For many organizations, making the transition from traditional Robotic Process Automation (step-wise, screen navigation, repetitive tasks, back-office processes, un-attended etc.) to Intelligent Automation (automated decision making, search and validation, human interaction, Context Learning and response) is a daunting challenge.
This challenge is rooted in a lack of understanding what exactly Intelligent Automation (IA) is, when and where to apply it, and the inability to assess the tremendous value as a result of the proper implementation.
Companies have made great progress in implementing Robotic Process Automation (RPA) because these use cases are easy to identify as they are located at the business function and people process level and are easier to identify, document and then automate. Unfortunately, these use cases are only scratching the surface of the value and wide-ranging capabilities of Intelligent Automation.
Discovery: Where Do You Look For Opportunities For IA?
One suggestion is having the Business Process SME’s review existing unstructured data, including document management systems, File Transfer systems, (EDI, FTP etc.) Email, Web portals, etc.
Ask yourself these questions:
- How do we currently receive business/client unstructured data for further processing?
- How do we get this data into a structured digital format?
- Where is the data located within our business process workflow?
- What is the current Operational cost to maintain these document mgmt. systems?
- How do we extract and move this data into our applications, databases, and front-line ERP systems for further processing?
- How reliable and accurate is the data extraction?
- What is the cost impact to downstream systems that require this data to be accurate?
If you go through this exercise you will find that a large percentage of your operational costs reside in the manual human steps of transposing data from documents, emails, invoices, forms, websites etc. into your ERP systems. Most of these activities are human based, or handled by complex, costly and rigid systems integration solutions – making it easy to find plenty of high value IA opportunities to reduce costs and increase accuracy, resulting in a major impact on your bottom line and client satisfaction.
Many of the current unstructured document ingestion processes are either manual (high cost for staffing) or currently handled via very complex systems integrations, requiring multiple IT competencies, technologies, and expensive IT staff (costly to maintain, change and update).
Some companies try to reduce the human cost by transferring the business process offshore. The staffing is less costly initially, however the business loses as they no longer have visibility into their business processes and the accuracy of their data, which again can introduce losses in revenue and the ability to make change in an agile customer product focused way.
Identification And Evaluation
Engage your internal IT organization and proactively identify and document how unstructured data is ingested into your applications and systems. Then weigh the current operational costs of maintaining these integration systems, and manual human data extraction and inputs.
By doing this, you will identify tremendous opportunities to save on operational costs by migrating your current data ingestion systems into a more holistic, business-controlled data ingestion process through Intelligent Automation platforms. The IA platform will interact with your unstructured data, and accurately identify and translate your data into your ERP systems.
Intelligent Automation not only can ETL (extract, transform and load) your data but it also has the capability to interact with your human workforce, specifically SME’s, in real-time to ask questions about the validity of data being extracted, and also take feedback from business SME’s in real-time, which reduces data inaccuracies. Intelligent Automation can continuously learn from each interaction with humans, collect the right decision to make about your data, and store it correctly so that the next time AI encounters a question about data it can look to its knowledge base to make the right decision automatically.
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