The Business Process Outsourcing (BPO) industry, also called Business Process Management (BPM), is expected to reach $336 billion by 2025 according to Everest Group. The industry took off in the 2000s amid accelerating globalization by offering internet-driven cost of labor arbitrage. During this phase BPO was focused on data entry tasks for back-office functions in finance and human resources. BPO companies primarily competed on price, and their primary value proposition was cost savings.
Once the labor was moved to the cheapest corner of the world, the industry started moving further upstream. In the 2010s BPO organizations began leveraging Robotic Process Automation (RPA) and other automation technologies to drive process efficiencies. These companies developed deep expertise in common shared services processes, such as Procure to Pay (P2P), Order to Cash (O2C), Record to Report (R2R), and Hire to Retire (H2H).
Some companies started going deeper into industry-specific processes in insurance, banking, financial services, healthcare, and pharmaceuticals. They also started moving into front office processes impacting customer experience (CX). During this phase, BPO vendors grew into becoming process experts and close business partners of their clients.
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Once end-to-end processes are streamlined, the next level of value can be realized by leveraging information generated in various parts of enterprise data flows—this includes vendor, customer, and employee data—to help businesses gain a competitive advantage. In this post-pandemic phase, BPO organizations are beginning to evolve from Business Process Outsourcing to “Business Data Services.” Data is the new oil for business transformation, and BPO companies are evolving as a result.
You’ve likely heard it (perhaps too many times) before, but 80-90% of enterprise data is unstructured. To automate processes such as P2P and O2C, BPO companies have started processing documents including invoices, POs, and contracts. Industry-specific processes such as customer onboarding, loan and insurance underwriting, and claims processing involve a number of additional documents such as ID cards, passports, bank statements, pay stubs, deeds, claim forms, and more.
BPO companies have been automating document processing for years, initially using OCR and Data Capture solutions, and now using Intelligent Document Processing (IDP). IDP solutions are a huge improvement over OCRs from the past. However, most IDP solutions still struggle to process the long tail of semi-structured documents such as invoices, POs, and bills of lading (BoL). They also struggle to process more complex and unstructured documents involved in shipping and logistics, underwriting, claims processing, and contracts. This results in continued investment in a large number of data entry personnel and high processing costs. With the slowing global economy, there is greater pressure on BPOs to bring these costs down.
One of the reasons why many IDP solutions struggle to process different types of documents is their fixed or closed approach. Some of the solutions use proprietary AI models that can quickly become outdated with rapid advancement in cloud-based AI models from Google, Microsoft, and Amazon, as well as open-source AI models. Others are overdependent on one or two OCRs and/or document AI solutions. Although these products can perform well for some use cases, they often leave BPO companies searching for other solutions for help them with more complex use cases.
Super.AI takes a different approach to solving unstructured data processing challenges, making it a good fit for BPO companies that encounter a wide variety of document and other unstructured data processing use cases. There are four key reasons why super.AI can process even the most complex documents with a guarantee to help BPOs achieve the next level of efficiencies:
Super.AI apps are created in a scripting language.The app “Data Program” allows users to quickly create new AI apps that break the processing of a complex document into smaller tasks and use the best available AI for each. Moreover, super.AI uses three levels of AI to achieve the best results.
For example, a bill of lading with tables, handwritten notes, signatures, and approval stamps can be broken into sections, and a different AI model is used for each in the first level of processing. Next, the app uses a semantic layer to extract key/value pairs from data. Finally, the app uses a custom AI layer trained for BPO’s customer-specific data to achieve the highest level of accuracy.
Super.AI is the only IDP vendor with ready-to-deploy crowd-sourced data labeling/processing workers. These workers are carefully curated using sophisticated onboarding tasks, measured continuously using 150+ quality assurance measures, and kept engaged and performing at the highest level using in-app gamification. These crowd workers can provide BPO operators with surge capacity that can be ramped up and down to fulfill sudden changes in demand.
Super.AI uniquely allows BPOs to make trade-offs between quality, cost, and speed and deliver AI apps that guarantee the selected outcomes. Super.AI intelligently combines output from AI, humans, and software eliminating the need to tinker with field-level confidence levels to ensure Service Level Agreements (SLAs) compliance.
Super.AI was designed from day one to process any data type—documents, emails, images, videos, and audio—allowing BPO operators to move up the value chain by automating increasingly complex use cases. BPO operators have come a long way from the early days when they were viewed as drivers of cost-saving via labor arbitrage to becoming Business Data Service providers and trusted partners of their customers. Next-generation IDP vendors such as super.AI can help BPOs accelerate this transition and move up the value chain of service offered to their customers.
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