As the amount of data companies need to process in day-to-day operations becomes larger and larger, business processes get more sophisticated and intelligent automation needs gain urgency. There are many competencies that companies have to possess to keep pace with these requirements. Digital transformation is the overall framework which can be defined as the process of using digital resources to improve existing business models and culture or creating new ones.

Intelligent Automation as a Part of Digital Transformation

One of the main goals of digital transformation of an organization is to automate operations and processes. This not only about getting faster or more profitable, but also getting able to make better decisions and becoming more strategic.

Overall, we conclude that digital transformation is a more complex type of technology-enabled business transformation, which needs to address the strategic roles of new digital technologies and capabilities for successful digital innovation in the digital world (Yoo et al. 2010). We define it as the process through which companies converge multiple new digital technologies, enhanced with ubiquitous connectivity, with the intention of reaching superior performance and sustained competitive advantage, by transforming multiple business dimensions, including the business model, the customer experience (comprising digitally enabled products and services) and operations (comprising processes and decision-making), and simultaneously impacting people (including skills talent and culture) and networks (including the entire value system).

Digital Business Transformation and Strategy: What Do We Know So Far? by Mariam H. Ismail, Mohamed Khater, Mohamed Zaki

With this perspective in mind, we can understand why intelligent automation is such an important stage of digital transformation. It is so important because it is impossible to digitize processes and operations without utilizing intelligent automation tools. Companies need to free their employees from manual work as much as possible. So that employees can become more productive and focus on high value tasks instead of repetitive, time-consuming jobs.

Handling Data with Data Extraction

As we mentioned above, the amount of data companies need to handle is getting larger. It is not easy to process such a big amount of data, especially when it is generated through many types of documents. When data is arrived in various formats and mostly unstructured, we need a solution to extract, interpret and validate data. Data extraction tools are built to meet this demand and OCR technology has been enriched by AI assets.

Since it is possible to run intelligent automation only after proper data extraction from documents, it is critical to choose a well-developed data extraction solution.

What are the Stages of Intelligent Automation?

Although requirements and KPIs for intelligent automation differ from industry to industry, there are some common principles and methodologies to be used as a guideline.

  • Data Extraction: This is the first step towards successful automation. OCR and ICR engines working through AI based software, make it possible to extract data in various formats, from any type of document.
  • Data Processing: Once data is extracted and digitized, second step is processing it. Machine learning technologies come in help at this stage. Data validation and classifying is ensured with high accuracy.
  • Data Distribution & Automation: As a final step, structured and validated data should be distributed to relevant authorities and core systems. Integration to ERP or DMS software is undertaken at this stage and final automation tasks are completed.