Choosing the right processes to automate

Rob Mills Rob Mills
June 13, 2019 AI & Machine Learning

How to get RPA right

So, you’ve built your robotic operating model and you’re starting to deliver processes for your digital workforce, but how do you know if you’ve identified the right processes for automation?

When you ask your business team, they’ll quickly tell you their ‘pain points’ and identify these as the goals for your automation. But are these actually the best and most strategically relevant processes to automate as part of your wider digital transformation?

One of the most important steps when beginning any project at scale starts with determining the correct tasks to automate in a business. This is a critical first step in setting up robotic process automation (RPA) and getting the most bang for your buck.

This is your opportunity to realise quick return on investment (ROI) and demonstrate the efficiency of RPA to stakeholders, while also evangelising a culture of continuous innovation within your organisation.

Overcoming automation friction

When an RPA journey begins, there’s usually a pocket of enthusiasm within the business, whether from a senior executive who has a vision for digital transformation, or from a business unit itself. They may want to do more with less or have a particular business problem they’re trying to solve.

This enthusiasm provides the initial thrust to create a pipeline of processes to be automated. And it’s that thrust that’s needed to get a project going.

When an RPA project doesn’t scale, however, it’s often because it isn’t achieving wider cultural adoption or is weighed down by an ill-defined approach to pipeline management.

There are a number of challenges that lead to this. The most common is where there’s a big initial push to collect a list of processes, but no wider structure is in place to ensure that it’s consistently reviewed, so processes keep on churning.

We often see the start of many RPA journeys targeted towards a limited to a number of business areas, because they are often the most enthusiastic or the friendliest team, rather than the area that can provide the biggest ROI.

Pipelines also tend to be populated by processes where people feel most comfortable letting go of, rather than the best candidates for automation. While sometimes that can be the same thing, often they’re not.

Another challenge is when prioritisation isn’t strongly enforced. This leads to processes being automated in order of receipt or who shouts the loudest, rather than those that have a sound business case in place.

In many cases, a lot of time is wasted up front doing deep dives into many processes, rather than applying basic criteria to see if there are any show stoppers to focus on. Where pipelines are managed most effectively, organisations have a well-defined process for submitting, triaging and defining candidates for automation.

Without having a proper structure in place, it’s sometimes difficult to get past the friction. This can limit an organisation’s success in rolling out a transformational digital workforce.

Discovering the right processes

Proper process discovery helps organisations identify the processes that are automation-ready and primed to deliver the greatest returns. By ranking processes by ease-of-automation, hours returned to the business and even potential savings, organisations gain an ongoing automation pipeline to support continued growth and scalability.

When process discovery is done effectively, it minimises the cost of process discovery itself. It shortens the time to automation because the structure is in place and expands the digital workforce across the organisation faster. All of which helps to deliver ROI.

The good news is that there are some fundamentals that can help most organisations to build a robust and consistent pipeline. There are three key stages in process discovery that deliver the right business outcomes.

The first is process triage. By applying high-level criteria, you can quickly identify the processes that have potential for automating immediately and those that don’t. Focus on areas of the business where there’s the highest likelihood of return, rather than those that people are most enthusiastic about it.

Once you have identified potential processes, you can then move onto the second step of creating a shortlist by assessing them further in terms of suitability for automation and their potential benefit.

The final step analyses and defines the processes in much greater depth, both from the perspective of looking at the process itself and the associated meaningful insights. By carrying out a detailed analysis at a task level, you can definitively identify those processes to be automated and prioritised.

The benefit of this approach is that processes are prioritised based on business objectives and ROI. As new processes are added to the pipeline, they go through the same process.

By frequently assessing these processes, you can give regular feedback to the business. Decision makers and stakeholders actually see progress, which helps automation projects maintain momentum.

This article originally published in Computerworld.

  • Experfy Insights

    Top articles, research, podcasts, webinars and more delivered to you monthly.

  • Rob Mills

    Tags
    Artificial Intelligence
    © 2021, Experfy Inc. All rights reserved.
    Leave a Comment
    Next Post
    Mobile Cobots on the Move – The Next Wave in Industrial Growth!

    Mobile Cobots on the Move - The Next Wave in Industrial Growth!

    Leave a Reply Cancel reply

    Your email address will not be published. Required fields are marked *

    More in AI & Machine Learning
    AI & Machine Learning,Future of Work
    AI’s Role in the Future of Work

    Artificial intelligence is shaping the future of work around the world in virtually every field. The role AI will play in employment in the years ahead is dynamic and collaborative. Rather than eliminating jobs altogether, AI will augment the capabilities and resources of employees and businesses, allowing them to do more with less. In more

    5 MINUTES READ Continue Reading »
    AI & Machine Learning
    How Can AI Help Improve Legal Services Delivery?

    Everybody is discussing Artificial Intelligence (AI) and machine learning, and some legal professionals are already leveraging these technological capabilities.  AI is not the future expectation; it is the present reality.  Aside from law, AI is widely used in various fields such as transportation and manufacturing, education, employment, defense, health care, business intelligence, robotics, and so

    5 MINUTES READ Continue Reading »
    AI & Machine Learning
    5 AI Applications Changing the Energy Industry

    The energy industry faces some significant challenges, but AI applications could help. Increasing demand, population expansion, and climate change necessitate creative solutions that could fundamentally alter how businesses generate and utilize electricity. Industry researchers looking for ways to solve these problems have turned to data and new data-processing technology. Artificial intelligence, in particular — and

    3 MINUTES READ Continue Reading »

    About Us

    Incubated in Harvard Innovation Lab, Experfy specializes in pipelining and deploying the world's best AI and engineering talent at breakneck speed, with exceptional focus on quality and compliance. Enterprises and governments also leverage our award-winning SaaS platform to build their own customized future of work solutions such as talent clouds.

    Join Us At

    Contact Us

    1700 West Park Drive, Suite 190
    Westborough, MA 01581

    Email: [email protected]

    Toll Free: (844) EXPERFY or
    (844) 397-3739

    © 2025, Experfy Inc. All rights reserved.