Artificial Intelligence (AI) has been around for over half a century now. The term was coined only back in 1956 with John McCarthy, who worked at the Massachusetts Institute of Technology (MIT). Since then, we have come a long way from expert systems to deep learning-powered Chatbots, capable of doing many tasks in our day-to-day lives without any human assistance. With all these advancements in research and applications, one would ask the question, “How to use AI in your business?” Well, to answer that question, I have created a list of 10 different ways you can use Artificial Intelligence (AI) in your business.
1. Create an automated system for data extraction – from websites or PDFs
You may not realize it, but today, many websites provide APIs for their data dumps. The information available through these APIs is stored on structured databases, which you can easily extract by using software tools that support the respective API calls. These tools classify the information according to a given pattern and extract relevant information like names, phone numbers, and other details which you might find helpful in your business applications. Expert systems are also used with AI, where various concepts are associated with each other. An example of a concept could be a person working for a company. Once this association is identified, the expert system can extract all information about the mentioned concepts with the help of RemoteDBA.com.
2. Apply predictive analytics and forecasting capabilities on top of big data
Predictive analytics is a significant benefit of applying AI in business applications that require predicting future outcomes based on historical data. For instance, if you want to predict the outcome of an election using historical voting trends or calculate the risk probability of defaulting on a loan based on certain credit scoring model results, it would make sense to use predictive analytics when available at hand. It is also known as foresight which helps businesses take necessary action before others do so. Apart from just analyzing past data, deep learning models are also used to predict future results based on current conditions and trends observed.
3. Use machine learning for smart trading strategies in the stock market
With the rise of advanced trading tools like HFT (high-frequency trading) and algo-trading, it is becoming increasingly difficult to predict stock movements accurately with a human eye alone. It has given birth to a new industry known as “quantitative finance,” where experts use intelligent algorithms to analyze data & predict prices to make optimal decisions during trading hours. AI applications have been around for some time now. Big players adopt them primarily because of their accuracy rates and the cost-saving benefits they can provide over conventional methods such as using expensive paid experts.
4. Create chatbots for customer support
We are well aware of the importance of providing good customer service in today’s competitive business environment where customers can switch to a different brand if they find any competitor offering better services than your own company. Chatbots have been around for several years. Recently, these intelligent bots are mainly used by companies to provide 24/7 live chat support and automated self-service options on their websites that replace FAQ pages with more dynamic conversation flows between the user and the website software. As we know, all things are equal. People prefer choosing human interactions over robotic ones due to our emotional attachment towards other humans, which you cannot achieve through automated processes alone. Hence, it is vital to introduce chatbots as support and ensure that businesses do not lose the human touch when dealing with their customers.
5. Use AI for intelligent data visualizations
Data can be presented from different perspectives when analyzed using algorithms that can understand how concepts are associated with each other. As we know, visualization is a great way to easily give information, which helps organizations convert data into meaningful intelligence by automatically extracting and correlating various related concepts. The output would then contain a set of charts & graphs linked to specific key terms that helped conclude the nature of the subject matter under analysis. It saves time and provides new insights that might have been otherwise missed out on due to a lack of manual effort on the part of the analyst.
6. Use computer vision for smart security systems
It is becoming increasingly important for organizations to find ways to provide cost-effective measures to deal with elevated threats and a growing need to ensure round-the-clock monitoring in the context of limited control over resources. They must also not compromise on the quality of surveillance footage they want to record. One of such ways would be to use AI applications capable of simple object detection, tracking & recognition from live camera feeds by converting them into a digital format using advanced algorithms. It allows organizations to monitor risks better and do so more efficiently than using traditional methods alone.
7. Implement predictive maintenance to reduce downtime costs
Companies often struggle to minimize the operational costs associated with unplanned repairs & maintenance, which usually involve interrupting critical processes until the equipment is fixed or replaced. By using advanced AI techniques, companies can now predict when certain parts will fail to plan for their replacements beforehand and minimize downtime using predictive maintenance schemes already adopted by several organizations around the world.
8. Use big data analytics for better decision-making capabilities
As we know, large amounts of data need to be processed in real-time to derive meaningful insights from them that can help businesses make better decisions. Here tools like Spark come into play where organizations can use it alongside deep learning algorithms as a unified platform. It will help implement big data applications that allow companies to collect relevant data and process it using their in-house clusters or cloud services like Amazon EMR, HDInsights & Azure ML.
9. Use data marketplaces for better collaboration with suppliers and partners
When there is a large amount of information involved and different parties are required to collaborate to derive meaningful insights, it’s always good to have a central place where you can store all your data securely without worrying about its security. Using a dedicated resource like Data as a Service (DaaS), organizations can bring together various AIs that provide specialized expertise from their respective fields on the same platform while ensuring that sensitive data remains secure using end-to-end encryption. It allows companies to use diverse sets of knowledge from a single place that can help them achieve more in less time.
10. Use virtual assistants for better customer service
As we know, building trust and loyalty are essential to the success of any business since it allows businesses to remain financially stable over the long term while delivering superior services to their customers. Companies are now using to interact with their customers by providing them with personalized advice via virtual assistants that use natural language processing (NLP) techniques to answer queries within seconds. This technology was first introduced by Apple Siri and has now been adopted by companies across various industries, including fashion, banking &financial services, travel & hospitality, healthcare, etc. With artificial intelligence already undergoing rapid transformation, many companies are now exploring ways in which they can use AI in their existing business models without making any changes to their workflows. By using many tools & techniques such as computer vision, NLP, deep learning & predictive analytics for specific applications, organizations can now foresee new opportunities while maximizing the benefits that they derive from implementing them.