Robotic Process Automation (RPA) to Hyperautomation

Global robotic process automation (RPA) software revenue is projected to reach nearly $2 billion in 2021, an increase of 19.5% from 2020, according to the latest forecast from Gartner, Inc. RPA market is expected to grow at double-digit rates through 2024.

The current pandemic has forced companies to look at automation to improve overall operational efficiency while reducing the cost of manpower. Increased demand for RPA stems from safeguarding businesses from future global catastrophic effects like COVID-19, where automation can continue to ensure fast, intelligent, and error-free business solutions.

Gartner predicts that 90% of large organizations globally will have adopted RPA in some form by 2022 as they look to digitally empower critical business processes through resilience and scalability, while recalibrating human labour and manual effort.

Robotic Process Automation (RPA) technology is at the peak of its hype cycle and its adoption across enterprises has uncovered few challenges that need to overcome:

  1. Scaling up RPA across their organizations

    Forrester found that only 29% of organizations are satisfied with RPA utilization rates, with 62% of respondents reporting that their bots are working their prescribed tasks fewer than three hours per day.

  2. RPA Orchestration

    The more bots that organizations build to automate tasks, the more they bump into the complexities of managing these bots. RPA benefits are often negated due to the need for managing different process flows.

    An RPA solution needs to be resilient enough to withstand the changes to the applications you're automating. "Otherwise, you’ll have a flow management problem keeping pace with that." — Archie Roboostoff

  3. Broaden automation lens

    As organizations mature in their automation approaches, they're increasingly taking a big-picture view of how to solve business problems, rather than simply identifying tasks or processes to target with RPA bots

    "A lot of times when you solve those task problems, you're uncovering a lot of other much more systemic problems with the process itself." — Dave Easter

  4. Governance

    The broadening vision and a drive to scale RPA in the enterprise will result in organizations taking a more mature approach to RPA and automation governance. Since RPA is mostly driven by business, there's been a lack of control and governance over the use of more standard tools for shared services across the organization.

    LeClair sees more companies taking a "strike team" approach to automation orchestration as a way to ramp up governance capabilities while still providing flexibility and alignment with the business. These teams will include stakeholders from across the business who will formalize pipelines where the business can come up with automation ideas and run those through the strike team, which will then run process discovery and review those ideas and put in place guardrails and guidelines that can ensure that ideas have been validated, vetted, are viable projects, and are well-designed.

Turning process discovery into a science will be crucial not only to ensure that RPA bots designed to automate processes are robust and resilient, but also to weed out less applicable use cases and ensure that organizations are building RPA automations that maximize utilization rates.

Smart and mature enterprises explore a broader tool box of process discovery, process mining, process automation, and data ingestion to help them orchestrate true end-to-end processes that actually bring an enterprise’s customers, employees, and suppliers closer together.

To address all these problems, the Robotic Process Automation (RPA) combines with Artificial Intelligence (AI) is only the best solution.

Intelligent Process Automation (IPA) blends the rules-based automation capabilities of RPA with the sophistication of AI, together with the trial-and-error learning capabilities of Machine Learning and fundamental process redesign. IPA includes the process discovery analytics and orchestration components covers functionality crossovers and integrations in existing RPA and business process management product maps.

The mind boggles at the possibilities of what a highly sophisticated, constantly-learning and ever-evolving smart automation system can offer the world of business.

Hyperautomation is typically an advanced form of automation, serving the purpose of completing tasks and processes faster, smarter, more efficiently and with less error across large-scale enterprises. Harnessing the power of AI, RPA, and Machine Learning (ML), hyperautomation is designed to process massive volumes of data end-to-end seamlessly to optimize business processes across multiple and diverse areas. As automation will be adopted at much higher volumes in the coming years, the need to automate more complex tasks will become more apparent.

Gartner predicts the scope of automation over the next few years to evolve from distinct tasks and transactions based on static and rigid rules to automating knowledge work. However, this will require a new automation strategy that focuses on optimizing digital processes from IT infrastructure through customer-facing applications.

Hyperautomation allows businesses to automate entire processes previously compartmentalized by traditional RPA, bringing integration, DevOps, monitoring and management together into one process. This will enable the automation of end-to-end workflows that manage complex reliance across diverse platforms and further boost efficiency and productivity at a much larger scale than ever before.

“By 2022, 65% of organizations that deployed robotic process automation will introduce artificial intelligence, including machine learning and natural language processing algorithms.” - by Gartner

Our Approach to Hyperautomation:

As a first step, Business leaders / owners & Enterprise architects need to set the vision for this initiative.

  • Business Goals & Outcome Alignment
  • Strategy to integrate Process Automation (RPA), Business Process Management (BPM) with Artificial Intelligence (AI)
  • Define & Socialize Automation Roadmap

Transformation leaders need to define strategic roadmap for long-term, medium-term & short-term objectives to accelerate business transformations that enables an end-to-end automation.

First step is identification of an end-to-end process, optimization/scaling of processes, structured & un-structured data inputs and decision intelligence. As next steps, assembling various components to integrate Process Automation (RPA), Business Process Management (BPM) with Artificial Intelligence (AI) comprising the right tool sets is needed.

Currently available renowned platforms are:

  • Microsoft’s Power Automate RPA solution with Power Apps low-code and workflow applications.
  • SAP’s BPM RPA offering with enterprise cloud, integrated with S/4HANA ERP.
  • Pegasystems integrated BPM RPA solution.
  • Oracle, Appian & many other leaders have partnered with UiPath, Automation Anywhere and Blue Prism.

Mostly, all the above-mentioned platforms are combined with AI, ML, NLP, OCR and cognitive bots technologies.

Hyperautomation is exceptionally scalable service and adjusted to accommodate peaks and valleys and drive benefits listed below without dropping quality.

  • Operational Costs
  • Quality & Accuracy
  • Increases Productivity
  • High Scalability
  • Faster & Easier Implementation
  • Improved regulatory compliance
  • Improved customer experience

So, the global RPA industry is not simply gaining in popularity across the board, it is now rightfully being viewed as the future for global business. As the world moves toward a collective leveraging of automation’s efficiency, speed, and accuracy, new technologies like hyperautomation and IPA have room to stand out. Businesses not adopting a competitive RPA strategy will soon be left behind in a world where automation is soon to become industry standard.