How Artificial Intelligence Positively Influences Business Decision Making

How Artificial Intelligence Positively Influences Business Decision Making

Artificial Intelligence (AI) is an umbrella term that encompasses various fields of study. These include natural language processing

Artificial Intelligence (AI), a term covering fields like natural language processing and machine learning, has become integral to the business world with the boom of Big Data. AI and data analytics technologies, including AI in marketing, are reshaping business operations and decision-making processes.

Chatbots and recommendation systems illustrate the use of AI, but its potential in business AI decision making, is still largely untapped, with more and more large-scale organizations turning to intelligent data analytics.

AI’s ability to process vast data in real-time offers valuable insights to address persistent business issues, identify operational inconsistencies, and detect anomalies. It empowers businesses to rethink their processes and pinpoints the root cause of challenges they face.

By employing explorative and predictive data analysis, AI can mitigate risks and enhance the efficiency of business decision-making. Thus, AI provides businesses with the tools to seize short-term opportunities and fuel long-term profit and revenue growth.

How to benefit from artificial intelligence in your business today

One important role that AI plays in benefiting companies today is speeding up the process of decision-making. A wider variety and volume of data, better computational processing speed and power, and cheaper data storage solutions are collectively enabling AI technologies to analyze big data sets for delivering useful results.

Since the datasets are usually huge and complex and not something like a hard drive space you would find on a computer, machine learning extracts meaning from them in a way that humans cannot. Therefore, machine learning is now able to drive fruitful business decisions without human intervention.

Numerous companies allow business process automation technologies to power operational efficiency. Yet the decision-making process is usually a stumbling block no matter how powerful and well-designed the automated workflows are. Organizations take time in making decisions. Sometimes it is because the responsible people are occupied with numerous other tasks, they’re worried about the decisions left to them, or because a single approval step in the organization involves too many decision-makers. Such situations create loopholes in business processes which may negatively affect profitability and productivity.

It is now possible for organizations to set up machine learning frameworks that do not only analyze data but also determine general trends in the approval processes so that well-structured decisions could be automated. For instance, when there is a question or a choice to make, machine learning tools will be able to tell the user what their previous answers to that situation have been in the past. Such tools can provide intelligence on decision-making trends over some period.

Once the machine learns a certain number of correct options, it will be able to make predictions on its own, without any kind of human intervention. This means that quick decision-making will speed up important workflows and processes across the organization.

Benefits of Artificial Intelligence to Business Decision Making

Handing over some part of the business decision-making to AI brings many benefits:

Quicker decisions. The pace of business has dramatically accelerated in recent times and there are no signs of it slowing down. In such an era, speeding up decision-making is extremely important. For example, oil companies can alter the price of gas according to demand with the help of AI-powered pricing. Statistics show this could increase their profit margins by almost 5%. Travel sites, retailers, and other services similarly use dynamic pricing on a regular basis to improve their margins.

Handling multiple inputs. When it comes to taking input from multiple sources and handling many different factors simultaneously, machines are much better than humans. This is because they can process a lot of data at once to make complex decisions and give a prediction or suggest the best possible decision.

Reduced fatigue. A lot of psychology studies prove that when people are forced to make many decisions in a limited time, the quality of those decisions keeps declining. This is the reason you see candy and snack bars near cash registers at supermarkets; shoppers get exhausted with so much decision-making while shopping, so they find it much difficult to resist the sugar craving at the point of sale. On the other hand, algorithms have not so many weaknesses. They make equally good decisions at any point in time, hence helping executives avoid making bad decisions due to exhaustion.

Non-intuitive predictions through more original thinking. With AI, executives can identify patterns that may not be very clear to human analysis. For example, AI helped a major drugstore discover that people who bought diapers also tended to buy beer at the same time. Such unique insight, if incorporated in decision-making, can have an immediate and significant effect on the business.

Artificial intelligence and marketing decisions

A lot of times the complexities involved in marketing decisions create a hindrance in making accurate predictions. Complexities include a good understanding of the customer’s wants and needs and the ways to align products with these requirements. Businesses must know what their customers want and then design the products accordingly. Similarly, they can make good short and long run marketing decisions with some insights on the changing consumer behavior.

AI modeling and simulation techniques use valuable insights into your buyer personas. Implementing these methods into the decision-making process helps organizations to improve brand loyalty by predicting consumer behavior. AI systems can help real-time decision making through a decision support system which also assists in forecasting, data mining, and useful analysis of the recent trends.

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Business expert systems

Expert systems are specialized problem-solving software. Through the combination of a knowledge base and artificial intelligence, expert systems can replicate the knowledge and reasoning of experts in a subject area. For example, MARKEX is an expert system that can apply its vast marketing knowledge to provide better data and analysis. The outputs from an expert system typically include recommendations and assessments for a specific input problem. You can make use of expert systems in marketing and sales to positively influence and streamline business decision-making.

As a business owner, you can deploy expert systems for each area of concern. For instance, if you are looking to boost sales then you can implement a sales expert system to improve sales decisions. The benefit of using these expert systems is that they take a systematic approach to business decision making that is not influenced by human factors such as biases or inconsistency. The other benefit is that instead of hiring expensive consultants or full-time sales experts for solving business issues, you can make a one-time investment into an expert system.

Opinion and sentiment mining

Opinion and sentiment mining

Data mining is described as the automated process of gathering data from various data sources. Opinion mining and sentiment mining are types of data mining. With opinion mining, you are searching the web for opinions (about your business). On the other hand, with sentiment mining, you are searching the web for feelings and reactions to your business. The result? An impressive dataset that gives you loads of information about how customers and leads are interacting with your business.

Opinion and sentiment mining is a method for marketers to know about how their products are being received by audiences. Where is artificial intelligence in all this? Well, since manual mining (and analysis) of such data will require you to put endless hours into the process, artificial intelligence can automate the process for you! With the boom of social media, there are massive amounts of data available online. It is nearly impossible to carry out mining manually. Artificial intelligence enables us to automate the process of opinion and sentiment mining.

In contrast to data mining, social computing is a technology that helps marketers better understand the behaviors and social dynamic of their target audiences. When you combine opinion and sentiment mining with concepts of social computing, the result is better business decision making. This is because, through these technologies, you can simulate, monitor, analyze, and predict customer behavior. Through this, you can plan out your next move effectively to ensure the best results.

Automated business processes

Automation in businesses is not just about assembly lines and product manufacturing. Instead, artificial intelligence has enabled businesses to automate just about anything. Several businesses already make use of automation in various business functions for marketing and sales. Through this, businesses can reliability hasten processes and improve business decision making through accurate insights.

For instance, in marketing, the process of campaign management and market segmentation can be automated through artificial intelligence. This enables businesses to take quick action and make their decision-making more efficient. You can generate invaluable insights about your customers while marketers can focus on making the actual decisions.

In distribution, automation can help businesses monitor and control the supply chain more effectively. Businesses can monitor their product demand and accurately predict how much does the business need to generate to meet the sales requirements for the upcoming fiscal period.

How artificial intelligence will affect the future of business decision making

In a couple of years from now, organizations may be able to combine machine learning with other interface and technologies, and also have two-way communication with the machine in order to discuss decisions and approvals.

Even though algorithms cannot completely replace managers, machine learning can provide much valuable support and guidance when it comes to management. According to Gartner’s forecast, by the end of 2018, over three million workers worldwide would be under the supervision of a ‘roboboss’ in rule-based work areas where automation can monitor performance. Moreover, ‘virtual career coaches’ could be implemented in order to give real-time suggestions to many workers, so performance could be improved across the organization. The efficiency and speed of such systems would be much more than a human could manage.

Two possible scenarios are predicted in the next ten years:

The first is that humans will be dealing with an AI agent that has the capabilities and attributes of a person supervising a department and will be making decisions on behalf of the management.

The second is that smart machines will continue to evolve and become smarter with more capabilities. They will provide more support to managers for making informed, accurate, and faster decisions without any involvement of C-level executives. However, exceptional decision-making would still require input and approval from the board.

Successful examples

Now that you understand how artificial intelligence improves business decision making, we take a look at two successful examples of well-known brands, Arby’s and Macy’s, and how they make use of artificial intelligence for making reliable business decisions.


Arby’s is a fast-food quick-service sandwich restaurant chain that is based out of America. The restaurant makes use of artificial intelligence, in the form of data mining, to determine the best segments for targeting their ads. Through the analysis of opinions and feedback of customers, Arby’s determines which of their ads are the most effective and on which target markets. With each ad, Arby’s can also determine which channel (social, print, among others) is the most receptive. This enables them to target appropriate ads to relevant audiences using the best channels to achieve maximum returns from their advertisement campaigns.


Macy’s is a chain of department stores based out of America. The business makes use of artificial intelligence in the form of sentiment analysis (mining) of Big Data. For example, Macy’s was able to find out that people who discuss jackets on Twitter, also make use of terms such as “Louis Vuitton” and “Michael Kors” in their tweets. Through this information, Macy’s was able to identify that they should offer discounts on these jackets’ brands in the future. Therefore, Macy’s promoted these specific brands for their advertising campaigns using this insight in order to attract more customers.

To Sum Up

Artificial intelligence is an ever-growing technology that is becoming a vital ingredient for the success of businesses in this age. Through artificial intelligence, businesses can develop new ideas, gain better insights, and improve business decision making.

Utilities such as real-time interactive dashboards, automated marketing campaigns, and expert systems enable businesses to take risk-free and profitable decisions. Artificial intelligence technologies such as CRM software, data mining, machine learning, and process automation positively influence business decision making. Predictive analytics can enable organizations to identify business opportunities and seize them at the right time.

We are currently at the crux moment in time for the adaptation of artificial intelligence. Right now, is the make or break situation for businesses that want to establish themselves in the future. Incorporating artificial intelligence for smarter business decision making is the next move into the future that will act as a catalyst for positive across your entire business.

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John Kopanakis

About John Kopanakis

John is a co-founder of Mentionlytics supervising Business Development and Business Processes. He is a Professor of Business Intelligence with interests in Data Analytics and Innovation.

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