The COVID-19 pandemic has expedited digital adoption across the globe. With an augmented need of digital workforce, organizations need to define their digital strategy to scale-up automation with tactical and strategic objectives. While scaling-up does not imply enhancing the digital workforce alone; it also entails a defined strategy to augment business operations with various facets of enterprise transformation such as simplification, automation, intelligent automation, and immersive experience. Several industries have adopted a combination of rule-defined bot scripts (RPA) and advanced technologies like artificial intelligence to supplement productivity gains.
Impact of intelligent automation on document extraction
In this blog, we talk about document extraction. Organizations deal with a lot of unstructured/ semi-structured documents on a daily basis. The staff assigned to work on business operations encounter a plethora of documentation as part of medical claims, invoices, purchase orders, goods receipts, evidence reports, and many other types of documents depending on the industry and organization. A significant amount of human time and effort is spent in going through each of these documents and segregating them, in order to queue them with the right category and classification. Further to this, another piece of the puzzle is to extract the precise information and ingesting it to the respective downstream applications to achieve data and process sanity.
While OCR has been a standard practice of extracting data from business documents; variations in templates have always remained a key constraint of this technology. For instance, there are tons of scanned invoices received by the business from vendors, but only 30% are in the same format and structure. This means that the data to be extracted resides at a similar document location with limited or no change in its definition. For all such invoices, rules are configurable in OCR and effective productivity and throughput is achieved. However, for high variability invoice scenarios; either it raises a lot of exceptions or the output is not that accurate.
Intelligent automation can help synthesize large volumes of information and automate processes and operational workflows in an organization. Research indicates that nearly 80% of companies will adopt intelligent automation by 2025. Intelligent automation is an evolved answer to such scenarios to drive in enhanced productivity and seamless extraction of key data points by recognizing labels and values, and further applying review/ feedback learning to augment the efficiency. This aids in tackling variations and template complexities. Natural Language Processing (NLP) and machine learning further substantiate this process - enabling handling of customer sentiments, feedbacks, reviews etc. alongside template variations with significantly high confidence score.
Is RPA an intelligent automation?
The short answer is - no. RPA in its rudimentary form only mimics human action. It involves teaching a robot a script to follow hardcoded rules and logic. A bot is required to address all decisions and scenarios, and they are all pre-coded. Thus, prevails the question – How to make it intelligent and drive a solution that resonates decision making like humans, in the absence of a rule-based situation.
This is where the amalgamation of complementary technologies like artificial intelligence and smart RPA operations comes into play. RPA can work as a data orchestrator that triggers key actions basis a set input mechanism like downloading attachment from emails, broker/ customer portals and saving them at a secured location. This, further triggers artificial intelligence/ machine learning modules to extract key information from document sources.
Once the extraction is done, bot ingests the information to the respective downstream applications. While you might need custom scripting as part of setting up the platform for once, but this is still minimal.
While the solution is imperative to drive operational gains and productivity, deployment with effective compliance, security controls, cloud policies, and data governance is critical considering any use case might involve customer data. This is where the intersection of domain and technology drive through data security and controls, taking into account how these controls are unique to each organization and every business unit.
Organizations deal with customer data and with the ecosystem of process, people and technology, data flow has multiple handoffs and touchpoints across multiple business units and applications. Accordingly, it’s imperative for these digital platforms to establish requisite policies and compliance to encrypt data – both at rest and in-transit.
Further, onboarded platform partners/service providers should agree to comply and bound with your organization’s data governance policies and protocols. Equally critical is the fact that data is accessed by right stakeholders with appropriate segregation of duties.