Deep learning, as defined by MathWorks, is a system of artificial intelligence that is built around learning by example. Multiple industries have already understood the benefits that deep learning brings to their operational capabilities. Deep learning is best demonstrated by the implementation of the technology in self-driving cars, as well as medical research to detect and tag cancer cells automatically.
The Importance of Deep Learning
Deep learning is an extremely lucrative field. Aberdeen Research explains that as much as 43% of today’s businesses are already utilizing AI in some form. AI is a major disruptive technology and 30% of respondents of a 2017 Pricewaterhouse Coopers survey mention that within the next five years, they believe that machine learning will make a significant and indelible impact on their industries.
One attractive aspect of deep learning is that it doesn’t need a human being to teach it what to recognize. Given the right parameters and enough examples, a neural network can figure out the same answer to a potential problem as a human being can.
In business, the areas where deep learning already has applications include Natural Language Processing (NLP), Image Recognition Software (IRS), and Speech Recognition Software (SRS). Each of these technologies has advanced by leaps and bounds since its initial conception, and they offer a lot of promise to industry application that can be experienced today, even while the technology is improving on itself. Among the areas that businesses can benefit from deep learning are:
Personalized Customer Service Options
Businesses have had a long and arduous relationship with customer service. The implementation of customized methods of delivering customer service offers a way for companies to interact with their customers on a deeper level.
The modern world of marketing focuses less on customer service and more on customer experience – delivering an all-around stellar representation to the business to convert customers into brand evangelists. Marketing Week mentions that eight out of ten customers state that customer experience is crucial to their view of a company. Personalized customer service forms the cornerstone of that customer experience.
Chatbots already exist in a support role for many small businesses, and Business Insider notes that as much as 44% of US consumers prefer dealing with a chatbot. Humans are still necessary for cases that the bot can’t handle, so having staff ready to deal with customer interaction is always a concern. But with chatbots coping with most of the mundane problems that customers face, response times are lower, and the overall customer experience sees marked improvement because of it.
Corporate Hiring Practices
Businesses always want to get the best skill within the field. To this end, a lot of companies have dedicated HR managers that deal with sorting through employee résumés as they come in for an advertised position. However, because of the nature of the job, artificial intelligence is well suited to perform the necessary culling of the initial applicants. Forbes reports that within the next ten years, as much as 16% of HR jobs will be replaced by artificial intelligence.
Currently, the most challenging part of dealing with corporate hiring for more than half of recruiters, according to Ideal, is the shortlisting process. Machine learning has the potential to make for a much smoother hiring practice and even detecting outliers that might be a good fit for a job, while eliminating individual biases that might exist within the initial screening process.
In some applications, AI combines multiple facets of the technology to put together a virtual hiring professional. Digital Marketing Institute mentions the use of an AI assistant for HR professionals named Robot Vera that performs the tasks of finding best-fit candidates for companies. From locating the potential hires on job-seeker sites, to calling them and using voice recognition software to gauge the value of their responses, the AI can locate and determine whether the candidate might be a good fit for the company. Once the most suitable candidates are acquired, they can be sent to the human resources manager for final approval.
Financial institutions have implemented artificial intelligence automation successfully in many insurance and banking companies. Tech Republic reports that more than one third of businesses applying AI into their management systems have seen between a 2- 5% increase in revenue. While this is encouraging news for companies involved in the insurance and banking industry, deep learning can have an application in companies that aren’t part of this field.
By developing an AI that can automatically scan through invoices and records submitted to the company, the business can potentially catch situations where invoice numbers don’t match, or order and supply are different. Application of deep learning AI in this way could save the company time in sorting out orders, as well as saving money, since too much or too little tax paid on orders could lead to losses in revenue or potential audits from the financial authorities.
Supply Chain Management
Supply Chain Management (SCM) is another field in which AI and machine learning have made a substantial impact. A Teradata report entitled The State of Artificial Intelligence for Enterprises notes that SCM is one of the areas of AI that commands huge investment backing. SCM requires the collection and processing of large volumes of data to ensure that a company is well aware of their products’ location and schedule for delivery at all times. By combining deep learning with other emerging technology like Internet-of-Things devices, companies can reliably get updated data about the state of their products regardless of where they are.
Additionally, data collection from customers can be used to forecast the demand for a product and so prepare supply chains to deal with an impending increase in demand. The Network Effect mentions that supply chain management using AI can leverage real-time sales data as well as historical data to develop forecasts of product demand that are eerily accurate. The more data the system receives, the better it can be at offering insights into customers current and future needs.
Companies are complicated mechanisms, and because of this, they can fall prey to fraud that may go unnoticed for years. A McAfee report from February 2018 estimates that cybercrime may responsible for costing the world economy around $600 billion. Because of the significance of the impact of fraud on the global economy, it is crucial to develop systems that can consistently detect and notify companies of potentially fraudulent activity regarding their company finances.
Financial companies have already started leveraging artificial intelligence to track customer data points for detecting potential fraud. Mastercard mentioned that the company implemented an AI system in 2016 for this specific purpose. Within the insurance industry, artificial intelligence can offer an added degree of security for dealing with customers that may present fraudulent claims. While predicting human behavior may be difficult, determining patterns from existing practice is something that AI can be trained to do. In this case, it can provide valuable insight as to the behavior of criminals that may otherwise have operated under the radar.
Deep Learning in Service to Business
Technology continues to advance at a rapid pace. Multiple emerging technologies have already become disruptors of business operations and replaced entire areas of activity because they provide a more efficient method of doing things. Artificial intelligence has the potential to change the landscape of not just business operations but the entire world as a whole. For companies, this means that adopting AI early and engaging deep learning for business activities will benefit the company significantly as time goes on. The development of AI for business application isn’t likely to slow down any time soon. For entrepreneurs, now is as good a time as any to invest in the new wave of technology.