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5 Best Practices for Process Automation Implementation in 2024

Updated on Jan 11
5 min read
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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Some of the challenges facing process automation adopters are that they might:

  • Not know which processes to automate first
  • Not know which tool is right for them
  • Be unfamiliar with the technical requirements
  • Be in the dark about the implementation cost 

These challenges may lead to costly, failed automation initiatives.

To prevent this, we will go over: 

  1. How to identify processes to automate,
  2. How to identify the technical and business requirements,
  3. How to choose the right type of automation tool,
  4. How to choose the right vendor,
  5. And the top three points to remember when starting process automation.

1. Identify processes to automate

  • Do you have a data-driven approach to identifying which processes to automate? You should have a clear understanding of your as-is processes and choose the most suitable ones to maximize the impact of automation.
    • Process mining and a digital twin of an organization (DTO) can help your business see the full picture of your processes. The tools can also allow you to identify the best processes to automate.
      • If you’re still unsure which processes need automation most, then choose the most-repetitive tasks in your day. Data-entry processes are also easy to automate. Plus, look at where your business experiences the most human errors. Then consult an automation specialist about how to reduce those errors with a better workflow system.
  • Do you understand your processes in enough detail so you can automate them, taking into account process and input variations? You shouldn’t try to automate an old process with flaws in it and inefficient data-processing policies. First, streamline, simplify and improve your workflow. Then you’ll end up with a faster, better-automated system.
    • You can leverage the different process intelligence tools, such as process mining, task mining, or DTO to do that. This process ensures you understand processes in enough detail to identify deviations, bottlenecks, and the root causes behind such failures.
      • Moreover, you can understand whether the process would not need any automation, per se, but rather a simple modification. 

Read more on how process mining enables automation.

2. Identify technical and business requirements

  • What are the technical requirements to automate the chosen processes considering your current software stack?
  • Do you work in an industry that requires your data-sharing policies to follow specific regulations? Then you need an automation solution with extra security features. For example, the Health Insurance Portability and Accountability Act governs healthcare industry systems.
  • Get other team members onboard and finalize requirements in your process automation plan to get buy-in.
    • List them exactly what benefits they will receive from the new system. 
    • Explain which problems the new system will eliminate. 
    • Discuss with your team managers how much control the bots should have and when humans need to step in during customer interactions.

3. How to choose the right type of automation tool?

Even before choosing the specific process automation tool from a specific vendor, it is helpful to decide what type of automation tool to use. We have previously discussed the three main process automation tool.

If you are unsure about choosing the right type of process automation tool, ask yourself the following four questions in chronological order:

  1. Does the process involve only a single software tool?
    • Yes: Automation tools of the software involved in the process. Most software has at least basic automation functionality built in.
    • No: Low code, RPA, or business function-specific automation tools like ITPA. All of these tools can handle the automation of multiple software tools.
  2. Is the process specific to a business function?
    • Yes: Specialized automation tools like ITPA could reduce the time to develop the necessary automation feature
    • No: RPA or low code is likely to be more appropriate solutions
  3. Does the process involve legacy tools?
    • Yes: RPA. Since RPA has been developed to use the UI like a human, RPA is the go-to tool to automate tasks that involve legacy systems.
    • No: Other solutions
  4. Does the process or the software user interfaces change frequently (e.g. monthly)?
    • No: RPA needs to be reprogrammed with process changes that are labor-intensive.
    • Yes: Other automation tools are easier to program and may not require reprogramming with UX changes. This is because they automate by extracting data from other programs via APIs. APIs are designed to be rarely changed as changes break any program that depends on those APIs

4. How to choose the right vendor?

There are numerous vendors in every automation category. In addition, all these tools have their learning curves. If your team is familiar with an existing tool, it could be useful to go with that tool especially if the process to be automated is not significant or complex.

For more details, read our in-depth guide to “How to Compare Robotic Process Automation Vendors” here. And if you are interested in leveraging an RPA solution afterward, we have a data-driven list of vendors prepared

5. Key points to remember when starting process automation

1. Start in phases 

According to most experts, you should start in phases. The main reason for this approach is that it’s much easier for everyone. You set concrete goals that you want to achieve through these RPA programs first. 

Then you test the initial small process automation to see if it matches those goals. If it doesn’t, then you can try another program. You can cancel the trial easily before you make a big investment of time and money into overhauling your whole system.

2. Train your team beforehand

Also, make sure every team member gets adequate training before you launch the new process automation tools. It’s not enough to train your employees about what their new roles are in the process. They have to understand the overview of how the entire process works. That way, they’ll know how to resolve any unexpected problems that may come up during the transition phase. It won’t be possible to train your staff completely in one day. 

Still, pre-training is essential to ensure a better roll-out and prevent disasters on the first day of launching your new software. Always do an in-house test run first where possible before you begin using the programs with live customers.

3. Have an escape route

Finally, you will need to make sure that your RPA developer includes an “escape route” in your program for emergencies. A real human should have the ability to step in and take over the program at any time. You never know when you’ll need to take charge so that business can continue as usual. Expect that sometimes unforeseen problems occur with the first automation.

If crossing over to automation still makes you feel uneasy, then read about our Three Alternatives to Consider Before Automation Investment.

For more on process automation

If you are interested in learning more about process automation, read:

If you believe your business would benefit from adopting a business process automation solution, we have a data-driven list of vendors prepared.

And for broader use cases, head over to our Automation Hub, where you will find vendors under different automation categories.

If you still have questions about process automation tools, feel free to contact us:

Find the Right Vendors
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.

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AIMultiple.com 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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