Almost all business today is being conducted on web-based systems. Collection and collation of data are done on the cloud, form filling, data extractions, website testing and a large number of other repetitive tasks have all become web-based. Most businesses have started moving virtually all their operations on to the cloud in search of greater efficiency at a lower cost.
Organizations are increasingly finding that most infrastructure, software, and processes are now only being offered on web-based platforms. Now, with the emergence of Robotic Process Automation (RPA) as one of the key drivers of efficiency, the cloud and RPA have become closely linked.
Technologies like the cloud and the Internet of Things are helping businesses and individuals become more interconnected. Being able to send, analyze, and interpret data on the go is one of the key drivers of IoT, and the cloud helps facilitate this. With RPA, all of these tasks can be more efficient.
Web-based RPA has changed the way people think of robotics. The strategies used have to be changed to adapt to the cloud. By using a web-based RPA, customers can see quicker results from using robotic automation.
RPA on Cloud– Quicker, more convenient solutions
Developing on-site RPA solutions for businesses can be cumbersome and time-consuming. This is why automation companies are making a move towards pre-built, customizable cloud-based RPA solutions, where a network segment has already been built into the cloud. This way, a client can start using the bots as soon as they start using the cloud.
Web-based RPA also allows for customizable solutions, where customers can choose specific processes to focus on so that bottlenecks can be addressed easily. By maintaining a library of ready-made RPA components, web-based systems can combine a number of automated processes to efficiently automate new processes. This provides a higher degree of personalization without installing an unwieldy system-based RPA solution.
Scalability
As businesses grow over time, there is often a need for new bots to increase efficiency. By adopting a web-based RPA solution, it is possible to put new bots into service when they need it. Businesses often will not need many bots when they start out, and their needs will increase slowly, but surely with time. It is also possible that certain bots will become obsolete and will need to be phased out. For example, if a certain department shuts down, or service is stopped.
Since the process of upgrading and updating cloud-based systems is a lot easier than system-based bots, a web-based RPA system can be a lot more flexible. It also takes away the need to buy a higher capacity than what is needed when you first install an RPA system.
Lowering costs
Easy customization and flexibility mean that companies can pick and choose what they want to automate. This means lower costs of licensing, and operations. By taking some simple steps to understand and identify what they want to automate, where the technology can be used, and the potential savings, a web-based RPA system can help companies save a lot of money. In a system-based RPA, companies will often have to purchase a lot more licenses than necessary to avoid high installation costs, and increased downtime, the cloud-based system can provide extremely cost-efficient.
Safety
The cloud has become ubiquitous. Almost all companies rely heavily on cloud-based solutions for more efficiency. By using robots instead of humans, they can also increase the safety of their operations. The robot’s access rights and scope of work can be clearly defined, so as to minimize any potential problems and minimize human errors.
The ability to scale and replicate cloud services improves the competitiveness of businesses.
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In the early days of automation, robotic process automation or RPA brought the promise of radical transformation and improvement. Organizations could automate mundane, repetitive tasks, potentially giving back thousands of work hours to the business and reducing FTE efforts. Hyper automation will eventually transform traditional automation capabilities into impactful automated processes.
The original types of automations were not integrated or even necessarily connected to automate end-to-end tasks or processes – leading to fragmentation. A decentralized approach and focus on “a bot per user” have increased technical debt for enterprises, putting true digital transformation out of reach.
Over time, enterprises cobbled together disparate automation technologies to protect their original investments in RPA and were forced to assume the risks involved in integrating them.
What is Hyper Automation?
Gartner coined the term “hyper automation” to define this integration of technologies, encompassing RPA, machine learning, artificial intelligence, and these technologies’ growing sophistication. Despite RPA’s massive market share, it was fast becoming apparent that RPA alone could not keep pace with today’s digital transformation requirements, necessitating hyper automation – but this had its own share of issues.
Organizations choosing to automate via RPA as well as those venturing into hyper automation report a significant trade-off in terms of growing complexity, mounting technical debt, and a snowballing total cost of ownership (TCO) – which does not make sense in the long-term.
As we enter a new era in digital transformation, it is time to revisit our automation approaches and level up.
During COVID-19, we saw several years’ worth of digital transformation (3-7 years, according to McKinsey) take place in a matter of months. As we enter the next phase marked by consolidation, maturity, and long-term sustainability, organizations should rethink one of the core tenets of digital transformation – automating business processes.
Robotic process automation (RPA) is entirely task-based, where you define precise rules to guide workflows in business process automation. Let’s say you are setting up an RPA software for invoice automation. At the invoice registration step, you can configure RPA to read from a file/folder, but every new source has to be manually configured. As you receive invoice submissions from multiple sources like cloud-drives, email, etc., the RPA script has to be updated and managed accordingly.
Over time, this leads to RPA becoming more of a white elephant than a genuine value generator, as you will be spending outsized efforts on updating, cleaning, and maintaining your automation scripts as your enterprise grows into diverse functions/areas.
A survey found that over 4 in 10 enterprises are having to spend more time and resources to maintain RPA than originally expected.
Another issue is deployment timelines. Enterprise leaders start with the best of intentions but adapting RPA to a typical enterprise’s scale, and process complexity takes time – often up to three years. More than two-thirds of deployments take anywhere between 1 and 3 years, delaying your time-to-value. And once RPA is in place, just 4% are able to scale, mainly due to the complexity of projects (57%).
This leaves you with mounting technical debt and sunk costs, further increasing your TCO.
The Hyper Automation Journey
Improving on this approach, Gartner introduced hyper automation as the next phase of maturity, which would take advantage of AI/ML to cut down some of the inefficiencies of traditional RPA.
The rise of hyper automation, the no. 1 strategic technology trend from 2020
Gartner calls hyper automation “the application of advanced technologies, including artificial intelligence (AI) and machine learning (ML), to increasingly automate processes and augment humans,” with the ultimate goal of enabling AI-driven decision making.
It was the no.1 technology trend from 2020, poised to simplify several of the complex scenarios that would stymie traditional RPA.
Here’s a simple AP automation example: If you are using automation to extract invoices, RPA would require you to pre-train the engine and create separate templates for each supplier. Hyper automation improves this through ML so that the data extraction isn’t template dependent. Similarly, when it comes to validating invoices, hyper intelligent automation can crosscheck via intelligent OCR, in contrast to RPA, which only reads specific ERP fields or structured information.
But even hyper automation does not match up to the promise of true digital transformation. Breaking down the above scenario, you will find frequent human involvement (often at preventable intervention points). For example, hyper automation-based invoice extraction still lacks continuous learning capabilities. ML models are mostly a “black box” that cannot be adapted to business user behavior. For invoice validation, you still have to write complex scripts – only now, it is compatible with both structured and unstructured information.
For this reason, hyper automation remains confined to the “promising trend” segment, with limited real-world usability. Research names only Amazon and Google as key players, owing to their rich AI/ML capabilities.
Does this mean enterprises who need immediate and effective outcomes from automation are left in the lurch unless they are willing to spend on a 5-year-long ROI generation roadmap?
Progressing to HyperApps – a pragmatic model with human-in-the-loop
HyperApps combine the functional principles of RPA, the intelligence/cognitive capabilities of hyper automation, and the self-service convenience of SaaS apps to enable automations that show value in months and last for decades.
Continuing with the scenario of invoice automation, here is how a HyperApp would do it:
Invoice registration – Business users can integrate their preferred invoice source through a simple, point-and-click UI.
Invoice extraction – Any exception not covered by existing formats is routed to the business user. The user’s behavior is taken as a learning point, and the ML will adapt its future actions accordingly.
Invoice validation – All validation rules are pre-configured; business users can toggle a rule on/off for a specific supplier when validating.
True cloud native – Pushing new configurations to existing automation implementations is easy, allowing for constant upgrades of the HyperApp’s business process automation capability.
HyperApps introduce a few important changes to the RPA-to-hyperautomation maturity curve.
First, HyperApps rely on self-service, empowering business users to set up automated workflows and configurable business rules. What the HyperApp eliminates is the dependency on technical resources to make business configuration enhancements and changes. HyperApp designers can also add new functionality to the app and business users can turn them on based on their needs.
Second, HyperApps are modular, with their components reusable as you grow, by applying the same components to multiple scenarios. This brings down the total cost of ownership and generates cost savings, while also shrinking time to value because of its turnkey nature.
Finally, the human-in-the-loop user interface can replace the bulk training ML approach in cases where it is not possible to create a pre-trained ML model. This business user-led approach allows enterprises to build or enhance ML capabilities with their own business data.
As you can see, HyperApps address the key impediments to traditional RPA and hyper automation. They ensure fast deployment and low maintenance, adapting to complex processes during business growth. They also keep a human in the loop to power continuous learning, reducing your efforts for manually configuring AI/ML models. Importantly, HyperApps are already in action at several enterprises, enabling long-term digital transformation without having to wait for technology or infrastructure maturity.
Learn from the frontlines and level up today with Hyper Automation
Demonstrating a remarkable improvement over RPA alone, one of the world’s largest automobile manufacturers was able to achieve 85% straight-through processing (STP) for invoicing processes in just a 12-week period. The company first tried RPA in their AP automation journey to replace manual execution. But it was too rigid and rules-based, unable to handle frequent changes in invoice templates as the manufacturer added new vendors, new invoice formats, new types of suppliers, etc., as part of its growth journey.
RPA solutions couldn’t keep pace with the company’s 5000-strong supplier network, processing 150,000 invoices per month.
An Invoice Processing HyperApp successfully addressed this by learning from 12 months’ worth of historical invoices and continually updating itself whenever it encountered an exception. Using a HyperApp, the manufacturer can process one invoice in three minutes vs. the pre-automation 24-hour turnaround. And unlike most implementations, it saw measurable ROI in six months.
At JIFFY.ai, we help organizations around the world with their digital transformation roadmaps by making it possible to level up their automation projects. This pragmatic progress from RPA to hyper automation and finally, to HyperApps has proven to bring about battle-tested outcomes.
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