Get More Control Over Your Data with Intelligent Automation

Mark:                    So have a sales office in France and Switzerland and we have a development locations in Central America as well as in Southeast Asia. We partner with Broadcom. Well CA Technologies/Broadcom, IBM, HelpSystems, Sencha, and others. The majority of our business is focused on the IBMI hardware platform. Obviously we do other things with cloud and so forth, but that's our main driver and HelpSystems is an acknowledged leader in the IBMI software solutions area. So hence our partnership. We've helped over 400 enterprises since 2010 at CM First worldwide. And now I'd like to turn things over to EJ who will tell you a bit more about health systems and we only have 30 minutes for the webinar today so it's going to be a fast ride. Got some live demos. Some housekeeping first. Audience members please type in your questions into the dashboard on the right side of your attendees screen and take it away EJ.

EJ:                           Yeah. Thanks Mark. It's a pleasure to present to everyone today and talk about the exciting topic of intelligent automation. To start, I'd like to give a very high level overview of health systems. Our company is focused on two broad categories, security and automation, to bring value to our customers and help them build better IT. On the security side, we provide a broad portfolio of solutions that include data security, such as our go anywhere secure file transfer software and our clear swift data loss prevention product line that is in the Gartner magic quadrant. Our core security IGA product offers an integrated identity and access management suite, including tools to manage bot profiles. And finally, we have our infrastructure protection products that secure networks through methods such as pen testing and vulnerability assessment services. And we're going to dig into a little bit deeper into our RPA and intelligent automation offerings as we go through the demonstration today.

                                So let's take a look at today's agenda. What we'd like to talk with you all about is defining intelligent automation, how to supercharge robotic process automation with intelligent automation, we'll take a demo of automate and automate intelligent capture working together and look at those, and hopefully we'll have some time for Q&A at the end.

Mark:                    Great. So first you're going to see up our first polling question for today's audience. And that question is going to be, where is your organization in the automation journey? So we have a range of answers here. You have to select one of them. You're just getting started by researching IA, are you identifying opportunities and/or selecting an actual use case, are you evaluating and/or piloting an IA solution, or have you actually implemented IA and measuring success. And I would include RPA in that as well. And then last, we're expanding or scaling our IA solutions.

                                So we'll give our audience members about 10 more seconds here to answer. Let's see the answers please. So a third of you are just getting started by research. That's a common answer. It has not been deployed [inaudible 00:04:24] of organizations. However, there's a good chunk of you that have already implemented IA and you're actually measuring your success. We got some other areas that these we'll be getting into in the presentation, talking about how to implement and measure it. And then also people are evaluating opportunities and piloting a solution. So what do you have next for us?

EJ:                           Yeah. So next let's define and talk about what intelligent automation is. Most organizations are already familiar with RPA. RPA software robots manipulate and communicate with business systems and applications to streamline processes, reduce the burden on human employees, and empowers them to spend more time on truly strategic initiatives. By transforming how organizations work, RPA enables your business to increase productivity, reduce costs, eliminate costly errors, and increase productivity with a scalable digital workforce using logic based and repeatable tasks.

                                On the intelligent side of intelligent automation is machine learning and artificial intelligence. Machine learning focuses on the use of data and algorithms to imitate the way humans learn, gradually getting smarter over time while AI is the science and engineering of making intelligent machines, essentially intelligent computer programs. There are varying definitions of intelligent automation in the marketplace, but it's basically intelligence that is integrated and [inaudible 00:06:13] with automation to broaden the range of processes that can be streamlined within the business.

                                So in essence, intelligent automation considers the entire lifecycle of automated processes across tools and functions in relation to overall complexity to accelerate digital transformation within the organization. So what are some popular business processes to automate? We see our customers working with application integrations. So automating workflows across your applications and systems, including IBMI. Your CRMs, ERPs, AWS, VMware, and other type of cloud offerings. Microsoft applications. RPA bots can check an email inbox, look for files in SharePoint, and automate Excel reports for more efficient and accurate processes. Data entry and extraction. So keep data moving to your green screen form emails, databases, and your business applications without the human errors caused by manual data entry.

                                RPA bots can transport and transform data out of your IBMI and into useful formats like Excel, PDF, CSV, and more. Report generation and distribution automation. So manipulate data and automate reporting across the organization. Web browser type tasks. So streamline web data extraction with automated navigation, inputs, imports, download activities, just to name a few. And user provisioning. So here you can automate your user provisioning for tools like Microsoft Exchange, active directory, or even sync active directory with the IBMI EIM table for single sign on type activities.

Mark:                    Great. Our second polling question based on what you just saw on the past slide here is, where would you deploy your first bot or where have you deployed it? Would it be in a functional area like HR or accounting, would it be across applications or systems to bring critical data together, or would it be to replace existing labor and mimic individual tasks, or perhaps your unsure or don't know where to begin? So I'll let our attendees choose one of those following options, a functional application, labor, or unsure.

                                Just have a few more seconds here. Organizers and panelists don't vote so EJ and I can't stack the deck here. And as for the results, so split pretty evenly between either function or to replace existing labor and mimic tasks. And that's pretty good and a couple of people are unsure. So I thank you very much for those responses. That's similar to what we see in RPA too is that replacing existing labor in a functional area seems to be a driver for a bot deployment. And now back to EJ and small market data and then we'll get into live demonstrations.

EJ:                           Yeah. This next slide ties directly into some of our poll responses so a good segue. So let's talk about why intelligent automation matters in 2021. We'll do that by looking at some recent industry and analyst reports on how intelligent automation is shaping business in organizations. A couple of key trends from Forester advise that intelligent automation suites will provide one quarter of all robotics process automation solutions and 20% of enterprises will expand intelligent document extraction investments. A recent Gartner report notes that augmenting automation with intelligence will lead to greater cost savings for organizations. In fact, Gartner predicts that by 2024 companies that leverage automation and intelligence will reduce operational cost by nearly 30%.

                                Because of the advancements that intelligent automation will bring, RPA technology will continue playing an increasingly pivotal role in automating and integrating all possible business processes to drive lasting value for the organization. Technological progress is helping drive hyper automation as well. Gartner expects that by 2023 organizations will be able to run a full 25% more task autonomously. And Deloitte reports that 47% of organizations have already combined RPA and AI as part of their intelligent automation strategy.

                                So how does intelligent automation supercharge RPA? To do that we need to further define intelligent automation and introduce intelligent document process into the conversation. So at the beginning of our session, we define automation. Now let's further the conversation again by talking about intelligent document processing and get a better understanding of that concept. Intelligent document processing or IDP leverages unassisted machine learning and AI to extract data across all types of documents as they enter into your organization, making that data usable across your business. IDP reduces human touchpoints, streamlines manual processes, and eliminates costly steps like document classification, manual data entry, and document analysis.

                                Delivering on the promise of digital transformation with an intelligent automation platform requires the ability to harness both structured and unstructured data, but not all data is created the same. Traditional robotic process automation excels at processing structured data, which is organized, clearly defined, and searchable. So we think about things like Excel spreadsheets. But structured data only accounts for about 20% of data within a company. Unstructured data accounts for that other 80%. It is typically qualitative and difficult to interpret and analyze because it has no predefined structure. So think of things like emails, photos, audio files, blogs, videos, any other type of qualitative data. But thankfully intelligent document processing handles most unstructured or semi-structured data allowing for a more encompassing solution.

                                So here's another good question. Not a poll question, but still a question to ask yourselves internally. Are you manually processing items today such as mortgage documents and AP invoicing, insurance claims, customs declarations, bills of lading, essentially common data points in non-common places? If you answered yes to any of these scenarios, then intelligent document processing could help you achieve greater results. And now that we spent some time talking about RPA and defining what intelligent document processing is, let's talk about what are some of the strength differences between the two and why do they compliment each other so well. As we previously mentioned, RPA excels at structured data and repeatable processes were in document or pre-post document automation may need to occur. IDP on the other hand, shines working with either structure or unstructured data and utilizes internal machine learning for intelligent document classification and processing, making large volume processing both logical and affordable.

                                So working together. RPA and IDP extend automation capabilities and enable organizations to maintain control of critical information with speed and accuracy. With a robotic process automation, you can automate interactions between critical applications, data entry and extraction, and reporting. Whether or not you use the latest most sophisticated systems, RPA can be layered on top of your current systems to eliminate and streamline business processes. And because it builds upon your existing technology, RPA can provide immediate relief for limited staff and remove the need for temporary workers as your workload expands. Intelligent document processing further accelerates and supercharge your automation efforts by combining RPA with machine learning and artificial intelligence, constantly steps like document classification, manual data entry, and document analysis are eliminated.

                                So now we're going to look at a few demos of our products, but before we do that, let's highlight the two specific products we are going to see in action, which are automate RPA and automate intelligent capture or AIC. So what is automate RPA? At a high level, automate is a leading automation solution designed to go beyond basic robotic process automation to integrate front end and backend automation workflows across your organization. Our RPA software offers superior flexibility and scalability, integrating processes that span across departments, teams, people, and processes in your business. Automate plus enables your organization to build solutions without writing code, leverage intelligent workflows, and centrally managing administrator all automated assets like workflows, tasks, users, servers, and agents across a truly distributed network.

                                So what is automate intelligent capture? Automate intelligent capture is an intelligent document processing solution that uses patented unassisted machine learning algorithms and AI to streamline document processing and eliminate costly steps like document classification, manual data entry and sorting, and document analysis. By automatically learning data fields on documents, eating without having seeing them before, automate intelligent capture enables you to reduce manual processing without endless hours of building templates while improving the accuracy of your data. Our advanced capture solution simplifies document processing without the need for a complicated or time consuming setup. Eliminate gathering and sorting of a documentation and tackle your biggest document processing challenges.

                                Plus, when combined with the top rated HelpSystems automate RPA product, you can achieve more speed and accuracy over critical information in your business. It's all possible, but it just takes the right solution to get you there.

                                So how does intelligent capture work? That's very important to take a few moments to talk about AIC and kind of how it processes data, how we get data into the system, and how we get data out of the system? So the typical steps in an intelligent capture process begin with input. So again, how we get documents and data into the process. You can leverage multiple document import options across your business, including automated email monitoring, watch network folders, direct connect scanners, local and remote multifunction devices, and even fax technology to input batches of documents and kickoff processing. Next is the classification of documents. So this is our patented document classification technology. It mimics the way a human would identify documents by looking at multiple document characteristics, including size, location, and type of information, tables, pictures, or logos, and other attributes like physical and semantic content.

                                Then we have the capture process. So using unassisted machine learning algorithms, our solution automatically learns data fields without building any templates. This allows for a fast setup of previously unseen documents, no matter whether the software has seen it before or not. And during this process we can also validate our data. During capture and data extraction, automate intelligent capture applies data validation parameters. Extracted data fields can be passed to a database to verify extracted information or to pull information that's not present within the document to ensure greater security and accuracy. And verification. In some cases of our data we have to verify that.

                                So here we can easily correct any exception fields that were not extracted properly. Exceptions can be caused by a number of factors, including things like a poor quality scan, characters touching lines, or marks on the form, but with minimal effort, it is possible to correct any exceptions and documents with no exceptions can simply pass straight through the system. And last, but not least is the export of our captured data. So our IDP solution provides multiple data element export paths, including CSV files, XML files, and direct database connections be an ODBC or even JDBC. Image file export options include TIF and full text PDFs. For XML files XSLT is also supported to simply import into other systems.

                                So now let's take a closer look at automate intelligent capture and assisted machine learning in action. So unlike legacy capture products, you not need to start building templates, expressions, keys, or other programming structures. Automate intelligent capture learns by observing the way a user interacts with the documents. For instance, we may want to find the value for a new data field. Just like how you would train the employee, you would show them what you're looking for and just like a human, automate intelligent capture will learn from what you show it and remember it for the next time. As we're seeing in the demonstration video, on the first viewing of the document type several fields were not have found, but just by watching an end user find and select the correct fields, on the second pass of the same document type automate intelligent capture was then able to identify all fields correctly. This is how automate intelligent capture can get to 95% automation within days, not months and without needing a programmer to do so.

                                So kind of reinforce what we just saw on our demonstration there, let's take a look at two different, in this case, purchase order invoices. To process these documents and automate and most every other competitor, these two documents need to be viewed as two separate document types. In automate intelligent capture however, it's just a purchase order. It's common data in uncommon locations. This often holds true for other types of documents like bank statements, AP invoices, credit memos, and even mortgage documents. So on that topic, what are some typical business problems or use cases intelligent automation can solve? A great example is accounts payable and invoicing scenarios. The handling of these documents is traditionally driven by manual time-intensive processes, which add cost to simply process the invoice. Humans are prone to errors here as well, which can increase the cost of processing.

                                Combining automate and automate intelligent capture, the unstructured data is made structured and automate can evaluate and validate the data, even entering it into multiple systems of records downstream and generating reports. So now let's take a look at a demo of AP invoice processing again with automate RPA and automate intelligent action working together. So in this example, we have two specific workflows we'll be using to perform our intelligent automation. We have an initial workflow within automate that is using a foul monitor to watch for new files added to a network share. So here we're going to add in some new files. In this case, AP line item invoice files to our watch folder. And in a moment we'll see automate begin to process the task.

                                We'll get a visual indicator in the lower right-hand portion of the screen when any automate task begins. We could also use automate to maybe download forms from a website or get the documents from a mailbox or even do pre-validation of the document before feeding them in automate intelligent capture. So now that the files have been moved into AIC, AIC will begin processing them. AIC is going to perform text extraction and OCR, classification of the documents to define what type of document is, extract all of the data we specified should be captured, and then export the data pallet as a CSV file.

                                So our second automate workflow is monitoring a network location for files processed by AIC. Once the exported files are moved into the watch directory, automate is going to both create a processed inventory report and import the CSV file into a SQL database. Automate can also take that exported data and maybe enter it to a downstream application or another scenario depending on the use case. So here we can see automate creating the report, writing the contents to an Excel file. Automate is also going to email the completed report as well. So this shows how automate and automate intelligent capture can easily work together to seamlessly handle business processes that involve the use of documents that contain unstructured data.

                                So another great use case example for intelligent automation is mortgage documents. These documents often require extensive training and manual review. Traditional processing methods are often slow, error prone, and cost both time and revenue. AIC can automate this process by auto classifying and transforming unstructured data into structured data based on the document type, combined with automate to evaluate and validate the data, a robust solution can be created. So let's take another look at an example of automate and automate intelligent capture working together.

                                This time with a commercial loan PDF that contains multiple documents. So in this document set a promissory note and autopay agreement and a boarding data sheet were all included. Data needs to be pulled from all the documents and the extracted results need to be compared against data that resides in the company's core system for reconciliation purposes. The process is triggered by a file mantra being pulled by automate, which initiates the batch processing of the loan file by automate intelligent capture. Automate intelligent capture uses machine learning to accurately classify sections of the file into various user defined document types and pull user-defined data from those documents, learning as it goes. The values are then exported again, in this case, a CSV files. Automate can then detect the existence of the exported files and use them as a trigger to start a new process, which could enter the data into an application or as we're seeing in this example, comparing the export values against data from other systems.

                                The comparisons are done in this case, evaluating the two fields and writing into Excel, if there isn't a match between the two data points. Notifications of the process results can be sent via email reports or even as a slack notification as we'll see here in the video.

Mark:                    Great. Well, so impressive demos there EJ.

EJ:                           Yeah.

Mark:                    [inaudible 00:29:15] from demos. Amber, have we received any ... I don't see any questions on the dashboard. One I had from another customer was, "We've got unstructured data. We're [inaudible 00:29:31] insurance application where they've get tags from different kinds of legacy systems. How would you handle that EJ in terms of getting that organized in a way that would be understandable to the IDP process?

EJ:                           Yeah. So we would train the system essentially to classify out those different document types. The system is going to look for and read documents kind of the same way humans do. So we as humans don't typically start left to right, up and down when looking at a document. We look for keywords, phrases, fonts of a certain size, or geographic location to determine what type of document that is. And then once we're classified the documents then we know exactly what information we need to pull off of that particular document type. And so that's kind of how we would approach it. We would use the physical or semantic content available on the document to begin that building process.

Mark:                    Great. Well unfortunately we're out of time here, [inaudible 00:30:36] 30 minute allotment. For people exiting the webinar, there's a short survey for you to fill out and both EJ and I wish you a great day and a great week and we look forward to the next time that we get to communicate with each other. Thanks very much for your time. Goodbye, everybody.