Artificial Intelligence in Business Management

                                    
                                    (Image Source: Narrative Science)

Demand for structured data has been rising over the past decade. Businesses are in need to adopt in-house data warehouses. The business analytics software reaches public and private databases in search of data to spur their AI strategies. Due to the increasing demand, data is becoming a valued commodity, and businesses are beginning to compete for the most lucrative reserves.

Until recently, businesses did not realize that they were sitting on a treasure house of unstructured data and had no clue what to do with it. With the innovative advances in Machine Learning, data mining, and AI, businesses can now use data produced by consumers and users. For example, Moz uses AI to predict customer churn using a deep learning Neural network that analyses user actions and can predict the behavior of users. Since several factors from the past cause actions performed by customers within the system, it makes it easier to mine valuable business insights and decrease existing customers, having an enormous effect on overall company growth. 

 Lately, online consumer activities such as search queries, clicks, or purchases were critical data sources for large enterprises. However, as it turns out, data is plentiful in our physical environments and offline experiences as well. Big companies like Amazon have established various corporate surveillance strategies in their grocery stores. The sensors and actuators installed in the stores can collect data about consumer preferences and behaviors. 

Drones, AI personal assistants, and even the Internet of Things (IoT) are tools that can turn every single moment of human life into valuable data. This data becomes a driver of price-setting algorithms that reacts to changes in consumer demand.

Uber has begun using this model in its price mechanism. Businesses that stand on the edge of such innovation will have the best prospect to extract value from consumer behavior.

One of the most promising paths is sentiment analysis that uses NLP (Natural Language Process) techniques to understand users' emotions and feedback dynamics. With sentiment analysis, one can identify negative and positive reviews of their products on e-commerce platforms such as Amazon. Moreover, knowing the sentiments related to competitors can help companies assess their performance and find ways to improve it. 

Another benefit of sentiment analysis for managing online reputation is automation. Since it can be hard to process tons of user feedback manually. One of the most efficient solutions that will set you apart from your peers is turning the data input into piped in business intelligence software. 

AI is becoming a real competitive advantage for businesses, from chatbots to a business analytics tool, intelligent generators, to business tools. It promotes automation, cost reduction, and intelligent decision-making. However, to develop their AI strategies and train their machine learning models, businesses need high-quality data.

Tech giants such as Facebook and Google have solved this problem by leveraging the user-in-the-loop model, where users generate data via posts, comments, or search queries. Some businesses gain access to data by reaching out to public and commercial databases, crowdsourcing data collection and classification services, collaborating with data-driven companies, etc. 

On the whole, AI is changing the way we deal with data in business. From changing unstructured data to the structured and thoughtful acquisition of data, AI-powered tools are backing business and will continue to do so.