The Future of RPA
Robotic Process Automation current market size is estimated at USD 1.57 billion with 2021 to 2028
expected to grow at a compound annual growth rate (CAGR) of 32.8% (Source Grand View
Research). That’s a very strong growth rate prediction and an insight into where the future is
headed. Of course, these are estimates but it gives great insight into what the industry could
potentially be worth in a few years. With that here are a few key projections and future RPA trends
to be aware of.
RPA and the Cloud
Currently, RPA vendors mostly sell a licence or subscription to companies to use its software. It varies
quite a bit and at times this can be quite complicated. Anything from $995 per individual user to
$5000 per individual user and this goes up as the size and scale of the requirement rises. RPA has
been adopted mainly in-house by using a company's own tech infrastructure and servers and
allocating resources or computers to RPA solutions.
What is starting to take shape recently is the migration to cloud-based solutions. Cloud computing is
basically the idea that all of the hardware and computing power is shared via off-premise servers but
in order to qualify as “cloud”, this should be done over the internet. The advantages of cloud
computing include an organisation’s ability to provide really sophisticated and very powerful
solutions to clients almost instantly and on a massive scale. It provides fast applications and services
because there is no need to install software locally or buy hardware to accommodate expansion.
Cloud computing and cloud services also provide pay per usage, so the costs are dramatically
lowered for businesses and this effectively means all companies can harness the power of the cloud.
So it's logical to assume that the adoption of RPA will be widespread over the next decade.
RPA and Artificial Intelligence
Currently RPA largely works on defined rules but this is starting to change with more complicated AI
technology. As the two merge it is likely that bots will make more judgemental decisions that involve
unstructured data. Machine learning is a subset of AI and deep learning is a subset of machine
learning and they all fall underneath the AI umbrella. These subsets will add massive value to the
RPA space especially in the vision and language processing areas. It will allow documents to be
viewed holistically and decisions made and interpreted for subsequent automation.
As AI gets more and more familiar with RPA the complexity and the intricacy of the use cases grow.
It will be able to provide insights and predictive modeling to help humans make smarter and faster
These are two words that seem to be cropping up more and more. Think of intelligent automation
like a digital worker, a robot able to operate more and more like an employee. As improvements are
made to AI algorithms the digital worker will be able to execute processes in real-time, running
through repetitive tasks as it sees fit and allowing the human to concentrate on creativity and areas
of business away from mundane repetitive tasks. This next step in RPA is likely to further change the
way RPA is viewed. Academics and experienced people in the industry advise that business will be
better served treating bots like employees, adapting the culture of the workplace to view RPA bots
are valid work partners, there to tackle the nuts and bolts of workflow process. All areas of company
infrastructure also need to work on how they view and deal with the digital workforce and there is a
lot to do and plan for in the future around this topic.
What Happens After Intelligent Automation
Once RPA reaches the level of a digital worker that’s able to make decisions and execute processes
mostly unattended, it will be interesting to see where it goes from there. The tech will likely run into
new issues around creativity and strategic thinking. There is not much research or planning around
what will happen at this point but it is clear to see that the future for RPA looks very bright.
Written By Neil Liddle
Associate Instructor RPA
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