Decision making for any business is a key aspect of the success path of the organization. The process involves evaluation of the alternatives to identify the best possible solution to the problem. This ensures growth and success path for the business. Traditionally, these decisions are either taken using standard predefined procedures or using manual calculations. These methods are very crude and involve heavy calculations, even for a smaller data problem.
In the age of digital revolution, organisation consider data as resource. In fact, this resource provides crucial insights for driving the market. With increasing data, these traditional methods become ineffective and the chances of error increases. By automating such tedious tasks, organisations try to minimise the error and reduce solving time. However, the bigger picture involves understanding the patterns in the data and provide useful insights to the company.
Assuming an example of a spend-monitoring application which tracks all the spend in real time and gives out total spend segregated based on the buckets assigned by the user. Now, if the user wishes to decide where to cut down on the expense, he/she has the entire historical expenses available and can decide where to reduce expenses. However, if the user wishes to identify patterns like possible expenses on weekends and based on the pattern predicting the expenses of the upcoming weekends post reducing the costs, he/she might not be able to do that without the use of AI tool and predictive analysis.
Going by the example, AI becomes the driver for most decision making as it not only gives the historical overview. But also, can predict the upcoming implications based on the decision of the business. This gives companies more certainty in the market where volatility and uncertainty are the predominant forces.