10 golden rules for successful RPA implementation
Our extensive experience of Robotic Process Automation (RPA) implementations allows us to see what makes the success cases really shine. To help with your RPA journey, we’re sharing our ten golden rules to achieve successful RPA implementation and avoid common pitfalls.
Rule 1 – Integrate RPA into your automation framework
RPA is now part of a broader automation landscape, serving as a stepping stone towards a comprehensive automation framework. It should be seen as one element in an extensive automation roadmap, with the understanding that some bot-enabled workflows may eventually become obsolete. Process evolution over time, driven by platform upgrades and technology, leads to enhanced integration and AI-based automation opportunities.
Rule 2 – Establish a sustainable RPA value model
It’s easy to list the benefits of automating simple processes, but as process complexity grows, calculating ROI becomes challenging due to various factors and interdependencies. A realistic business case should accurately reflect potential value, savings, and associated costs. It’s crucial to develop a robust and replicable business case for each automation opportunity. Notably, we’ve seen significant disparities in RPA licence costs among firms in the same region and varied success in achieving sustained ROI, independent of licensing expenses. Choosing the appropriate RPA tooling is essential to recognising long-term value and savings.
Rule 3 – Treat RPA as a corporate platform
RPA’s ease of deployment and clear value excite technology and business teams, but you must avoid rushing its implementation and taking shortcuts. RPA must meet the same standards as other enterprise technologies, addressing security, data privacy, credential, documentation, and workforce impact from the start. For good automation governance the RPA platform should co-exist within the existing business digital ecosystem.
Rule 4 – Establish clear ownership and support for RPA processes
RPA ownership and support must be clearly defined to ensure smooth operation and quick resolution of issues. The responsibility for RPA processes should be shared between business units and IT teams, ensuring both parties are aligned on goals and support mechanisms. This collaborative approach ensures that RPA initiatives are effectively managed, maintained, and evolved over time.
Rule 5 – Prioritise process optimisation
When starting an RPA initiative, including the pilot phase, technology teams must adopt a comprehensive approach to automation candidates. Successful RPA programs establish pipelines for potential processes, covering intake, evaluation, and prioritisation for 12-18 months ahead. These pipelines are usually managed by a Center of Excellence (COE) or a dedicated team.
Rule 6 – Lay groundwork for effective automation governance
The key is not just automating processes but ensuring correct implementation. This involves consistent methods to identify, prioritise, and manage automation targets, alongside robust operational and governance frameworks to maintain program stability and agility.
Rule 7 – Strategise AI integration with caution
RPA is deterministic, while AI operates on probabilities. However, there’s a strong alignment between process automation and Machine Learning (ML). Integrating ML into RPA workflows significantly expands the range of automatable tasks, enabling innovative applications beyond the capability of software bots alone. These range from extracting structured data from scanned images to complex decision-making algorithms. Despite this potential, it’s crucial to recognise the enduring challenges and limitations of ML in Intelligent Automation (IA). Our key advice is go slow to go fast, taking incremental steps that are well understood with clear value returns.
Rule 8 – Embrace innovative approaches to intelligent automation
In the early stages of RPA initiatives, prioritise improving customer experience, enhancing internal capabilities, and meeting automation goals aligned with the business case. As these programs grow and confidence in RPA increases, it can become a key driver of innovation, evolving from automation of existing processes to discovering novel uses for RPA.
Rule 9 – Design with people in mind
Despite advancements, fully autonomous bots remain in the realm of science fiction. Human involvement is crucial for automation success, from strategising to implementation, monitoring to maintenance. Straight-through processing isn’t always feasible, especially when leveraging probabilistic technologies like Machine Learning. Carefully devising scenarios for bot-human interaction is imperative for optimal outcomes.
Rule 10 – Cultivate an automation-centric culture
Adopting the right mindset towards automation means prioritising maximum process automation and then integrating human labour as needed. This mindset is crucial as automation possibilities continue to expand. RPA has successfully automated many repetitive tasks, and with the integration of AI and ML, it’s poised to cover a broader range of processes and decision-making domains within automation. To succeed in automation, organisations must shift their perspective and view it as the primary model for all types of work.