For the early centuries and long since before the invention of computers and other agents of artificial intelligence, common sense has been the prevailing aspect of establishing vital business decisions. However, the continuous progression in the business environment has led to the emergence of a diverse range of risks beyond the capacity of anticipation of common sense. This has thus called for a search for alternatives in anticipation of risks and making of informed decisions. Artificial intelligence agents have emerged as a dependable tool in making informed decisions in the economic market.
The continuous innovation and improvement of artificial intelligence agents have established their application in the diverse and ever-changing economic environment (Duan, Edwards, & Dwivedi, 2019). Artificial intelligence agents can be used in transforming the portfolio decision making by the engaging in market decision making, use of customer relationship management (CRM), recommender systems, problem-solving capacities, opinion mining and its application in augmented analytics.
Customer relationship management is an artificial intelligence agent that manages the business customers and potential customers by evaluating the company’s relationship and interactions with the current customers and potential customers. This plays a vital role in bolstering the organization/company’s relationships. The company/organization can evaluate the customer’s lifetime value by applying artificial intelligence’s buyer persona modelling (Bhattacharya, 2019). CRM also aids the organization/company in managing multiple inputs as it enables them to establish the approximate current and future market demands (da Silva, Santana, Mastelini, & Barbon Jr, 2018). Moreover, the efficiency by which CRM manages and wheels the various diverse aspects of customer relations and consequently managing complex decision-making processes. The large data sets that it can process in within short periods provide valuable insights into the decision-making process and thus making the process less tiresome and faster.
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Application of recommender systems is critical in enhancing the process and outcome of decision-making. The recommender systems are AI agents that recommend products or services to users. The recommender systems utilize the users’ explicit and implicit responses to evaluate their products and service preferences (Huseynov, Huseynov, & Özkan, 2016). These systems have long been in used in the music industry in recommending content to users (Duan, Edwards, & Dwivedi, 2019). Their expansion has however been eminent in other economic fields. The recommender systems can enhance the consumers’ preference. Analysis of the users’ implicit and explicit responses also enable the companies to make key decisions in crafting of user-specific targeted contents and thus reduce bounce rates and increase the profit margins of the organization/company.
Customers needs are diverse and ever-changing depending on the customers’ environment. Establishing of marketing decisions are thus crucial aspects in the economic environment (Huseynov, Huseynov, & Özkan, 2016). Poor marketing strategies lead to poor results. Artificial intelligence agents can be used to run simulations and exhibition methods that are essential in providing insights into the users’ personalities (Huseynov, Huseynov, & Özkan, 2016). In doing so, the AI systems assist in predicting the users’ behaviour. This is made possible by the real-time data collection, analytics in trend and forecasting of the AI systems.
Artificial intelligence systems and agents are also involved in problem-solving which consequently influence decision-making portfolio. AI systems such as expert systems are specially crafted to enhance problem-solving. They achieve this milestone by replicating the understanding and intellectual techniques of experts in particular fields (Bhattacharya, 2019). The expert systems engage expert thinking processes to evaluate and provide recommendations or solutions to the problem presented (Bhattacharya, 2019). The expert systems thus enable swift and most preferable decision making.
AI systems have provided essential tools in the opinion mining process. Opinion mining is a branch of data mining and computation linguistics denotes the computational techniques for pull out, categorizing, understanding, and evaluating the sentiments articulated in numerous online news platforms, social media remarks, and other user-generated content (Bhattacharya, 2019). The AI systems provide a means through which the trillions of comments expressed in various platforms. These analyses can be used in establishing answers to questions in various fields such as socio-economic and geopolitical fields among others. sentiment analysis has often been applied in opinion mining to establish thoughts, affect, bias and other emotive states in online content. These are thus essential in the decision-making process. The results of the analysis of these billions of data enable fast and precise decision-making.
Augmented analytics established by artificial intelligence systems are also influential in the decision-making portfolio. Great decisions are made when there is accurate and precise information collected from vast perspectives. The continuous evolution of data and analytics in the offering of support to internal decision-making processes to the provision of continuous intelligence support throughout the process has revolutionized the decision-making portfolio (Duan, Edwards, & Dwivedi, 2019). This thus establishes a competitive advantage in the organization’s external environment.
Artificial intelligence can transform decision making portfolio through various approaches. These include approaches include engaging in market decision making, use of CRM, recommender systems, problem-solving capacities, opinion mining and its application in augmented analytics. AI thus plays a major role in decision-making processes.
