Given below are two statements: One is labelled as Assertion (A) and the other is labelled as Reason (R).

Assertion (A): The objective functions in business organizations are complex requiring the processing of large volume of data.
Reason (R): AI-based non-linear models can solve the problems of arriving at a dynamic solution to business problems.

In the light of the above statements, choose the most appropriate answer from the options given below:

Correct option is B

Statement A is correct:
Objective functions in business organizations (like profit maximization, customer retention, process optimization) are indeed complex and often require the processing of vast and varied data. This is especially true in large firms using ERP, CRM, and data analytics tools.

Statement R is also correct:
AI-based non-linear models (like neural networks, deep learning, and fuzzy logic systems) can be applied to solve complex business problems and derive dynamic solutions in changing environments.

However, R is NOT the correct explanation of A:

• A is talking about the nature of business objective functions and data complexity, which arises due to multi-objective, multi-departmental goals and real-time business demands.
• R is about how AI can offer dynamic solutions, but it does not directly explain why business objective functions are inherently complex.

• The complexity of objective functions is due to interdependent variables, conflicting stakeholder goals, regulatory constraints, etc. AI may solve or manage this complexity, but it is not the reason why the complexity exists in the first place.

So, while both statements are independently true, they are not in a cause-effect relationship.

Information Booster:

• Business objective functions often involve multi-variable optimization under constraints—e.g., balancing cost vs quality vs speed.

• Data complexity arises from high volume (big data), velocity (real-time updates), variety (structured/unstructured), and veracity (uncertainty).

• AI and non-linear models are tools to interpret patterns, detect anomalies, and predict outcomes, but they do not cause the complexity in business goals.

• The complexity is more strategic and managerial in nature—AI is a technical response to managing that complexity.

• AI enhances decision-making by learning from data, but business strategy still requires human judgment and domain knowledge.

• In fields like finance, marketing, and logistics, AI is increasingly used for personalization, dynamic pricing, and route optimization.