Robotic Process Automation (RPA)
Robotic or Software Process Automation is next generation technology that reinvents how businesses operate today. Companies of all sizes are adopting RPA because of its speed, quality, consistency, accuracy, audit trail, and quick ROI. Business leaders are embracing automation because digital workers (BOTS) can learn and be taught to act like humans, work 24/7/365, handle a high volume of repetitive tasks with precision, which leads to cost-effective error-free output.
Digital Workers (BOTS) are an alternative to traditional IT automation, and companies are currently using these BOTS to perform rule-based, repetitive tasks. Eventually, businesses will be able to automate more complex tasks using these Digital Workers. So, what are some examples of industries and functions where Digital Workers are particularly helpful?
Energy and Utilities:
Electric, Gas, and Water
Oil and Gas
Functions that can be automated:
- HR and legal
- Finance and Accounting
- Marketing and Procurement
- Data Monitoring
- Collections Payment Processing
- Customer Relationship Management
Benefits of Digital Workers/BOTS
- Flexible and scalable virtual workforce
- Enables employees to achieve productivity gains of over 40 percent
- Analyze massive amounts of data rapidly and accurately
- Solve complex problems in seconds/minutes
- Produce work that is error-free and free of duplication
- BOTS do not get tired and work 24*7*365
- Quick learners that can be taught to perform new tasks easily
- Minimum investments as BOTS work in existing landscape
Choosing the right processes to automate is the key to success.
Not all processes are suitable for automation. Processes that have high elements of human decision-making are not good candidates for automation, while other processes that have repeatable tasks are perfect candidates for RPA. It’s of the utmost importance to set criteria for selecting which processes to automate and which processes should not be automated. We recommend developing a proof-of-concept to determine the feasibility of RPA and to avoid automating the wrong processes.
Our RPA division can take you every step of the way. The following services are also available ala carte
- Advisory and Strategic Planning: Assessing the technology environment to define the roadmap.
- Platform Selection: Choose the right RPA tool which matches your requirements. We help our clients decide which tool is best suited to their business.
- Creating a pilot: Proof-of-concept to validate feasibility.
- Development: Design, implement, test, and deploy the RPA solution.
RPA Technology Partnerships
In addition to being your implementation partner, Wise Men has reseller status of the 3 leading RPA tools, thereby being a single purchase point for our clients. Our strategic partnerships with UiPath, Blue Prism, and Automation Anywhere help us to provide advanced RPA software solutions and meet the increasing demand for large-scale RPA deployments.
UiPath is a robotic process automation platform for end-to-end high-scale automation. UiPath software offers solutions for enterprises to automate repetitive office tasks for rapid business transformation. It converts boring tasks into automation process using multiple tools.
UiPath drives the “automation first” era – championing a Robot for Every Person and enabling robots to learn new skills through AI and ML.
UiPath Robot runs the processes designed in Studio in the same way that a human user would. It can work either by assisting the human user, or completely autonomously without supervision for virtual or remote environments.
Blue Prism RPA is the most Productive, most Adaptable, most Scalable, and most Secure, true Enterprise RPA platform.
It will enable businesses of all sizes to build a solid foundation of Intelligent Automation and harness the power of cognitive technologies such as Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Sentiment Analysis, and more.
Blue Prism has emerged as the trusted and secure RPA platform of choice for the Fortune 500.
Blue Prism is designed to automate any application that can be accessed from a Windows PC through the Graphical User Interface (GUI).
Automation Anywhere offers the most intuitive RPA solutions that anyone can use with ease.
Enterprise-grade technology with the intuitive experience of a consumer application.
RPA+AI platform of products has automated business processes for some of the biggest brands in the world.
Automation 360 is the leading cloud-native end-to-end intelligent automation platform used by the world’s top enterprises to double their amount of automated processes at a fraction of legacy RPA systems’ infrastructure—with 3X faster scaling.
Artificial intelligence (AI) analyzes the actions of machines and mimics cognitive functions of humans, such as learning and problem solving. AI today includes several technology disciplines such as Machine Learning (ML), Natural Language Processing (NLP), and many others.
Robotic Process Automation (RPA) is a process automation technology that uses Digital Workers or BOTS to automate repetitive tasks and manual processes and augment the work of your employees. RPA interacts seamlessly across desktop software, traditional enterprise browser-based systems, and web sites to aggregate data, transform it into actionable information, trigger responses to communicate with other applications, and execute repetitive work.
Robotic Process Automation (RPA) has made a global impact by increasing efficiency, productivity, and profitability across all industries. Today, RPA is no longer limited to predefined tasks and data. AI-based “cognitive BOTS” can handle ambiguity and making decisions like humans do.
Combining RPA and AI mimics human activity through machine vision, speech recognition, and pattern detection capabilities. This combination can handle structured, semi-structured, and unstructured data. Machine Learning allows BOTS learn how to process, as well as improve processes, that lead to probabilistic behavior.
Difference between RPA and AI:
RPA and AI are 2 horizontal technologies that are distinct in their goals & interfaces. The role of RPA is to save worker’s time. RPA is built by engineers via a graphical interface or GUI, which is used to arrange the sequence of tasks the RPA automates. For the most part, RPA is based on rules; if-then statements that tell a program what to do under certain conditions.
Artificial Intelligence is an umbrella term that includes rules engines like the kind mentioned above. But that’s not the exciting side of AI, and it is usually not what people mean when they refer to AI. AI consists of programs that are capable of rewriting themselves in response to their environment or the data they’re exposed to.
AI is a horizontal technology that makes decisions about data. Sometimes it makes decisions, or predictions, based on rules that humans manually write, and sometimes it makes decisions based on numeric parameters that it arrives at after much trial and error
Advances in AI allow us to make more accurate decisions about the data we are looking at. In some cases, that accuracy can surpass human accuracy.
RPA and AI overlap in that you can infuse RPA with AI. Useful applications of AI powered RPA often include image recognition and text analysis.
Here are 2 examples of how AI-Powered RPA can make technology even more efficient:
Cognitive Document Automation (CDA):
CDA processes structured and unstructured content, especially in business systems that entail the handling of documents. An AI-powered RPA solution can become increasingly efficient over time. As more documents are processed, the solution learns how to intelligently manage variations independently of the channels the information is exchanged through (whether electronic channels like email and web portals, or physical paper). CDA delivers the greatest accuracy, efficiency, consistency and dynamically adapts to your evolving processes.
Intelligent Screen Automation (ISA)
ISA uses artificial neural networks to analyze an image of an application. For example, where applications are running on Citrix or other remote desktop environments, and only image data is available, there is no direct access to the application and its objects. As virtualization is used almost everywhere, this becomes an increasing issue for RPA solutions to connect and work with environments that only return image data. ISA addresses this issue by automatically creating user interface objects for the robot designer to use in building the software robot. This results in significantly faster BOT development and avoids the issue of screen resolution standardization, because the robot does not depend on screen position to select menu items or buttons when performing tasks.
How does it work?
CDA and ISA are the most requested AI technologies to be leveraged with RPA, but there are many more. For example, AI services such as Google Vision, IBM Watson, and several chatbot services can be easily leveraged by an enterprise-class RPA solution. In those cases, AI is consumed as a service to help the BOTS intelligently perform tasks.
- RPA is the “hand”
- AI is the “head”
RPA automates manual work of collecting information. AI automates the brain work by analyzing the information provided by RPA and either making a decision, or making a recommendation, to the human analyst so he or she can make quicker and more frequent decisions.
RPA and AI together make up the new smart digital workforce that frees human workers to be more effective at their jobs and aids in better decision-making.
RPA provides a quick method of automation; however, it has its own limitations. While RPA works well in situations where processes and decision-making are clearly defined, it is difficult for RPA alone to be smart. In any RPA-based automation, it is important to split the larger processes into 2 categories: Silent Automation and Human Intervention
There are many instances where it is not practical to automate a big process fully. Most processes require a human component to make decisions and continue to the next level. In such cases where a vast amount of knowledge cannot be effectively defined into algorithms, it is not conducive to do RPA and human intervention is necessary. That’s when Machine Learning (ML) comes into play to solve the “knowledge problem.”
Machine Learning (ML) has matured to a degree where it can be applied to solve real-life problems. ML essentially works on the principle of encapsulating large amounts of data (or knowledge) into a form of mathematical model that can be then utilized to help solve complex problems through automation.
The Machine Learning Process
Machine Learning works as follows: Employees work in a particular department, or do certain tasks, for many years on end and apply the knowledge gained to decide what actions to take and eventually that creates a process. ML is applied to a multitude of problems where there is access to large volumes of historical data which can be used to predict or define decision-making in certain areas. Unlike developing an algorithm, ML is based on building a repository or knowledge base. Think introducing a SMART AGENT into the RPA process. The Digital Worker (BOTS) becomes “smarter” and more efficient by gathering additional information to make more accurate decisions.
Many RPA platforms, like Blue Prism, UiPath, and Automation Anywhere have developed robust ecosystems and are implementing their own powerful Machine Learning algorithms. With the combination of RPA and ML, BOTS are now capable of capturing previously unidentifiable data like signatures, images (in under 100 milliseconds), complex application automation, and image recognition optimization.
RPA Technology Partnerships
ML uses Artificial Intelligence (AI) that enables systems to learn from data without being explicitly programmed. ML is based on the premise that technology can process data and learn from the data, to help make better decisions based on new information without constant supervision of programmers.
For example, Netflix and Spotify use ML to store what movies or music a subscriber is watching or listening to in a repository and will be able to create a pattern from that data, improving accuracy as it learns and offers movies/music specifically tailored to a user’s liking. Spotify’s ML algorithm uses your data (i.e., the songs you listen to) to create a weekly two-hour long playlist of music that it thinks the subscriber will enjoy.
Netflix, YouTube, Amazon, and many other services use ML in a similar fashion by employing your viewing/browsing history to recommend content or products that they think you will watch/buy. It may seem as if companies like these know you as well or even better than you know yourself! That is because the more you use these, or any other, services, the more they learn about your viewing/purchasing pattern, resulting in recommendations that are more and more accurate. This is machine learning in action.