RPA automates tasks but can be tricky to scale across an organization. Process intelligence helps by providing data to guide automation efforts. This data helps identify the best processes to automate, build a strong business case, and measure success.
Welcome to part 5 of an ongoing series of blog posts demystifying “process intelligence” - the terms, the technologies, and how and why process intelligence can help improve your business. In our previous blog post, we discussed building process transparency. For this week’s blog post, we will be looking at scaling process automation. Process Intelligence helps automation leaders build a data-driven approach to their automation strategy, including when deciding what to automate as well as building a quantifiable business case.
As always, you can learn more about this and related topics by reading the Skan Process Intelligence Playbook here.
Adoption of RPA is growing fast, but one question remains: will it scale?
Tools that enable the automation of digital tasks and processes, such as robotic process automation (RPA), have surged to the forefront of transformation and IT budgets over the past several years. Below are just a few of the impressive data points which highlight the technology's incredible growth in recent years:
- According to Gartner, worldwide RPA revenues have grown from $518M in 2017 to nearly $2B in 2021, enabled by RPA’s ease of implementation as well as the market-wide acceleration of digital transformation.
- In PwC’s 24th Annual Global CEO Survey, 49% of chief executives said they plan to increase their investment in digital transformation by 10% or more, outpacing other priorities like realizing cost efficiencies, improving cybersecurity and data privacy, and growing their leadership and development programs.
- In another study, Deloitte’s third annual Global RPA Survey found that 53% of organizations have already “started their RPA journey.”
Despite this impressive market growth, RPA has not been without its share of challenges, particularly in growing from pilot projects to scaled, organization-wide implementations. For every RPA success story, there is an example of underwhelming results, unclear ROI, or failed implementation. In fact, Deloitte found that only 3% of the survey’s 400 respondents reported that their organizations have been able to scale their digital workforces.
Challenges with scaling RPA
So with all of the success and excitement surrounding the technology, why have so many companies struggled to scale RPA throughout their organization?
We have found that there are often four common challenges which impact the success of RPA efforts (this was also the topic of my recent keynote presentation at Reuters Insurance AI and Innovative Tech conference)
- #1 Using the wrong tools for process discovery: As we have stated in many previous blog posts, existing process discovery tools and approaches (such manual process discovery, process mining, and task mining) as don’t deliver a comprehensive view of processes, which makes it very difficult to build a solid foundation for an automation strategy.
- #2 Challenges with building a quantifiable business case: Building an automation business case is about more than deciding where to automate within a process, it is about understanding the exact time and effort that will be saved as well as the impact on customer experience and business KPIs.
- #3 Approaching automation as a goal, not an outcome: Too often automation is treated as a hammer in search of a nail. However, the goal should not be to find as many automation opportunities as possible, but to improve the process in the best way possible, whether that is through automation, process redesign, or training.
- #4 Not quantifying results and ROI: Scaling RPA requires proving that any investment and effort delivered results. Quantifying results and ROI is particularly important in the early days of automation implementation or pilot to show early results and success to build the future business case for even more resources to scale.
Without a detailed, data-driven foundation and understanding of the end-to-end process, overcoming these challenges becomes difficult if not impossible. Building this data-driven view is where process intelligence can help.
How using Process Intelligence can help overcome these challenges
Process Intelligence overcomes many of these critical challenges mentioned above by enabling organizations and automation leaders to use data to guide, measure, and validate their first automation efforts. Below are just a few of the ways Process Intelligence can help build a data-driven approach to automation:
- Letting data guide automation strategy: Automating based on guesswork or 'instinct' is a sure path to failed implementations, error-prone automation workflows, and significant bot rework. Process Intelligence uses data and machine learning to identify the best and most impactful opportunities for automation, to ensure that the correct processes and tasks are being automated to maximize ROI.
- Generating a comprehensive automation business case: Building internal support for automation beyond an initial pilot requires a strong and well-defined business case. Process Intelligence makes it simple to define and quantify key baseline operational metrics such as transaction volume, turnaround time, and total effort, as well as quality metrics such as SLA and allowable error rate.
- Accelerating the creation of RPA workflows: Another important RPA metric is the time and effort involved in building and managing the implementation. With Process Intelligence, RPA developers can save significant time and effort by leveraging detailed, exportable Process Design Documents to easily and quickly build RPA workflows within leading RPA tools such as UiPath, BluePrism, Automation Anywhere, and more.
- Quantifying and validating ROI of automation efforts: When building the business case for scaling RPA, it is critical to measure both the pre- and post-automation operational performance metrics. Process Intelligence makes it simple to quantify and demonstrate clear ROI for an RPA implementation to help build the business for additional investment for future efforts and scaling.
Ready to learn more? Read the Process Intelligence Playbook
Modern process optimization requires a data-driven approach. That means considering all the technologies that can help you do that.
Wondering where to get started?
Skan’s actionable Process Intelligence Playbook explains approaches to process discovery, how to get started with process intelligence, and illustrates process transparency in action through real-world applications.
Ready to learn more? Download Skan’s Process Intelligence Playbook today.
