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7 Steps to Successful RPA Implementation in 2024

Written by
Cem Dilmegani
Cem Dilmegani
Cem Dilmegani

Cem is 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 focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

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7 Steps to Successful RPA Implementation in 20247 Steps to Successful RPA Implementation in 2024

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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.

With a process mining solution or task mining tool, companies can:

  • prioritize automation opportunities
  • understand processes in detail which facilitates RPA development
  • track process changes after RPA implementation to ensure that the deployment is successful.

Feel free to read our research on process mining, task mining and the top companies in that industry to learn more.

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 and implement an RPA solution

This step requires you to decide whether to implement a pre-built RPA solution or develop one using Python RPA tools and libraries. Businesses lacking the necessary resources and skilled personnel to create or deploy an RPA bot can consider collaborating with RPA partners.

1. 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.

2. 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:

RPA use case design workfusion
Figure 1: A use case design. Source: WorkFusion’s Automation Quickstart Guide

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.

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:

Guide to Choosing an RPA Technology Partner

4. Facilitate your deployment with other tools

Businesses encounter several challenges when deploying an RPA tool, including:

  • Uncertainty about which task to start with
  • Inability to monitor the entire deployment process
  • Complex processes and industry-specific operations that may require more advanced solutions.

1. Pick your task mining/process mining solution

When selecting a process mining tool, consider the following:

  • Determine if the tool provides task mining, ML applications or simulation capabilities like DTO, and assess whether these features are necessary for your needs.
  • Explore whether certain RPA tools offer process mining capabilities within their automation platforms.
  • Evaluate open-source process mining tools, especially if you have a skilled team capable of leveraging such tools effectively.

Learn more on which tool to choose by reading our data-driven benchmarks, such as:

2. Choose the AI/ML providers

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:

5. Test your solution & run a pilot

Conducting RPA testing and running a pilot based on the results, is essential for ensuring the successful deployment of RPA solutions.

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.

Once the testing is completed, it’s crucial to proceed with running 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.

6. 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.

7. 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:

If you need more answers about RPA, read our comprehensive whitepaper on the topic:

Get RPA Whitepaper

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:

Find the Right Vendors

Image credit: © 2022 IBM Corporation

Cem Dilmegani
Principal Analyst

Cem is 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 focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

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.

Cem's hands-on enterprise software experience contributes to the insights that he generates. He oversees AIMultiple benchmarks in dynamic application security testing (DAST), data loss prevention (DLP), email marketing and web data collection. Other AIMultiple industry analysts and tech team support Cem in designing, running and evaluating benchmarks.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology 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.

Sources: Traffic Analytics, Ranking & Audience, Similarweb.
Why Microsoft, IBM, and Google Are Ramping up Efforts on AI Ethics, Business Insider.
Microsoft invests $1 billion in OpenAI to pursue artificial intelligence that’s smarter than we are, Washington Post.
Data management barriers to AI success, Deloitte.
Empowering AI Leadership: AI C-Suite Toolkit, World Economic Forum.
Science, Research and Innovation Performance of the EU, European Commission.
Public-sector digitization: The trillion-dollar challenge, McKinsey & Company.
Hypatos gets $11.8M for a deep learning approach to document processing, TechCrunch.
We got an exclusive look at the pitch deck AI startup Hypatos used to raise $11 million, Business Insider.

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Amit Dasgupta
May 03, 2021 at 14:15

Nice. Very in-depth.

Rohit Bansal
Jun 21, 2019 at 10:01

Thank You for sharing a complete guide on best practices to be followed for implementing RPA. it’s Very Informative and Useful For us

BoTree Technologies
Apr 18, 2019 at 10:10

Thank You for sharing a complete guide on best practices to be followed for implementing RPA.

Flournoy Henry
Oct 31, 2018 at 19:43

Great article. FYI, at the bottom you have a typo or wrong link in the following. The link is to a BluePrism study, not UiPath:

We leveraged UiPath

Dec 28, 2018 at 17:37

Hi Flournoy,
Well spotted! Thanks for letting us know, fixed it

Oliver Harris
Jul 02, 2018 at 16:28

You put together a very thorough article. The simple truth is that sometimes projects fail, for a very large number of reasons. According to IBM Systems Magazine, up to 25% of technological projects fail downright, while up to half of them require extensive revisions by the time they are set to go. I’d like to add some basic pitfalls that I stumbled upon: not choosing the right processes to automate in the beginning, trying to implement robotic process automation on your own, not setting clear objectives for your automation strategy and not ensuring the scalability potential of your software robots.

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