By now, we’re all familiar with the promises of automation: efficiency gains, relief from talent shortages, greater efficiency, and increased margins. Sounds like a no-brainer, but if that were true, there wouldn’t still be so many organizations that have not automated all or any of their processes. But why the ongoing hesitation?

Let’s take a look at three common concerns that hold decision-makers back from implementing business process automation – and whether these concerns are still valid today.

Automation Barrier #1: Business Disruption

Will implementing automation cause delays that could impact your ability to meet SLAs? What happens when legacy software won’t play nice with your new automation solution? Or when you have to adjust your existing processes to accommodate the solution? Delays that arise from mistakes are problem enough; delays that arise from a planned implementation are unacceptable for service providers.

Automation Barrier #2: Lackluster ROI

One of automation’s biggest promises is cost reduction, which makes it all the more ironic that unforeseen cost explosion is one of the most common fears holding organizations back from automating. And there’s a good reason for that fear. The ROI timeline for automation takes a hit any time the flow is interrupted by any of the examples given for Barrier #1. A recent survey from Economist Impact found that 50% of businesses feel they aren’t achieving significant value from their technology investments.

The reality of designing an automation solution for an organization, then quickly deploying it and leading the needed organizational change management – all while maintaining data privacy and top-notch customer service can be a difficult balancing act. If any part of this balancing act stalls, ROI quickly evaporates, and that’s why investment proposals for automation draw understandable scrutiny.

Automation Barrier #3: Data Privacy

Nothing can halt a project faster than a potential data leak or security breach. Any loss of control or transparency is paired with the genuine fear of reputational harm or a hit to customer retention.

Picture, for example, a healthcare file. One file can carry the entirety of someone’s personal data; if this complete data set is then visible to one individual, the risk for potential misuse is high. Or consider the potential for downstream mayhem if customer service reps respond to a claim incorrectly on account of inaccurate system data.

Even traditional automation solutions with higher-than-average 95% accuracy rates can introduce huge risks when cross-referencing a record in error, exposing additional personal data.

Are these automation fears valid today?

The truth of the matter is that these fears are all still valid to some degree. Automation solutions like intelligent document processing have improved over the years, but important gaps remain around continuity of service, disaster preparedness, skilled resources, and data security to name a few. Until now, there hasn’t been a viable solution for covering these gaps in automation.

Collective intelligence offers a solution. By combining human and artificial intelligence, we’re able to optimize the solution to improve the way people interact with the data entry tasks previously considered impossible to automate. Let’s revisit the three automation fears with a collective intelligence approach:

Business disruption is the biggest fear when introducing new ways of working. With an on-demand option to support document and data processing, organizations need not fear those dreaded automation gaps. Collective intelligence can and should be tapped whenever it doesn’t make commercial sense to try to tweak an automation solution to cover difficult to automate tasks.

As far as ROI is concerned, transactional access to any number of skilled workers means processing volume spikes no longer pose a threat to the bottom line, and with collective intelligence, you know up front exactly what scaling up or down will cost. Not to mention lessening the financial impact of bad data feeding and training your algorithms.

With access to 2.3 million crowd contributors who can simultaneously label data at an accuracy rate of 99%, you can build a solid and accurate foundation for AI and machine learning at unprecedented volumes and speeds.

Lastly, while crowdsourcing in name alone may not imply high levels of data security, collective intelligence solutions like ScaleHub can offer foolproof data privacy with techniques such as snippeting and scrambling sensitive data. When a particular process requires that a document be viewed in its entirety, ScaleHub can be easily configured to access a select group of crowd contributors.

A webinar on this topic can be viewed here.

Now based in Australia, Torsten Malchow is Chief Revenue Officer at ScaleHub. Email him at torsten.malchow@scalehub.com