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                Infopulse - Expert Software Engineering, Infrastructure Management Services

                Juxtaposing Automations: the Difference between RPA, Intelligent Automation, and Hyperautomation

                Reading time: 8 minutes

                Businesses have completed a formidable scope of transformations over the past several years, digitizing manual work and retiring legacy technologies. However, gaps still remain in corporate abilities to run lean, streamlined operations.

                For instance,   admit that they still need to use 3 or more disparate systems to obtain the required result and spend a lot of time on manual data cleansing. The same holds true for other teams and industries — from ecommerce and healthcare to telecom and insurance.

                Some companies ended up with a much larger portfolio of standard operating procedures as a result of adopting new digital solutions without reengineering their business processes first. Soundly, there is a viable trifecta of solutions for addressing the process scope creep — RPA, intelligent automation (IA), and hyperautomation.

                RPA vs. Intelligent Automation vs. Hyperautomation: Concepts Defined

                Robotic process automation (RPA) is the lowest level of business process automation. Largely powered by pre-programmed scripts and APIs, RPA tools can perform repetitive manipulations or process structured data inputs. For example, extract data from forms, copy files, or validate inputs. However, even the most basic RPA solutions can save teams a tremendous amount of time and effort. For instance, automating three business processes with the help of RPA led to a   for one bank.

                Intelligent automation (IA) is a step-up from rules-based RPA software. Powered by machine learning (ML) and artificial intelligence (AI), intelligent automation technology can handle a wider array of tasks, requiring baseline analytics and conditioning logic. For example, analyzing the document tags before assigning a proper status to it or reviewing the provided context to pre-suggest the best reply.

                IA solutions still rely on baseline automation, courtesy of RPA. However, such tools have extra “intelligence”, supplied by machine learning and deep learning. Therefore, they are capable of handling more complex cognitive tasks and even end-to-end workflow execution. Respectively, the efficiency and productivity gains of using IA solutions are much higher. For instance, one bank relied on smart automation to streamline corporate credit assessments, which led to an   in staff productivity.

                Hyperautomation, in turn, is the pinnacle of intelligent automation, which leaders are now aiming for. The RPA technology still remains at the center of such solutions, but it is further augmented by a wider range of AI capabilities — such as intelligent document processing (IDP), optical character recognition (OCR), and natural language processing (NLP), as well as adjacent solutions such as low-code/no-code platforms, event-driven software architecture, and intelligent business process management suites (iBPMS) among others. Thanks to a wider range of technical capabilities, hyperautomation tools can be deployed for semi- (or fully) autonomous end-to-end process execution across systems.

                Benefits of Hyperautomation vs. Intelligent Automation vs. RPA

                Hyperautomation Intelligent automation RPA
                Main use case End-to-end, company-wide process automation. Digital Organization Twin (DTO) creation. Cognitive automation of multi-step tasks and standard operational workflows. Scripted automation of simple, repetitive, tasks, requiring data and/or UI manipulations.
                Core technologies AI/ML use cases including OCR, ICR, NLP

                Intelligent business process management suites (iBPMS)

                Low-code/no-code tools

                Other types of task automation tools

                Event-driven software architecture

                Integration platform as a service (iPaaS)

                Low-code/no-code tools
                Packaged software

                Robotic process automation (RPA)

                AMachine learning / deep learning

                Optical character recognition (OCR),

                Intelligent character recognition (ICR)

                Process mining


                Software bots/scrips

                Macro-based automation

                If/then logic automation

                Basic business process management

                Advantages End-to-end process automation across a wide spectrum of business and technology processes

                Unprecedented levels of agility

                Advanced data modeling and analytics

                Ability to fully eliminate human intervention and supervision

                Self-service operational analytics and contextual insights

                Reduced level of human expertise required to configure and manage automation tools post-deployment

                Workforce performance augmentation cross-processes

                Wider scope of supported tasks/use cases

                Extra analytical insights

                Suitable for customer-facing processes

                Higher predictability and quality of task execution

                Seamless scalability across other use cases

                Rule-based task automation for back-office processes

                Improved staff efficiency and productivity

                Reduced error rates

                Implementation difficulty High. Requires a certain degree of digital infrastructure maturity, as well as a meticulous cross-system orchestration to deliver the most gain. Longest time-to-market, but highest ROI in the long-term perspective. Moderate. IA tools require unconstrained access to data, as well as a suitable target environment for deployment. May not be applicable to legacy systems. Slower time to market, but higher ROI. Low. Most RPA tools are non-invasive and conducive to a wide array of business applications. Fast time-to-market, proven ROI.

                Which Automation Technology Can Best Meet Your Current Needs?

                When presented with several options, it may be tempting to go after the most advanced technology — hyperautomation — as a means to future-proof your business. Yet, we must caution leaders that hyperautomation software requires high levels of digital infrastructure maturity:

                • An established cloud-based data lake
                • Robust data pipelines or data fabric
                • Automated IT infrastructure configuration, provisioning, and orchestration

                Likewise, hyperautomation (similar to IA and RPA) delivers the highest ROI for well-standardized business processes. “If you don’t have a really solid business process, you will be limited to how much you can automate. Because if you’ve got one business process that’s been run 30 or 40 different ways, then it will be impossible to harmonize with other processes,”   John Barraclough, Senior IT Director: Digital Transformation, Procter & Gamble.

                  also warns that by 2024, over 70% of larger enterprises will have to manage over 70 concurrent hyperautomation initiatives which require strategic governance or face significant instability due to the lack of oversight.

                That is why we recommend a bottom-up approach to enterprise automation. Start with employing simpler RPA solutions for redundant, error-prone, and repetitive processes. Based on the feedback, prioritize subsequent areas for improvement — more complex workflows, where extra “intelligence” is required for effective execution. Then look into “stitching together” workflows, requiring switching between applications.

                Types of Business Processes to Automate with RPA and Intelligent Automation

                According to  , cost reduction and alternative to more expensive IT modernization are the top two reasons for selecting RPA over other options.

                The projects of Infopulse clients also suggest that RPA adoption across different functions drives significant gains in productivity, customer experience, and business unit performance. The benefits above are particularly prominent when RPA tools are deployed for the following types of business processes.

                Discover the capabilities of RPA in enhancing the quality, speed, and productivity of your business processes in our in-depth research.

                Financial Workflows

                • Account reconciliation
                • Invoice processing
                • Bank statement reconciliation
                • Revenue management
                • Budget management
                • Financial reporting (including P&L)

                For example, our client, an Oil & Gas company, managed to save 12 weeks per year for each of the 6 FTE processes automated with the help of RPA.

                Commercial and Sales Functions

                • Order and transaction processing
                • Inventory management
                • Shipping information verification
                • Order tracking
                • Customer onboarding
                • CRM, ERP, SAP functions
                • Customer query processing
                • Vendor relationship management

                A construction company managed to significantly improve the speed of customer issue resolution and CSTA with an intelligent automation platform our team created for them.

                Juxtaposing Automations: the Difference between RPA, Intelligent Automation, and Hyperautomation - Infopulse - 1

                HR and Payroll Processing

                • Candidate application tracking
                • Employment history verification
                • Candidate screening and evaluation
                • Employee onboarding/offboarding
                • Benefits management
                • Business expense processing
                • Payroll management
                • Tax compliance

                Corporate Data Management

                • Data extraction, replication, validation
                • Metadata discovery and management
                • Data labeling
                • Data cleansing
                • Distribution of log files
                • Automatic report/documents generation
                • Data entry and verification
                • Data migration status tracking

                With the rapid boom of big data, this RPA use case alone can drive significant improvements in productivity, as well as cost containment. That was the case for a Nordic municipality. Infopulse team helped the organization migrate large-sized data records from legacy systems and implement an RPA solution for automating standard data-related workflows.

                Help Desk and IT Service Management

                • Ticket creation and prioritization
                • User credential management
                • Incident management
                • Business application configuration
                • Access permission configuration
                • Password resets
                • Network support management

                The pressure on ITSM teams has increased dramatically with the widespread adoption of remote work. Greater reliance on cloud-based applications and virtual desktops also multiplied their scope of work. To enhance your ITSM capabilities we recommend looking at comprehensive solutions such as ServiceNow, rather than standalone RPA tools. ServiceNow comes with an array of native digital process automation capabilities, low/no-code tools, as well as the ability to add custom process automation for company-specific workflows.

                Learn more about RPA use cases across functions and industries from our previous post.

                What It Takes to Adopt RPA, IA, and Hyperautomation

                The road to adoption will differ for businesses, depending on the clarity, complexity, and standardization of existing business processes. At the lowest level, we are talking about simple automation of different digital tasks — data entry, records consolidation, or input verification. Such RPA scenarios take the least time to develop and deploy. However, positive business outcomes will also be bound to granular, yet minor improvements in speed, efficiency, and accuracy.

                For more complex business process automation scenarios, we recommend following the next framework:

                Conduct In-Depth Business Processes Analysis

                Rule-based, fully or partially manual, and repetitive processes are the prime contenders for RPA. However, these actions often underpin more complex workflows. Strategize which other elements of the process can be set on automatic execution or performed semi-manually — meaning an RPA assistant can be triggered by a human user for extra support. At the same time, assess the current gaps in workflows, which require switching from one system to another for obtaining data or input. These disparities can be linked together with the help of RPA.

                Identify Automation Options

                Once you have an initial list of requirements for process automation, assess which type of technology could best fit your needs — simple rule-based automation or AI-enhanced execution. Additionally, consider your current technology portfolio. Many RPA tools are vendor-agnostic. Yet, they may offer pre-made connectors or ready-to-use automation scenarios for some of the business apps your company already uses.

                The best RPA and IA platforms we recommend are:

                • UiPath
                • Microsoft Power Automate
                • Azure Logic Apps
                • Blue Prism

                To learn more about each platform, sign-up for our on-demand “RPA in action” webinar.

                Develop and Execute an RPA Implementation Roadmap

                To justify the investments and prevent operational disruptions, we recommend phasing RPA adoption in three stages:

                1. Baseline RPA automation: Development and deployment of RPA bots, created with standard platform components and connectors for a selected set of business processes. Treat this as a trial stage for assessing the solution’s performance and value. Encourage a selected group of business users to test and give feedback on the solution.
                2. Application of IA. Operationalize the collected user insights and make amends to reduce cultural resistance to the new solution. Scale the standard bots across a wider range of business processes and encourage the company-wide usage. Then select a new set of contenders to augment them with AI/ML capabilities.
                3. End-to-end system integrations. After analyzing the results of the second automation round, look into interlinking the remaining elements of your business systems. This stage will likely require more intensive data management transformations. Consider upgrading some of the earlier implemented models with extra capabilities such as OCR or NLP to further enhance their performance. Implement system-wide orchestration of all existing automations to further improve performance and security.

                If you need help at any stage of your RPA adoption journey, to help you refine your strategy and support your execution.

                Originally published   December 20, 2021 Updated   December 20, 2021