Process automation was born out of the need to solve specific challenges in certain parts of a large operation. Robotic Process Automation (RPA) rapidly became a household name at the turn of the millennium. Businesses across industries started deploying RPA bots to automate recurring and repetitive tasks to achieve process efficiency, reduce cost, and eliminate manual efforts.
Even though RPA matured with time and its adoption spread far and wide, this technology had limited scope for expansion. Enterprises are now wrestling with how to be more strategic with automation. The ability to automate more of the end-to-end processes is currently a top priority. Nearly 50% of respondents in a survey see the value of an end-to-end automation platform as a meaningful way to achieve process optimization.
Process optimization enables businesses to optimally utilize existing resources while eliminating the cost and time-intensive factors and errors left behind with too much human involvement in each process. In order for enterprises to optimize and fast-track each business process, RPA tools are employed in recurring tasks prone to human errors.
Initially, tactical automation for optimizing business processes was restricted to finite use cases, with a maximum of 100 RPA bots deployed. Besides the inherent shortcomings of RPA bots, owners faced other bottlenecks when scaling automation to other key business use cases. The use cases get too hard to automate at important points in the process since most of these points involve interactions with unstructured data or documents or human-human interactions; the final configuration of the automation is built to stop and start again, with workflow passing back and forth to a human operator(s).
And there are other internal barriers to scaling automation as well, including:
Lack of IT readiness: Nearly 37% of respondents in a survey agreed that the absence of IT readiness is one of the top-most challenges faced while scaling automation to other key processes. The existing IT infrastructure is not adept at handling the various complexities arising out of seamless automation implementation.
Lack of skills: 31% of respondents cited the lack of skills and understanding as the second-most barrier to implementing end-to-end business process automation.
Identifying the right processes to automate: Manual efforts fail to discover the right process candidates for automation. Manual process mapping and discovery can omit granular variations and discrepancies existing in each task; hence, the outcome is not good enough to ensure the success of automation implementation. Nearly 30% of respondents agreed that this was a major roadblock.
Resistance to change: Often, the existing human workforce is not open to change and considers automation a direct threat to their position in the organization. This mindset translates into their reluctance to share valuable inputs during process discovery and eventually resist the automation implementation efforts. 22% of respondents agree this was another barrier to building an
end-to-end automation platform.
Besides the aforementioned barriers, others are added to the list, such as implementation cost, lack of clear vision, lack of holistic approach to integrating RPA into broader transformation, and unrealistic vendor promises.
Robotic process automation can be considered a harbinger of major changes gradually emerging in the technology space. And, despite its shortcomings and obvious challenges, other tech-based capabilities are coming together with RPA to ensure a far-reaching automation implementation. Assembling the right capabilities with the best expertise has broadened the space from RPA to what is often called Intelligent Automation (IA), also sometimes called connected automation or hyper-automation.
Even though technology providers have yet to standardize the flavors of additional capabilities being integrated into their offerings, a fair mix of the same is already available.
The available automation platforms offer the following capabilities to power end-to-end process automation:
Low/no-code capability: Nearly 21% of the respondents in a survey agreed that low code/no-code capability is one of the prime factors to consider in the available automation platforms. LCNC tools enable rapid design and deployment of human-RPA bots interactions. It no longer requires a custom web app each time a human is needed in the loop.
Process orchestrator: It is considered the heart of the automation platform sequencing work by applying business rules or engaging predictive ML models that move the work down the correct path. In order for the business rules or data to reveal the rules that define how work is getting done, insights need to be embedded into the design process and the process orchestrator. And that can be achieved by delving deeper into the wiring of the orchestrator. According to the same survey, nearly 18% of respondents cited this element as one of the key capabilities powering end-to-end process automation.
Complex document processing: A deeper understanding of how processes get performed has helped illuminate IA’s solvable challenges. And to do that, full document processing with natural language understanding is already available, taking the shape of another key enabler, as cited by 3% of the respondents from the same survey.
Other capabilities include advanced automation analytics, automation script tools, machine learning decision models, and customizable web-based dashboards.
In a nutshell, an end-to-end automation platform connecting people, processes, and data is the basic foundation for intelligent automation. With the dawning of this new realization, enterprises are working towards finding an automated solution that can seamlessly combine multiple capabilities to extend the use-case and value of comprehensive automation solutions.