluation of hypotheses to arrive at the best possible decision. Just as your intuition guides you in selecting the most likely scenario, AI’s mathematical optimisation guides it toward the most optimal prediction.


As we explore the decision-making process of AI, it becomes evident that these systems are not just cold, calculated machines. They mirror the intricate cognitive steps humans take to navigate complex information. Whether it’s a detective solving a case or an AI generating predictions, the path to informed decision-making follows a similar trajectory. The fusion of human intuition and AI’s mathematical optimisation paves the way for a new era of cognitive decision-making, enabling us to harness data’s full potential for transformative insights. Here at BlueSky, our MetaOPT platform provides our clients with options for ingestion of the evidence and visible phenomena and using IBM Cplex and sophisticated modelling to present, visually, the various hypothesis, but then take it further and determine, given your individual nuances and constraints.  Decision-making at the speed of light.

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