Semantics AI Technology is vastly improving time to value, have you considered it to extend your business process automation agenda?
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Introduction
Business owners are always looking for ways to keep processes efficient and effective. Technology is advancing at a pace, and it is the innovation it brings that is making a difference to the strategic objectives that are required by a business. Automation is pivotal to achieving this.
Automation has evolved over time, and it’s no longer limited to just basic computing and manufacturing processes. Several new technologies have been introduced that help organisations achieve better results with automation. The latest addition in this list is semantic AI technology, which is used within automation systems today. Read on to learn about what semantic AI technology is and its impact on automation adoption by businesses around the world.
Semantic AI technology is a new trend in the field of artificial intelligence. It deals with applying natural language processing (NLP) and deep learning technologies to unstructured data to understand its meaning so that it can be used for automation and advanced decision-making. This enhances the process of automation by providing insights into the context, structure, and semantics of data.
It is now something that cannot be ignored.
So where is the link between Semantics Technology and Business Process Automation?
Over the past few years, automation has become a big word in every sector. Several businesses are implementing automation to save on time and cost, increase efficiency, and reduce errors. Over time, automation systems have incorporated machine learning and artificial intelligence within them to deliver better performance and results. Though several new technologies are used within automation systems today, semantic AI is the most recent addition that helps organisations achieve more with automation.
The ultimate goal of Business Process Automation (BPA) is to increase the efficiency and effectiveness of a business process by reducing manual intervention, improving output quality, increasing control and predictability, reducing costs, increasing flexibility and much more. Semantics plays an important role in automating many kinds of business processes that are content-intensive and document-driven even further.
Semantics AI Technology must be able to retrieve large amounts of information with speed and quality and deliver context-based quality results. The benefits of semantic AI go way beyond basic search – it can help you capture the implicit, hidden or intangible ‘intent’ of the information – be it finding it, analysing it, comparing it and extracting it.
There are many ways Semantics AI technologies are adding true time to value within organisations. You could be a large company that spends a huge amount of money on R&D projects that produce reports and documents which may be useful for years on many different occasions within the company or outside. Such documents can be made available in automated formats like PDF, but without being able to access the content programmatically it will be difficult to use such contents effectively. Semantics technology enables you to do this by providing context information about each paragraph, sentence, word etc., so that other applications could access these context tags to find out what they mean. This way your application can understand what exactly is written in those paragraphs and sentences which helps improve time to value for your organisation.
A company could want to analyse and compare two different reports or documents that have been generated in a different time period, or by different authors or by different software packages, it will be very difficult to extract the required information from these multiple documents and compare this information manually.
Below are a few use cases to consider.
Recruitment – Don’t compare whether certain terms occur in documents, but read the documents at the level of meaning. Ranking of the ideal candidates at the touch of a button.
- Overview of the best internal candidates for any skills
- More objective selection of candidates
- Acceleration of the selection process by up to 50%
- More time for interviews to ensure human fit
Automotive – In automotive an agenda that drives the need for Semantics AI Technology is the concept of requirements. Every time you, as a supplier, are given a requirement to consider they could be ones you have seen before and commented on, they could be new requirements or ones you have rejected. Whichever combination it is you could have hundreds of pages of requirements to consider. Over 45%-time savings have been achieved in this process using Semantics AI technology.
Insurance – Product managers and underwriters have to check large amounts of text on a regular basis with a high manual effort. Special broker wordings in particular contain many special agreements and are usually formulated quite individually. New incoming documents have to be checked in detail for risk whilst under a high time pressure, and in the case of new specifications by reinsurers, entire portfolios need to be analysed for specific contents and exclusions in order to avoid undesirable major losses.
And this is exactly where Semantics Technology can help: It understands text based on meaning, regardless of word choice – so you can find content you’re looking for, no matter how it’s worded – out of the box and without extensive training with your data.
Legal – Companies across industries can have a large legal team or you could be a medium to large legal company. In your company, many procedures on the same topic are processed. Despite always having similar presentations in the pleas and repetitive correspondence with companies, insurers and clients, these documents have to be read and analysed at great expense. Although content can be divided into certain categories, the problem is that similar issues are formulated in completely different ways, which makes it seem impossible to automate the processes. Imagine how much time you could use with semantics technology?
Conclusion
In conclusion, the use of semantic AI technology has expanded rapidly over the last few years. It is already being used by many businesses for various purposes and has helped them achieve better results with automation. The technology will continue to grow in popularity as more organisations realise how it can help them improve their business processes by enhancing their existing systems.
So, if you are an organisation that is analysing, comparing and extracting information from large volumes of data on a regular basis from multiple documents in multiple languages then Semantics AI Technology must be a part of Business Automation stack.