Five Common Applications of AI in Business
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Artificial Intelligence (AI), once a purely fictional concept prevalent only in movies and sci-fi novels, is now starting to revolutionize the modern world of business. With the steady improvements witnessed in the processing power available to computer systems, the data available to train models and the design of computer algorithms, AI technology has been refined into several business applications for both consumer and enterprise. The following are some of the most frequently-encountered use-cases where AI has successfully been leveraged to business to optimize processes and/or yield higher revenues for companies:
Chatbots use Natural Language Processing (NLP) and Machine Learning to accurately return the most suitable response to queries they receive.
Chatbots were some of the first large-scale applications of Artificial Intelligence; products of evolving research in a specific domain of AI known as Natural Language Processing (NLP). Business Insider estimates that nearly 80% of all businesses want to implement some kind of chatbot interface by the year 2020. Chatbots use the understanding of natural (human) language derived from NLP to try and simulate human conversation, and this functionality has been used in nearly every industry vertical to enhance business processes. Chatbots have seen two major kinds of applications in business:
- Customer Service Chatbots
- Business Process Automation Chatbots
Customer Service Chatbots
An example of a pizza-order customer service chatbot. This bot understands the Intent, Context and Parameters provided by the customer and successfully services the customer request.
Responding to customer queries and disseminating information about products/services are tasks that have traditionally been fulfilled by employee profiles such as Customer Service Representatives. However, some of the more mundane parts of these tasks are now increasingly being outsourced to virtual chatbots, who can provide several advantages over a human employee. For starters, chatbots do not require downtime or financial motivation to work, and can be accessible 24⁄7 to respond to queries. Further, delegating the first line of customer response to chatbots will allow automatic handling of several easily-answerable queries, freeing the human operators to focus on the more complex, customized responses to issues that need human involvement.
Customer service chatbots have been utilized by businesses in several industries such as Retail, Ride-sharing, Food Delivery, Technology and so on. Advances in Machine Learning & NLP have allowed chatbots to successfully extract the intents and entities in customer queries to understand the meaning behind those sentences, and then deliver a suitable response in turn. Chatbots can also remember pieces of information provided by the user for a predefined amount of time, further increasing the individuality of the conversations they have with customers. Chatbots allow businesses to stay engaged with their customers even during times of the day they wouldn’t be expected to be active, and they facilitate customers conversing with an official company channel in an easy, intuitive chat interface.
Business Process Automation Chatbots
Over and above customer service requirements, chatbots can also be used to expedite internal business process requirements. Since the basic algorithmic flow of chatbot creation is the same no matter which use case it is meant to handle, chatbots can be quickly customized out of a general framework and deployed for any internal use case. Examples of this could include a Human Resources (HR) Bot that is trained to obtain information about the user and accordingly answer any HR-related queries they may have, such as Benefits, Leave or Salary Package, or an internal Payments Bot that is capable of handling financial transactions between accounts belonging to users within the same company, by virtue of integration with their bank accounts.
The large amount of transactional/customer data available to most financial institutions means they are good candidates for applying AI solutions to their business.
Artificial Intelligence has enormous potential for financial fraud detection. Fraud is always a pertinent threat for financial institutions, and with the rapid increase in the banking population and number of financial transactions that take place every year, the risk of fraud increases as well. Fortunately however, this means there is a large amount of customer, financial and transactional data available to businesses now, and that leads to an improved likelihood of AI systems being able to correctly flag up the potential for fraud before the transaction takes place. One example of an idea that could be used in this space is to collect tabular information about every single customer that has applied for a loan, for example, and include a binary target variable that specifies whether the user has successfully repaid the loan in the given time frame or not. The customer data can be mapped to points in higher-dimension vector space, and a machine learning algorithm such as k-Nearest Neighbors or Random Forest could be used to teach the AI system to predict those vector points that have an increased likelihood of “not repaying their loans.” In this way, the system can predict the likelihood of any new applicant defaulting on their loan as a function of these characteristics of that customer, allowing the bank to take a decision on whether or not they should grant the applicant the loan request or not.
Cybersecurity is another area with huge potential for AI solutions, due to its abundance of data, complex characteristics and critical nature to businesses.
With the increasing proliferation of digital systems in every aspect of the business pipeline, the threat of cybercrime needs to be taken seriously by organizations around the world. According to a 2018 report by McAfee, the global cost of cybercrime is as high as $600 billion, which is nearly 1 percent of the global GDP. Artificial Intelligence has the potential to eliminate a significant portion of this threat in a variety of ways. Cybersecurity organizations could train AI systems to detect the presence of malware and viruses in every operation that is conducted, by making it learn from past datasets of software that has shown similar behavior. An AI software could handle biometric identity verification systems and create a global authentication framework that can dynamically alter access privileges based on location and network, perhaps with the help of a Blockchain system. Another exciting potential application of AI in this area is through the emerging area of Natural Language Processing. AI could be used to scour the web for important and relevant information regarding the latest articles, news and studies about cyber-threats. Such information would provide cybersecurity firms with an easy way to stay up-to-date on the latest cybersecurity trends and allow them to devise strategies to protect their own clients from cyber attacks.
Supply Chain Management
Efficiency in Supply Chain Management is of critical importance to businesses in the logistics sector. Being able to track incoming and outgoing shipments, and being able to flexibly allocate resources to every requirement at minimum cost to the organization is crucial to the financial health of the business. For that reason, Artificial Intelligence is already being utilized in the Supply Chain Industry to get the optimal solution for several stages of the process. Demand planning systems, for example, look at historical data and use this to forecast what kind of shipment demand an organization warehouse may face on a given particular day. This forecast can be adjusted by time period (days, weeks, months) depending on its suitability to the business. The machine learning system utilized in this software can take into account several variables that may influence this demand, such as temperature data or commodity prices, and can look to identify significant correlations between these variables that may not have been evident beforehand. Using weather data, for example, can be used by a machine learning system to improve transportation planning. Another fascinating application is, again, to use Natural Language Processing to see if a Vision-based model can accurately automate arduous processes such as classifying goods for import or export. One of the most recently popular applications of AI to logistics and supply chain has been the introduction of the Autonomous Mobile Robot. Amazon’s global warehouses, for example, employ over 100,000 robots that utilize a robotics technology known as Simultaneous Localization And Mapping (SLAM).
Amazon’s global warehouses employ thousands of robots to increase the efficiency of their supply chain.
Google recently created an AI system that could identify lung tumors in CT scans with accuracies better than those of experienced doctors themselves.
One of the most high-profile and potentially life-transforming applications of Artificial Intelligence has been in medicine and healthcare. AI-Applications in healthcare have sent waves across the entire industry and have even given rise to debates about whether human doctors will eventually be replaced by machines themselves. The appeal of healthcare as a vertical ripe for AI disruption is that it is an incredibly data-heavy industry, and this data has barely been utilized to improve best medical practices, some of which have stayed the same over decades and are in urgent need of an upgrade. AI Computer Vision systems, for example have shown great success in assisting doctors with the scanning of X-Ray, CT and MRI outputs, and helping them identify malignant features such as tumors and cancer cells. Natural Language Processing systems, can perhaps help clinicians and medical researchers scan huge volumes of journals and research papers to get up-to-date information on trends and best practices in the industry to inform proper patient care. An AI system can also augment the traditional question-based diagnostic methodology of doctors in identifying a disease, by giving them summary statistics of the patients they have diagnosed and helping them identify hidden trends in the data they may not have seen before. Lastly, AI can even be used in immunization and vaccine development at the research level, simulating scenarios that require time and effort for testing, and helping researchers develop new theoretical ways of fighting diseases, which can then be deployed in R&D and used to create new drugs or medicines.
These are merely five of the hundreds of possible industry verticals where there are ideas for Artificial Intelligence to make an impact on business. There will always be a need for human involvement in the operational aspects of running a business for transparency and accountability. However, with the growing maturity of AI solutions, it is possible and maybe even necessary for modern organizations to move ahead of their competition by improving their efficiency and financial health through AI deployment in various parts of the business. Businesses that have future-proofed themselves by creating a pathway for increasing AI deployment within their organizational structure are in the perfect position to make use of the increasing attention being given to Artificial Intelligence development in today’s software environment, and are hence at a great advantage in that regard.