AI solutions are increasingly becoming a staple of businesses across industries.Today’s headlines and commercial applications powered by increasingly sophisticated models may lead companies to try to solve their use cases with cutting-edge AI methods. 

However, whether you should employ complex techniques or would do better with simpler approaches depends on a range of variables. 

In this post, we take a step back and offer some pointers on how to determine if deep learning is the best tool for your issue at hand. It is important to consider not only the type of data you have available but also the nature of the problem you are trying to solve. 

Business Problems VS. AI Solutions

a table showing business problems and ho Ai can solve these problems

The table below provides a brief overview of some common business problems and the AI solutions that are best suited to solving them.

As can be seen from the table, there is no one-size-fits-all solution. The most appropriate method will depend on the specific problem you are trying to solve and the data you have available. However, by taking into consideration the nature of the problem and the data at your disposal, you can narrow down the field of possible solutions and choose the best AI one for your particular use case.

5 Examples of AI Solutions:

There are a variety of AI solutions available, each with its own strengths and weaknesses. In this section, we will take a look at five of the most popular ones and see how they are being used to solve business problems.

1. Time Series Analysis:

Time series analysis is a type of statistical analysis that is used to examine data points that are spaced evenly in time. This type of analysis is often used to predict future trends based on past data. Time series analysis can be used for a variety of business problems, such as predicting demand for a product or service, forecasting sales and revenue, and detecting fraudulent activity.

For instance, Walmart uses time series analysis to predict demand for products and optimize its inventory levels. This allows the company to avoid stockouts and ensure that its shelves are stocked with the items that customers are most likely to purchase.

2. Deep Learning:

Deep learning is a type of machine learning that is inspired by the structure and function of the brain. This type of AI is capable of learning from data without being explicitly programmed to do so. Deep learning is often used for image recognition and classification tasks.

For example, Google uses deep learning to power its image search engine. The company’s algorithms are able to identify objects in images and return results that are relevant to the user’s query.

3. Anomaly Detection:

Anomaly detection is a method of identifying unusual data points that do not conform to the expected behavior. This method is often used to detect fraudulent activity, such as credit card fraud or insurance fraud. Anomaly detection can also be used for other purposes. This includes detecting equipment failures in a manufacturing process or identifying patients at risk of developing a certain disease.

Anomaly detection is used by a variety of companies to detect fraud. For instance, PayPal uses anomaly detection to identify and prevent fraudulent transactions. The company’s algorithms are able to detect unusual patterns in data that may be indicative of fraud.

4. Simulation:

Simulation is a powerful tool for optimizing manufacturing processes or assessing the impact of changes to a system. By creating a model of a process or system, businesses can test different scenarios and identify the optimal solution. This type of analysis is particularly useful for complex systems. It would be impractical or impossible to test all potential solutions in the real world.

For example, Boeing uses simulation to test the impact of changes to its aircraft designs. The company’s engineers are able to create virtual models of planes and test different design modifications before any changes are made to the physical product.

5. Collaborative filtering 

Collaborative filtering is a recommender system that can be used to suggest products to customers or find similar items. Collaborative filtering algorithms make recommendations by identifying other users with similar interests. Then, suggest items that they have liked in the past. This method is often used by online retailers to recommend products to customers based on their past purchases.

Amazon uses collaborative filtering to recommend products to customers on its website. The company’s algorithms take into account the past purchases of similar users. Then suggest items that those users have liked in the past. This allows Amazon to make personalized recommendations to each customer.

As can be seen, there is a wide range of AI methods that can be used to solve business problems. The most appropriate method will depend on the specific problem you are trying to solve and the data you have available. However, by taking into consideration the nature of the problem and the data at your disposal, you can narrow down the field of possible solutions. Likewise, choose the best AI method for your particular use case.

Leveraging AI solutions with Kiimkern

As organizations across industries continue to face business challenges, they are turning to artificial intelligence (AI) for help. AI services can provide the ability to automate processes, improve decision making and accelerate innovation. Kiimkern, an AI solutions provider, is helping organizations solve business challenges with its services.

Kiimkern provides a set of tools and services that make it easy to develop, train and deploy AI models. The platform includes an end-to-end solution for data preparation, model training, and deployment. It also offers a suite of services to help organizations get the most out of their AI investments, including consulting, support and education.