Manual sorting of data is a prime example of not using your resources wisely. From content sorting to incoming emails, insurance claims, and social media sentiment, AI classification solves various needs for companies worldwide, shares Trey Norman, senior pre-sales engineer at Mindbreeze.
Have you ever gotten so many emails and wished there was a system to automatically forward them to the proper department without all the back and forth? Automated classification can be your savior to this repetitive task, eliminating the need for employees to sort, categorize, and forward manually.
Manual sorting of data is a prime example of not using your resources wisely when so many solutions on the market can do this for you.
For example, a large insurance company may have up to 700 claim tickets in its queue that may belong to 70 different departments. Without automatic classification, the team would sift through each claim and then pass them along to the proper department one by one. The efficiency of this process could increase by 30% with the right solution in place.
Now that the problem is established, this article aims to inform readers exactly what an intelligent automatic classification system is and the power of the technology running it – from training a business model to automatically categorize and clean labeled datasets to using artificial intelligence for keyword and image recognition.
An Introduction to Automatic Classification
An innovative and effective classification service extracts metadata from unstructured texts and uses their semantics to identify different document types, as well as keywords. The foundation for this type of system uses an integrated machine learning model that consistently learns and self-optimizes for greater use. A basis for machine learning is training a business model with real-world company data. Doing so makes automatic classification adaptable to specific and different business requirements across a wide range of departments and industries.
A key attribute of classification services is the need for very little or no coding experience.
Through human-in-the-loop machine learning and consistent user feedback, administrators can improve the accuracy of the underlying model. This permits the auto-tagging of incoming documents and tickets filed to support teams, FAQ articles, and emails. The best classification models can even support image recognition and sentiment analysis.
Key Questions to Consider when Thinking about Classification Models
Contracts, invoices, orders, delivery bills, purchase orders, and countless documents are processed and handled by companies every day. Before diving deeper, it is essential to identify the key questions.
How can companies guarantee handfuls of documentation are categorized correctly and appropriately assigned?
Are there fast and efficient processes that can quickly adapt to the circumstances and business requirements?
How can documents be classified without creating elaborate sets of rules? How can this be applied to your own company, and how can the AI perfectly adapt to your specific business and business needs?
Use-cases of Automatic Classification Explained
Since the beginning of time, we have used categorization, splitting, and separating to organize different subjects or groups. For example, we use boarding groups to separate who gets to go on an airplane first, and we use color coding to help us identify the importance of our personal notes. The exact same methods are used for businesses and their vast amounts of data. It only occurs on a much larger scale; take the insurance example from the introduction
As there are countless other examples of this in the real world and business – it’s important to note a few more. Automatic classification can be used in law firms to categorize legal cases, ensuring all relevant legal documentation is in the hands of the right team. Image recognition and classifying specific diseases and illnesses from X-rays and scans are also used to make healthcare more effective and diagnosis more understandable for patients and doctors.
Companies have also used AI automation to detect customer sentiment from Amazon reviews. Companies use this by putting product reviews into a model. The classifier can determine if the sentiment is positive, negative, or neutral based on the words used in the post. AI can detect emotions from the content alone and does not require access to the actual star rating method used by Amazon. The same applies to determining sentiments of social media posts on Instagram, Reddit, Twitter, Facebook, and more. High levels of sentiment analysis lead companies to significant breakthroughs in marketing and product development by generating an easily understandable glimpse of how consumers feel and talk about your brand. There is no need for a department to scrub the entire internet anymore, and we have AI to thank for it.
Again, thanks to AI automation, getting rid of the need to spend days at a time going through nuggets of data saves all sorts of hard-working individuals and companies time and money.
The very same technology is used to organize articles, blog posts, and different pieces of content on company websites and online publications. By recognizing file types and keywords, systems can identify and sort by authors, topics, and types of media and content.
Data compiled by enterprises continues to grow and grow every day, so the importance of having a system in place to sort through the clutter is becoming a necessity to keep employees from becoming overwhelmed and automating work that has no need to be done manually.
Many people wonder how certain emails ended up in a spam filter, and the answer is quite simple: automatic classification. A keyword or multiple keywords in the address, subject line, or body of the email was likely targeted and identified as a spam message – all made possible by supervised machine learning and training models to determine what is and isn’t spam.
The Overall Importance of AI Automation
From content sorting to incoming emails, insurance claims, purchase orders, and social media sentiment, automatic classification solves various needs for companies worldwide. Not only by eliminating redundant processes and accurately sorting data into the right hands but ultimately leading to revolutionary insights about your product, users, and customers. AI automation, and specifically automatic classification, can serve a purpose across your entire operation with minimal hands-on experience and money spent.
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