The 10 Golden Rules Of RPA Success

Having reached a level of maturity, robotic process automation (RPA) now plays a crucial role as a fundamental technology for enterprise automation. The landscape of RPA products and the accompanying ecosystems has undergone substantial advancements. As organizations strive to establish robust and expansive RPA initiatives, they face not only the hurdles from RPA's history but also the opportunities it holds in enabling autonomous enterprises in the future. This report outlines the top 10 essential factors that automation leaders should consider to achieve scalability, value, and resilience with the help of RPA.

Heed The 10 Golden Rules Of Robotic Process Automation

Technology and business leaders demand clear, direct benefits from investments in a digital workforce. Follow Forrester’s 10 golden rules to get a head start on future-proofing your RPA initiative.

Rule 1: Weave RPA Into Your Company’s Automation Fabric

The evolution of processes occurs over time, and advancements in platform upgrades and technology contribute to the emergence of enhanced integration-based or AI-based opportunities for process automation. RPA is now part of a larger landscape of automation rather than existing in isolation. It should be viewed as a stepping stone towards constructing an automation fabric that encompasses various technologies, allowing for end-to-end process automation throughout your organization. Consider RPA as one element within a comprehensive automation roadmap, while also anticipating the eventual obsolescence of certain bot-enabled workflows as time progresses. To do so:

• Broaden your automation roadmap.?Automation represents a vital and transformative aspect of modern business operations, marking the next significant phase in the digital realm. It is crucial to develop a strong and comprehensive plan for leveraging automation-driven advancements, incorporating Robotic Process Automation (RPA) while also outlining a broader strategy for fostering innovation and orchestrating the long-term transformation of organizational processes. VITAL, the agency responsible for overseeing corporate shared services within the Singapore government, employs RPA as a key element within its extensive approach aimed at providing a range of automated services. These services play a pivotal role in facilitating the digitization of numerous essential services targeted toward citizens and are implemented across various government agencies.
• Wrap RPA within the broader organizational structure for automation delivery. In the year 2023 and onwards, it becomes imperative to establish a unique organizational framework for automation, encompassing a charter that emphasizes transformation and appointing dedicated leaders. The integration of RPA into this organizational structure should be aligned with strategic objectives, operational goals, and governance within a larger framework.
• Explore transformation-centric use cases, not just cost avoidance. In addition to its role in automating repetitive manual processes, RPA plays a crucial role in connecting various digital business initiatives. Forrester clients utilize digital workers in innovative ways, such as facilitating large-scale enterprise resource planning transformations, bridging disparate systems to gain new insights, empowering purpose-built applications in areas like field service and logistics, enhancing customer experience (CX), and creating autonomous workplace assistants (AWAs). AWAs incorporate embedded monitoring, conversational capabilities, detection mechanisms, and decision-making abilities to accomplish employee or workplace tasks. They make informed decisions based on contextual information, the environment, user input, and workplace objectives, acting autonomously without requiring approval while providing valuable services.
• Secure business sponsorship from the C-suite. At the very least, an RPA initiative necessitates the presence of an executive sponsor who offers strategic guidance, procures funding, ensures stakeholder agreement, advocates for the initiative, and eliminates obstacles hindering its success. This individual holds a high-level position and possesses the authority and influence required to provide direction, support, and necessary resources for the initiative. The executive sponsor plays a vital role in the initiative's triumph by ensuring alignment with business goals and facilitating the acquisition of essential resources for achieving desired results.

Rule 2: Build A Sustainable Business Value Model For RPA

Enumerating the advantages of automating simple processes is relatively straightforward. However, as the complexity of processes increases, calculating the return on investment (ROI) becomes more challenging due to the multitude of factors and interdependencies involved. A practical business case should neither exaggerate the potential value or savings achieved nor underestimate the associated costs (refer to Figure 2). Emphasizing the significance of developing a robust and replicable business case for every automation opportunity cannot be overstressed. We have observed significant disparities in the price points at which different firms in a region procure RPA licenses, ranging by a factor of eight. Simultaneously, we have witnessed firms achieving or failing to attain sustained ROI with RPA, regardless of the licensing costs they incurred.

To prove the financial viability of your RPA efforts:

• Take a Balanced Scorecard approach to value.?Implementing large-scale RPA programs brings about various advantages across the business. Operationally, these programs lead to improvements in personal and process efficiency, as well as impact overall business performance. The benefits extend upward, eventually influencing key transformation metrics such as revenue, profitability, customer experience (CX), and risk. When operating at scale, automation teams should adopt a Balanced Scorecard approach to identify the sources of automation value and establish connections with tangible financial or nonfinancial outcomes for the company. For example, Arla Foods, a supplier of Scandinavian dairy ingredients, utilized RPA bots to decrease inventory wastage, streamline logistics by reducing truck downtime caused by administrative delays, and enhance wallet share with its largest customers by adhering more effectively to service-level agreements. Taking it a step further, analytics provider LexisNexis developed an Automation Results Tracker, which serves as a comprehensive platform for capturing the value of automation initiatives and reporting it to the CEO and board.
• Understand your cost structures. RPA teams need to take into account various cost factors, which can generally be categorized into four areas: the expenses associated with setting up infrastructure and acquiring licenses for RPA products and third-party applications, the costs of developing and testing automation, the long-term operational costs of running RPA, and the expenses incurred due to changes. During the early stages of RPA implementation, the focus is primarily on the first two categories, while more advanced programs must address the growing costs of maintaining bots and other associated overhead. Strategies for reducing RPA costs in scaled programs involve optimizing automation design, enhancing bot scheduling and utilization, conducting regular bot audits, and promoting efficiency through reusable components and standardized processes. Fannie Mae, a US-based mortgage finance company, achieved a 70% reduction in processing times and saved millions of dollars in operating costs by optimizing automation design and prioritizing high-value processes.
• Report value in ways that matter to business leadership.?Executives often perceive RPA programs, regardless of their level of maturity, as being purely tactical, cost-centric, or focused solely on improving productivity. Automation practitioners contribute to this perception by reporting only tactical metrics such as reduced FTEs or cost savings, along with hypothetical metrics like 'hours returned to the business,' which don't particularly interest the C-suite. However, automation brings value across various dimensions that do matter to the C-suite, including functional, economic, experiential, and symbolic aspects. It is crucial to capture and communicate the holistic value of automation in ways that are relevant to the business. KeyBank shifted its focus from intangible efficiency metrics to emphasizing measurable business impact, enabling them to successfully expand RPA and achieve significant outcomes such as tangible cost savings, revenue growth, and risk reduction in key areas such as mortgage originations, collections, reporting, and real estate capital document placement.
• Don’t ignore strategic and human impact. In the context of a broader automation initiative, the implementation of Robotic Process Automation (RPA) can contribute positively in intangible but significant ways. By liberating individuals from mundane and repetitive tasks, RPA enables them to redirect their time and effort toward innovation, creativity, customer-centricity, and societal impact. Although often intangible and undervalued, this outcome of automation holds immense potential as a valuable resource. As an example, during the onset of the COVID-19 pandemic, the RPA Center of Excellence (COE) at the UK's National Health Service estimated that by utilizing RPA, the NHS could save approximately 66 years' worth of nonclinical staff time by 2025. This saved time could then be utilized by staff to serve more patients or enhance patient care.

Rule 3: Treat RPA As An Enterprise Platform

The ease of deployment and evident value that RPA offers has generated enthusiasm among technology and business teams. However, it is crucial to resist the temptation of taking shortcuts during the initial stages. It is important to subject RPA to the same standards and guidelines as other enterprise technologies, considering its impact on security, data privacy, credentialing, documentation, and the workforce right from the beginning. To ensure good automation governance:

• Prioritize the user experience. As with any implementation of user-focused technology, the success of RPA workflows is closely linked to the?user experience. It’s very easy to succumb to a machine-centric or process-first view of automation design without considering how the automation will interact with and affect actual humans within the workflow. Design automation responsibly. Identify and develop processes and workflows for automation in close collaboration with business stakeholders and process experts. Bring user-centered design and design thinking principles to process and automation design.?Involve experts early?to help focus on user goals and design effective automation that helps achieve these goals.
• Formalize approaches to data privacy and resilience. Take into consideration the potential impact on customer and personal data when RPA bots are involved; ensure awareness of vulnerabilities that may affect data compliance such as GDPR. The fields of architecture guidelines and coding standards for RPA development are continuously progressing disciplines. These disciplines directly influence the resilience of bots, determining their frequency of breakdowns and the time required for fixing them. Establish control framework checkpoints to assess coding practices and significant architectural choices.
• Build platforms and support reusability. Creating an effective automation architecture necessitates the use of reusable elements. Construct your automation artifacts as a collection of components, business objects, or reusable code that can be combined to form other workflows, eliminating the need to start from scratch each time. For instance, develop reusable modules that can be repeatedly invoked for common tasks such as user authentication or structured interactions with third-party applications. To take automation a step further in the era of automation fabrics, recognize that automation itself becomes the application. Progress from individual, standalone automations to libraries of reusable components, and ultimately transition to platform approaches that encapsulate a comprehensive set of functionalities catering to specific roles or processes.
• Uphold software development and testing best practices. RPA simplifies the process of automation creation for business users, which is beneficial. However, the current solutions heavily rely on scripting, often spanning multiple workflows and applications. When dealing with more complex tasks, additional scripting becomes necessary, leading to bot failures caused by infrastructure issues, software reliability problems, and changes in application UI and data. Rectifying these issues takes at least a day, negatively impacting customer service and employee experiences (EX), and diminishing employee productivity. The development processes for bot software lack proper structure, and RPA platforms offer limited support for testing. Moreover, poorly designed automation can result in significant technical debt. It is advisable to adhere to organizational guidelines regarding code reconfigurability, readability, and performance.

Rule 4: Secure Your Bots With Zero Trust Principles

Regard your bots as digital workers and handle them accordingly. Create official guidelines to recognize and verify bots as nonperson users, while monitoring their access privileges. Just like human workers who have commencement dates, managers, training, and end dates, digital workers should also be subject to such provisions. Be sure to:

• Treat each bot as an IT asset. Effectively handle the lifespan and distinct identity of each bot as an individual IT asset. One way to initiate this is by implementing suitable naming standards to distinguish bots from human user accounts. Furthermore, IT teams should establish procedures and utilize analytics to consistently monitor a bot's account access and privileges, while also implementing alert systems to disable access when the bot is retired.
• Establish a strong foundation for managing identity and access.?Utilize the identical procedures employed for credentialing and authorizing access rights for human employees when dealing with RPA bots. Involve HR teams from the beginning to establish a shared understanding across the organization regarding the guidelines for effectively managing a hybrid workforce consisting of both bots and humans. Employ Forrester's identity and access management lifecycle to achieve a harmonious equilibrium between enhanced security measures, seamless user experience, and operational efficiency throughout your digital workforce.
• Apply Zero Trust principles to secure your bots. RPA presents a fresh vulnerability for both human and nonhuman entities, expanding the potential targets for attacks. It is crucial to adopt Zero Trust methodologies when it comes to securing bots, managing identities, and overseeing their lifecycle. From the beginning of the automation program, prioritize enforcing these fundamental practices. Safely store bot passwords in a centralized and encrypted location. The system responsible for running the robot must maintain a high level of security and be regularly updated. Implement secure code development techniques. Guarantee traceability of every action and decision made by the robots. Analyze access logs in conjunction with analytics to identify any irregularities. Conduct thorough penetration testing for each bot before deploying them into production.
• Remember that RPA bots can be an internal or external attack point. Emphasize the importance of security in the design process rather than treating it as a secondary concern. Apply rigorous security checks to RPA implementations, treating them on par with other enterprise systems. It is advisable to provide each bot with minimal access privileges while ensuring uninterrupted automation. When working within virtual desktop environments, create distinct environments for development, testing, and production purposes. Implement guidelines for centralized storage and code reuse. Ensure compliance with the security, resilience, and data access policies, procedures, tools, and controls established by the CIO and CISO, including the protection of personally identifiable information.

Rule 5: Prioritize Your Processes

When initiating an RPA initiative, including the pilot phase, it is crucial for technology teams to adopt a comprehensive approach towards processes. Numerous RPA programs that have achieved success have proactively established pipelines comprising potential processes, encompassing intake, evaluation, and prioritization for a period of 12 to 18 months in advance. These pipelines are typically overseen by a Center of Excellence (COE) or a dedicated strike team. To build a strong intake pipeline:

• Align RPA with the right use cases. Sometimes, tech teams who are enthusiastic about experimenting with RPA often overlook the fact that when you have a hammer, everything tends to resemble a nail. It is essential to consider other tools available in the intelligent automation (IA) toolbox that can yield more lasting results and enable greater intelligence. To begin the RPA process evaluation, you can employ Forrester's "rule of five" to identify straightforward candidate processes. For more intricate automation, Forrester's automation framework can be utilized. When it comes to quick data integration, it may be advisable to avoid relying solely on RPA and instead consider an API approach for better long-term outcomes. As mentioned by an RPA leader in an automaker company, we perceive [vanilla] RPA as the last resort for automation, once all other automation models have been assessed and rejected.
• For simple processes, use digital worker analytics. Forrester defines digital worker analytics (DWA) as a collection of platforms and methodologies that enable automated and systematic methods for collaborating, capturing, organizing, analyzing, standardizing, documenting, and ranking human activity on desktops and browsers. These DWA systems aim to facilitate the standardization, design, and development of digital workers for task automation and enhanced intelligence. Present-day task mining tools offer similar approaches, but they are a narrower subset of the broader functionality provided by DWA. DWA is characterized by its abundant data. While traditional process discovery led by consultants is time-consuming and labor-intensive, task analytics can now efficiently identify, prioritize, and document human inputs and outputs. It combines and ranks various task elements such as login data, screen navigation, mouse selection, search time, and field entry from multiple recordings. These task elements are evaluated based on rule-of-five factors, including task duration, the number of people involved in the process, and the number of systems utilized.
• For complex tasks, go deeper.?Multiple heterogeneous application environments may encompass complex processes. To comprehend such intricate processes involving various disciplines and stakeholders, design thinking and journey visioning prove invaluable. These tools facilitate a comprehensive understanding of processes by incorporating qualitative inputs regarding user behavior, motivations, and dependencies. By combining qualitative insights with concrete data through DWA (Data-Driven Automation), it becomes possible to discover both immediate and long-term automation opportunities. This, in turn, helps generate a pipeline of process candidates suitable for automation. An example of design thinking in action is when a prominent movie studio employed it to identify and streamline the metadata management process across diverse sources and types of digital assets.
• Process intelligence drives RPA success — but can also do so much more. Process intelligence plays a crucial role in enhancing RPA performance by providing real-time insights. It enables the identification of bottlenecks, optimization of workflows, and improvement of efficiency and accuracy on a large scale. With the capability to monitor, analyze, and optimize RPA processes in real-time, process intelligence becomes a valuable tool for achieving RPA success. The adoption of process optimization as an essential component of automation is evident, as 40% of process intelligence inquiries from Forrester clients in 2022 focused on process mining. Although process mining is still in its early stages, it holds immense potential for the future, especially as vendors incorporate AI features to achieve comprehensive business observability. To ensure the success of RPA programs at scale, deep integration of process intelligence into standard operating procedures is essential. Consider integrating RPA within a broader evaluation of your organization's processes and incorporate it into your process transformation program.

Rule 6: Lay Early Foundations For Effective Automation Management And Governance

The paramount importance lies not just in automating processes, but in ensuring the correct implementation of the automation process itself. This entails creating consistent approaches to identify, prioritize, and manage the pipeline of processes targeted for automation. Moreover, the automation process requires the establishment of robust operational and governance frameworks that provide stability to the automation program while retaining its agility.

Here are a few guiding principles:

• PDDs are crucial weapons in the fight against imperfect process selection.?Many early-stage RPA programs often opt for automating fragments of a process instead of the entire process itself. Only a few take into account the overall efficiency of completing the entire task in a seamless manner. This limited perspective disregards the interdependencies with external steps, which can introduce instability to automation. Additionally, teams lose the ability to effectively measure the value and impact of partial automation. Over time, process knowledge may accumulate without sufficient documentation. To address this, utilize process design documents (PDDs) as a vital tool to generate comprehensive and detailed documentation for your intended process. These documents should encompass specific details, ranging from the workflow to the keystroke level. By analyzing process execution dependencies and capturing variations, you can enhance your understanding of the process.
• Understand, document, standardize, improve, and then automate. Opting to revamp or establish uniformity in a procedure prior to implementing automation might lead to higher initial expenses; however, it generally yields a more enduring automation solution with enhanced returns. The toolbox for process enhancement encompasses a wide array of tools and methodologies aimed at assistance. Conventional methodologies like Kaizen, Lean, Six Sigma, cost of poor-quality analysis, and failure modes and effects analysis are available. Undertaking a comprehensive redesign of the entire process using these methods can be overwhelming, and many companies lack the required patience to embark on such an endeavor. Fortunately, faster and more intelligent data-driven approaches, such as task mining and process mining, have made this process more manageable, replicable, and scalable.
• Choose light and federated governance models.?While each organization has its unique ideal governance model, there are certain general principles and tradeoffs to consider. Centralized models offer greater control, particularly in the early stages of a program. However, these controls can hinder the speed of automation and limit scalability. On the other hand, federated models prioritize speed and agility but necessitate extensive collaboration between business and IT for the long-term development of operational processes and allocation of responsibilities (refer to Figure 3). It is recommended to aim for a federated hub-and-spoke model, establishing multiple automation design centers within the business and supported by automation strike teams (refer to Figure 4). Lastly, as RPA evolves from a self-contained capability to an integral part of an automation framework, governance models should align more closely with the organization's transformation office.

Rule 7: Plan For AI, But Don’t Rush In

The technology of Robotic Process Automation (RPA) is deterministic, whereas Artificial Intelligence (AI) operates with probabilities. Nevertheless, there exists a strong and inherent alignment between process automation and machine learning (ML). By integrating ML into RPA-driven workflows, the range of automatable tasks and processes expands significantly, enabling innovative applications that cannot be accomplished by software bots alone (refer to Figure 5). These applications cover a wide spectrum, ranging from straightforward tasks like extracting structured data from scanned images to intricate endeavors such as incorporating complex decision-making algorithms into RPA workflows. However, while leveraging this immense capability, it is important to acknowledge the enduring challenges and limitations of ML in Intelligent Automation (IA) endeavors. To succeed with AI/ML use cases in RPA programs:

• Start with baby steps.?Implementing RPA alone can be challenging, and the inclusion of AI introduces an additional layer of complexity. It is advisable to explore simple use cases, particularly those that expand existing stable automation into the realm of uncertainty. Initial opportunities arise within back-office processes in finance and accounting, procurement, and other fields that involve partially structured paper documents like invoices or purchase orders. However, before diving in, it is important to understand the specific AI or ML tools being utilized, as well as the accuracy and limitations of the underlying algorithms across various scenarios. Managing expectations with business stakeholders is crucial, as algorithms may not always deliver perfect results. It is essential to establish acceptable success rates and have a human backup plan in place.
• Focus on data quality early. According to data scientists, the effectiveness and precision of your machine-learning model depend entirely on the quality of your training data. As you advance towards more intricate AI/ML scenarios, it is essential to collaborate closely with data science teams to ensure data quality. Employing an automation fabric approach can be beneficial in this regard. By co-locating data science teams and automation teams within the same organizational structures or Centers of Excellence (COEs), it facilitates stronger alignment of strategic objectives, improved collaboration, and ultimately leads to enhanced automation capabilities.

Rule 8: Take An Innovation View Of Intelligent Automation

During the initial stages of RPA initiatives, it is crucial to give priority to customer experience, enhancing internal capabilities, and achieving automation goals aligned with the business case. However, as these programs expand and confidence in RPA increases, it can assume a pivotal role in driving innovation. Bring an innovation view to your RPA program by:

• Taking a business services view of innovation. Why not extend your COE or strike team's internal capabilities beyond delivering consulting, scale, and support as a business service to the entire organization? It's time to include innovation as well. In an innovation-driven culture, various aspects such as architecture, lifecycle management, measurement, reusability, training, capacity management, quality assurance, monitoring, and reporting are crucial for enabling automation to address new customer experiences and workloads effectively. The roles of your automation team go beyond mere scale and skill development.
• Fostering in-house automation skills. Employing RPA and related technologies to streamline processes necessitates individuals equipped with fresh skill sets and a demand for specialization. It becomes essential to establish distinct positions to facilitate this (refer to Figure 6). Numerous organizations attest to reclaiming a noteworthy albeit modest number of jobs that were initially lost to automation, which are now attributed to their automation initiatives. Interestingly, instead of fearing job displacement, many employees within these organizations eagerly embrace automation, enthusiastic about acquiring new abilities and enhancing the quality of their work.
• Support citizen development. Individuals lacking traditional application development or coding expertise are now assuming responsibility for creating their own automated solutions. The emergence of citizen developers is a prevailing trend in the corporate landscape. Encouragingly, most modern Robotic Process Automation (RPA) tools are increasingly catering to citizen developers by offering user-friendly, low-code design interfaces and seamless integrations with commonly used third-party applications. Nonetheless, supporting citizen developers necessitates additional competencies within automation Centers of Excellence (COEs) and close collaboration with technical teams. Companies such as Carlsberg, Singtel, and Spotify have achieved notable success in promoting RPA adoption throughout their organizations by prioritizing the development of frameworks, providing training opportunities, and establishing effective governance to facilitate the success of citizen developers.
• Leverage your co-innovation partners. A co-innovation partner refers to an external party strategically engaged in bringing valuable assets, alliances, and solutions to facilitate your transformation process. They play a crucial role in orchestrating the value derived from your internal and external ecosystems. Your foremost service partners, who are deeply integrated into your ecosystem, actively contribute to your digital objectives. These partners have already played a significant role in enhancing your automation initiatives across various services, having dedicated several years to developing advanced service lines, partnerships, methodologies, platforms, and assets aimed at driving automation throughout your organization. It is advisable to engage in discussions with your strategic partners regarding your automation goals and make use of their expertise to progress on your journey.

Rule 9: Design For Humans

Despite advancements, fully autonomous bots continue to be confined to the realm of science fiction, as the involvement of people remains crucial for the triumph of automation. Human expertise, effort, and assistance are frequently necessary to strategize, outline, implement, and ensure the stability of automation processes. Complete reliance on straight-through processing is not feasible for all situations. Furthermore, with the inclusion of probabilistic technologies like machine learning, the presence of humans becomes indispensable. It becomes imperative to meticulously devise scenarios that facilitate interaction between bots and humans (refer to Figure 7) to achieve optimal outcomes. ?To start your human-in-the-loop design:

• Architect human is a failsafe. Process flows between individuals and bots will be orchestrated by RPA platforms, often necessitating a safety net involving human intervention. In the realm of customer experience, a system must be in place to handle outcomes in the event of a bot malfunction. When RPA is integrated with machine learning to handle exceptional cases, human involvement becomes essential for training algorithms, validating outcomes, and managing process exceptions.
• Commit to a robust change management program. Ensuring a comprehensive understanding of the importance of automation is crucial for all parties involved, and it is essential for the organization to effectively communicate this reasoning. To minimize resistance towards the automation program, RPA program leaders should engage employees from the beginning, encouraging their input to identify the most suitable processes for automation. Furthermore, the organization should offer ample training and support to aid employees in adapting to the new system. Overcoming middle management's resistance to automation can be facilitated by strong executive sponsorships and clear objectives. Maintaining open and transparent communication throughout the process is vital. Roche Hong Kong's RPA program encountered challenges due to compartmentalized, function-specific thinking, but they successfully addressed this issue by establishing shared cross-functional goals, implementing dynamic budget allocation, and adopting event-driven planning to enhance their approach and impact.
• Put employee well-being at the center. The implementation of automation should not relegate humans to a secondary position; instead, it enhances their importance for achieving success. Undoubtedly, automation will impact employees' perspectives in novel and diverse manners, significantly influencing the reconfiguration of work. These outcomes stem directly from collaborating with intelligent machines and adapting to a rapidly advancing digital landscape.
• Focus on employee experience. Current research in the field of automation-related employee experience (EX) focuses on determining the most significant elements of EX. Among these elements, anxiety has gained prominence. With machines increasingly making decisions on our behalf, rapidly revealing skill gaps, and presenting challenges in terms of keeping up, the human workers impacted by the relentless progress of automation are likely to experience feelings of threat and disenfranchisement.

Rule 10: Foster An Automation-First Culture

Embracing the right mindset towards automation involves prioritizing the automation of processes to the maximum extent possible and then integrating human labor. This mindset is crucial due to the continuous expansion of automation possibilities. Robotic Process Automation (RPA) has already successfully automated numerous repetitive tasks. With the integration of Artificial Intelligence (AI) and Machine Learning (ML) and within the larger context of automation, it is poised to encompass an ever-widening range of process types and decision-making domains. To achieve success in automation, organizations need to shift their thinking and consider it as the primary model for all types of work. Forrester's robotics quotient framework can assist in developing the necessary organizational capabilities for automation. The right mindset requires your organization to:

• Evangelize an end-state vision for automation. Take proactive measures to promote the value and advantages of automation both internally and throughout the entire business. Begin by illustrating the benefits of automation through tangible real-life examples. Effectively convey the advantages, such as enhanced efficiency, reduced costs, improved accuracy, and the ability to concentrate on more valuable tasks. Elaborate on how automation can contribute to the organization's goals and vision. Communicate transparent roadmaps, address concerns, and involve individuals at all levels. Utilize workshops, hackathons, and other initiatives to stimulate the generation of ideas and encourage participation in automation across the organization. Schlumberger successfully generated enthusiasm for RPA by organizing enterprise-wide hackathons across multiple locations worldwide. These hackathons served as a means to collect ideas from professionals within the organization, create awareness and excitement regarding the potential problem-solving capabilities of automation, and reward teams that achieved significant business impact.
• Encourage experimentation and learning. Establishing a culture that prioritizes automation necessitates a readiness to embrace experimentation and knowledge acquisition. Motivate your staff members to venture into uncharted territories of tools and technologies, and encourage them to disseminate their learnings throughout the organization. Foster an environment that promotes ongoing learning, viewing mistakes as chances for growth and enhancement. Moreover, when an automation endeavor achieves triumph, commemorate it! Emphasize the advantages it brings and acknowledge the teams or individuals who contributed to its success. This will generate enthusiasm and drive around automation, solidifying the significance of an automation-centric culture within the organization.
• Teach them to fish! As you implement fresh tools and technologies, it is crucial to educate and assist your employees. This will enable them to comprehend the optimal utilization of the tools and maximize the benefits of automation possibilities. Offer continuous support as employees progress in their tool usage and confront new obstacles. Additionally, go beyond mere tool training by investing in empowering employees and citizen developers to acquire comprehensive IT skills encompassing tasks like creating PDDs, employing fundamental software development methodologies or testing, as well as non-IT skills like business case formulation and effective communication.
• Discuss emerging skills proactively and transparently. As automation continues to advance, the inevitability of job role elimination becomes more apparent. The widespread need for reskilling to adapt to transformed positions will become prevalent. Fortunately, automation also brings career prospects for numerous employees currently engaged in jobs beneath the surface.

RPA has its limitations. It is best suited for rule-based, structured, and repetitive tasks and may not be suitable for processes that require complex decision-making or human judgment. Additionally, implementing RPA requires careful planning, process analysis, and ongoing maintenance to ensure successful integration with existing systems and workflows. Overall, Robotic Process Automation has emerged as a valuable tool for organizations seeking to optimize their operational efficiency, reduce costs, and improve productivity. Its potential benefits make it an appealing option for businesses across various sectors.