RPA is one of the top technologies playing a major role in businesses’ digital transformation journey. It has many benefits and can be implemented to automate numerous back-office and customer facing processes. However, there are still misconceptions about what RPA is, how it works, and whether it will replace human labor.
In this article, we explore the top myths around RPA and the reality behind them.
Myth 1: RPA bots are physical robots
Unfamiliarity with the concept of robotic process automation (RPA) leads to the misconception that the solution involves actual physical robots that replicate human labor.
Reality: RPA bots are software bots that leverage screen scraping technology to detect user interactions with UI elements and replicate these activities.
Watch the following video which demonstrates how RPA software works:
Myth 2: RPA’s cost outweighs its benefits
Deploying a single RPA bot can cost a company $5,000-$15,000 per year. These figures combine many underlying costs such as, licensing fees, complementary software (e.g. process mining), and consulting costs.
Reality: Looking at statistics and reports from companies that have implemented RPA we realize that the ROI is typically high. For example:
- RPA can provide cost savings ranging from 20%–60% of baseline FTE costs for financial services.
- McKinsey estimates that leveraging RPA for automating business tasks offers a potential ROI of 30–200% within the first year alone.
- Vendor case studies show that implementing RPA to complete data migration processes can have an ROI of up to 628%.
Myth 3: RPA will replace human labor
It’s been reported that 17% of businesses face employee resistance when piloting an RPA project. This is because many employees believe that RPA bots will replace them and they will be out of a job.
Reality: Although RPA bots can reduce labor intensive tasks by 80%, and the cost of leveraging a bot to complete a process is ⅕ of the cost of hiring an employee to do it, RPA bots cannot replace cognitive human labor. This is because RPA bots still require human intervention to:
- Program or design the bot to achieve a task.
- Monitor bot performance and ensure its compliance to organization policies.
Additionally, value-adding processes, such as landing clients or making business decisions, require cognitive capabilities, human emotions and judgment, as well as social interaction abilities which a software bot doesn’t have.
Myth 4: RPA is error-free
One misconception about RPA is that it provides error-free results due to its consistency and reliable underlying technology.
Reality: Many case studies have shown that RPA is, indeed, prone to error due to:
- Organizational pitfalls, such as lack of IT support and RPA implementation strategy.
- Process pitfalls, such as choosing the wrong processes to automate (e.g. edge cases, processes that don’t have a significant impact on the business)
- Implementation pitfalls, such as choosing a solution that did not demonstrate scalability.
- Technical pitfalls, such as relying on a programmable solution when the team doesn’t have skilled talents.
- Post-implementation pitfalls, such as missing maintenance requirements, or disregarding privacy issues.
Read in detail about RPA pitfalls in our in-depth article about the top 21 RPA mistakes and the checklist to avoid them.
Myth 5: RPA can only automate rule-based tasks
Most observed RPA use cases are focused on rule-based repetitive tasks such as data entry, email downloads, periodic reporting, and updating business databases.
Reality: There are also cognitive RPA solutions in the RPA space which leverage AI capabilities to perform more sophisticated tasks. Cognitive RPA tools include OCR and NLP as core features which enable them to:
- Scan handwritten documents and extract the text from it to perform data manipulation processes.
- Detect sentiment in customer emails and prioritize the response according to the tone of the text.
- Screen resumes for keywords relevant to the job description in order to help the recruiting team make a faster decision.
For more on technology myths
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This article was drafted by former AIMultiple industry analyst Alamira Jouman Hajjar.
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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