Business management is an ever-changing environment. While we’re getting used to smart homes, virtual assistants, and AI-powered apps, enterprises already have software solutions working for them and managing some business processes literally with no human interaction. If you want to track where the field of business process automation is going and how to prepare for changes the next few years will bring, then keep on reading.
Size of the business process automation (BPA) market worldwide from 2016 to 2021 (in billion U.S. dollars).
#1. Robotic Process Automation
Robotic process automation or RPA is a term for the automation of repetitive actions so that they could be performed with no human workers involved. Even today, RPA already has about thirty applications including digital assistance, user registration, website scraping, profile updates, credentials verification, credit card applications, risk assessment, data management/transfer, and email reply generation. Technology enhancement and high investments in this field prove that RPA’s potential won’t stop its growth anytime soon. According to Hadoop, potential savings companies will experience with RPA by 2025 will account for $5 trillion to $7 trillion.
Robotic process automation is applicable to rules-based and data-driven scenarios. This technology, or better to say a tech stack of many, is trained to capture, analyze, and react to a certain situation. Although it can be programmed to learn and enhance its own performance over time, in a nutshell, RPA does not require machine learning to function. Market giants like Dell, IBM, Capgemini, and Cognizant use RPA in their professional routine to speed up the working processes and reach ultimate productivity without overstaffing their offices. So far, robotic process automation is best at three major activities — communication between systems (e.g., data transferring and updating), output back to a client (answers to most frequently asked questions), input into the corresponding process (typing if there’s a sample, filling in forms, etc.).
For business process management as it is today, RPA invasion means the shift in the work of business analysts. First of all, instead of actually analyzing and structuring data as they used to, business analysts will switch to deeply exploring the processes and divide them on repetitive and data-driven vs. creative and unpredictable ones. In its turn, this will help to shape firmly fixed rules and patterns for RPA software to process.
Also, RPA implementation will basically affect every team member in the whole enterprise as this software requires certain behavior changes to be adapted appropriately. If you are considering robotic automation to become the part of your work routine, check the readiness of your team first. RPA implementation requires closer employee tracking to gather necessary data about their actions, which isn’t the most comfortable thing for some people.
#2. Cognitive Automation
Technologies able to successfully solve tasks that traditionally require human intelligence are called cognitive technologies. They are products of the AI field, however, it wouldn’t be right to synonymize these two terms. Although cognitive technologies (CT) have been explored for decades, only recently scientific and technological achievements in the field of artificial intelligence made CT feasible for businesses.
In a nutshell, cognitive technologies are aimed at mimicking human capabilities including:
- reading and handwriting recognition with the help of Optical Character Recognition (OCR) and text analysis;
- speaking and texting through virtual assistants (chatbots) using Speech Recognition and Natural Language Processing;
- listening by utilizing Voice Analytics and Natural Language Processing as well.
Real-life application of cognitive technology is extremely diverse and broad, which proves versatility to be amongst its benefits. Most frequently, companies using CT admitted its application in logistics, manufacturing, customer service, research, marketing, and development. The top industries adding CT to their routine include healthcare, science, automotive, banking, aerospace, real estate, energy & water supply, travel, and agriculture. Although it all started with a single aim — to automize and improve business operations — many companies continue to reinvent their opportunities with cognitive technology use. For example, some enterprises embed CT in the services they distribute to customers to achieve the ultimate competitive benefits, while others utilize cognitive tech stack by gathering in-depth analytics about inner business operations to take the right strategic decision when needed.
The next five years will bring drastic changes in the ways modern companies are using cognitive technologies. Obviously, CT will spread over the various companies as well as attract massive investments. However, we recommend you to avoid jumping to any conclusions on whether your particular enterprise needs cognitive technology right now. High investments come with high expectations, so you got to be completely sure that your business processes will benefit from CT and that your team is ready for new technology on board.
The definition of cognitive technology oftentimes provokes people’s fear of being replaced by machines and leading to massive unemployment. This is nothing more but a misconception — CT doesn’t steal jobs from human workers, it just makes some of the actually generates new ones. Although we have to admit that cognitive tech stack will be the reason why a lot of jobs will be exposed to change their routines, reality check proves we’ve nothing to be afraid of with CT.
#3. Big Data Analytics
According to IBM, people generate 2,5 quintillion bytes of data daily. Altogether, individuals and companies created about 90% of all the digital data currently stored on Earth within the last two years. How to find valuable information in all that mess? And is that even possible in 2019? It actually is, due to the technologies behind Big Data analytics.
Due to the information overload we described in a few words above, one of the key struggles of businesses today is to set valuable information apart from useless terabytes of information. That’s where Big Data management technologies — tools enabling end-to-end data processing with collecting, analyzing, integrating and predicting abilities — come forward. Nowadays, companies experience an exponential increase in volumes of operations and associated with these operations data, regardless of their industry field. This means data management solutions that used to be so effective yesterday can turn into incompetent overnight.
The thing is — there’s no universal solution for everyone when it comes to Big Data tech application. Meaning, if a company decided to take its chances with Big Data analytics aiming at improving its business processes, the foremost decision to make at this point is about an exact process or operation that needs improvement. Obviously, not all the processes in the company are data-driven, so the critical starting point is to analyze where exactly Big Data analytics should be applied for its max productivity.
Since 2018, the use of Big Data analytics started turning into a mainstream practice rather than an exclusive high-tech tool for privileged enterprises. The foremost change to expect in the way businesses use Big Data analytics is driven by IoT rapid technological progress. Software engineers already started working on combining streaming analytics with machine learning to get an ultimate solution for real-time Big Data processing, reporting, and controlling. When achieved, this would be a huge step forward to the most stable and reliable cybersecurity where unusual behavior has to be alerted immediately and accompanied with adequate software reaction before personnel hasn’t been even notified yet. As the Internet of Things matures, it starts offering Big Data analytical insights never seen before, so no wonder this field will be thoroughly explored during the next five and more years.
Another two major trends connected with the growing interest to Big Data analytics include Hybrid Cloud architecture and freshly generated jobs to support these technologies. Lots of organizations will start combining Cloud data processing (both public & private), SaaS, and in-house data processing to enhance business productivity that altogether create a hybrid system of data storage and management. Due to these changes, more and more companies will be discovering their need to open a new position entitled Data Curator or something similar to that. The key difference between a Data Curator and already existing positions like Data Analyst is that the first one won’t be as close to the subject of his activity (gathered data and its analysis) but will define best practices for data management and take responsibility at the higher administrative level.
Burgeoning advancements in technology, as well as its application in the business field, inevitably impact industries in general and each individual employee in particular. Gone are those days when people could perform the same working routine over and over for years. Business operations aren’t like they used to be, so as the ways we’re doing things today. Despite the frightening for many forecasted scenarios, business process automation is here to do nothing but help us — increase productivity, shorten manual labor and paperwork, generate new positions, and save valuable resources. The technologies we reviewed in this post — robotics, cognitive automation, and advanced analytics — are basically just the tip of an iceberg, and we’re more than certain that the BPM field will surprise us more than once within the next few years. What are your forecasts for the nearest future of business process automation? We’re waiting for your feedback here.