How AI Will Change Decision-Making For Businesses

In recent years, Artificial Intelligence (AI) got off the ground and is making significant strides in the tech industry. From picking the restaurant people want to go to, they let AI assistants like Siri, Google Assistant, Microsoft Cortana, Amazon Alexa and the like take control. We are being accustomed to AI every day without even realizing it. For example, things like the autocorrect function in your smartphone keyboard and the automated tagging functions on Facebook are all controlled by the power of AI. In a nutshell, the AI industry is attempting to make computers mimic human intelligence, and they are halfway there by virtue of neural networking. In neural networking, they try to make transistors behave like neurons of the human brain. Machine learning pertains to the usage of artificial neural networks (ANNs) to facilitate learning at multiple layers. Deep learning is another learning model (which is a part of machine learning) and is based on data presentation rather than task-based algorithms. While the future of AI might allow machines to make decisions like human beings, the present is already influencing human decisions, especially for business. In this article, we will discuss some interesting ways of how Artificial Intelligence is (and will) change decision-making for businesses.

AI and Business Decisions

Prior to the debut of AI, businesses had to rely on inconsistent data. As a result, the decision-making process was not very precise. That is when AI came in to save the day. Now, with Artificial Intelligence, it is possible for businesses to turn to data-based models and simulations. The updated AI systems begin from zero and feed themselves with a regular diet of Big Data. This is intelligence in action and eventually provides sophisticated data models that can be used for precise decision making. Here are some examples of AI-based decision making.

The Finance Industry

Fueled by the significant changes taking place in the finance industry, Robo-advisors are gaining traction. Robo-advisors have evolved over time. Initially, they were stand-alone helping in a self-directed way the consumers in aggregation and trade execution. The second phase of evolution gave birth to the "integrated robo-advisors." The integrated robo-advisors model helped both the providers and the consumers. These advisors imparted assisted advice and predictive models. This model was especially popular among the retail and institutional products. Then came the ultimate cognitive robo-advisor model with economic and market outlook. Like its predecessor, the model helped both providers and consumers make finance-based decisions. It took complete advantage of Machine Learning and agent-based modeling concepts in order to provide enhanced and holistic advice.

PwC, one of the world’s largest professional services company, has amassed prolific amounts of info from the US financial data, US Census Bureau, and other public licensed sources to build $ecure. $ecure is a large-scale simulation model that allows making financial decisions of 320 million US consumers’. The model is created to help the financial services sector (FSS) companies map buyer personas, anticipate customer behavior and simulate market trends and the company’s future. $ecure has enabled these FSS companies in making real-time business decisions in seconds.

The Automotive Industry

The automotive industry has developed a hand full of AI applications for requirements right from vehicle design to sales decision-making support. Artificial intelligence has is the core reason behind the design of smarter driverless cars. Equipped with multiple sensors, the cars can learn the environment, circumstances and identify patterns. This data is put to use as an add-on safe-drive feature that warns drivers of collisions and lane departures. Like in the FSS, AI is used here to develop an automobile ecosystem model. Here, you have smart bots that map the decisions made at every level by automotive players including buyers, manufacturers, and transportation services providers. This AI decision making has helped carmakers predict the fashion of adoption of driverless vehicles in the future. In addition to design and manufacturing, AI has also helped companies in making better marketing and advertising decisions.

The Marketing Industry

There are a plethora of complexities when it comes to making a marketing decision. One has to be cognizant of the customer needs and expectations and align products to these needs accordingly. Also, having good insights into changing consumer behavior pattern is crucial to making. The market trends keep changing at a neck-breaking pace and prediction of the dynamic fashion is inevitable to make good marketing decisions.

The AI-based modeling and data simulation techniques allow you to get reliable insights into your buyer personas. These techniques enable marketers to predict consumer behavior under various circumstances. Through an AI-powered Decision Support System, it is possible to get support to make decisions with real-time and up-to-date data gathering. These systems can also help you out with market forecasting, and industry trend analysis. Here is a list of some common marketing systems that are being powered by AI:

Customer Relationship Management

Artificial Intelligence in CRM systems enables automation of a good number of functions, such as contact management, data recording, data analyses, lead ranking, etc. AI’s buyer persona models can also provide businesses with a prediction of every customer’s lifetime value. Sales teams can work more effectively with these features.

Recommendation System

Recommendation systems were first deployed in sites with music content. These systems managed to make its way through to different industries. The AI systems learn a user’s content choices and preferences and eventually pushes recommended content that fit into those preferences. This is a great way to reduce bounce rate. Also, businesses can use the information gained through machine learning to craft tailor-made targeted content.

Expert System

Over the years, Artificial intelligence has been trying to mimic the knowledge and reasoning capabilities of experts through Expert System. An expert system is sort of a problem-solving software application. Expert systems for marketing (for example, MARKEX) apply expert thinking process algorithms to provide assessment and recommendations for every problem.

Social Computing

Social computing helps marketers get insights into social dynamics and the behaviors of their target market. Through Artificial Intelligence marketers can analyze, simulate and predict the customer behavior. The AI software applications can also be used to data mine the social media networks.

Opinion Mining

Opinion mining is a type of data mining process that browses the web for opinions, reactions, and feelings of customers. It is a great way for marketing professionals to learn about how their products are perceived by the target audience. In general, the manual data mining analysis is tedious and require significantly long hours for completion. Artificial Intelligence has minimized the time consumption with its advanced search and analyses functions.

This type of AI is mostly used by search engines. Using AI the Search engines rank people’s interests in certain websites on a regular basis. These AI bots employ various algorithms to get to targets’ HITS and Page Rank, among all the other scoring systems. Here, a hyperlink-based Artificial Intelligence is employed, and bots look for clusters of linked pages. The AI system usually looks at these sources as a group for sharing common interests.

The Future of AI-Based Business Decision Making

Arguably, the opinions of people that say that AI will make employees obsolete can be only considered a myth. People will not actually lose their jobs to machines but will enhance their work quality with them. AI will help employees work more efficiently. The same thing applies to decision-making. Unlike the science fiction movies have shown us, AI will not make our decisions harmful but will only give us a better reasoning eventually enabling us to make better decisions. When business executives and decision-makers have reliable data analysis, follow-ups, and recommendations through AI-based decision-making systems, they will make better choices. These way businesses would enhance the work efficiency of every individual team member. AI would also improve the competitiveness of the businesses.

Conclusion

According to Gartner, today’s data is set to multiply to up to 800% by the end of 2020. Having this said, businesses get about 80% of data unstructured. These data are composed of images, audio, emails and the like. According to IBM, this data is the most abundant and compex raw material in the IT world. In this scenario, human capacity is not enough to go through all the data and make the best decision. No wonder that the majority of industries incline to the idea of involving AI in decision making.