Robotic Process Automation (RPA) stands out as a powerful transformative force, pledging not just heightened operational efficiency but also a significant reduction in manual workloads. As per Gartner, the RPA software market attained a valuation of $2.8 billion in 2022, reflecting an increase from the previous year’s $2.3 billion. The research firm anticipates that the RPA software market worldwide will sustain double-digit growth throughout 2023.
In response to this trend, vendors are swiftly advancing their RPA offerings, transforming them into comprehensive automation platforms with built-in features. Yet, venturing into the RPA journey parallels navigating uncharted waters, presenting enterprises with a challenging expedition. As organizations progress from the initial Proof of Concept (PoC) to deployment at scale, a plethora of challenges emerge that warrant meticulous examination. This blog, with a dedicated focus, intricately unravels the complexities embedded in RPA scaling challenges and illuminates the pathways through which enterprises can effortlessly surmount these obstacles, especially through the innovative integration of AI-based RPA platforms.
Unpacking the Key Challenges in Scaling RPA Implementation
Intelligent automation allows your skilled workforce to focus on more intricate, creative, and transformative endeavors. Software Bots work 24×7, are not prone to errors and complete tasks much faster. The outcome? The capacity to drive swift organizational change and generate comprehensive business value from every angle.
However, implementing automation at scale poses numerous challenges for several reasons. Managing stakeholders across various departments for end-to-end processes demands intricate coordination, especially in gaining buy-in from Subject Matter Experts (SMEs). Without securing their support, there’s a risk of insufficient adoption and, in some cases, outright resistance from current employees who harbor doubts and fears regarding the potential impacts of automation on their roles.
The top challenges in scaling RPA include:
Lack of an Enterprise-Wide Strategy: Most organizational processes are cross-functional by nature. Owing to time or budget constraints, numerous organizations tend to address process challenges reactively and in silos. Robotic Process Automation (RPA) is frequently viewed as a swift, ad-hoc solution, where bots are deployed to address immediate issues without a comprehensive strategy or rationale. This approach, over time, results in disrupted processes, organizational disarray, and a sense of disillusionment with the technology itself.
Unoptimized Processes: With the evolution and rising popularity of RPA, undue pressure has been placed on IT directors and managers to implement it hastily. Deploying automation without prior in-depth analysis and identification of key opportunities, as well as potential issues, can lead to the failure of RPA before it has an opportunity to scale.
Selecting the Wrong RPA Platform: Choice is critical! Opting for the correct RPA solution is a typical challenge in organizational RPA projects. Often, it’s during the implementation phase that organizations realize they’ve made the wrong choice. They discover its unsuitability when they can’t meet customer requirements, and their investment exceeds expectations. Hence, selecting the right RPA tools for implementation is of utmost importance.
Lack of Adequate Expertise: One of the primary reasons for the failure and limited scalability of RPA projects is the presence of siloed RPA ownership. When IT exclusively owns RPA, its deployment may become disconnected from business needs. Conversely, if the business side of the organization is the sole owner, issues such as bot errors and necessary process fixes may arise. Additionally, as RPA remains a relatively new technology, finding notable talent in this field can be challenging.
Challenges in Infrastructure: Infrastructure-related challenges pose significant hurdles during RPA implementation. Insufficient infrastructure, encompassing servers, network capacity, data storage, security measures, and scalability capabilities, presents significant obstacles to the success of Robotic Process Automation (RPA) initiatives. These result in performance issues, unreliable operations, limited scale, integration difficulties, and increased costs. To maximize the benefits of RPA, organizations must ensure they have a well-established and robust infrastructure to support the technology, enabling efficient automation processes and long-term success.
Strategies for Seamless Scaling
Organizations with a fragmented technology landscape have the biggest challenges when scaling RPA. To achieve scale, organizations must standardize and integrate diverse automation technologies for an end-to-end process management system aligned with the enterprise-wide roadmap. They require a flexible and secure hosting platform to integrate with existing siloed applications. Automation assets and workflows often suffer from limited reuse potential, lengthy discovery phases, and high development costs. The debate centers around adopting a best-in-class automation platform versus building one from scratch. Existing platforms, whether in-house or not, tend to be IT-focused, but emerging platforms adopt a more business process-centric approach.
To achieve widespread success in implementing intelligent automation, it’s crucial to reexamine how tasks are carried out. Several strategies can help companies. Some of them include:
Embrace an Outcome-based Approach:
Successful automation starts by defining clear, measurable objectives—ranging from cost reduction and operational efficiency to quality improvement and increased customer satisfaction. Continuous measurement and reporting of business value are integral to these initiatives.
Select a Representative Cross-Functional Process for PoC:
The initial pilots invariably select a simple, siloed process to automate as part of the RPA PoC. As a result, the pilot does not address any interoperability requirements, tackle the differences in systems and processes employed in different departments, or assess the ability of the selected platform to support the workflow customizations required. All of these then become issues when scaling the pilot.
Rethink End-to-End Processes:
Traditional business processes, often rigid and outdated, hinder adaptability to market changes. Transforming these processes into intelligent workflows driven by technology, supported by human input, and fueled by data enhances agility. This approach allows enterprises to tailor customer and employee experiences while ensuring adaptability and optimization in supporting workflows.
For example, think of a traditional customer onboarding process that involves multiple manual steps and paperwork. By reimagining this process as an intelligent workflow, several steps can be eliminated and the rest automated, resulting in reduced time to onboard customers, less staff needed, and an overall simplified experience.
By rethinking their end-to-end processes and embracing intelligent automation, organizations can navigate the challenges of scaling RPA and position themselves for future success.
Plan Agile Automation Delivery:
Departing from traditional waterfall development, large-scale automation necessitates agile prototyping and testing. Scaled automation teams benefit from a factory model with agile development, recognizing common tasks and issues. A central team dedicated to intelligent workflow development operates within a pod structure, ensuring agile and iterative automation delivery.
Future-Proof With AI-based RPA
Intelligent automation merges the swiftness and efficacy of traditional robotic process automation (RPA) with the flexibility and decision-making prowess of artificial intelligence (AI). Using AI algorithms for data analysis and decision-making, these systems can learn and adjust to dynamic conditions, enhancing accuracy and efficiency in processes.
Some common use cases include:
Streamlined Data Handling With Intelligent Bots
Employing AI techniques like ML and NLP, intelligent bots surpass traditional OCR by extracting unstructured data from PDFs, images, and handwritten documents. Their enhanced accuracy, rooted in AI capabilities, involves understanding document context and minimizing noise. These bots seamlessly integrate with company systems, automating data updates across functions like HR, customer services, and sales, ensuring precise and timely database updates from customer interactions, emails, reports, and enterprise applications
Enhanced Customer Engagement and Onboarding
Intelligent bots, powered with conversational AI, enhance customer services by addressing FAQs, making recommendations, and facilitating self-service tools. In customer onboarding, these bots guide customers, collect and extract data from documents, and verify information, ensuring a seamless and efficient process.
Optimizing Inventory Management
Traditional inventory management involves manual processing of tasks such as communication, work order generation, invoicing, and complex back-office processes. Intelligent automation (IA) transforms this by handling tasks like analysis and data mapping, markedly reducing human hours. Automated management of the supply chain, inventory tracking, and shipping significantly enhances the overall experience for customers and businesses.
Sales and Marketing Efficiency
Utilizing intelligent bots, you can achieve optimal pricing for your products and services. These bots analyze various factors, including changes in supply and demand, customer data (such as demographics and spending habits), historical data, and market trends, as well as your competition’s pricing.
Unlocking the Electroneek Advantage
Through intelligent process automation, combining RPA and AI seamlessly, Electroneek provides a solution ensuring a zero-touch process and innovative service design. Its cutting-edge RPA capabilities enable businesses to attain process perfection. The intelligent automation tools from Electroneek handle mundane and complex workflow elements, freeing up space for human creativity.
Integrate RPA technology and cultivate intelligent processes to achieve unparalleled agility, minimize effort, and enhance productivity with our RPA solutions.
The Nextgen RPA Platform comes with the following features:
- 5x time faster development
- Truly no-code platform with pre-build bots
- Pay-as-you-go business model, no bot licenses
- 80% cost reduction compared to legacy RPA tools
ElectroNeek is a proven leader in AI Innovations in RPAs. Some common use cases include:
- ERP and CRM Data Integration: Bridge Connectivity Gaps, Automate Routines, Generate Custom Reports, and Ensure Data Integrity
- User Support: Automate end-to-end ticket management, including creation, synchronization, processing, resolution of user issues, and continuous audits for improved service efficiency.
- Optimize Employee Lifecycle Management: Efficiently manage employee onboarding and offboarding by automating the creation or deletion of accounts in chosen systems based on customized roles and access rights.
- Unify Security Operations: Automate security operations by integrating the security stack to maintain a single version of the truth, streamline user onboarding, and eliminate routine tasks such as periodic backups and auto-remediation of incidents.
Concluding Insights: Charting the Future of Intelligent Automation
Expanding intelligent automation goes beyond just adding more bots to handle additional tasks. Instead, it involves actively exploring opportunities to deploy integrated AI solutions, aiming to enhance and optimize business processes. Through the synergistic integration of diverse AI types, businesses can innovate and devise entirely new solutions for addressing complex challenges, thereby achieving significantly elevated returns.