RPA, ML and AI
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Robotic Process Automation (RPA)

Robotic or Software Process Automation is next generaion technoogy that is reinvening how businesses operate today. Companies of all sizes are adopting RPA because of its speed quality, consistency, 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 repeatable tasks with precision, and produce cost-effective error-free output.

 

Digital Workers 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 digital and functions where Digital Workers are particularly helpful?

Digital


Energy and Utilities:
Electric, Gas, and Water


Oil and Gas
Oilfield Services


IT Managed
Services


Manufacturing


Healthcare

Functions that can be automated


  • Finance
  • Legal
  • Acccounting

  • Marketing
  • HR
  • Procurement

  • Data Monitoring
  • Storage

  • Vendor/Customer
    Relationship
    Management

  • Billing
  • Collections Payment Processing

Benefits of Digital Workers/RPA

  • Enables employees to achieve significant productivity gains of over 40 percent
  • Analyze massive amounts of data rapidly and accurately
  • Solve complex problems in seconds/minutes
  • Flexible and scalable virtual workforce
  • 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
  • Minimize investments in IT integration by working with 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 many 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 a la carte.

  • Advisory and Strategic Planning: Assessing the technology environment to define the roadmap.
  • Platform Selection: Choose the right RPA tool that best 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 Consultants enjoys reseller status of the three leading RPA tools, allowing for 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.
Blueprism
UIPath
Automation Anywhere

Machine Learning

RPA provides a quick method of automation, however, is has its own limitations. While RPA works well in situations where processes and decision-making are clearly defined, it’s difficult for RPA alone to be smart. In any RPA-based automation, it is important to split the larger processes into two 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 stage. In such cases where a vast amount of knowledge cannot be effectively defined into algorithms, RPA is not conducive to the process automation, and human intervention is necessary. That’s when Machine Learning (ML) comes into play to solve the “knowledge problem.”

Machine Learning is an emerging technology and 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 some form of mathematical model. The model can then be utilized to apply this knowledge for solving complex problems through automation.

The Machine Learning Process

 

Machine learning works as follows: Employees work in a particular department, or do certain tasks, many times or for many years, and apply the knowledge gained to decide what actions to take, therefore eventually creating 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 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 you’ll enjoy.

 

Netflix, YouTube, Amazon, and many other services we consume use ML in this way, employing your viewing/browsing history to recommend content or products that they think you’ll watch/buy. It may seem as if companies like these actually know you as well as (or even better than) you know yourself! That’s 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.

Artificial Intelligence

Artificial intelligence 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 is a process automation technology that uses software robots or “Digital Workers” 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 digital. Today, RPA is no longer limited to predefined tasks and data. AI-based “cognitive BOTS” are capable of handling ambiguity and making decisions like their human counterparts.

 

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 lets BOTS learn how to process, as well as improve processes, that lead to probabilistic behavior.

Circles AI

RPA and AI are two horizontal technologies that are distinct in their goals and interfaces.

The Role of RPA is to save workers time. RPA is built by engineers via a GUI, or graphical interface, 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’s 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 arrived at after much trial and error.

 

Advances in AI allow us to make more accurate decisions about the data we’re 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.

CDA

Cognitive Document Automation

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, and consistency and dynamically adapts to your evolving processes.

Here are two examples of how AI-Powered RPA can make technology even more efficient:

ISA

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 robot 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 the hand

RPA is the “hand”

AI is head

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.

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