Artificial intelligence (AI) is a technology that makes computers smarter. It enables businesses to carry out a wide range of tasks faster than humans could do, boosting sales and reducing errors.
AI is helping in a variety of industries including finance, retail, real estate, education and agriculture. It also offers more efficient access to data and predictive analysis.
Real Estate
Real estate is a highly data-intensive industry, and companies are using AI to streamline the process of property search. This includes offering properties that match homebuyers’ character traits, preferences, and values.
In addition to streamlining home search, real estate agents also use AI-based tools to generate more leads and convert customers. These include AI-powered chatbots that can answer potential homebuyers’ questions in a conversational manner.
Another important real estate use case for AI is predicting home prices. For example, Zillow uses an algorithm that analyzes a property’s photos and compares them to similar homes in order to estimate its value.
One other major use case is the banking industry, where machine learning is used to assess documents before loan applications are submitted. It helps save time by reducing the amount of manual analysis required.
Entertainment
Movie production, for example, is a complex and time-consuming process that requires various methods, such as screenwriting, location scouting, storyboard and shot list generation, budgeting, recording, scheduling, and editing.
AI can help automate these processes to make them more effective and efficient. It can also be used to improve the quality of content by analyzing user feedback and preferences.
For example, YouTube uses AI to prevent copyright infringement on its platform, which helps it to provide users with a high-quality experience and avoid legal issues. It also enables the company to monitor social media activity and provide personalized recommendations to its users. This ensures that all the users have a great time on the platform. It is also beneficial for the company in terms of generating revenue.
Agriculture
AI can help with many aspects of agriculture, from predicting crop yields to reducing the risk of crop failure. It can also improve efficiency by automating certain tasks and analyzing data to make decisions.
Soil Health Monitoring: Image recognition based technology can be used to analyse the soil texture and nutrient levels, helping farmers know what type of fertilizer to use to produce healthy crops. It can also help them identify harmful insects and pests that could ruin their crops.
Weather Prediction: Artificial intelligence systems can analyze weather and climatic conditions to predict potential issues and take preventive measures before they become a problem. This helps farmers avoid expensive damage and reduce environmental impact.
While AI is an exciting tool for agriculture, there are several barriers to adoption. These include cost, public perception, and a lack of skilled workers.
Healthcare
AI use cases in healthcare help reduce the workload of practitioners and enable patients to get timely care. In addition, these tools can improve patient adherence and prevent disease progression.
For instance, AI can sift through the world’s largest collection of clinical and molecular data to personalize treatment for cancer. It also can predict whether immunotherapy will work based on a patient’s DNA.
Similarly, medical imaging and scans backed by AI can detect diseases at an early stage to start treatments. This can save lives and improve outcomes.
Finance
Finance is a rapidly changing industry, and AI is helping to improve and automate many aspects of this field. From trading to insurance, these technologies can help companies reduce costs and improve customer service.
Artificial intelligence is also being used to speed up financial research and provide accurate market forecasts. This can be done by using natural language processing (NLP) to analyze the sentiment of financial news articles.
Another use case for AI in finance is contract analysis, which is a repetitive task that can be delegated to an intelligent machine learning model. An NLP model that has layered business logic can automatically interpret and record contracts at high speed.