How to Transform Your Business with Intelligent Automation
Authors: David Steiger and Andrea Frauchiger
Imagine robots that follow rules, interpret documents, and predict outcomes. Intelligent automation makes this a reality, expanding the scope of automation in your organisation.
In brief:
Intelligent automation integrates robotic process automation (RPA) and artificial intelligence (AI) to revolutionise document processing.
AI enables RPA robots to understand complex documents, classify unstructured content, and extract data from diverse layouts and formats.
Our Data Classifier solution leverages advanced AI technologies to extract essential data from unstructured documents.
Intelligent automation combines RPA and AI to expand automation possibilities. While RPA provides fast, rule-based automation, it struggles with unstructured data like scanned documents and emails, and this is where AI comes in. Our solution, Data Classifier, integrates powerful AI to enhance automation. It understands complex documents, extracts relevant information, and integrates seamlessly with RPA robots, automating manual tasks and eliminating errors.
RPA: A proven technology for quick automation
RPA allows straightforward automation of repetitive tasks using predefined rules. It is non-invasive, fast to implement, and enables quick time-to-market. Traditional RPA excels at handling structured data and automating routine tasks but relies on rigid rules and lacks cognitive skills for processing unstructured or ambiguous information, limiting the scope of automation.
As business processes become more complex, they hit the limits of traditional RPA, requiring more human judgment and reducing the return on investment (ROI) for RPA automation. The graph below illustrates that increasing process complexity diminishes the RPA automation capability and ROI, and there comes a point where human-in-the-loop assistance is needed.
Figure 1: Increased cognitive input demands affect the ROI of traditional RPA
Adding AI to RPA for deeper document understanding
Will integrating AI capabilities take RPA to the next level? The answer is yes. It allows robots to interpret unstructured content, classify documents, and make predictions.
How do RPA and AI work together?
Following a training phase, AI supports robots to handle document processing tasks like invoice processing. AI allows robots to understand and extract data from digitised or scanned invoices in varying formats and layouts, including unstructured data, such as emails containing complaints or general enquiries. Without AI, these complex documents would require extensive manual work or rule-based models.
With IDC predicting that 80% of global data will be unstructured by 2025, the challenge lies in efficiently managing and deriving insights from this vast and diverse data landscape. Addressing this challenge, AI offers a significant advantage through its prediction-based model that can continuously improve over time, setting it apart from rule-based RPA, which remains static.
For invoice processing, this dynamic nature of AI becomes vital. As the system undergoes a training phase, it continually improves its ability to recognise and extract data from new supplier invoices with each processed example. The adaptable models not only enhance automation rates but also ensure accuracy over the long term by effectively handling future variations in layouts and content.
Figure 2: AI enables RPA to understand unstructured data, expanding automation possibilities
Does AI deliver a high return on investment?
Integrating AI does require an upfront investment for development and maintenance, but it pays dividends in the long run by reducing costs and increasing automation rates.
For example, in client onboarding processes, transfer instruction documents come in varying layouts from different custodian banks. While the content of these instructions is standardised (e.g., security name, ISIN, quantity, and settlement instructions), the layout is not. This information is sent to the recipient bank via email, where it needs manual data entry (or robotic automation) into the core banking platform.
Without AI, an extensive set of rules is necessary to handle each layout, requiring high development and maintenance effort. With AI, on the other hand, a single prediction-based model can extract data from different layouts accurately. The AI approach is faster to build, more resilient to layout changes over time, and lowers overall automation costs.
By augmenting RPA with AI capabilities, organisations can achieve higher automation rates and savings across diverse document processes. The ROI makes AI a very compelling investment for intelligent automation.
Figure 3: AI has upfront costs but reduces long-term automation costs as business volume grows
Realise the full potential of intelligent automation
RPA needs to enhance its cognitive abilities to unlock the full benefits of automation. RPA with AI is more resilient and requires less maintenance over time, thanks to the learning capabilities of AI models. It expands the automation spectrum to documents and workflows previously dependent on manual work.
Solutions with advanced AI, such as Data Classifier, enable unstructured data to be seamlessly processed and understood. Our solution integrates into existing workflows, employing industry-leading technologies (e.g., optical character recognition, natural language processing, and machine learning) while adhering to secure data handling practices and protocols.
Explore how our AI-powered solution revolutionises data handling. Contact our experts to streamline your workflows and transform your business!
Originally published on our Synpulse website, we have updated this article with fresh insights. For a comprehensive overview, you can refer to the original article.