Applying machine learning and AI will push RPA to the next level.
Robotic Process Automation (RPA) has helped companies reduce the sea of worldly activities for humans, allowing them the ability to focus on more fulfilling tasks. RPA has drawbacks, however. Intelligent use of machine learning (ML) and artificial intelligence (AI) will push RPA to the next level.
Software developers have been developing applications to automate activities for several years, but RPA tools have democratized automation, taking it to nearly everyone's scope. Drag and drop tools allow frontline employees to take on and automate routine activities.
For example, a human resource (HR) officer can receive e-mails for leave requests on a regular basis. You will need to open these emails, retrieve relevant information, and translate it into a report that needs to be submitted to specific individuals. It is a routine task that is carried out on a regular basis which will take a few hours. However, an RPA tool can create an automatic workflow that searches an email address, collects the relevant data, compiles it into a report, and sends the email faster without any hands-on interference. This frees the HR worker to concentrate on higher-value tasks.
Taking RPA to the next step
The capacity for RPA to be implemented is important – most of the staff have duties that are the same every day and are clearly carried out by following a set of well-defined steps.
But what happens when the job needs precise human input? It can be a routine task but with enough variations every day that human intuition and experience are required. RPA allows variables to be used in the - stage. For example, it may be used to compile a financial report and the variables could be modified to look like some accounting code or financial entity. But what happens if the number of variables varies every day? What if the study has to cover the top 10 cryptocurrencies by market capitalization – something that varies every day? Or if the study just needs to alert anyone to a budget item that has unexpectedly gone from under-budget to over-expenditure?
It's pretty straightforward for a person to determine and apply an extra parameter or variable to a quest. AI would make it easier for tools to understand what we want and to increase questions and activities themselves. Instead of RPA routines being unique to a mission, they can be more abstract and automatically tailored to the data they are dealing with.
Many acts, though routine, require the wisdom and attention of a human being with expertise and experience. And that's where the next generation of RPA software will exploit AI. Humans are really good at answering the question, "What else is important or interesting? ”. AI will help RPA tools go further than just adding more variables to the query. AI will allow RPA to take the next step and answer the question, "What else? ”. In addition, the implementation of AI to RPA would cause these tools to extend the reach of what they can do.
Leveraging data and AI for RPA
The advent of GPT-3, Generative Pre-Trained Transformer 3, is an influential technology that uses AI to manipulate large quantities of language data on the Internet. By training an extremely large neural network, GPT-3 can understand and produce human and programming languages with near-human output. For example given a few pairs of legal contracts and plain English documents, the process of drafting legal contracts in plain English will begin to be automated. This kind of advanced automation was impossible with classic RPA software without data leveraging and state-of-the-art AI.
This example of the influence of data and AI is bringing us to a new issue. What else can be changed by using AI data in RPA?
One example of this is delivering emails to digital marketers. Marketers also send automatic emails to clients and prospects. Collecting a goal list and then generating and submitting an email is a typical use case for RPA, but AI can be used to evaluate data and refine certain messages in order to increase the probability of a message leading to good conversion. By adding AI to RPA, advertisers will better target their consumers with customized ads so that they are more likely to respond positively.
Next-generation RPA will grow from digital transformation
This depends on providing enough data and effective research software to build templates of what to send to and the right times. This would create stronger partnerships between line-of-business management and IT teams collaborating to find opportunities for RPA use.
In the past humans will analyze this data and make decisions on where to deliver messages. With AI, you can now perform the research, develop the theory, validate it and then optimize it to ensure that the next campaign is more successful.
Although RPA has provided considerable benefits when it comes to automation, the next generation of RPA can provide more benefits through the use of AI and machine learning through optimization. It's not about faster automation, it's about better automation.
It is possible that as RPA becomes more popular, companies will also be looking into how easily software bots can be sourced. Some researchers argue that we will see the rise of "Robotics as a Service" as businesses search for opportunities to deploy RPA by payment models and to be able to receive expert assistance.
With companies experiencing a digital transformation and perhaps expanding their efforts to counter the effect of Covid-19 on their workforce, data is becoming increasingly relevant. The optimization of RPA would significantly benefit from improved market digitization. As companies build data lakes and other new knowledge sources that are available by APIs, it is important to enabling RPA tools to be open so that they can be configured.
There are though, several difficulties. Automation has many benefits, but it may not be worth automating every process. Businesses should concentrate on routine procedures that are most valuable since this is where most business gains can be obtained.
There still has to be a human strategy in motion. If a job previously done by a person is automated, a new function or operation must be established for that person. Although it is possible that automation will minimize or avoid mundane and unsatisfactory jobs, the time that automation produces must be spent. Having anyone eligible for higher-value jobs is pointless if there is no higher value work for them.
If automation is well-targeted and implemented, the market gains are easily understood. The next wave of RPA will go further, not only by automating repetitive operations but also through seeking ways, with the use of AI, to optimize automation and produce better performance. Applying neural networks would allow RPA to move deeper and produce better results for companies, both large and small, as they aim to deliver greater value to their consumers.
