Business process outsourcers (BPOs) that provide services focused on document processing have new competition, and if they are not prepared to respond, their viability—even in the short term—may be at  stake.

The New Competition

So who are these new competitors? They are modern-era companies offering transactional data services in a new way and utilize back-office services to lower both costs and turnaround times.

They are companies like Crowdflower, CloudFactory, and Amazon Mechanical Turk (AMT) that provide “micro-task” services that are likely the future of transactional data services. Survey after survey shows that clients of BPOs prefer transactional, modern and automated services that can be flexible and easy to purchase. These micro-task providers can take-on projects and turn around the data in a faster, more efficient way than most of the large-scale incumbents.

Invoice Processing in Your CRM Workflow

Want to add invoice data extraction to your customer relationship management (CRM) workflow? Simple: go to the provider’s website, create an account and then use an integration from a provider like Appiant and you’re done. While large enterprises might be happy with large-scale outsourcing contracts, there are millions of smaller and mid-sized companies that would prefer a more “on-demand” approach. If typical adoption cycles ensue, as expected, more large enterprises will follow suit simply as a way to be more flexible themselves while enjoying lower cost.

As a result, these types of ad hoc transactional services will grow to compete with legacy data services provided from large BPOs such as Cognizant, etc.

Tightly-coupled Processing Ecosystems

In addition to the ease of use that these new providers can offer, they also support a more tightly-coupled processing ecosystem by including the provision of this work in a more-dynamic way through a Web Service. The result is an extended platform ecosystem that enables companies to have more visibility into these outsourced processes while automating the intersection between the on-premise systems and those of the service provider.

Human-Machine Collaboration

These new entrants are also technology savvy, and have approached human and machine tasks as a collaboration. These companies are even early adopters of  machine learning to automate much of what is currently accomplished manually, not only as a way to provide more-efficient services, but to improve the overall accuracy of the data and to expand their service offerings.

The end result is a transactional service offering that is more accurate and more adaptive than what most companies realize today with traditional provider relationships.

 

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