According to a 2019 survey performed by O’Reilly, 85% of businesses were utilizing or considering the use of AI.
These companies weren’t just big tech firms like Facebook and Twitter, but also businesses ranging from retail to logistics and transportation. The reason? Process automation is everywhere - and it has boosted AI to provide incredible value and ROI to companies in almost any industry.
Yet, organizations struggle to define their process automation strategy. There is a simple explanation for this - there are just too many options and it is easy to get different terms mixed up when they are used interchangeably.
We’ve decided it's time to shed light on the various forms of process automation, including Artificial Intelligence (AI), as well as Robotic Process Automation (RPA) and Intelligent Process Automation (IPA) - and what makes for a good process automation strategy
Let's get to it!
RPA vs. AI vs. IPA - same, but different?
To better understand the differences in technology and what they offer, it’s important to take a look at each individually.
What is Robotic Process Automation?
Robotic Process Automation (RPA) is the term used to describe software that can be programmed to do basic and repetitive tasks across a variety of applications. It is essentially software that can control other software. RPA has been used for rule-based logic and is suited for automating processes where it is easy to mimic human behavior. It also operates as a foundational technology for solutions like AI and IPA.
RPA is excellent at automating simple and straightforward tasks. Businesses that handle large volumes of data such as ERP and CRP are perfect candidates for RPA. They can also greatly benefit from the speed and consistency it offers.
The easiest way to think about RPA in action is to imagine it as a sort of digital assistant - it can reroute documents, sort and file attachments, and perform actions if a certain keyword pops up - and has high processing capabilities.
But - and that is important - it lacks any form or sense of intelligence. When it comes to learning or teaching the system, RPA tools only make sense if there are processes in the organization that you can write very clear rules for: if an email with an attachment is received, it will be logged in Google Drive or Dropbox - in every single occurrence. RPA will only do what it's told - no more, no less, and it doesn't change its behavior based on past experiences.
Got it. What about Artificial Intelligence?:
Artificial intelligence (AI) is an umbrella term that covers several different technologies. These can be utilized to allow machines to learn, adapt, imitate, and “think” similarly to intelligent beings. Several variants within AI provide different capabilities. We've briefly covered them here, but we've also written another, more in-depth article on AI.
- Machine Learning (ML) - It is the primary subset that allows computer systems to learn and adapt to data. This provides computers with the ability to collect and analyze data, and then make a decision based on what it finds.
- One of the most extensive uses of machine learning would be Google Maps. It collects billions of different data points every day. This way, when someone requests direction to a certain address, it can use the data to provide the quickest route.
- Deep Learning - Deep Learning is more of a subsection of Machine Learning. It provides AI with advanced “thinking” capabilities that analyze data at a greater level. It utilizes an artificial neural network quite like the ones found in humans. This gives computer systems a more in-depth way to analyze and draw conclusions on the data it's given.
- Cognitive Computing - Cognitive Computing is another complex AI variant. It works to provide improved interaction between people and machines. It does so by recreating the human thought process through understanding human language and media.
- Natural Language Processing - Also referred to shortly as NLP, it primarily deals with providing machines a way to interpret and produce human language. The ultimate goal for NLP is to facilitate smooth and seamless interactions between computer systems and people using them. This technology is found in translation software and virtual assistants like Amazon Alexa or Siri.
While RPA can provide you with repetitive automation on basic linear tasks, AI takes that to the next level. It provides a way for you to “teach” a machine to perform a certain task and allow for variables and changes. This can be applied to almost any industry so the applications are endless.
“While RPA can deliver significant benefits, we believe that Artificial Intelligence will be the truly disruptive technology of the future.” - Accenture
Let's take the example from before: Say you wanted to sort the email you receive into different folders or rows on an Excel sheet based on sentiment. You want to make sure that customer complaints reach the support team, CVs end up in HR, and payment and account info reaches billing. You could try and write multiple rules for these tasks - but you'll soon realize that there are too many variables, keywords, and conditionals that apply. AI focuses on thinking like a human and can be taught to determine sentiment and make those decisions based on previous interference.
What is Intelligent Process Automation?
Intelligent Process Automation (IPA) combines RPA with Machine Learning, as well as other AI techniques and Digital Process Automation (DPA) to operate and automate digital processes. It's a bit of a gimmick to add intelligent in front of it when essentially it's about adding the two. If we want to get to the nitty-gritty of it, RPA combines web scrapping and workflow automation with AI, while IPA combines the more intelligent disciplines of AI e.g. NLP or data extraction to process automation. But from an operational perspective, RPA and AI are the two sides of the same IPA coin.
The main difference is combining RPA's process-driven and AI's data-driven focus. The data concerned is often unstructured, or semi-structured at best, and the automation of processes that incorporate unstructured content, such as text and images is not only process or data-oriented - but also focus on outcome actions. It doesn’t need big training data samples or rule-based training, which are complex and beyond the reach of most companies.
The limitations of process automation
Process automation tools can and are highly beneficial - but it's not the case of one-size-fits-all. However, each of them comes with its own set of downsides, which you should be aware of before deciding on the specific product offering or even strategy.
RPA is unable to process non-digital data with unstructured inputs
Traditional automation tools like RPA are linear in the way they operate. This means that they need to have designated inputs and outputs which doesn’t allow for much flexibility or variability. If you want to make changes to a process then the tool itself will need to change to accommodate the additional tasks or functionality you require. If you need a solution that can process a lot of non-digital data and save you from a lot of manual work, AI or IPA tools will be a much better choice for you.
Multiple input sources
RPA tools, and to some extent AI tools are limited to receiving and interpreting inputs they understand. This can be an unrealistic expectation when dealing with many different input sources. Take supplier invoices as an example: most businesses deal with multiple suppliers to meet their needs. These suppliers will use different layouts for their invoices. This makes it difficult for an RPA tool to interpret the many different formats offered, or make data extraction a highly complex and technical process.
RPA lacks cognitive function
RPA is not AI and therefore lacks the adaptability it offers. While AI can utilize data to adjust its process, RPA is stuck with the preprogrammed settings and this can put a shelf life on the programs. To stay up to date with new processes, the bot has to be updated and configured properly which requires additional time and money.
AI and IPA can be biased
The ‘magic’ that lies behind AI is that it can be trained to recognize certain traits in data and use it to relieve you of your task. However, you can’t give your AI-powered tools full autonomy, as it can backfire. For instance, if you train your recruitment tool to spot fitting candidates (but feed it with data where most past candidates were male, just like Amazon did), you can unwittingly introduce gender-bias into your hiring process. In the worst-case scenario, not spotting such things could lead to serious financial repercussions and fines.
Building your process automation strategy
AI, IPA & RPA can be excellent tools for certain cases - it just takes a little bit of planning and understanding your process management. Here's a couple of scenarios:
Automating Purchase Order through RPA tools
Keeping data cohesive across multiple systems, such as sales software and a CRM, can be a pretty daunting task. It is quite vulnerable to mistakes and inconsistencies. Utilizing RPA tools for recurring processes, such as Purchase Order requests, can be an excellent option. The RPA tool can be programmed to approve or deny requests based on the criteria outlined by you - if the process is unilateral. If approved, it can be automatically generated and sent to the required individuals.
Improve Customer Support with AI
Detecting the sentiment of emails or messages is where AI solutions shine. Be it setting the right mindset, routing messages to the right person to handle invoices, complaints, or requests, or analyzing NPS surveys, AI tools that are trained on your data can take on an enormous workload of reoccurring processes that require intelligence.
Transferring data between systems by using IPA
There are excellent tools and systems out there that can provide several benefits to your business. Unfortunately, many of these systems are built by different developers and generally don’t work well together. IPA tools can be programmed and trained to interpret a wide variety of data and interface with multiple systems. This can save time and money by removing the repetition that is involved when jumping between systems.
Process automation provides an excellent way to relieve your employees from simple repetitive tasks. It can excel with tasks that have an easy-to-define beginning and endpoint and offer you a way to leverage business process management intelligence to its fullest. Automating your mundane tasks and jobs can reduce costs by freeing up employees to focus on more important assignments. It also provides a more efficient and accurate result and process.
Building a strategy is no easy task, so here's what you should keep in mind: if you have a project with clearly defined rules – go with RPA. Respectively, if your data is unstructured and requires cognitive judgement, AI might be a much better fit. As for IPA, it offers the same capabilities as RPA and AI - but doesn’t require big training data samples or rule-based training, which might prove best if you require instantaneous results.
The combination of the above-mentioned process automation technologies has significantly elevated productivity. They offer an exciting way to automate complex tasks by leveraging such solutions, as machine learning and natural language processing. The enhanced capabilities process automation offers can help improve your business, scale better, and remain competitive in an ever-evolving marketplace.