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Semantic Automation in 2024: The Future of Process Automation

Intelligent automation, which can be defined as a new generation of automation, combines RPA technology and AI capabilities. It gives businesses a competitive edge over traditional, rule-based RPA automation. This is why 40% of the companies invested into Intelligent automation in recent years.

However, because intelligent automation is a new and vaguely defined topic, such statistics should be taken with a grain of salt. More practitioners can get on the same page about intelligent automation if we align on the capabilities of its parts like semantic automation.

Software robots with semantic capabilities are an important part of AI-powered automation. We are likely to see more investments in this area in the coming years. Semantic automation is capable of assisting employees for both repetitive and semi-technical tasks. In this article, to guide CEOs & executives, we will explore what semantic automation is, its importance, and its use cases.

What is semantic automation?

Semantic analysis is the process of inferring meaning and intent from text. Semantic automation integrates AI-powered semantic analysis into automation solutions such as RPA and intelligent automation. This enables software bots (also called digital workers) to better understand the context of the processes in which they are used.

Why is semantic automation important now?

Business adoption of traditional automation solutions such as RPA goes hand-in-hand with cognitive and intelligent automation technologies: More than a quarter of organizations implementing and scaling RPA are also implementing cognitive automation.

This is because the range of processes that can be automated with RPA alone is limited to simple and repetitive processes. On the other hand, enterprise hyperautomation requires automation technologies to have a higher level of understanding of processes, interfaces, documents, etc. in order to automate as many business processes as possible.

Semantic automation gives software robots a level of understanding that allows them to imitate human language understanding and information processing. This allows bots to understand:

  • The differences between different types of unstructured documents rather than just extracting information from predetermined fields,
  • What is present on a user interface so that they can work with different types of applications with inconsistent user interfaces,
  • Domain-specific language and terms.

What are some use cases for semantic automation?

Document processing

Extracting data from documents is challenging because there are many types of documents such as invoices, reports etc. And even the same type of documents comes in a variety of formats. Problems such as skewed images, blurred text, different handwriting, or color differences further complicate the process.

With a semantic understanding, bots can read and analyze unstructured information from documents with different formats or layouts and increase the accuracy of data extraction.

Chatbots

It is difficult for computers to have human-like conversations. This is because natural languages can be very irregular and require intuition that comes from the lived experience of humans. When you add the irregularities of informal languages such as typos, missing punctuation, abbreviations, etc., it is not surprising that chatbots often fail to understand what the user actually wants them to do

Bots that have semantic understanding and knowledge of domain-specific language can understand the context of the conversations and use common sense and reasoning to provide answers to queries.

Sentiment Analysis

Semantic Automation improves RPA bots’ capacity for sentiment analysis by assisting them in thinking more as humans do. Thus, before replying to humans, bots might analyze their emotions. Consider an insured who had recently been involved in a terrible auto accident. It is crucial for the chatbot to comprehend the emotional context that the insured experience in order to optimize claims processing.

Additionally, since these tools can more accurately forecast human emotions, they can help businesses with their marketing and sales efforts.

Semantic automation is an emerging technology, and a better understanding of human language will open up more possibilities and business applications in the future.

If you want to start your automation journey and don’t know where to start, feel free to contact us:

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Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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