I would consider the addition of AI/ML to your RPA to be the most important Cognitive RPA feature.
E.g., UiPath AI Fabric allows you to consume information from AI/ML and use the result in logical decisions and to inform human teammates. You can set up a feedback loop to continue training your model to improve efficiency and confidence.
Let's say you're willing to automate a 95% confident decision without human intervention. Anything under that confidence you'd route to a human. You can use the results of the human decision to teach the model and make the AI/ML even more effective. This allows your AI/ML to get smarter and ever more helpful (cognitive) as time goes on.
Search for a product comparison in Robotic Process Automation (RPA)
Cognitive RPA enables you to design more complex and less rule-based processes using AI-powered bots integrated with third-party cognitive services, mainly from Google and Microsoft.
The RPA and AI technologies collaboration provide automation capabilities that level up business values. These unique capabilities include optical character recognition (OCR), text analytics, document processing, content recognition, machine learning, etc.
Using the cognitive functions of RPA extends the opportunities of process automation, enabling intelligent bots to make decisions while learning the role as they become valuable sources in your digital workforce.
-Reducing operational costs and barriers: RPA can automate your legacy systems and connect your operations with AI technologies, unblocking all restrictions.
-Arrange and process unstructured data: RPA bots with cognitive automation capabilities capture and manipulate data from unstructured sources, making the data structured in a way you need.
-Automation of business processes: With RPA Cognitive Automation, you can save time and money by automating any business process, reducing human interaction, and increasing productivity.
-End-to-end customer experience: Increase customer happiness by providing quicker response times, higher accuracy, and consistent results.
Cognitive AI we develop includes the skills to percept and to control anything on a screen in a similar way, a human employee does. A Cognitive AI is independent of the position of objects and can even act flexibly on the appearance of such objects.
Benefits:
1.) it is very easy to tell it, what to do and how to do it since I can "tell" her, how I am operating the processes and she will do it in the same way. The consequence is that also employees without an IT background can automate processes. Cognitive AI is a no-code way to automate. Low-code solutions by definition still require coding skills and only speed up a developer.
2.) It is very fast to tell it how to operate a process because I don't have to deal with complex technical selectors and activities.
3.) For Cognitive AI it does not make a difference, if it looks through cameras in order to operate physical devices such as tablets, smartphones or machines or if it just "looks" at the perfectly rendered screen of your desktop in order to automate PC applications.
4.) Cognitive AI does not require the object IDs ordinary RPA solutions use, so the processes that have been automated do not stop when the object IDs change - e.g. very common after system updates. The consequence in "old school" RPA projects is, that a full DevOps team of developers is necessary to keep already automated processes running... A very expensive consequence of automating the process of an employee with a moderate salary in order to transfer the know-how into a bot that needs to be maintained by software developers.
And since I am a software developer myself, I tell you something: we don't like this kind of work. We love to develop and evolve new and powerful systems rather than maintain IDs in broken processes...
And there is still enough stuff to automate - the industries are still at the beginning of the digital transformation.
Please feel free to DM me for more details about Cognitive AI.
This is not to say that there isn't value in combining machine learning and RPA to enhance the values of the solutions you build - there is value there, and we're big proponents of building AI and ML into the solutions we deploy.
But let's not confuse machine learning with cognition, and I think we're all better served just focusing on the specific problems we're solving and how rather than how the sausage is made in terms of the tools we use.
@Evgeny Belenky,
Marketing is "cognitive RPA is the next evolution of RPA!" .. a more practical and useful thing to say is "We can automatically process your invoices and push the data into your operational systems and processes." - okay, so you're solving an invoicing problem for me. using machine learning, and using RPA.
That's something that is tangible and not just stringing the words together.
There are all kinds of compliance requirements out there, so you're posing a pretty broad question, but generally speaking, security compliance requirements are going to involve continuous monitoring, access management, encryption, endpoint and network security, and patch management.
It's going to come down to a log management solution, many of which provide reports that help you prove compliance. But because compliance usually has governance requirements, meaning that upper management is aware of risk issues and can provide proper direction, your log management solution needs to be able to present the technical details in a way that is meaningful to potentially non-technical people. So it's important to vet the solutions well, so that you know they work for your environment and your C-suite.
There are also compliance consultants who can bring expertise to the table. And if you're not sure where to begin, consider implementing a risk assessment so that you know what areas are most applicable to your business.
There are a number of options to get these kinds of documents scanned and categorized. Among them are use of stand-alone OCR technology (Tesseract or Google Cloud Vision, for example) or implementing an RPA tool with bots to handle the process. Either can be done in-house or, if you don't have the time/resources/expertise among your staff, by a consultant.
The route you take will depend on things like budget (of course), volume of documents, how fast and how often you need the work done, and the availability of your staff to do or oversee the process. Either track will require a learning curve, both require some knowledge and training, and standalone OCR will likely require notably more coding expertise.
In either case the starting point is to gather examples of all the docs you are going to need scanned and determine how they should be categorized and whether there is any data within them you need categorized.
Next, whether you go with standalone OCR or RPA bots, the tools are going to have to be trained to recognize the text in docs and, in the case of RPA, to know what to do with it. OCR can identify text within an image, and convert it to machine-readable text and then you'll need a system to categorize it. RPA can do both.
The last stage is evaluating the accuracy of the scans and the classification process and, often, adjusting the process to increase accuracy before it is validated as "working". Even then, you will want to monitor the results.
Dear PeerSpot community members,
Welcome to the latest PeerSpot Community Spotlight, where we sum up the most relevant recent postings by your peers in the community.
Check out the latest questions, articles and professional discussions contributed by PeerSpot community members!
Trending
Here are some topics that your peers are discussing at the moment:
What is your recomme...
Dear PeerSpot community members,
This is our latest Community Spotlight for YOU. Here we've summarized and selected the latest posts (professional questions, articles and discussions) contributed by PeerSpot community members.
Check them out!
Trending
See what your peers are discussing at the moment!
What were your main pain points during the SIEM product purchase process?
What...
Hi PeerSpot community members,
This is a fresh-from-the-oven Community Spotlight for you. Here, we've summarized and selected the latest posts (professional questions, articles and discussions) by PeerSpot community members. Check them out!
Also, please share with us your feedback and suggestions by commenting below!
Trending
See what is trending at the moment and chime in to discuss!
...
Hi peers,
We're happy to share our new bi-weekly Community Spotlight with you. Here you'll find recent contributions by PeerSpot community members: questions, articles and trending discussions.
Trending
See what your peers are discussing at the moment!
What to choose: an endpoint antivirus, an EDR solution, or both?
What is your recommended IT Service Management (ITSM) tool in 2022?
W...
All companies, from large to small, are trying to keep one end of the digital transformation, but for large companies, the situation is more than opening a social media account.
Sometimes they may have to manage millions of data. And the faster they do these transactions, the more they get ahead of the competition.
Although RPA is a well-known tool in digital transformation in the world, it...
I would consider the addition of AI/ML to your RPA to be the most important Cognitive RPA feature.
E.g., UiPath AI Fabric allows you to consume information from AI/ML and use the result in logical decisions and to inform human teammates. You can set up a feedback loop to continue training your model to improve efficiency and confidence.
Let's say you're willing to automate a 95% confident decision without human intervention. Anything under that confidence you'd route to a human. You can use the results of the human decision to teach the model and make the AI/ML even more effective. This allows your AI/ML to get smarter and ever more helpful (cognitive) as time goes on.
Cognitive RPA enables you to design more complex and less rule-based processes using AI-powered bots integrated with third-party cognitive services, mainly from Google and Microsoft.
The RPA and AI technologies collaboration provide automation capabilities that level up business values. These unique capabilities include optical character recognition (OCR), text analytics, document processing, content recognition, machine learning, etc.
Using the cognitive functions of RPA extends the opportunities of process automation, enabling intelligent bots to make decisions while learning the role as they become valuable sources in your digital workforce.
-Reducing operational costs and barriers: RPA can automate your legacy systems and connect your operations with AI technologies, unblocking all restrictions.
-Arrange and process unstructured data: RPA bots with cognitive automation capabilities capture and manipulate data from unstructured sources, making the data structured in a way you need.
-Automation of business processes: With RPA Cognitive Automation, you can save time and money by automating any business process, reducing human interaction, and increasing productivity.
-End-to-end customer experience: Increase customer happiness by providing quicker response times, higher accuracy, and consistent results.
Hi Evgeny,
Cognitive AI we develop includes the skills to percept and to control anything on a screen in a similar way, a human employee does. A Cognitive AI is independent of the position of objects and can even act flexibly on the appearance of such objects.
Benefits:
1.) it is very easy to tell it, what to do and how to do it since I can "tell" her, how I am operating the processes and she will do it in the same way. The consequence is that also employees without an IT background can automate processes. Cognitive AI is a no-code way to automate. Low-code solutions by definition still require coding skills and only speed up a developer.
2.) It is very fast to tell it how to operate a process because I don't have to deal with complex technical selectors and activities.
3.) For Cognitive AI it does not make a difference, if it looks through cameras in order to operate physical devices such as tablets, smartphones or machines or if it just "looks" at the perfectly rendered screen of your desktop in order to automate PC applications.
4.) Cognitive AI does not require the object IDs ordinary RPA solutions use, so the processes that have been automated do not stop when the object IDs change - e.g. very common after system updates. The consequence in "old school" RPA projects is, that a full DevOps team of developers is necessary to keep already automated processes running... A very expensive consequence of automating the process of an employee with a moderate salary in order to transfer the know-how into a bot that needs to be maintained by software developers.
And since I am a software developer myself, I tell you something: we don't like this kind of work. We love to develop and evolve new and powerful systems rather than maintain IDs in broken processes...
And there is still enough stuff to automate - the industries are still at the beginning of the digital transformation.
Please feel free to DM me for more details about Cognitive AI.
Kind regards,
Michael
@Michael Wilczynska I interested to find out more about Cognitive AI
Marketing...!
This is not to say that there isn't value in combining machine learning and RPA to enhance the values of the solutions you build - there is value there, and we're big proponents of building AI and ML into the solutions we deploy.
But let's not confuse machine learning with cognition, and I think we're all better served just focusing on the specific problems we're solving and how rather than how the sausage is made in terms of the tools we use.
@Evgeny Belenky,
Marketing is "cognitive RPA is the next evolution of RPA!" .. a more practical and useful thing to say is "We can automatically process your invoices and push the data into your operational systems and processes." - okay, so you're solving an invoicing problem for me. using machine learning, and using RPA.
That's something that is tangible and not just stringing the words together.