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Robotic Process Automation automates high-volume, repetitive tasks by emulating human-machine interactions. It has captivated the business masses, thereby pushing the RPA market value to US$ 2.65 bn in 2021. Yet surprisingly, some reports state that many companies spent less on RPA tools in 2022.
RPA has undoubtedly paved the way for automation and digital transformation initiatives, enabling new-age technology across on-premise hardware footprints. However, the bot-based architecture hinders scaling end-to-end enterprise process automation. And for obvious reasons, the latter is superseding RPA:
It is unscalable: Enterprises struggle to scale automation using only RPA bots. However, when too many bots are deployed, they become fragile and hinder automation scalability.
Maintenance is challenging: It goes without saying any ‘maintenance-hungry’ tool impedes deployment and scalability. Because of the fragile nature of RPA bots, maintenance becomes a hassle for the IT team.
Other tech stacks required: In order to bring every process under the automation umbrella, enterprises need added capabilities beyond RPA’s limits.
It can trigger governance issues: IT-driven governance and best security practices become harder to implement when too many bots are deployed organization-wide.
RPA can turn into economic debt: RPA bots will require another army of bot managers for maintenance. RPA bots are deemed a savior for human workforces, relieving them of cumbersome tasks. But the reality is different. Here, humans are needed to take the load off of the bots. Definitely not an economical approach.
Presently, the enterprise automation industry is dominated by standalone solutions, namely:
However, industry heavyweights are more interested in defining process efficiencies economically by acquiring multiple automation solutions sourced from a single platform. Therefore, to enable process efficiency and resiliency, once siloed collection of technologies is clubbed together to create an automation stack for a uniform end-to-end application.
Robotic Process Automation faltered when enterprises attempted to expand bots’ capabilities in need to scale automation. And this very need compelled enterprise software giants to look beyond RPA for a more comprehensive one-stop-platform solution. As a result, enterprise process automation emerged as the missing piece of the technology puzzle. However, it is still at the nascent stage but evolving nevertheless. It sounds like the dawn of a new era in automation software, promising big opportunities for savvy investors and end-to-end platform players.
Enterprise automation refers to the full-service integration of automation, streamlining complicated processes and driving operational efficiencies. It enables a synergy of people, processes, and systems to augment human intelligence, foster seamless customer journeys, and scale unlimited possibilities for organizations.
Enterprise intelligent automation is born out of the need for an end-to-end automation platform bringing people, processes, and data together.
Lack of IT readiness: Organizations rely on internal IT support to implement automation solutions. But only a handful are equipped with the right technology, infrastructure, and security. And most of them lack appropriate standards controlled by an intelligent automation center of excellence. The CoE ensures that only a select group of people can access data to avoid any plausible risk to the organization. Addressing this shortcoming should be a top priority for businesses.
The skill crisis: Skilled workers with sound knowledge of automation, AI, and other evolving technologies are hard to come by. And in the absence of this, implementing, maintaining, and scaling automation capabilities will remain a distant dream. Expecting every recruit to understand technical jargon elucidates the short-sightedness of recruiters. But redirecting the hiring focus only to tech experts will only supersede the allocated budget. Instead, companies can re-utilize their existing resources by upscaling their knowledge through proper training.
Process fragmentation: Fragmented processes are significant barriers in the enterprise automation journey. Process fragmentation occurs when critical business processes are not managed in a unified workflow. These standalone processes increase the risk of error and delay the seamless transition. Hence, care should be taken first to break down those existing siloes and bring them on a unified platform.
Lack of clear vision: Having a clear vision and strategy is a prerequisite for Intelligent Automation implementation at scale. And some of the crucial decisions owners have to take should center around key business objectives for automation – maximize business performance and returns on investments. So, enterprises should make well-informed choices about how and where automation might be applied and the level of change the organization is willing to undertake.
Apprehensive mindset: None of the above-mentioned remedies will work if the top-notch decision-makers in a company are apprehensive about trusting and adopting automation. This apprehensiveness is the byproduct of the constant fear that worries most decision-makers – automation replacing humans altogether. Therefore, educating people about the various benefits of automation followed by a constant assurance of scaling unlimited possibilities through a human-machine synchronized approach.
According to a survey on workforce automation, over 90% of respondents2 saw immense value in their enterprise intelligent automation pursuit. And organizations that have achieved scale started their journey with a clear vision and strategy. Many such organizations have approached automation as an enterprise-wide challenge and started building an arsenal of new tech capabilities required to extend the scope of RPA bots. Their objectives are similar – augmenting their workforces and broadening the scope of work.
The additional technologies and capabilities needed to continue stretching the use case and doing more strategic E-E automation could be summed up in the following points:
When integrating smart automation resources in a singular platform, the size and complexity of the use cases grow two to ten times what RPA is capable of alone. Intelligent enterprise automation comprises the following key components:
Choosing the proper process to automate at a detailed level is probably the most significant challenge enterprises face. To successfully automate, a complete understanding of the work and how it is executed is essential to route the work for automation properly. And that involves understanding process complexities and business rules. By leveraging process mining and task mining tools, highly detailed data about what happened is collected and fed to machine learning models.
When humans and machines are connected, enterprise automation becomes more capable, a fact that debunks the myth of automation and AI replacing humans. By bringing humans in the loop or citizen developers, automation doesn’t need to stop and restart. However, in the absence of tech experts, low code/no code technologies are employed to enable rapid design and deployment of human-bot interactions. These little applications optimize digital information, predict what additional information might be needed or valuable, and receive the input back.
Taking the friction out of processes and delivering fast, accurate fulfillment requires strategic process transformation. And to reach that point, enterprises need data in their structured or semi-structured form. Because technologies like AI, ML, and automation cannot work in the absence of data. By using different tech capabilities, data is reduced to its distilled essence and fed to the system orchestrator. This determines the correct workflow that should follow the initial interaction.
These different components should be connected in a meaningful way to make the automation build simple and effective. Enterprise automation is increasingly a part of its transformation or digitization programs.
Enterprise automation maturity model presents a comprehensive view of how business automation has evolved over the years delivering greater outcomes, the present technologies and strategies employed today, and where automation is heading.
The different stages of the model include:
Currently, most enterprises fall into one of the first three stages, while a handful is striving to reach stage four. Enterprise process automation is a continuous journey that is still evolving alongside other tech capabilities. And it will take quite some time before enterprises enter the final two stages of the maturity model.
Enterprise automation has already exceeded its expectations, as responded by 31% of companies surveyed in a report on the imperatives for automation success. Hence, the following year will likely be significant for intelligent automation.
Enterprise automation offers a full spectrum of intelligent solutions to address process inefficiencies at the granular level. As a result, it is the best bet for businesses to build a fully functional autonomous enterprise where every component works seamlessly towards a greater goal.
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