In one of our previous articles, we have discussed how Robotic Process Automation and Test Automation are different although they are often mistaken with each other. In this article, we are going to discuss an effective RPA governance model and how it can directly improve the chances to scale up the automation efforts and optimize ROI.
Concept of Digital Labor made its entrance into Information Technology to deliver value at speed by offering its non-invasive technology. The business units whose efforts are prone to human error welcomed it almost immediately. Especially the Business Process Automation using metaphorical (soft) bots quickly gained popularity and soon became the most matured automation solutions. But over a decade later, key stakeholders are still resisting the idea of pulling a trigger for org-wide implementation of RPA. Gartner says 60% organizations who adopted RPA will face challenges to scale the solution by 2022 and it will restrict their ROI and market dominance. But most of the successful proof of concepts are either shelved or being limited to the small-scale implementations tested against one specific business unit or functionality. The leaders are reluctant to sign up for a longer journey because it is a cost incurred decision – whether they DIY the solution or buy it from a vendor. Any mishap will not only cause financial regression but also damage the organizational structure and affect the very core of the business. Poor organizational design create confusion in roles, disturbs functional coordination, slows down the decision-making process, thus creates complexity, stress, and conflicts. But a well-structured implementation method can safe-guard the organization during the leap and accelerate its growth.
Let us start from the most obvious question ‘Why full implementation of Robotic Process Automation is prone to failure, though the POC was a success?’
Proof of Concepts are generally efforts to create a successful demonstration to a theoretical notion. They aim at proving the functional effectiveness, ease of development, and technical compatibility of the software with the existing IT environment. But for a business-wide implementation, the additional questions to be addressed are –
- Will the demonstrated use cases align with the organization’s strategic goals?
- Is the POC successful because it is intended for a smaller function? Will it stay accurate when it is scaled?
- Will it bring agility or increase tech debt due to over-reliance on automation?
- Is your workforce upskilled to welcome automation and leverage its capabilities?
Automation strategies built for specific business units will struggle to meet the needs of modern heterogeneous IT landscapes that may include cloud-native infrastructure, rapid deployment demands, large data workloads, and stringent security & regulatory compliances. The stakeholders must realize that RPA will unveil a great deal of automation scope that grows beyond the realm of IT governed enterprise systems. It is critical to create orchestration between every integration to make it a smooth sail. Bringing the existing operational model and RPA solution together will yield long-term benefits when it has a foundation of implementation framework. Three key drivers of any enterprise RPA implementation model are: A Governance model backed with an Operational model and portfolio prioritization strategy that is designed to serve the unique demands of the business. Because RPA is not about technical delivery but to substitute human efforts in various business processes. We have a hexagonal approach towards RPA implementation that is proven to improve the flexibility, scalability, and agility of the adaption.
Organizational Structure and Governance
There are many ways to normalize Automation in an enterprise but in our experience, the business-led one will sustain better.
- A well-designed organizational structure and well-defined roles and a governance strategy to support it, are undeniably critical for any transformational engagements, especially the ones you would like to scale with time. Governance and Risk Management Frameworks creation is often underrated in mid-sized organizations. And that is the most common pitfall behind unsuccessful org-wide adoptions.
- A governance framework should be in place whether the organization believes in central delivery team (CoE) and/or decentralized teams. Along with the org structure, defining proper reporting mechanisms, roles & responsibilities as well as the delivery model are key.
- When the automation portfolio is defined and aligned to the organizational structure, a practice of proactively documenting and aligning unique use cases to the organization’s mission-critical priorities will mature the model as it grows.
Process Delivery and Deployment
As they say, ‘If you can’t define it, you can’t automate it’
Setting up demand mechanisms (top-down and bottom-up), performing a clear process analysis, and delivery mechanisms are important before robotizing any processes.
This is a very time-consuming step during proof of concept. When organizations plan to scale it org-wide, delivery pipelines and deployment mechanisms may differ between processes and business units. Defining the development standards and conventions for each delivery process can save the day when business rules adjustments are needed.
Though the mission is org-wide RPA adoption, visualize the implementation as different building blocks layered and forming a structure. Each block is a use case of automating an inefficient manual process to achieve a predictable and definitive outcome while bringing down the operational expense.
Attended Robots, Unattended Robots, and Orchestrator require different infrastructure to function as planned. Depending on the process you are trying to automate, you would need an on-prem, cloud, or hybrid infra to support the function. Just like for human workers, bots would need Development, Testing, QA, and Production Environments. Database configurations for the information flow and Process Recording should be done for safe and effective robotization. Different bots would need different levels of admittance into the systems and logs. A well-defined Credential Management to give necessary access to the bots will keep the automation truly automated.
A deep analysis of work-load, frequency, data types, and risk factors will enable predictability in implementation.
Today’s digital businesses are enabled by many third-party software bound to various SLA agreements. Bringing in a soft bot to mimic human actions would require it to interact with all those platforms and software seamlessly. Checking terms of the contract, license agreements, and offered support levels before infusing RPA into the process is inevitable. Vendor requirement analysis should be performed before settling for a suitable vendor.
Performance and Risk Management
This is another aspect that is different from POC to actual implementation. While presenting a proof of concept, the vendor/teams tend to visualize the benefits of implementation. But documenting predictable pitfalls with clear descriptions and training the bot for immediate action to take during incidents is very important.
KPI formulation, quality compliance, timely audits (internal as well as external), CoE enforcement, and regular reviews are some of the best practices. Auto-healing/repairs are great, but the bot should at least be trained for Auto-rollbacks when it detects a potential risk.
People and Skillset
The former GitHub CEO said, ‘You are either the one that creates the automation or you’re getting automated’. Though AI and Automation identify as the most disruptive sources of 21st century, there are technocrats who supports a notion that Automation is rather an underprop than a substitute for Human efforts and frees up their time so they can attend to tasks that need more strategic and creative skills.
But to ensure all that, RPA implementation needs a complete buy-in from all the business units involved and not just the c-suite. The leadership should communicate the expectations from the Automation bot and work force post-implementation. A long-term resource planning that clears the required skills and competencies that would be needed to co-work with, improve, and maintain the adopted automation solution. Perhaps a clarity on how the bots impact their career would really build confidence and motivation to cope with the change. A strategic workforce management is the key to building a sustainable organization.
Robotic Process Automation is a quick window to Business Process Automation that opens door to a range of opportunities such as improving build and process quality, increasing control, and bringing in better flexibility. But lack of clear RPA vision, strategy and a misguided approach of implementation and in the absence of an operating model can quickly turn it into a catastrophe.
A bot can work 24X7. It is usually 5 times faster than a human. A bot is consistent, predictable, compliant, and scalable. RPA solutions are proven to cut down OpEx by at least 50% and they leverage existing IT assets to maximum potential. They save employee’s time and efforts so they can upskill themselves and focus on higher value activities thus, higher work satisfaction. So, clearly RPA is here to stay for years to come. Many industries including yours will adopt automation partially or completely to form a more sustainable human-machine collaboration org-wide. ‘Scaling’ will be the most essential aspect to consider before plugging in.
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