Robotic process automation (RPA) has the capacity to deliver an extensive range of business benefits through the automation and optimization of business processes. As organizations wrestle with tough trading conditions and growing requirements to do more with less, process automation provides many of the required efficiency gains. Recognition of these benefits is reflected in the sustained growth in the value of the RPA industry, which this year is estimated to be worth around $4 billion rising to over $14 billion by 2029.
And since the evolution and widespread availability of generative artificial intelligence (GenAI) solutions, which now intersect with RPA implementations, robotic process automation looks like being an ever more popular source of competitive advantage.
As long as it’s implemented properly.
For real return on investment, there’s no such thing as a plug-and-play RPA. Even the most low code (no code) RPA software requires careful planning, execution, and management. What follows is a whistle stop review of RPA itself, followed by a roadmap of the most important considerations for launching a successful RPA initiative.
So what is robotic process automation and what is it used for? RPA technology uses software robots (or ‘bots’) to automate the repetitive, rules-based tasks that form important but mundane components of most business processes.
These bots are capable of carrying out these often tedious tasks at a speed, volume, and degree of accuracy that far exceeds human capabilities. In this way RPA adoption not only accelerates productivity and promotes process excellence, but also frees human workers to focus on more important value-adding duties.
Traditionally RPA tended to be deployed to optimize administrative tasks such as form-filling, standardized reporting, matching POs to invoices and goods receipts, or data extraction and insertion. The integration of artificial intelligence into process automation – creating ‘intelligent automation’ – has changed the scope of what’s possible with RPA. With GenAI instructing and interacting with RPA bots this more cognitive automation is capable of executing more complex tasks – including interpretation of unstructured data or use of conversational interfaces.
With the range of RPA-derived business benefits continuing to grow, the importance of successful RPA implementation is growing right alongside.
It’s important to understand that every RPA project is different. Each RPA implementation is shaped by the individual characteristics of an organization – including (but not limited to) budgets, technical capabilities, legacy techstacks, RPA vendor preference, executive buy-in and change management culture.
That said, there are key considerations that need to be addressed if the RPA journey is to be a successful one, offering a strong and enduring return on investment.
First things first, be 100% clear on RPA implementation goals – what you hope to achieve and what will justify the expense of deploying RPA solutions. Defined goals and success metrics matter. Though they may evolve over the course of the RPA adoption process, these considerations must be the project’s guiding principles.
Pinpoint those tasks and processes within the organization that are suitable for RPA automation, based on two key criteria:
Ensure they are the correct type of task for RPA software – these tend to be rules-based, high volume, and repetitive tasks that are potentially prone to human error or involve handling large volumes of data.
Analyze which workflows would benefit most from cost and time savings.
These considerations are crucial. As many as 30-50% of initial robotic process automation (RPA) initiatives fail because businesses target the wrong processes, automate inefficient processes, or fail to consider what happens to processes post-automation.
Determining the scope of the RPA implementation process should include research into indicative costs, timelines and required resources. This might have both internal and external components. Resource investment will be determined by the type of RPA implementation you opt for (we’ll get to that), so it may be worth talking to a range of RPA vendors.
Consult internally on project goals and scope – not only to get a view of likely team resource commitment but also spark stakeholder buy-in. Consider all the teams that could be impacted by RPA technology deployment and get them involved early, starting with IT. Even if an RPA vendor characterizes their solution as ‘no code’, IT professionals will be at the core of any integration with legacy systems.
Beyond IT, seek stakeholder support from across the business, from operations to HR and finance. This is the beginning of your project team that will maximize output and minimize disruption from RPA implementation.
All of this planning and scoping is essential if executive-level sponsorship of the RPA deployment is to be achieved. Without this support, there’s no RPA implementation.
There are a range of different models for implementing robotic process automation. The first of these is in-house RPA development, where an internal team develops and manages the software robots. This approach tends to incur lower long-term costs while offering maximum control, customization and internal expertise. But (and it’s a big but), the upfront investment and requirement for technical expertise is high. Realistically this model is most appropriate for digital businesses with specialist technical skills.
In the majority of cases, organizations will work with RPA vendors according to one of the following models (or occasionally a hybrid of different models when using multiple vendors):
RPA Vendor Partnership: The vendor builds and deploys RPA bots based on clearly defined organizational needs. This approach offers easy adoption (and therefore quick implementation) and allows the organization to draw on vendor expertise. Customization options, costs, and potential for vendor lock-in (not all RPA solutions play well with others) will vary according to the individual solution.
RPA as a Service (RPAaaS):RPAaaS is a cloud-based model for using RPA tools where the RPA software and infrastructure is hosted and managed by a third-party provider rather than building and maintaining it within the organization’s techstack. This vendor-managed option offers rapid deployment, low upfront investment, and easy scalability. On the flip side, there is increased vendor dependence, reduced control and customization, and reliance on third parties to manage security and RPA infrastructure.
Within each model, there are many RPA software vendors providing a range of different solutions. Take some time to compare the features, integrations, level of internal resource required, level of external support provided, scalability, and pricing.
The key is to find the RPA solution most suited to achieving your implementation goals within the constraints of budgets, time frames and in-house technical capability.
Whatever pre-built out-of-the-box integrations your selected RPA solution provides, successful implementation relies on close collaboration with your vendor on bot design and deployment. RPA development needs to be rooted in business best practices and bot behaviors optimized to replicate (and improve) human workflows.
Through this collaboration, RPA bot design will be shaped to incorporate all required application interactions, data transfers, error handling, logging, notifications and triggers.
One of the biggest RPA implementation challenges is cultural rather than technical – winning the hearts and minds of the workforce around the benefits of process automation. Successful implementation requires teams to be open minded as a minimum, and ideally to embrace the new working practices.
But deployment of RPA technology necessitates changes to the roles and responsibilities of employees, and there may be resistance from individuals fearing job losses. So it’s vital to be transparent in addressing such concerns, to emphasize the positive impact to the individual, and to provide comprehensive training where required.
Test, test, and test again. Work with your RPA vendor to pilot limited bot deployment in discrete areas and monitor bot activity and performance data closely. The integration of software robots can then be ramped up gradually and safely – using proper access controls, encryption, and security monitoring. Any RPA developer will tell you that this initial deployment is not the end of the story: using performance data and user feedback bot activity can be refined over time.
Like every other enterprise IT asset, RPA software requires clear, centralized oversight and governance. This will entail creation of policies for RPA bot access, data security, change controls, and even disaster recovery. Similarly, controlled, monitored procedures need to be established for robot maintenance, issue tracking and upgrades.
But RPA governance also entails careful monitoring of changes to tasks, processes or workflows that intersect with automated processes. An RPA tool won’t detect or react to these changes without instruction – but the changes could compromise, or entirely derail the process automation.
No RPA use case is a one-and-done exercise. It’s iterative, evolutionary. Therefore it’s vital to measure the impact of process automation against expectations and project success metrics in order to refine RPA software performance. It’s also important to track any unintended consequences of the RPA implementation (whether positive or negative), and to identify potential new use cases for future automation.
Using process mining techniques and technologies, the Celonis platform can enhance RPA implementation in several key ways.
For the uninitiated, process mining analyzes event log data from IT systems to automatically discover and map end-to-end business processes as they actually operate, and unlock value opportunities. The insights this accurate, objective data provides can be used to great effect before, during and after RPA deployment.
With the RPA roadmap still at the discovery and planning stages, for example, the Celonis system’s insights can be used to identify the processes that will deliver the greatest boost to project success metrics in response to automation. Not only does this avoid automation of inefficient processes, but also provides a data-informed rationale for any RPA investments.
Once RPA software has been deployed – whichever vendor or vendors you opt for – Celonis can provide real-time monitoring and measurement of bot effectiveness. Additionally, with its living, breathing visualization of business processes, the Celonis system can track the impact of automation across all business functions.
However, where RPA implementation gets seriously supercharged with Celonis, is the use of process mining insights to provide situational awareness of end-to-end processes. With this awareness, process anomalies across your tech ecosystem can be detected, triggering the activation of RPA robots to intervene with process countermeasures.
Again, this level of RPA orchestration and activation is available via the Celonis platform’s insights, whether you opt for one (or more) of the many excellent specialist RPA vendors or choose Celonis’ own Action Flows solution. Action Flows enable the automation of diverse business workflows tailored to your requirements, offering a robust mechanism to initiate and manage an RPA software robot, or multiple software robots.
But no matter how you choose to activate your RPA robots, process intelligence data from the Celonis system creates an intelligent foundation for process automation success.
There are many RPA implementation challenges to overcome, and RPA strategy is bespoke to every organization. Common to every successful implementation, however, is the cast-iron certainty that RPA success can only truly be achieved through commitment to continual improvement.
Organizations change, trading conditions change, and business priorities change – requiring processes to flex and adapt. Where processes need to adapt, process automation needs to adapt. This means RPA software performance must be carefully monitored and RPA solutions shaped and reshaped by data-driven business insights.
The process of process optimization never stops. But the benefits RPA can deliver make the effort well worth it.