According to a Deloitte survey, 78% of those who have already implemented RPA expect to significantly increase investment in RPA over the next three years. However, 50% of RPA projects are at risk of failure due to a lack of visibility into existing processes to automate and a lack of familiarity with available RPA vendor solutions.
To resolve such issues, RPA vendors such as IBM developed frameworks for RPA implementation. In this research, we build upon existing frameworks to explore the steps required to prepare, implement, and launch a successful RPA project. If you are considering starting to work with RPA, read our guide that starts from the beginning of the journey: process identification.
1. Have visibility into the existing processes with the help of internal interviews and processes mining
Processes can be understood by interviews with the operators that currently run the process but relying only on this approach is
- Costly – Interviews take time
- Error-prone – People have imperfect memories and are prone to numerous cognitive biases
An alternative is to combine interviews with analysis derived from task/process mining. Process mining software enables companies to analyze their logs to understand real-life process flows. Task mining companies augment this log data with video recording of employee actions. Of course, these vendors also automatically remove non-public personal information NPPI from these video materials.
Using real-time data and event logs, these solutions show the actual conditions of processes, help identify bottlenecks, unnecessary steps, and provide factual insights.
2. Improve and simplify the existing processes
Processes evolve due to regulatory pressures and market pressures. Though they are sometimes improved with top-down lean or 6 sigma projects, these are few and expensive. Therefore, most processes have significant potential for improvement.
Just consider the use of fax machines in the US healthcare system. Numerous media including Vox report how the US healthcare system relies on hospitals sharing records with faxes or hand delivered documents because digital healthcare records were not built in a compatible manner across different institutions.
So before proceeding with the RPA implementation, it is worthwhile to look for improvements in the process as process improvements can
- Simplify the process
- Make it more understandable therefore reducing the necessary programming and auditing effort
- Improve customer experience
3. Choose your partners
There are numerous RPA partners and consultants that can help roll out an RPA solution. While it may seem like a quick & cheap approach to use just an RPA solution for RPA deployment, case studies indicate that companies save significant time and money by using a best-of-breed approach (i.e. using process mining and machine learning tools in combination with RPA).
To learn more about how to pick an RPA technology partner, download our whitepaper:
4. Develop your solution
Initially, a detailed process map needs to be prepared identifying which parts of the process will be automated. Here’s an example from WorkFusion:
The contribution of subject matter experts from your organization is critical while preparing the process map. This is especially relevant if the process is not well documented. In our experience with large companies, most processes are not well documented.
After the role of RPA bots in the process is clarified, RPA bots can be programmed. Trade-offs such as quicker deployment vs more flexibility need to be weighed carefully while developing the solution. Following a well-established lean software development and quality assurance processes will ensure that business and technical teams are aligned and progressing.
A recent development is the launch of RPA marketplaces which provide reusable plugins/bots to facilitate RPA development. Implementation teams would be well advised to check out their RPA platform’s marketplace for readily available code and not reinvent the wheel. Feel free to read more on reusable RPA bots.
Developers can use Argos Labs’ “Python to Operations” toolkit to build customized plug-ins using Python, or reuse a plug-in from marketplace and save developing time.
5. Pick your task mining/process mining solution
While it is not mandatory to use a process mining solution for an RPA implementation, having a process mining solution helps companies
- prioritize automation opportunities
- understand processes in detail which facilitates RPA development
- track process changes after RPA implementation to ensure that the deployment is successful.
6. Select your RPA solution
We have a detailed guide on how to choose your RPA technology provider. Since RPA is an evolving field with new solutions such as no code RPA, it is helpful to spend a bit of time to understand the latest list of things to pay attention to while buying an RPA solution.
To see the full list, feel free to visit our list of RPA software vendors on our website.
7. Choose the AI/ML providers to facilitate your RPA deployment
While RPA is great for automating rules based tasks, it is hard to automate more complex tasks such as getting data from documents with RPA. RPA providers may provide ways to program such functionality but since this is not their focus area, the solutions do not tend to be the best performing ones. It makes sense to research AI/ML providers that solve the specific problems you are trying to solve.
Some common RPA use cases that can be accelerated by AI/ML providers include:
- Invoice automation is easy to automate with almost complete no-touch processing by integrating an API.
- Numerous RPA projects include document data extraction and specialist ML providers provide competitive solutions in that field. Check out our comprehensive, data-driven list of document data capture companies.
8. Test your solution
The importance of RPA testing can not be overemphasized. We explained 3 different types of RPA. For example, in attended automation, minor differences in users’ systems such as some users using MacBooks or even different screen resolutions can lead to unexpected bugs. All major scenarios need to be thoroughly tested before the pilot. Using historical data enables more realistic tests
9. Run a pilot
- Set targets for the pilot: These could be about accuracy (e.g. share of successfully processed invoices) or automation (e.g. cases completed without human intervention).
- Run a live pilot: Each day, the team in charge of the process reviews a random selection of bot output.
- Evaluate pilot results: Run a detailed evaluation considering rare cases and difficult inputs. Only finalize the pilot when previously agreed targets are met.
10. Go live
- Design the governance of a new, bot-driven process with support from the current team. For example, put in place mechanisms for maintenance to keep the bots functioning as the process changes.
- Clarify roles and responsibilities
- Build a fallback plan: A fallback plan will be helpful if the RPA solution requires rework after roll-out. Though such a plan would not be used most of the time, it is quite beneficial to be prepared when fallback is needed.
- Communicate new processes to all relevant stakeholders.
- Analyze results:
- Monitor results: During implementation, process mining tools can track bot performance and measure the level of automation to see if the project achieved its goals
- Record savings and analyze results to inform future RPA projects.
11. Maintain the RPA installation
In line with changes in the market and regulation, you will need to change your processes. Putting in place a capable team in charge of the installation is critical for the future success of your RPA installation. Companies either set up RPA Centers of Excellence (CoEs), work with service providers or train their business personnel on maintaining existing RPA installations and building new automation. To support the team in charge of RPA deployments, process mining tools can help them monitor changes in processes and as a result, identify when RPA bots need to be maintained/modified.
For more on RPA implementation
Before RPA implementation and after the solution rolls out there are necessary steps to follow. Read in-depth about:
- Identifying and Prioritizing Processes to Automate with RPA
- 3 Steps to Get Buy-In For RPA
- 5 Ways to Measure RPA Post Solution Success
If you need more answers about RPA, read our comprehensive whitepaper on the topic:
If you believe your business will benefit from automation solutions, scroll through our data-driven lists of:
And we can guide you through the process:
Image credit: © 2022 IBM Corporation
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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