How AI can Solve Business Problems? Here's What You Need to Know

• Read original article here

Datafloq enables anyone to contribute articles, but we value high-quality content. This means that we do not accept SEO link building content, spammy articles, clickbait, articles written by bots and especially not misinformation. Therefore, we have developed an AI, built using multiple built open-source and proprietary tools to instantly define whether an article is written by a human or a bot and determine the level of bias, objectivity, whether it is fact-based or not, sentiment and overall quality.

Articles published on Datafloq need to have a minimum AI score of 60% and we provide this graph to give more detailed information on how we rate this article. Please note that this is a work in progress and if you have any suggestions, feel free to contact us.

When it comes to solving real-world business problems, artificial intelligence (AI) is all the rage. In the past, businesses generally needed to invest a significant amount of time and money to develop insights. Today, we can take advantage of AI, its subsets like machine learning and deep learning, and also data analytics to gain actionable insights from massive amounts of data at little cost. With rapid advancements in AI, here's how the technology is being used specifically to solve business problems.

If you're still wondering what all the fuss around AI is, you should know that artificial intelligence can take things that are normally impossible to do by humans and make them possible. For example, take a look at the way it operates autonomous, “driverless” vehicles.

What makes AI different in business? AI is able to see patterns in large volumes of data and make predictions that humans cannot. This has a lot of applications in almost every industry.

While the self-driving car example is now well-known, besides the automobile sector, AI is also being used in finance, retail, software, insurance, and healthcare, where large amounts of data are collected. Big companies such as Google, Facebook, Amazon, and Microsoft are using AI to help them with their products and recommendations. These companies recognize that AI can help them not only create better products but also make better decisions.

Artificial intelligence is considered to be a major disruptive technology in many spheres of our lives. While data science techniques give us new ways to use the available data, AI is an important technology driver behind it.

More specifically, the use of a subset of AI, machine learning (ML) in solving business problems is a high-value way for enterprises to drive innovation and increase their competitive advantage. When data is analyzed, patterns can be found that can lead to new discoveries and provide new insights. When this happens, it can lead to new product and service ideas that can create value for customers and the business.

Machine learning is used in finding a solution to many business problems. It is used in not only marketing for customer segmentation, but also in sentiment analysis, fraud detection, supply chain optimization, and overall, to boost productivity.

Here are some ways how machine learning is being applied in business:

One of the most practical ways that ML is being used in a business is to help companies identify patterns in big data sets. A common task is to predict customer behavior based on past experiences; for example, predicting how likely a customer is to make a purchase. Customer analysis based on machine learning algorithms is being used by banks, telecommunication companies, and online retailers to provide recommendations for specific products or services. For example, Netflix uses a recommendation engine that predicts what people will want to watch based on past viewing history.

Machine learning can be used to generate personalized marketing campaigns. In some cases, this was found to have driven up conversion rates by as much as 30%. It can be used to personalize your online experience based on your actions and preferences, thus improving the overall customer experience.

Perhaps the most direct application of machine learning is predicting customer responses to marketing campaigns. An algorithm might use machine learning to predict whether customers will be interested in particular products or services, and if so, how likely they are to purchase.

Another popular use of data analytics and machine learning is for customer segmentation, which typically involves using a number of different variables to determine what type of customer a person is. For example, the customer might be a low-value, low-spending customer or a high-value, high-spending customer. When done right, this can lead to increased sales by marketing the right products to the right people.

Data science is getting more widely accepted in business. Because of the high-volume influx of data in today's digital world, traditional tools to solve business problems are faltering. Data analytics – a branch of data science – techniques are now being used for better and faster solutions. The business world is becoming data-driven, that's why more organizations are recognizing the need for data scientists and analysts to help them.

The need of today's enterprises is to extract actionable insights from large data sets. For this, it's critical that data science experts understand the business problem, the business domain, and the business environment. That's why businesses are actively recruiting data scientists and analysts because of the high value they provide.

Before a company makes a decision to deploy data analytics, it needs to ask the following questions:

Many businesses have started using data analytics as an integral part of their strategy and operations. Collectively, we know that there is a huge amount of data available, but we need to be able to find out what we can learn from that data and how we can use it to make good business decisions and improve operations and decision-making. This is also where AI steps in, along with data analytics in the form of business analytics.

Business analytics refers to the process of applying data analytics tools and methodologies to business settings. In business analytics, the aim is to extract meaningful business insights from data to ultimately achieve a company's goals.

Here's how business analytics is used:

Sales: An analyst can determine the number of leads necessary to fill the sales pipeline by understanding key metrics, such as the lead-to-customer conversion rate.

Budgeting and predicting: By determining the budget and investments required to achieve a company's future growth goals based on historical revenue, sales, and cost data, an analyst can identify what budget and investments are required.

Risk management:An analyst can mitigate business risks by determining their likelihood and costs, and by making cost-effective recommendations.

Product development and innovation: Through understanding how consumers have responded to previous product features, an analyst can guide future development, design, and user experience.

Many times, companies respond to questions on the use of AI in solving business problems by giving the example of “automated customer service”. While this is an important use case for AI technology, it doesn’t fully capture what the essential issue is in this instance. The core issue here is understanding customers and predicting what they will do. The correct answer, in this case, is that a business problem can be solved by using machine learning to predict customers’ preferences, anticipate what they will buy, and predict the likely responses of the customers.

Here's another example of how AI can be used to solve a real-world business problem: have you ever wondered why it is that in retail, personalization is the rage? AI comes into play in the form of recommender systems. These are pieces of software designed to identify patterns between one customer and another in order to recommend products or services to each customer. In the same way that recommenders can spot patterns, AI can also help spot patterns across a wider range of situations to recommend products and services which are relevant.

In the past few years, a lot of technology companies have started using AI and its subsets such as deep learning to build algorithms to make business predictions. Any ML model that makes data-driven predictions or decisions involves various types of datasets. You can, for example, train an ML model on the input-output dataset of a gas turbine, following which it can make predictions about the next day's energy consumption without actually knowing what a gas turbine is or how it works.

As any high-level company executive will tell you, there are too many factors involved in the decision-making process. AI systems can aid human managers in this process. Various techniques such as data mining and machine learning can be used to extract knowledge from the past and then provide recommendations for future actions. In the energy industry, this can be used to optimize a plant operation or provide other recommendations to management.

Pipeline companies are using AI in their operations to monitor operations in real-time, detect patterns in operations that might not be obvious to human eyes, and develop proactive approaches for managing operations to prevent potential problems.

Such applications are rapidly advancing. Not only the energy industry but other industries, too, are also benefiting from such AI systems. Artificial intelligence can also be used to improve investment decisions. How? By making use of the availability of vast amounts of data, energy companies can determine where to allocate resources in order to achieve maximum return on investment.

A prime example is a solar power. As the cost of solar energy continues to drop, the industry is rapidly expanding. However, financial investments and research and development (R&D) aren't static, and investors face the risk that the growth of this industry will slow down before they can reach their desired returns.

However, AI can be used to help them find the most profitable energy sources and areas of investment. For example, by using machine learning, the system can identify optimal sites where to install solar panels. In this way, investors can save a lot of money and attain a very good return on investment.

Previously, businesses generally used traditional methods to be able to gain insights and make decisions in order to stay competitive. Now, with copious inflows of data, they have started using artificial intelligence along with data analytics in decision-making, and other aspects of the business. With a significant amount of time and money being spent on AI, it’s also become imperative for companies to have a strong understanding of what AI can do for their business.