Which Business Case is Better Solved By Artificial Intelligence (AI)? 8 Best Examples & Explanations
Last Updated January 24, 2023
Artificial intelligence (AI) has the potential to change a wide range of industries and has grown in popularity in recent years. Businesses are rapidly turning to artificial intelligence (AI) to increase productivity, cut costs, and gain a competitive advantage.
About 37% of businesses are already using AI, and many more have plans to do so. Artificial intelligence (AI) technologies are used by nearly every sector to increase business effectiveness, customer satisfaction, and employee output.
If you miss out on not riding the current Artificial Intelligence waves, you will surely lose a huge opportunity to grow your business and end up in the dark. In contrast, others grow tremendously by solving their business cases through artificial intelligence.
Here we will discuss the best business case better solved by Artificial Intelligence in various sectors and how you can benefit from it.
We will also look at the specific sorts of AI technologies typically utilized to answer these business cases, as well as the benefits that can be obtained.
Overall, this conversation will thoroughly explain how artificial intelligence can improve business outcomes and drive growth.
Don’t miss out.
Want to avoid the nitty gritty backstories, then:
What exactly is conventional or traditional programming?
Conventional programming refers to the traditional method of writing code to solve a specific problem or achieve a particular task. The most common way to accomplish this is by using a programming language. The instructions are written in a specific format and follow the rules or syntax. Conventional programmers must carefully craft the instructions to ensure that they are accurate and will produce the desired results.
Conventional programming typically involves using a programming language such as C, C++, Java, or Python and writing code using a text editor or integrated development environment (IDE).
In contrast to the traditional approach of explicitly programming tasks, the more recent AI-based programming methods, such as Natural Language Processing, Robotics, Machine Learning, and Computer Vision, enable systems to learn from data and improve their performance over time without being explicitly programmed for a task.
Which Business Case is Better Solved By Artificial Intelligence (AI)? Complete List
In recent years, artificial intelligence (AI) has gained widespread interest as a potent tool for addressing complex issues in the corporate world.
The capacity of AI algorithms to process and analyze vast volumes of data, recognize patterns, and make predictions has the potential to change a variety of industries.
Here are the Best 8 Business Case are Better Solved By Artificial Intelligence (AI)
1 . Predictive maintenance
Predictive maintenance employs artificial intelligence to predict when equipment or machinery will malfunction, allowing for preventative maintenance. This can save downtime while also increasing overall machine reliability. It is feasible to spot trends and abnormalities that suggest a potential failure by using AI to evaluate data from sensors on equipment and machinery. As a result, it permits maintenance to be performed before the equipment fails, minimizing downtime and repair costs.
One of the critical advantages of predictive maintenance is that it allows businesses to move away from traditional maintenance schedules, which are typically based on time or usage. Instead, maintenance is performed based on the actual condition of the equipment, which can be much more efficient. Predictive maintenance can also reduce costs by reducing the number of unplanned maintenance events, which can be more expensive and disruptive.
Several AI technologies can be used for predictive maintenance, including machine learning, computer vision, and natural language processing. Machine learning algorithms, such as deep learning and neural networks, can analyze sensors’ data and identify patterns that indicate a potential failure. Computer vision can analyze images and videos to detect potential issues like wear and tear, cracks, or deformations. Natural language processing can analyze text data, such as maintenance logs, to identify potential issues.
Collecting and analyzing massive amounts of data is one of the predictive maintenance’s biggest hurdles. A lot of effort and money may need to be put into this. Additionally, businesses need the resources and personnel to gather, store, and analyze data properly. A correct perception and understanding of the data are also crucial for making the right choices and moves.
2. Customer Service
AI-powered chatbots and virtual assistants can handle a high volume of customer inquiries, providing quick, accurate responses. This can improve customer satisfaction and reduce the workload for customer service representatives. AI-powered chatbots can also be integrated with customer data to provide personalized responses and recommendations.
Chatbots and virtual assistants powered by AI can be combined with consumer data to deliver personalized responses and recommendations. This can assist in increasing consumer engagement and loyalty. By analyzing client data such as purchase history, preferences, and browsing behavior, chatbots and virtual assistants can provide personalized recommendations and offer pertinent to the user.
Various AI technologies can power chatbots and virtual assistants, including natural language processing, machine learning, and computer vision. Natural language processing (NLP) permits chatbots and virtual assistants to comprehend and respond to natural language client inquiries. Learning from consumer interactions, machine learning can be used to enhance the performance of chatbots and virtual assistants over time. Computer vision can analyze photos and videos, enabling chatbots and virtual assistants to comprehend and respond to visual client inquiries.
Utilizing AI-powered chatbots and virtual assistants for customer support requires a significant investment of time and resources, which is one of the major obstacles.
Additionally, businesses must have the necessary infrastructure and skills to gather, store, and analyze client data. Furthermore, it is critical to ensure that chatbots and virtual assistants can provide accurate and helpful responses and that they can be integrated with other systems and tools to provide a seamless customer service experience.
3. Supply Chain Management
AI can analyze data from various sources, such as inventory levels, sales data, and weather forecasts, to predict demand and optimize the supply chain. This can help businesses to manage their inventory better and reduce the risk of stockouts or overstocking.
Data from enterprise resource planning (ERP) systems, customer relationship management (CRM) systems and Internet of Things (IoT) sensors can be analyzed with AI to understand demand trends better and predict future demand. This data makes having the proper supplies available at the right moment more accessible. When applied to logistics and transportation, AI can help determine the most effective pathways and types of transit.
One of the critical challenges of using AI for supply chain management is that it requires a significant investment in time and resources. Businesses must also have the proper infrastructure and expertise to collect, store, and analyze data. Additionally, it’s essential to ensure that the AI systems are properly integrated with existing systems and tools to provide a seamless experience for users.
4. Fraud Detection
Large volumes of data can be analyzed by AI systems to find trends and abnormalities that may suggest fraudulent activity. This can assist financial companies in reducing fraud losses and improving the overall security of their systems.
The advantages of using AI for fraud detection include the following:
- Efficiency: Artificial intelligence (AI) systems can handle massive volumes of data fast and accurately, making it simpler to spot patterns and abnormalities that can point to fraudulent conduct.
- Scalability: As more data is collected and added to the system, the AI’s ability to detect fraud improves.
- Automation: With AI, the process of evaluating transactions and spotting potential fraud may be automated, eliminating the need for human participation and reducing the likelihood of mistakes being made.
- Customization: Artificial intelligence systems are particularly flexible since they may be trained to detect many forms of fraud or to zero in on a single transaction.
Disadvantages of using AI for fraud detection include:
- Complexity: AI systems can be complex and challenging to implement, requiring specialized knowledge and resources.
- Cost: Implementing and maintaining an AI system can be expensive, and the price may be prohibitive for some companies.
- False positives: AI systems may flag honest business dealings as fraudulent, leading to unnecessary holdups or extra expenses.
- Evolving Fraud: Fraud techniques are growing and changing over time; the AI system must be continuously updated and retrained to stay up to date and detect the latest fraud techniques.
5. Credit Risk Assessment
Credit risk assessment is where AI can significantly benefit businesses, particularly in the financial industry. AI can assess credit risk and make lending decisions by analyzing large amounts of data, such as financial statements, credit history, and demographic information, to identify patterns that indicate a borrower’s creditworthiness. This can improve the efficiency of the lending process and reduce costs by automating the credit risk assessment process and reducing the need for manual reviews.
Advantages of using AI for credit risk assessment include:
- High accuracy: AI algorithms can analyze large amounts of data to identify patterns that indicate a borrower’s creditworthiness, improving credit risk assessment accuracy.
- High processing speed: AI algorithms can process large amounts of data quickly, which can help to make lending decisions in real-time, reducing the time it takes to approve or decline loan applications.
- Continuous learning: AI algorithms can learn and improve over time, which can help to adapt to new types of credit risk and improve overall performance.
Disadvantages of using AI for credit risk assessment include:
- High costs: Implementing AI-based credit risk assessment systems can be expensive, particularly for small businesses.
- Data Quality: Inaccurate or incomplete data can negatively impact the performance of AI-based credit risk assessment systems.
- Privacy and security concerns: AI-based credit
6. Sales Business Cases solved by Artificial Intelligence
AI Solved “Sales” business cases are areas where businesses can significantly benefit from implementing AI technology. By analyzing customer data, such as purchase history, browsing behavior, and social media interactions, AI can predict sales trends and help businesses optimize their sales strategies. As a result, the sales team can bring in more money and do better.
One of the best things about using AI for sales is that it can quickly and accurately look at a lot of data. AI algorithms can find patterns and trends that may not be obvious to human analysts at first glance. Because of this, companies can refine their sales strategies.
Businesses can also improve their lead generation with the help of sales tools that AI powers. AI can help businesses find potential sales leads and rank them by how likely they will become paying customers. This is done by analyzing customer data. AI can also automate sales by giving customers personalized suggestions and offers based on how they act and what they like.
Improving customer service is yet another significant advantage of utilizing AI in sales. AI-powered chatbots and virtual assistants can react quickly and accurately to client inquiries.
Utilizing AI for sales involves a substantial investment of time and resources, which is one of its most significant obstacles. Additionally, businesses must have the necessary infrastructure and skills to gather, store, and analyze client data.
7. AI for healthcare and medical applications
Artificial intelligence (AI) for healthcare and medical applications has the potential to transform the way medical data is evaluated and used to improve patient care. AI can be utilized in healthcare in a variety of ways, including:
- Diagnostic assistance: X-rays and CT scans are only two examples of the kinds of medical images that AI might analyze to help doctors make diagnoses. In radiology, for instance, AI systems can be taught to spot telltale cancer signals in medical images, allowing radiologists to make more precise diagnoses.
- Treatment planning: Electronic health records and other patient data can be analyzed with AI to plan health care better. For instance, AI algorithms can analyze patient data for patterns that point to the best course of treatment.
- Predictive analytics: Large volumes of medical data can be analyzed by AI to reveal patterns and trends that can be utilized to predict future health consequences. For example, AI algorithms can indicate a patient’s risk of developing a particular disease or identify patients at high risk of readmission to the hospital.
- Clinical decision support: With the help of AI, clinical decision support systems can be developed to aid doctors in making decisions in real-time. These systems can utilize information from a patient’s electronic health record, vital signs, and other sources to aid in making more educated treatment decisions.
- Drug discovery: AI can analyze large amounts of data from drug discovery research to identify new drug candidates and predict their potential efficacy and toxicity.
8. Finance business cases solved by artificial intelligence
AI for financial forecasting can be used to analyze and make predictions about various financial data, including stock prices, currency exchange rates, and economic indicators. Some specific ways that AI can be used for financial forecasting include:
- Stock market forecasting: Artificial intelligence can evaluate stock market data and forecast stock prices. Businesses that want to make better investment and trade decisions may benefit from this.
- Currency exchange rate forecasting: Foretelling the future of currency exchange rates by analysis of historical data is possible with the help of AI. This can be useful for businesses involved in international trade and need to make currency hedging and risk management decisions.
- Economic indicator forecasting: To forecast future economic conditions, artificial intelligence can examine past data on economic indices like GDP and CPI.
- Risk management: Financial data can be analyzed with AI to spot threats like shifts in the market or swings in exchange rates.Fraud detection: Financial data can be analyzed with AI to look for signs of fraud, like unusual activity or questionable transactions.
Some of the advantages of using AI for financial forecasting include the following:
- Speed and accuracy: AI can quickly and accurately analyze a lot of financial data, which helps businesses find patterns and trends that humans might overlook immediately.
- Predictive capabilities: Based on historical data, AI systems may forecast future financial trends, helping firms make better investing, budgeting, and other financial decisions.
However, there are certain drawbacks to employing AI for financial forecasting, including:
- Complexity: AI algorithms can be complex and challenging to understand, making it difficult for businesses to trust the predictions and decisions made by the AI system.
- Lack of transparency
Artificial Intelligence (AI) is a cutting-edge technology with great promise for organizations across multiple industries. AI’s most common use cases require significant data analysis and predictions, such as predictive maintenance, fraud detection, customer service, and supply chain optimization.
These use cases are well-suited for AI because of its ability to process large amounts of data and identify patterns that humans might miss. AI can also be used for solving sales business cases, financial forecasts, and in healthcare and automating repetitive tasks, which can help companies save time and increase efficiency.
However, it’s important to remember that not all business cases are suitable for AI, and it’s crucial to consult with experts and conduct a thorough analysis of your business needs before implementing AI solutions. By identifying practical business cases and utilizing AI effectively, companies can significantly improve their operations, reduce costs, and gain a competitive edge in the market.
Thanks for Reading!
Tech Content Writer
Danesh is a scientist and a content writer with more than 2 years of experience. He is also a published author of a science fiction children’s book titled Imaginary Tales.
AI has always been in his mind and soul ever since the cult classic movie 2001: A Space Odyssey inspired him to become a writer. Seeing a lot of stigma and misconceptions on AI, he has decided to found Ava Machina as an Hub for people from different backgrounds to gather and learn about AI through expert insights as well as redirecting them to the right source.