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How Intelligence Ignites Business Problem Solving

2 minutes

Knowledge can come from multiple sources. Previous articles explored why we desire information and how to obtain it. This article examines the primary sources of knowledge, how they can be used, and how they complement each other.

Knowledge Types

Content is the easiest knowledge source to identify and classify. It begins as observable attributes, which are collected into data and transformed into usable information. Human intelligence is more difficult to utilize. Experience is valuable but transforming it into applicable expertise without an identifiable intermediate stage can be challenging.

Digitized content is situational, quantitative, objective, structured, and intermittent. Attributes are any representation that can be quantified into data which consists of classified attributes which can be quantified into information which is interpreted data. Human intelligence is practical, qualitative, subjective, free-form, and insightful. It starts as experience which is unusable and must somehow be quantified into ready to use expertise with no intermediate state. The question is how can we transform experience into expertise?

The business problem framework transforms experience into expertise using a structured approach specifically tailored to solving business decision problems.

Human intelligence can be transformed from unusable experience into ready to use expertise by using the business problem framework which queries experience into goals, options, and resources using a simple yet sufficient query suitable for most business decision problems.

Knowledge Coverage

While determining whether content can provide the information we need can be difficult, its capabilities and limitations are clear once identified. Human intelligence, on the other hand, is less defined. Partial expertise exists across a broad spectrum, making this important but difficult-to-identify knowledge source harder to utilize effectively within a quantitative context.

With digitized content it is very clear to differentiate between what information can be correctly provided and what cannot be provided. With human intelligence it isn't so clear since there is a large area where partial expertise is available that exists between correct expertise and no expertise. The question is how to manage this area of ambiguity?

The key to effectively using intelligence is recognizing areas where partial expertise exists and combining them with other sources of expertise to create a more complete and useful approximation of actual expertise.

The inductive approach manages ambiguity very well by using correct expertise as-is, supporting partial expertise with external sources and declaring areas of no expertise as out-of-scope.

Knowledge Utility

This is where the distinction between knowledge sources becomes clear and where their complementarity nature for solving problems is most apparent. Content provides structured objective information, while intelligence provides complex subjective expertise.

Which is better digitized content or human intelligence? Digitized content is better for precise analysis and solution optimization whereas human intelligence is better for convoluted processes and future forecasts. They're complementary and therefore we need to find out how to maximize the utility of each source.

The complexity of business decision problems requires human intelligence to lead the process, supported by other experts, data, and machines.

The data effectiveness methodology structures the problem so that human intelligence makes a full contribution to problem structuring which is a convoluted process, makes a partial contribution to problem parameters which consist of future forecasts and precise analysis, and no contribution to solving the problem which is an optimization problem. Each source contributes to the solution according to its strength.

Utilizing GAI

The emergence of capable GAI models adds a powerful new knowledge source. Their ability to perform broad reasoning creates new opportunities, but effective use depends on combining this capability with human intelligence that can recognize and correct flawed reasoning.

Generative AI is similar to human intelligence in terms of types while it is partially similar to both human intelligence and digitized content in terms of utility. However, it is very different in terms of coverage in that it provides correct reasoning and incorrect reasoning with no area where it clearly states not having reasoning capability which is a major issue that needs to be properly handled. As seen GAI therefore complements the other sources rather than replaces them and will soon be fully integrated into Advanata.

Putting Everything Together

Solving complex business decision problems requires more than a single source of knowledge. Advanata was designed to maximize the contribution of each source, coordinate their efforts through a structured framework, and transform diverse inputs into practical solutions rather than additional analysis or intermediate outputs.

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