A study of the possibilities of Using Artificial Intelligence to improve Business efficiency

In today’s information age the utilization of data and technology for the betterment of business is very important. Everyday due to technological advancement,  businesses are facing intense competitive environment. For achieving sustainable growth in business, company should consider the importance of adopting right and updated technology.

Adopting technology in a right manner and as per business requirement is not an easy task.  Now days technologies like Internet of things (IoT), Web 2.0, data science, big data, cloud computing, artificial intelligence (AI), and blockchain are leading the business organization totally on to the different efficiency level. In this study we are going to understand the various possibilities of adopting Artificial Intelligence to Improve the efficiency of various aspects (Production or manufacturing, sales, advertising, marketing, operations, CRM etc.) related to business.


Understanding Artificial Intelligence:

The concept of artificial intelligence is to achieve human level intelligence and decision making ability with the help of computers and machines. Artificial intelligence uses logical algorithms to implement simple to complex applications i.e. the simple algorithms are used for simple applications while complex algorithms are required for more multifarious applications.

The structure of AI based applications depends on important things like objectives of application, data requirement and its logical connectivity, for example – Smart Washing machines is a simple AI based application, the objective of this application is to wash clothes effectively. The timing of wash, number of cloths, water requirement, detergent requirement, drying time etc. are some data requirements of this application.. Combing all these data inputs in a logical structure we can achieve the objective of Smart and effective washing just like human do.

We in this digital era are surrounded by no. of such AI based application like GPS based route and traffic mapping, price estimation by online cab services, recommendations for online shopping, computer operated games, self driving cars, automated production houses,  AI based retail store (Amazon Go in US or Watasale in India) and many more.


Artificial Intelligence in Management:

Machine learning is the most general form of Artificial Intelligence used for developing business objectives today. Machine learning is primarily used to process large amounts of data (Big Data) quickly. These types of artificial intelligence are algorithms that appear to "learn" over time, getting better at what they do the more often they do it. For example: If Production house wants to improve the efficiency of assembly line by detecting various anomalies, error chances, capacity deflation, they need to analyse each and every data frames produced during entire production hours. To achieve it manually is very complex task but Machine learning can rapidly analyze the huge amount of data as it comes in, identifying patterns and anomalies. If a machine in the manufacturing plant is working at a reduced capacity, a machine learning algorithm can catch it and notify decision-makers that it's time to dispatch a preventive maintenance team.

In machine learning there are two types of reasoning algorithm. One is linear reasoning and other one is non- linear reasoning. Traditional Machine learning systems supports linear algorithms for analysis and reasoning.

Deep learning, on the other hand, is a subset of machine learning, utilizes a hierarchical level of artificial neural networks to carry out the process of machine learning. The artificial neural networks are built like the human brain, with neuron nodes connected together like a web. The hierarchical function of deep learning systems enables machines to process data with a non-linear approach.

Tradition approach consider one factor (single direction or linear) to analyze data whereas hierarchical function of deep learning systems consider various factors (multiple direction or non-linear) to analyze data.

For example: A traditional approach to detecting fraud or money laundering might rely on the amount of transaction that ensues, while a deep learning non-linear technique to weeding out a fraudulent transaction would include time, geographic location, IP address, type of retailer, and any other feature that is likely to make up a fraudulent activity.

          If we consider the present stature and usage of Artificial intelligence in Management, it is very much evident that, the possibilities of implementing AI based approaches in management operations for improving the efficiency is very bright.


Artificial intelligence facilitates many complex tasks like high level forecasting, decision making under uncertainty, error detection, automation of routine works etc.

 But Intelligence whether its human or artificial need right inputs at right time for effective analysis and decision making, so adopting technology for improvement is incomplete until objectives are not clear and aligned with technology. So there is lot of scope for research in the field of developing AI enabled management environment.