Robotic Process Automation (RPA) is gaining acceptance gradually in variable industries however we have spoken to a number of executives and there are plenty RPA failure stories, too. A recent survey showed that >40% of RPA projects fail to deliver expectations in terms of
- implementation time
- implementation cost
- cost savings due to RPA
- benefits to analytics
We outlined some of the most common pitfalls that lead to these gaps between reality and expectations. We explain these points in detail below:
1- Lack of time commitment from local team
2- Lack of leadership buy-in
3- Lack of IT support
4- Lack of support from Analytics/Data function
5- Lack of support from HR
6- Unclear responsibilities
7- Company lacking a clear RPA strategy
8- Choosing a process that changes frequently
9- Choosing a process with insignificant business impact
10- Choosing a process where errors are disproportionately costly
11- Choosing a process that involves higher level cognitive tasks
12- Choosing a complex process. Though its sub-processes are simple, process itself may be complex if it has too many sub-processes
13- Choosing a process where better custom solutions exist
14- Striving for end-to-end automation when it is not cost-effective
15- Pursuing in-house RPA development with in-house teams that do not have enough capacity
16- Choosing a solution that requires intensive programming
17- Not relying on RPA marketplaces and other readily available tools
18- Choosing a solution that did not demonstrate scalability
19- Not building for scalability
20- Not taking maintenance needs into account
Organizational pitfalls: Alignment is key for any project’s success
Especially in projects where there’s no outside implementation partner, organizational alignment is key because your organization will be responsible for the whole solution. Both local team and leadership needs to be fully on-board, with top management regularly reviewing progress and local teams devoting significant time to automating processes getting help from departments like Strategy.
Adjacent teams that rely on automated processes also need to be notified and convinced in advance and especially in the beginning of automation they should be on the lookout for any issues.
These are not only implementation related issues. Once the RPA solution is rolled-out, it will require maintenance as processes are changed to make them more efficient, effective or compliant with new regulation. Satisfying these maintenance needs are important and can be challenging if companies do not dedicate enough resources and management attention or if they do not clarify responsibilities.
Do not forget to get support from these key functions
IT roadmap needs to be examined before choosing the RPA solution. For example, if IT is planning to migrate to Citrix, that will have implications for the RPA tool that was chosen. Additionally, IT can act as a coordinator in these tech purchase decision by other units. If there’s already an RPA implementation in the enterprise, IT should bring both things together and help them learn from one another’s experiences. Shadow IT which leads to different divisions using a myriad of tools leads to sub-optimal IT costs and data silos.
Data/analytics is on most senior leaders’ agendas and bots have the potential to create significant amounts of data. If analytics function is involved early on, the format, frequency and other important decisions about the data created by bots can be considered early on. This leads to bots creating valuable data as opposed to simple diagnostic information which is the case in most bot installations. However, benefits of RPA to analytics should not be too exaggerated as we explained before.
HR is important to get aligned with or else RPA training programs may never take their place in the corporate training schedule. RPA training is important to reduce reliance on RPA consultants and empower employees.
Company having a clear RPA strategy is key for sustainable RPA deployments
There are various models of RPA deployment or maintenance. It is important to decide the company’s RPA approach to ensure that teams do not waste time creating an RPA approach from start and end up creating redundant responsibilities. As PwC report points out, an RPA Center of Excellence (CoE), IT, finance, or the teams in charge of the process could be responsible for the RPA deployment. Additionally, company could be relying on external consultants for RPA deployments.
Process pitfalls: The most important decision is the process to be automated
Ideal process is impactful, simple, does not require high level cognitive tasks, lacks a custom solution and is difficult to be automated with non-RPA techniques. Let’s explain all these points:
Business impact is key to excite the organization. RPA project on a process with low business impact will have little momentum. Processes with high business impact tend to be high volume, high effort processes that touch the customer. Nothing like telling a CEO we can approve loans in 2 minutes rather than 2 days.
Process should either be fault tolerant or these needs to be a system for quality assurance. Since RPA bots rely on UX to complete tasks, they can make errors when there are UX changes. In highly critical tasks, RPA may not be the best choice. However, RPA can be deployed for almost all processes as long as the cases that lead to costly errors are verified via other mechanisms (potentially including manual controls) as well.
Process should not rely on ill-defined, high-level cognitive tasks. Reading an email that explains a number of tasks which include communicating with a client and reviewing an advertising image are quite simple tasks for a marketing professional. However, currently these are not well defined tasks and are therefore not suited for automation. For example, it’s difficult to explain what makes a good advertising image. This does not mean that such automation programs are impossible. An automated system could use crowd-sourcing to pick right advertising but it would be costly, slow and hard to program which are not the right qualities in good automation software projects.
Process complexity is a separate issue from high-level cognitive tasks. A process can involve only low level cognitive tasks like adding numbers, copy pasting and so on. However, based on different input, different set of instructions may need to be executed. For example, depending on a user’s answer to a question, a different department may need to handle the user’s request through a different process. Such a process can be complex and it would be difficult to extract the correct process flows in different scenarios.
Currently it requires a lot of manual process data extraction, interviews and lengthy pilots to successfully automate such processes. However, this is an ongoing area of research for RPA vendors and startups which aim to auto extract process data from logs and videos to successfully automate complex processes. We call these self-learning automation solutions. Newer solutions called cognitive automation or intelligent automation (depending on the company promoting the solution) are able to watch as automatable work is performed by humans, learn the automation needed and takeover when ready. We are investigating such innovative solutions and listing them as they become available.
RPA is not the only mode of automation. Replacing legacy systems or building powerful API interfaces to legacy systems can help you automate numerous processes with less effort than building RPA solutions. Because RPA systems use imperfect screen scraping solutions, upgrading legacy systems offer faster and more accurate automation solutions.
Once RPA demonstrates its value, keeping the organization focused is hard
The first process to be automated will likely be selected with a robust process. If that automation delivers significant value, all senior managers will be excited to jump aboard and start automating their processes. This can lead to a loss of focus as RPA experts are stretched thin due to demands from varying departments. Automation of less critical processes will deliver less value than initial pilot and this can lead to an “automation-fatigue”. Though numerous departments spent significant effort to automate processes, they will end up with little improvement.
To keep the organization motivated, RPA experts should be focused on a limited number of high impact projects. As RPA expertise increases in the organization, individual teams will start to take initiative and automate their own processes. This is the ideal state as automation will improve operations without the need for significant time commitment from senior leadership
Full process automation is desirable but it may not be economical
Many processes are 70-80% automatable without great difficulty. However, as the level of automation increases, businesses face diminishing returns. Automating a process completely may be five times more expensive than automating a process up to 80% because that additional 20% will require automation code that’s a lot more complicated than the code required to automate up to 80%. Process redesign, keeping human in the loop for edge cases are all solutions to operate 80% automated processes with optimal efficiency.
Implementation pitfalls: RPA development is a focused effort, rely on teams that have capacity to deliver
Deploying RPA bots require understanding processes and programming RPA bots. While these take weeks, they require focus. Unless there are teams within the organization who have the time for RPA deployment, it would be wise delay the project or rely on consultants to complete the RPA implementation.
Technical pitfalls: RPA is an evolving field, don’t buy outdated solutions and leverage the full capacity of your chosen solution
Especially when you are outsourcing your RPA setup to a consultant or BPO, keep in mind that they may have a conflict of interest. For example, programmable solutions take longer to implement and therefore result in longer billable hours, however programming time may be reduced with low code/no code solutions.
Bankers, especially those on the technical side, like to boast how their banks are actually tech companies and how they are using the state of the art. However when we start discussing how they implement RPA solutions, some have not even heard about self-learning or low code/no code solutions. We discussed self-learning solutions above, another interesting new area is no code RPA solutions. While normal RPA solutions require intensive programming, no code RPA solutions replace time consuming coding with recording and drag and drop interfaces which aim to democratize RPA.
Reusable RPA plugins/bots which are available on RPA marketplaces, reduce RPA development time and save your teams from reinventing the wheel. Ensure that your teams make full use of the RPA tools that your chosen RPA platform offers. Most leading RPA companies have RPA marketplaces with reusable code. Feel free to read our RPA marketplace or reusable RPA bots articles to learn more.
Finally, using bots that have been proven in large deployments reduces future risk of scalability issues. Most major RPA providers have large (100+ bots in a company) deployments so this should be a smaller concern. However, it would be good to check your RPA software providers’ largest deployment size.
Post-implementation pitfalls: These can slow down or even stop an organization’s transformation
Scalability is widely quoted as a major issue especially for Fortune 500 organizations looking to scale up their RPA implementations. Managing an RPA installation involves starting and stopping bots due to business necessities, managing the maintenance process, ensuring that error rate is acceptable. RPA management should also demand very low time commitment to ensure that RPA’s benefits are optimized.
Complexity of managing an RPA installation can grow quickly as number of bots, issues encountered by bots and processes impacted by bots grow. Ensuring that bots are audited post implementation, simplifying bot architectures and following a gradual approach to automation can help facilitate management of RPA installations.
Given increasingly large RPA installations, it seems that vendors are effectively tackling this problem. For example, UiPath in partnership with IBM, Accenture, EY and PwC rolled out RPA bots in Sumitomo Mitsui Financial Group to automate activities in 200 processes leading to 400K hours of annualized savings. 400K hours/year is approximately worth 250 FTEs making this one of the largest RPA deployments globally.
Maintenance is the most important post-implementation challenge. Changes in the regulatory or business environment will sooner or later require changes to bots. Since most bots are programmed, following software best practices in programming allows bots to be maintained with relative ease. Still, changes need to be prioritized and necessary effort needs to be devoted to bot maintenance.
RPA essentially adds a new responsibility to process owners. While they will likely be managing a smaller workforce that produces higher quality results, they will need to allocate time to manage and maintain their bots.