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Artificial intelligence was once a tricky invention from science fiction books and movies. Today, things have changed and are now a part of business reality.

AI is reimagining the business world by increasing innovation and productivity and allowing organizations to think bigger. AI can help businesses improve their products, processes, and decision-making. With today's technology, organizations should be able to achieve organizational agility supported by AI.

Read on to learn what AI in decision-making is. We will explain examples and applications of AI in decision-making. Follow along.

What is AI in Decision Making?

Let's start with the basics. AI decision-making is data processing completed entirely or partially with the assistance of artificial intelligence algorithms to establish and maintain positive customer relationships, target marketing efforts, improve problem-solving, and predict market changes.

AI is ushering in a new era of innovation across industries, and executives should strive to be at the forefront of incorporating AI into their decision-making processes. Humans can use artificial intelligence to predict the outcomes of their actions and speed up the decision-making process.

Artificial intelligence is used to aid decision-making in both small and large businesses. The capabilities of data-driven AI are no longer restricted to high-end companies with large budgets or technology firms with specialized knowledge. Everyone can benefit from this rapidly growing profession by collaborating with the right set of data scientists.

Different Degrees of AI in Decision Making

Humans may not be wholly reliable or consistent in their decision-making, but they bring essential skills to the table. Similarly, there is a place for AI in decision-making.

The degrees to which AI and analytics can be deployed to pursue faster, more consistent, more adaptable, and higher-quality decisions at scale are represented by decision automation, decision augmentation, and decision support.

The differences are in the analytics techniques used at various stages of the decision-making process, as well as who (or what) ultimately makes the decision:

Decision Automation

The system makes the decision based on prescriptive or predictive analytics. Its advantages include decision-making speed, scalability, and consistency.

Decision Augmentation

Using prescriptive or predictive analytics, the system recommends a decision or multiple decision alternatives to human actors. Its advantages stem from the synergy of human knowledge and AI's ability to rapidly analyze large amounts of data and deal with complexity.

Decision Support

The decision is made by humans, who are assisted by descriptive, diagnostic, or predictive analytics. Its primary advantage stems from integrating data-driven insights with human knowledge, expertise, and common sense, including "gut feel" and emotions.

Applications of AI in Decision Making

Continue reading to find out how artificial intelligence can help in decision-making.

Decisions in Business Operations

Machine Learning algorithms come to the rescue in areas that rely on a constant flow of heterogeneous data, such as several financial reports, payrolls, procurement, employee productivity analysis, and predicting future churn rates.

In short, AI automates routine administrative tasks and alters the entire working environment. It allows employees and executives to make more timely and relevant decisions.

Consider HR as an example of a business decision. The entry, categorization, and evaluation of data from employees and applicants are critical but monotonous in HR. The initial stages of recruitment are typically difficult: defining a position needed for a department, determining all the candidate criteria and areas to cover, sourcing, selecting the CVs, and so on. AI comes into play here.

AI-powered solutions aid recruitment by sourcing better candidates and analyzing their interviews. Finally, an HR team can make an informed decision about hiring a suitable candidate.

Overall, AI can leverage business intelligence and make a company data-driven in many aspects, including decision-making, regarding internal business processes.

Marketing Decision-Making With AI

Each marketing decision involves several subtle complexities. To match products to customer needs and desires, one must be aware of and understand these needs and desires. Similarly, understanding how consumer behavior changes is essential for coming up with the best marketing choices, both now and in the future.

Techniques for AI modeling and simulation provide trustworthy insight into your buyer personas. Consumer behavior can be predicted using these methods. Your artificial intelligence system can support decisions through a decision support system by gathering current and real-time data, forecasting, and trend analysis.

Performance Assessment

First, it relates to people's performance evaluations and decisions. Employee performance reviews are conducted on an ongoing basis rather than every six or twelve months. Despite this, the integrity of the employee evaluation process can be jeopardized by human error and potential biases. AI has the potential to reduce human errors and make employee performance data more transparent.

AI can also recommend online courses, training, and development programs to employees based on their performance history. Many People Management software vendors have enhanced their performance monitoring software with Artificial Intelligence capabilities. Another aspect of performance evaluation is marketing. AI solutions allow you to evaluate precisely which tactics work and which do not. Then decide how to tweak them and which approaches to try.

Finally, evaluating the performance of some aspects of a business is a way to understand the overall performance of the company, its growth potential, and which decisions should be made to invest in that.

Customer-Related Decisions

AI can be helpful in customer service management, personalized customer communication, customer behavior evaluation, and predicting consumer trends and patterns.

Today's speech recognition technology significantly improves the customer experience. These systems inform customers about the status of their shipments and initiate conversations with them to manage unforeseen events, changes in last-minute deliveries, or incident management and feedback.

Artificial intelligence allows for the automatic identification and profiling of potential customers. New customers, for example, can be identified and characterized using predefined profiles. Based on the analysis of this data, it is possible to forecast the behavior of new customers and ways to attract them. Advertisers also use neuromarketing to influence consumer thinking and behavior.

This can help your marketing department understand how to connect with your potential customers.

For example, if you're a SaaS company, it may reveal that hosting educational webinars is a better way to attract and retain customers than social media advertising based on how customers react.

You can use AI to decide how to improve the customer experience. Artificial Intelligence allows you to understand your customers better and determine which tactics to try.

Recommendation System

This AI system was first used on music-related websites. Since then, the recommendation system has been expanded to various industries. It works by learning about the content preferences of the user.

It advances the content that corresponds to the preferences. As a result, the bounce rate is reduced. Furthermore, the information learned by the AI system may be used to target relevant content better.

Examples of Artificial Intelligence in Decision Making

Following are some areas where artificial intelligence is used in decision-making.

Infervision-Healthcare

A crucial aspect of AI and machine learning, image recognition, and analysis can be used for medical diagnosis, potentially saving lives.

Cancer has recently become one of the leading causes of death. Radiologists use CT scans to diagnose cancer.

Every day, radiologists must review many CT scans to make a diagnosis. It is a time consuming and laborious task.

China, which lacks radiologists to review over 1.4 billion CT scans yearly, is looking to AI to fill some of the void. With the doctor-to-scan ratio incorrectly balanced, overworked healthcare professionals may experience fatigue, leading to errors.

Infervision has created an AI trained with appropriate algorithms to review CT scans and detect any early signs of lung cancer.

It makes radiologists' jobs more accessible because they can use AI data to diagnose cancer more accurately and efficiently, allowing them to treat it more effectively.

Volvo-Manufacturing

In recent years, the automotive industry has used IoT and AI well. As more and more sensors are installed in vehicles for security, they generally produce more data. Autonomous vehicles produce even more data.

Volvo relies on IoT and AI to uphold and maintain its reputation for safety. Volvo installed sensors in 1,000 cars as part of a 2015 project to detect and analyze driving conditions and monitor the vehicle's performance in hazardous conditions.

The data gathered is then uploaded to their cloud. Volvo works on this data with Teradata to perform machine-learning-driven analysis across its collected data.

Volvo's early warning system analyses over a million weekly events to predict vehicle breakdown and failure.

BP plc- Energy

The British oil and gas giant BP plc, which operates in 72 countries, has pioneered AI and big data in business processes.

No matter where the well is physically located, BP has installed sensors in its gas and oil wells to continuously collect data and monitor and understand the well's operational conditions.

Analyzing this data allows BP to monitor and optimize its equipment's performance and keep track of maintenance requirements to ensure smooth and uninterrupted operation.

The sensors installed in the oil and gas wells collect data on chemicals, temperature, gas, humidity, vibration, and other factors.

The collected data is then processed using big data technologies and AI to make business decisions, such as improving operational efficiencies and cost-saving.

Teva and Hoka-Retail

MakerSights employs a product decision engine for retail and promotes informed decision-making throughout product development and go-to-market processes.

Teams use it to quickly validate assortments and product attributes and gather feedback from target customers for a product hypothesis, all while providing an excellent mobile user experience.

Teva and Hoka use MakerSights to make AI-based product decisions. MakerSights' AI-driven technology provides Teva and Hoka with a proper decision framework for each product creation process step.

It also aids them in identifying strategic opportunities and anticipating issues and complications.

Underwrite.ai - Financial Services

Accepting fraudulent loan applications and incurring losses is always a risk in the financial services industry.

By examining numerous data points from credit bureau data sources, Underwrite.ai assesses credit risk for small business and consumer loan applications to cut down on such losses. 

This gives them access to credit risk for individual customers.

Their system collects portfolio data using advanced artificial intelligence algorithms to identify patterns for good and bad loan applications.

Benefits of AI in Decision Making

AI in decision-making has numerous benefits throughout the entire company lifecycle. AI forecasting accuracy has vastly improved over time. Because of more accurate models, human stakeholders may be able to rely on AI to make more informed decisions with greater confidence. Here are some areas where AI can help with decision-making:

Business Automation

In automated systems, human biases and inadvertent errors are less likely. AI can assist businesses in saving money by reducing their reliance on human labor. AI-powered automation is driving the Fourth Industrial Revolution. Manufacturing, marketing, and sales are all becoming more automated. Automation of routine tasks can help businesses save time and money. Forecasting supply and demand can help to optimize revenue streams.

Opinion Analysis

Advertisers learn about customers' preferences through online searches, blog posts, surveys, comments, emails, tweets, and other user-related actions. This information helps them improve client satisfaction and relationships. This is commonly referred to as opinion mining, the essential method for getting inside customers' heads.

Natural Language Processing (NLP) models powered by artificial intelligence can analyze sentiment on any data type. NLP models have improved their understanding of human emotions. Brands may use AI to mine social media and listen in real-time to their customers' needs, allowing them to provide more personalized product experiences.

High ROI and Better Judgement

Standardized data, simplified business processes, automated market sentiment research, and AI-powered CRMs enable businesses to make better decisions. AI can detect changes in the industry and assist decision-makers in optimizing the supply and demand pipeline in real-time. AI has the potential to reduce the percentage of incorrect conclusions while also lowering administrative costs, resulting in a high return on investment.

AI allows businesses to cultivate a culture in which humans and AI coexist. It democratizes artificial intelligence by involving all employees in AI-driven activities. A culture like this would enable businesses to use technology and people to iterate processes quickly and with fewer resources. Companies need the right tools to pull off such an ideal solution. Most businesses fail in this area, negatively impacting AI adoption in the workplace. On the other hand, the pursuit of perfection is a continuous process. Companies must ensure they can take appropriate actions and use their resources best.

Standardizing Data

Quality AI solutions require valuable data to achieve the desired results. Businesses collect raw unstructured data from various sources, such as public datasets, internet scraping, internal company activity, market research, and lead purchases. This raw data was previously manually processed and examined. All data is processed automatically by AI now. Several AI approaches can normalize data regardless of the source. AI models can also quickly adapt to various data types to produce meaningful results.

Future of Business Decision-Making with AI

AI is undeniably the future of decision-making for both businesses and consumers. It has been a radical shift from deciding what we need based on our circumstances to AI deciding what is best for us.

Similarly, all AI needs to do for businesses is align with where and how you want your business to grow.

AI will make decisions based on the desired results to give your business the best ROI possible.

Allowing AI to assist in implementing strategic decisions has numerous advantages, including the ability to develop new ideas while gaining better insights. 

Accelerating decision-making allows an organization to spend less time on the task at hand and more time launching more effective campaigns with higher ROI. If a company invests in an AI-powered data-sorting tool, it will complete the task twice as quickly and with better results than an individual.

Conclusion

We are at a point where machines can do far more, and businesses that do not investigate what they can do for them will fall far behind! AI is a rapidly evolving technology that companies must consider to remain relevant in the future. AI techniques have enabled a more precise understanding and laid the groundwork for businesses to gain even more insights into their customers' behavior.

With technological advancements this rapid, it will be interesting to see what the next ten years bring.

Where do you believe technology will take us in the next decade? Will artificial intelligence take over our jobs?