Over the past few weeks, we’ve already explored how RPA has evolved from being a simplistic automating tool to the sophisticated, self-learning system companies are scrambling to hook up their offices to these days. Recent advancements in fields such as processing power, machine learning, connectivity, and artificial intelligence have all led to new benchmarks for what an RPA should be doing for you, and it’s no longer necessary to employ a large number of bots to get your work done. It’s not how many bots you use, but how you use them, that will help you get the most out of your implementation.
Imagine walking into your office one day, firing up your terminal and pulling up your robotic process automation software’s (RPA) control panel. Even as you start typing in your query, the console tells you that the report has already been generated, vetted, shared and seen by the CEO.
RPA consists of bots that can work on their own or as collaborative units towards a common purpose, and this makes an RPA implementation both powerful and scalable. Bots, after all, are small software packages sitting inside the container we call an RPA. These bots can be customized on an individual basis which means that there is a lot of flexibility in terms of what each bot can be made to do.
One of the biggest paradigm shifts in recent times has been the emergence of intelligent bots in addition to the rule-based ones.
Rule-based bots, as the name suggests, operate on the basis of predetermined rules. This is ideal for structured environments and interactions, such as reading data of fixed-format documents and screens or accessing datasets which have security rules binding on them. Think of a train that has to run on tracks – that’s rule-based bots. These bots are highly specialized but limited in their scope and function. They will process only the information they’ve been explicitly programmed to understand.
Rule-based bots are ideal for managers across the organization, especially those who should have restricted access to data and administrators who are tasked with processing
normal business actions like collating bills and invoices, digitizing expense records and maintenance sheets of infrastructural assets, etc.
* Makes it easy for RPA administrators to restrict areas of information away from employees’ eyes
* Predictable run-times, performance, and output
* Pre-validated safety – a rule-based bot will be deployed to public use within the organization only after it has been cleared by administrators (that it does not affect data or exposes confidential information)
* Cheaper to acquire
* Wide variability in documentation will result in false entries
* Difficult to structure every possible scenario
* Limited function, limited scope… limited utility
* Need a large number of bots to perform all functions
The earliest definition of a robot was that it was an instrument capable of working within definite, fixed parameters. In RPA, this was no different until vendors started incorporating AI and ML into the code that made up their bots. These bots, which can anyway operate within rules, can also color outside the dotted lines and explore unstructured scenarios and datasets if they’ve been set up to learn properly.
For instance, invoice formats differ greatly between organizations. A telecom company that deals with hundreds of vendors cannot keep upgrading its bots each time a vendor changes its format, which is where intelligent bots are employed. These bots will be trained to understand what each piece of data can represent – what are the line items, the key dates, the invoice amounts against each item, taxes, etc.
In other words, they have unlimited function and unlimited scope which means that the same bot can be used in different contexts and scenarios, even those it has not been explicitly programmed for. It’s ideal for upper management where any additional insight it can provide can be impactful across the organization and where the users are already cleared for access to sensitive information.
* Self-learning nature means that it doesn’t need to be reprogrammed/upgraded every time a dataset it needs access to is structurally modified
* It may be able to identify correlations missed by human minds
* Unpredictable run-times and output since there’s no way of determining these factors in unstructured contexts. Within structured contexts, these are no different from rule-based bots anyway.
* More expensive to acquire, but lower running costs
* Data needs manual review during learning cycles
Depending on where you are in your business journey, you might choose one over the other. But while making this decision, remember, where you’re headed is more important than how far you’ve come.