Companies who did large implementations have all noticed their costs and timelines doubled than what is budgeted initially. Yet, PwC’s 2017 effectiveness benchmark report found that users spend half their time focused on mundane, repetitive tasks of gathering data from various systems. This led to many of the systems reaching the point of diminishing returns. This led to many of the systems reaching the point of diminishing returns. This led to many of the systems reaching the point of diminishing returns. On the positive side, the speed, scale and cost of automation are evolving better with help of robotic process automation, natural language processing, machine learning systems that offer companies new opportunities to improve process performance and realize significant cost savings.

For Labeling processes, some of these technologies can be implemented in short sprints, focused on specific sub-processes, with manageable costs. This approach of “small & smart” automation leads to “fast” implementation of flexible and adaptable technologies that fill the gaps left by your current or legacy enterprise tools or document systems, thereby enabling much higher productivity for labeling teams.

Small automation can improve the productivity of individual labeling processes by 80 to 100 percent and overall labeling functions by 30 percent or more.

Small automation does not replace large enterprise systems or your future big automation initiatives. These are easier to implement and much less expensive. They can be applied to individual processes or tasks without having to go through complex cross-functional discussions (negotiations) and coordination in large projects. Icing on the cake is that small automation doesn’t depend on standardization (unlike your large projects) leading to higher flexibility and adaptability.

Applying small automation is a new way of working, and company leaders will need to ensure that both they and their teams have the right knowledge to be successful.

Some of the areas of “small” automation in Labeling include auto-impact determining, reducing documents using NLP, risks identification through data or triggers, translation automation, compliance checks minimization, QC reduction, and few more areas.

Please reach out to us to discuss how we are helping customers achieve these automation goals using our patent-pending LABELai which is a cloud-based modular focused labeling technology