Kyle Hoback

Logan Margulis

Sasha Zinchuk

Veronika Andreeva

SAP has about 425,000 customers in 180 countries, including 92% of the Forbes Global 2000, making it one of the most popular enterprise digital ecosystems. Many of these customers will build, on top of the powerful SAP software, processes involving not only SAP but other applications, with people working among components of the IT landscape to accurately get data where it needs to be.

There are many ways to get data into SAP, so how are enterprises best able to not only optimize SAP data entry but also the operational processes that support them? WorkFusion provides a transformational solution — not just because it is the only leading Intelligent Automation software vendor with a SAP certification, but also because it is able to solve business problems associated with processes using SAP.

We discussed this at length in our recent webinar: How WorkFusion’s SAP Certification Enhances Intelligent Automation for Order Management. In a process such as Order Management, SAP may be the core system of record, but it typically is touched multiple times by a team of people, while they work with other systems.

Making the process more efficient is not just about getting data into SAP but having an automated approach to the specific use cases — such as Sales Order Updates, Order Block Removal, and Reimbursements, to list only a few examples.

Throughout the webinar, we had very engaged attendees who wanted even more information. For the benefit of the broader community, we’re sharing some of their Q&A here. We would definitely love to follow-up on any additional questions and comments other attendees or readers might have. Let us know by emailing us at. Learn@workfusion.com

What is the difference between RPA and Intelligent Automation?

Robotic Process Automation (RPA) is a rules-based technology that handles repeatable, structured activities in support of the execution of a business process, typically geared towards leveraging the user interface to get or input data. Intelligent Automation includes RPA as a core capability, but also includes additional capabilities, including native machine learning and workflow, to solve larger business problems.

Applied to a use case like Sales Order Updates, RPA is used to enter data into SAP, but Intelligent Automation also solves the sourcing of emails, classification of the type of sales order received, and extraction of relevant data from the email, in addition to the entry of the data into SAP at the end.

SAP has purchased Contextor to provide its own RPA tool — how is your software different?

There are many options for customers to enter data into SAP. WorkFusion realizes this, which is why we achieved the SAP certification, but our focus is actually more on full use cases — such as Sales Order Updates, Order Block Removals, etc. — not just the ability to automate entry of data into enterprise applications like SAP.

Can WorkFusion support automation of emails, including reading inboxes, managing automatic replies, and working with attachments?

Absolutely! Many processes built to run on the WorkFusion Intelligent Automation Cloud start with automating receipt of emails and their attachments. This includes processes like those we discussed in the webinar, like Sales Order Updates and Reimbursements, but goes outside the scope of SAP processes for things like Customer Inquiries in Banking and Appeals Intake in Healthcare.

Would you recommend Outlook automation through WorkFusion’s Recorder or through Web Harvest Components (i.e., code)?

Either approach can work. Typically, developers will approach the problem by building code first, but business users may start with Recorder. Often, a coding approach will more easily incorporate further robustness and error handling, but business users can add actions to account for these scenarios as well.

On a related note, in many scenarios, the question is asked whether automation should source data via API or the user interface (UI) — in this case, emails via Outlook. If the team already doing the work has built considerable rules and filters into Outlook, then it may make more sense to not reconfigure this logic and just connect via Outlook. However, if the approach for the automation is simply to ingest all emails and route from within the automation, then maybe connecting via SMTP is the better method.

We would love to hear more about specific scenarios. Let us know by emailing us here learn@workfusion.com

Does WorkFusion only integrate with SAP, or can it connect to other enterprise applications, like green screens, command line, web pages, etc. that are typically part of a full process?

WorkFusion Intelligent Automation Cloud can connect to a full portfolio of enterprise applications, including mainframe applications, web applications, Excel tools, command line prompts, etc. to automate end-to-end processes across multiple systems.

Do you have plans to automate Ariba, SuccessFactors, Concur, and other SAP cloud applications?

We already provide the capabilities to automate with any web application, inclusive of SAP, Salesforce, and Oracle. If there is a customer need for further certification of our software with SAP applications, we are happy to consider it. Please reach out to learn@workfusion.com for more information.

What SAP processes are available out-of-the-box today for automation using Work Fusion?

Any process using SAP can be automated easily by a business user using WorkFusion’s out-of-the-box SAP integration capability. Additionally, WorkFusion is in the process of building out additional pre-packaged use cases to handle key organizational processes, such as those within Order Management.

We find that in most cases, each organization has customization in their deployment, so while we provide pre-built packages, we also offer training through our Automation Academyto enable resources to quickly build out automation processes.

Besides Order to Cash, what other SAP processes are available today?

The WorkFusion Intelligent Automation Cloud enables any SAP process to be automated through our platform by business and technology users. This means the capability can immediately start transforming any manual work that is executed in your SAP applications to date. Please reach out to us at learn@workfusion.com to learn more.

Does WorkFusion use a proprietary OCR tool and what is the extraction accuracy?

When the text is in images, a third-party software is utilized to first convert it from image-to-text. Then, yes, WorkFusion provides a proprietary platform for customers to perform common text processing actions like data extraction and classification, typically powered by our AutoML capabilities.

That means solutions with our software typically leverage Optical Character Recognition (OCR) for its classic abilities to convert images to text, but then use newer methods (our AutoML capabilities) for structuring that text into meaningful data. Extraction accuracy, then, is typically much higher than the average person is able to do on their own, but still dependent on the quality of source data (DPI of documents) and the quality of the training set used for machine learning.

Please reach out directly at learn@workfusion.com for more details.

Does the SAP certification impact RPA Express (now known as Intelligent Automation Cloud Express)? Is this a good entry point?

Yes, Intelligent Automation Cloud Express (formerly “RPA Express”) has supported SAP automation since version 2.3.

Is there a trial version we can use for PoC?

WorkFusion Intelligent Automation Cloud Expressprovides the capability to test the SAP integration. Try it for free, and scale it with Intelligent Automation Cloud Business and Enterprise.

Does this SAP certification also apply to SAP Business 1?

WorkFusion’s certification is specifically for Netweaver and S/4 HANA, However, since Business 1 is based on HANA, integration is possible, although it may be without the fully certified connectivity.

How to call an Excel macro in Intelligent Automation Cloud?

There is an Excel automation class for Bot tasks, both for code and codeless automation. Also third-party libraries can be used to operate Excel files, such as Apache POI.

Is window control — which figures out the GUI element (for example, .GUIOrdernumber) only for SAP or other ERPs like Oracle? What happens when the GUI Elements name changes (.GUIOrderno) as part of updates/upgrades? Do we need to manually change the automation workflow?

WorkFusion’s Inspector can find objects in most enterprise applications. SAP NetWeaver and Oracle ERP desktop apps are built using different technologies, so the majority of their UI controls will have different class names. If a GUI element class changes after an update, you need to create a different selector or you can create a unique selector without using the class attribute. For example, use Name, Unique ID, Text, other attributes or relative selectors.

Is it possible to show (execute) the whole business process you showed in the webinar?

Yes! Reach out directly at learn@workfusion.com for a demo.

Can you show the code?

We would be happy to do so during a demo or site visit. Please reach out directly at learn@workfusion.com to schedule.

Originally published at WorkFusion.

There’s an emerging, even more compelling growth story to be told around process automation: Entirely new businesses can form around RPA, and small businesses can grow bigger through the exponential productivity that automation software allows.

R-Path Automation and Telogical Systems are two great examples of this business creation and growth.

Automation consulting firm R-Path helps businesses use robotic process automation and RPA tools to improve business outcomes. Its client, Telogical, had maximized what they could do with their staff and wanted to scale rapidly — but didn’t want to hire more workers to do the kind of data-gathering that can be quickly and accurately automated.

R-Path shortlisted four software solutions and recommended WorkFusion’s Intelligent Automation Cloud.

Jim Frost, R-Path’s Solutions Partner, said, “Telogical’s business revolves around monitoring telecommunications providers’ offerings, pricing, packages, and more across hundreds of sources a day. The primary use case was to automate monitoring that data and compare it to Telogical’s master data.” Telogical also delivers competitive insights to their clients via email and wanted to automate that end-to-end process too. In short, they required a versatile solution that could address multiple, disparate challenges, and WorkFusion’s software fit the need.

Capability wasn’t the only factor. As a smaller company with big growth ambitions, Telogical didn’t want employees to spend valuable time learning a complex, cumbersome software product. They knew time was better spent quickly delivering value to clients. Compared to other products, they recognized that WorkFusion’s dashboard is more intuitive and easier to understand, because it was built for business people without software-development expertise.

“You can be a very smart business user and, with good training, start using WorkFusion a lot faster. With WorkFusion’s help, we were up and running in a production environment within one day.” — Jim Frost, R-Path Automation

R-Path also was careful to choose an automation software product that comes with a supportive, proactive team behind it. RPA is still a new capability and getting the most out of it often means spending some time with the people who know it best. WorkFusion customers benefit from support and education, often in the form of courses on the Automation Academy training platform.

WorkFusion applauds the team at R-Path Automation, and what they are achieving for their clients. To learn more about R-Path and how they saved a client 10,000 hours of manual effort by automating a single use case, read this short case study. https://bit.ly/329Ot5G

WATCH: R-Path leaders share their POV on RPA and how they put automation to work for clients here: https://bit.ly/329Ot5G

Originally published at WorkFusion.

By Kyle Hoback

Banks and other financial institutions around the world may be reconsidering their data security after news that a major cloud-focused bank was hacked via its Amazon Web Services (AWS) infrastructure. As a cloud infrastructure is often thought essential for the future of artificial intelligence in banking, large institutions weighing robotics and AI technologies may be feel deadlocked trying to innovate while maintaining control of data.

How can change agents leverage the promise and capabilities of AI in the banking sectorwithout shifting sensitive data off-site? WorkFusion’s Intelligent AutomationCloud provides enterprises the ability to transform their operations with machine-learning powered Intelligent Automation — all without the need to send data outside their firewall. On-premise AI is a proven reality in a vast range of processes, including Know-Your-Customer (KYC) Identification Validation, Standard Settlement Instruction (SSI) updates, and management of Customer Inquiries. Let’s look at these in more detail.

KYC Identification Validation

To adhere to KYC regulations, when any customer seeks to open an account, almost all financial institutions must obtain and verify identity documentation, such as driver’s license and passport. This data is inherently sensitive and can jeopardize a customer relationship if released. Where financial institutions today must manage hundreds of ID types — across countries, states, and other jurisdictions — AI cuts across the various layouts of these documents to gather key information. Other Intelligent Automationcapabilities navigate institutions’ KYC and other systems of record, reconcile KYC sources of data, and route rare exceptions to operators to ensure high accuracy. Even in financial institutions with hundreds or thousands of applications each day, it’s common for WorkFusion customers to see a 70% increase in employee productivity after implementation, even in a fully on-premise environment.

Standard Settlement Instruction (SSI) Updates

SSIs are shared between financial institutions to expedite domestic and international payments with details about currencies, accounts, routing institutions, and various details. While accuracy of SSIs is vital to the efficient exchange of money, the method of sharing SSIs is often uniquely formatted PDFs, bank-to-bank emails, or unstructured SWIFT Query messages, limiting the ability of rules-based automationapproaches such as Robotic Process Automation(RPA). Intelligent Automation, however, can handle the judgment required in the work. Financial institutions that employ large teams of people to handle SSI Update volumes often see an automated solution with WorkFusion cut time spent on each document from 30 minutes down to only 30 seconds, without sacrificing quality — and again without the need to send data outside the firewall.

Customer Inquiries

While financial institutions, like all enterprises, are increasingly seeking to provide more digital methods of communicating with their customers. However, in many scenarios, end customers simply send various requests via email. With hundreds of types of requests, and numerous corresponding actions that must be performed for each type, the abilities of AI to understand the various nuances of individual emails — the intent of the request and each request’s relevant data — is critical for automation. Applying AI and Machine learning in bankingwith Intelligent Automation software for customer inquiries typically automates over 60% of what was previously done manually, cutting customer wait times from weeks or days down to hours or minutes, and with WorkFusion, no need to send data externally from the organization.

These three examples of artificial intelligence in bankingare just a few of the many AI use cases in bankingfor business process automationwhere WorkFusion customers achieve high operational impact inside the security of their firewall. With WorkFusion’s Intelligent AutomationCloud, there is no need to send data off-site to implement machine learningand achieve the benefits of using AI in banking.

Want to learn more about our process automation software? Connect with a member of our team to ask any questions or request a demo: learn@workfusion.com

Originally published at WorkFusion.

by James Lawson, Director, Strategic Markets, WorkFusion

You want to transform your business through automation. You hope to improve service, cut costs, and enhance compliance. You know you need to invest in an Intelligent Automation (IA) platform, powered by Artificial Intelligence (AI). Perhaps you’ve already had some success with Robotic Process Automation (RPA) or you’re at the start of your journey and looking for a future-proof solution. With unstructured data and complex processes, you know intelligence will be key.

Selecting an Intelligent Automation vendor is hard

There’s a huge amount of hype and noise. Many companies talk a good game, but a survey of 2,830 AI startups showed that 40 percent don’t use AI in a “material” way. Some startups compensate for their lack of product with a data science team, building custom solutions each time. Others lack AI altogether, relying on people in the background to give an appearance of intelligence.

Gregory Koberger on Twitter: "How to start an AI startup1. Hire a bunch of minimum wage humans to pretend to be AI pretending to be human2. Wait for AI to be invented / Twitter"

How to start an AI startup1. Hire a bunch of minimum wage humans to pretend to be AI pretending to be human2. Wait for AI to be invented

All the leading RPA vendors have offered an Intelligent Automation vision, accompanied by glossy presentations. Yet years later, most RPA software vendors just have a “python container,” meaning you need to build the AI yourself. Even if they have AI case studies, too often these are dependent upon “integration with third-party cognitive services.” Both approaches are costly, with multiple license fees, complex maintenance, and high technical debt. It begs the question: Why not go direct?

AI is a complex field, with its own terminology. Executives want to talk about business processes, problems and solutions. Data scientists instead want to talk about feature engineering, hyperparameter optimization and model ensembles. This makes it difficult for business people to understand what they are buying in practical terms. At worst, jargon is used as a cover for “vaporware” that doesn’t exist yet.

Is the AI genuine?

Intelligent Automation is underpinned by AI. Choose a provider that genuinely offers its own capabilities, then evaluate these capabilities. If your solution is being packaged off-site, without clarity into development or maintenance, this is a red flag. “Integration with third-party cognitive services” is another red flag.

Is there a clear first use case?

To achieve return on investment you need to solve a business problem. AI for the sake of AI is just a waste. Finding your first use case can be a hurdle. Choose a vendor who helps you identify and evaluate that first step. Even better, look for proven use cases and pre-built accelerators like Expert Bots. Minimize your risk and maximize quick wins; prove the technology to your stakeholders.

Can you identify other use cases?

To achieve real transformation, finding one use case isn’t enough. Choose a provider whose capabilities would enable you to tackle multiple problems. You can achieve huge ROI via process automation of a few big complex tasks, or a wide range of smaller ones. Also, look for capabilities that help you quickly discover and implement new use cases.

Can it automate complex processes?

Traditional RPA tools offered by RPA vendors can automate simple, repetitive tasks, with structured data. Real-world business processes are much more difficult to manage. Choose a vendor who can work with complexity. Look for the ability to work with a wide range of inputs — regardless of source, language or format. You want to trust your automation solution to complete complex work, so you can automate whole roles — from categorizing incoming emails through to extracting data from documents, reconciling transactions and evaluating risk.

Can you implement without ‘Big Tech’?

The big tech companies like Microsoft, Google, Apple and Amazon are leaders in AI, particularly for consumer applications. However, their main strength is on general applications (like translation, virtual assistants and photo enhancement). Their services often come packaged with their cloud-based offering and can compromise control of your data. There’s also a risk they use the experience to launch a competitive offering. Choose a vendor that can support you for your specific use case, without dependency upon Big Tech. Tailor the solution to your specific process and requirements (from customs documentation to insurance claims or banking settlement instructions). Don’t compromise on data privacy.

Can you implement on-premise?

With increasing regulation, tight data privacy laws across the developed world, and numerous data leaks, consider looking for a solution you can deploy on-premise. Even if you’ve successfully transitioned to off-site solutions in the past, choose a vendor who can implement on-premise or in your private cloud servers. This gives you maximum flexibility later on. It also validates that they have genuine AI capabilities, without tricks in the background. Learning robots should learn from your data.

Does it continue to learn?

Businesses are dynamic and change. Bots need to be updated. According to McKinsey, one in three AI use cases needs to be updated monthly, and one in four refreshed daily. Choose an automation solution vendor whose robots can continuously learn. When your business processes or data changes, you’ll want bots that adapt.

Is it low-code?

Data science skills are expensive and in short supply. There’s a shortage of around 150,000 people in the USA, according to Linkedin. Choose an automation solution that lets you implement quickly and maintain easily. Ideally, business analysts and subject matter experts can contribute too.

Can your team learn to use the tool?

In the short term, a robotic automation company or a consulting firm might help you start your Intelligent Automation journey. Longer term, you’ll want to be in control. Choose a vendor whose automation solution is easy enough to learn, and who offers the associated training. This way, you can reduce implementation costs and scale up beyond the first few use cases.

Can it be deployed quickly?

Creating a learning bot and automating a process will only have value if used in the real world. Choose a vendor whose technology you can deploy quickly. Examine the time to value: How quickly will you get from process discovery through to implementation in production? Is it a matter of years, months or weeks? This becomes especially important when you seek to scale automation across the organization.

Can you use normal hardware?

A lot of AI development takes place on custom frameworks with custom hardware. Google, for example, has custom Tensor Processing Units (TPUs) with Application-Specific Integrated Circuits (ASICs). Many others use Graphical Processing Units (GPUs), originally designed for video gaming, but well-suited to AI applications. Choose a vendor who allows for the use of normal hardware. Using your own hardware will enable you to get started faster, at lower cost.

Can you speak to a reference customer?

Before you buy, you should speak with another customer. This may give you key insight into achieving success, as well as validate that you’re making a good decision. Choose a vendor who can provide references. Use those references to assuage any doubts, identify any challenges, and better prepare for success.

Partnering with WorkFusion

WorkFusion is here to help. We want to solve your business problems. While yes, we have great technology (which is why Everest Group named us a leader in Intelligent Document Processing), our focus is on making sure you select the right automation solution for your use case, with short time to value and high ROI. Contact us to find out more or see a demonstration of how WorkFusion’s Intelligent Automation Cloud can work for you: learn@workfusion.com

Originally published at WorkFusion.

Decades after his untimely death, Alan Turing continues to be an inspiring visionary. He was an outstanding mathematician and a pioneer of computer science and artificial intelligence. In 1936, at age 24, he published “On Computable Numbers, with an Application to the Entscheidungsproblem,” a scholarly paper that is widely recognized as foundational to the field of computer science.

But he is best known as a World War II hero who devised code-breaking machines that ultimately helped the Allies defeat the Axis Powers.

This is the story that was dramatized in the Hollywood movie, “The Imitation Game,” starring Benedict Cumberbatch. Turing worked for the UK’s Government Code and Cypher School at Bletchley Park. He led “Hut 8,” a group responsible for cryptanalysis (deciphering coded messages). He devised new techniques to speed up the interpretation of German cipher, improving upon previous methods. This allowed the Allies to intercept and crack enemy messages, which may have shortened the war by two or three years and saved an estimated 14–21 million lives.

His memory also lives on in the Turing Test, which he developed after the war, in 1950. The idea was to test a machine’s ability to exhibit intelligent behavior equal to or indistinguishable from that of a person. In this test, a person would communicate with both a machine and a human, and then try to determine which was which.

For decades, his legacy was downplayed. Because of societal factors against homosexuality, people in the UK government worried he posed a security risk. In 1952, Turing was arrested and convicted under “gross indecency” laws, resulting in chemical castration and imprisonment. He died two years later by cyanide poisoning in what is believed to have been a suicide. In 2013, the British government issued a posthumous royal pardon and officially apologized for how he was treated during his lifetime.

Turing’s recognition on the £50 note is a much-deserved accolade for one of the greatest minds of the 20th century.

Mark Carney, governor of the Bank of England, said in a statement: “As the father of computer science and artificial intelligence, as well as a war hero, Alan Turing’s contributions were far-ranging and path-breaking. Turing is a giant on whose shoulders so many now stand.”

More than just Turing’s face, the new currency bill design will also include his signature, a depiction of the code-breaking machine he built, and other significant imagery.

In this redesign, Turing replaces two great inventors of the Industrial Revolution: Matthew Boulton and James Watt, creators of the Boulton & Watt steam engines. These engines made automation of factories and mills possible. Boulton also applied modern techniques to the minting of coins, while Watt developed the concept of horsepower and is the namesake of the international unit of power: the watt. It seems therefore fitting that Alan Turing will replace them, because today his innovations, his “engine,” and his inventions form a foundation upon which we automate business processes, in the midst of what some economists are calling “the Fourth Industrial Revolution.”

Contact us to learn more about WorkFusion’s Intelligent Automation Cloud and how automation software can transform your business and carry you into the future of work.

Originally published at WorkFusion.

WorkFusion is honored to be recognized in the Visionaries quadrant in Gartner’s first-ever “Magic Quadrant for Robotic Process Automation Software” report. We are also proud to share this analysis, which considers the global vendor landscape for RPA and business automation solutions. You can get a complimentary licensed copy of that report here, or read on for some key takeaways:

Gartner defines robotic process automation (RPA) as “a digital enablement technology that predominantly leverages a combination of user interface (UI) and surface-level features to create scripts that automate routine, predictable data transcription work.”

In this report, the authors also offer some context for the market. Per the Gartner report, “As a market, RPA is still relatively small, with a total revenue of slightly less than $850 million in 2018. However, RPA is the fastest-growing software subsegment officially tracked by Gartner, with year-over-year growth of more than 63% in 2018.”

At WorkFusion, our view, which is substantiated by numerous case studies, is that for enterprises to successfully automate a wider range of use cases, especially ones that entail unstructured and/or dynamic data, and deliver meaningful business results, there is a need for technology that combines RPA and AI.

We believe that is why this Gartner report named WorkFusion in the Visionary quadrant. According to the report authors:

Visionaries in a market are innovators that drive the market forward by responding to emerging, leading-edge customer demands and by offering new opportunities to excel. Typically, these vendors appeal to leading-edge customers and may even have minimal mainstream presence or name recognition. Their ability to deliver sustained and dependable execution in the mainstream enterprise market is not sufficiently tested. The vision of a vendor is not expressed solely in its technological innovation. The vendor must also show insightful understanding of market trends, as well as visionary marketing, sales, along with related product and business management strategies.

Gartner also says, “Vendors in Magic Quadrants are evaluated on two axes: Ability to Execute and Completeness of Vision.”

To that end, WorkFusion is placed highest on the “Completeness of Vision” axis in the Visionary Quadrant — which we believe seems to confirm that our vision for work and business process automation is second to none.

Importantly, the report specifically applauds our central advantage, AI-driven RPA. (See the report for details.)

Regarding the overall market, WorkFusion customers and partners know that RPA solutions are appealing to business users, with what Gartner describes as an “emphasis on resource reduction, easy efficiency and accessibility of the scripting environments.” Further, the analysts say, “Business executives are finding it increasingly difficult to understand why they need to wait for IT to integrate their existing technology solutions. The net result is a tremendous pent-up demand to democratize process automation and data integration.”

We are delighted our unique vision for automating work and business operations has been solidly validated by this industry analysis and energized by the prospect of making WorkFusion the de facto industry standard that defines the future of work, business operations and intelligent automation.

Gartner, Magic Quadrant for Robotic Process Automation Software, 8 July 2019, Derek Miers, Marc Kerremans, Saikat Ray, Cathy Tornbohm

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

Originally published at WorkFusion.

WorkFusion recently hosted an informative webinar on the opportunities and benefits of intelligent document processing (IDP) — featuring Anil Vijayan of Everest Group and Arnesh Sahay, a Solutions Engineer for WorkFusion, and moderated by Veronika Andreeva, Product Marketing Manager for WorkFusion.

More than 300 people have attended the free webinar so far, and it’s still available on-demand at: http://learn.workfusion.com/2019-may-intelligent-document-processing-webinar-ft-everest-group

The 30-minute presentation was followed by a Q&A, and many attendees had additional questions that we wanted to answer with some detail here.

Scroll for the answers to these topics:

• How are ML/NLP algorithms utilized in WorkFusion’s IDP solution?
• What does the initial training for a WorkFusion ML model look like? After an initial model is trained, how does the software perform quality control against the model’s output?
• How does WorkFusion’s IDP solution solve for high volumes of data?
• Can IDP be used to read data from emails? Can we schedule bots to run at varying times for different processes?
• What does a false positive mean in the context of machine learning processes?

How are ML/NLP algorithms utilized in WorkFusion’s IDP solution?

WorkFusion Process AutoML is a proprietary technology for Cognitive Automation. AutoML automatically finds the best machine learning algorithm and method of automation for myriad use cases. There are several different open-source algorithms leveraged in this process, depending on the type of model being trained.

In machine learning, a hyperparameter can consist of annotators, feature extractors, and various post-processors that are used to control the learning process. In order to produce the highest-performing machine learning models for any given use case, WorkFusion Process AutoML performs a series of experiments to test different “optimal” hyperparameter configurations by systematically varying one or more components and examining its effects on the result. This process is called hyperparameter optimization.

The two most common use cases deployed in WorkFusion software include information extraction: gathering data from unstructured documents to enter it into systems; and classification: classifying documents, transactions and emails into workflows. AutoML enables operations teams to train their own machine learning models without the need for a data scientist.

AutoML SDK goes further and allows Machine Learning Engineers (MLEs) to step in and extend machine learning models for any information extraction or classification use cases. This SDK consists of the following components, each responsible for a specific task in a pipeline:

Parser — remove HTML tags from documents, create an AutoML SDK Document

Annotator — split text into tokens, add boundary elements, add Named-Entity Recognition (NER)

Feature Extractors — analyze tokens in documents and create features

Algorithms — aggregate all field documents and feature extractors, provide a model and its statistics

Post-Processing — apply normalization logic and field grouping

Some examples of machine learning algorithms that are supported in AutoML SDK include Support-Vector Machines (SVM), Deep Neural Networks (DNN), Regression, etc.

What does the initial training for a WorkFusion ML model look like? After an initial model is trained, how does the software perform quality control against the model’s output?

The amount of data needed in the first iteration of training for an ML model depends on the complexity of the use case. An efficient information extraction model for a Prescription Intake use case can be created with as few as 50 documents, whereas a complex multi-class classification model for an Email Intake use case may require more than 100 documents per category.

Automatic Quality Control (AutoQC) refers to the use of statistical methods in monitoring and maintaining the quality of products and services. In a WorkFusion automation workflow, the AutoQC sub-process chooses an optimal, cost-effective combination of automated machines and cloud workers that always delivers at or above the acceptable quality level.

The main concept of AutoQC is to take samples from a defined batch of items (records, documents, etc.) and verify the quality of each individual sample. After sample verification, the whole batch is considered as accepted or rejected depending on the Rejection Limit parameter set in the process. Therefore, AutoQC enables customers to maintain a continuous quality check without needing to verify the entirety of the total batch.

Can IDP be used to read data from emails? Can we schedule bots to run at varying times for different processes?

Email Intake is a standard use case that WorkFusion has solved for several different customers. The data within emails (subject, body, attachments) are all parsed via RPA; any attachments are sent to the Optical Character Recognition (OCR) sub-process for conversion to a machine-readable format (XML/HTML); the body of the email is merged with the converted attachments; and a single document is sent to the AutoML process. The case study below outlines how WorkFusion tackled an Email Intake process at a large U.S. bank which was inundated with massive email volumes and riddled with inefficiencies due to human error. (View our Email Intake Case Study to learn more.)

Scheduling bots is a simple and straightforward process in WorkFusion’s Control Tower. The image below shows how process owners can create their own schedules for various processes to kick off at designated times. This is a typical view in Control Tower:

What does a false positive mean in the context of machine learning processes?

When evaluating the performance of machine learning models, we need to be able to differentiate between values that were identified correctly and incorrectly. When looking at a Binary Classification use case, a correctly classified document can be either a True Positive (TP) or a True Negative (TN). Alternatively, an incorrectly classified document can be either a False Positive (FP) or a False Negative (FN).

The definitions below pertain to a simple HotDog/NotHotDog use case, as presented on HBO’s “Silicon Valley” and help illustrate the differences between the four possible outcomes:

TP — Image is a hot dog and the model identified it as a hot dog. (Correct)

FN — Image is a hot dog, but the model identified it as not a hot dog. (Incorrect)

TN — Image is not a hot dog, and the model identified it as not a hot dog. (Correct)

FP — Image was not a hot dog, but the model identified it as a hot dog. (Incorrect)

A binary classification model is relatively simple to calculate for. But when evaluating a more complex multi-class classification model, confusion matrices are often used to map out the four possible outcomes against each possible category, to better understand the performance of a model.

How does WorkFusion’s IDP solution solve for high volumes of data?

WorkFusion’s Enterprise Architecture is designed to handle high volumes of data coming in across multiple different business processes. To address the increased load on the systems, single instances can be scaled either horizontally or vertically. Horizontal scaling consists of provisioning more servers for ML, OCR, and RPA as needed to respond to increased volume growth, and vertical scaling refers to dedicating more powerful hardware to each component.

As different business lines within an organization start using WorkFusion, it is important to keep data segregated across different departments. There are two approaches to address this: One is to instantiate new environments that correspond to business units as they are onboarded and have sufficient volume (this is the most common approach); another is to set up a shared multi-tenant instance with scaled ML, OCR and RPA components.

Related: Learn more about a recent Everest Group assessment of leading IDP vendors across the industry and/or download a complimentary licensed copy of the report.

If you have any questions about this webinar content, or if you’re curious how well-suited Intelligent Automation would be for your transformation program, we welcome your inquiries anytime at learn@workfusion.com.

Complimentary research report: Intelligent Automation — Accelerating From Short-term Wins to Long-term Strategic Business Outcomes

IDP Webinar Follow-up: We Answer 6 Bonus Audience Questions was originally published in WorkFusion on Medium, where people are continuing the conversation by highlighting and responding to this story.

Automating purely to reduce cost isn’t transformation. It’s optimization. That’s fine if you’re only interested in short-term wins, but leading enterprises — the ones who beat born-digital competitors, keep customers and delight shareholders — are looking beyond cost reduction and envisioning long-term success.

Led by distinguished analyst Sarah Burnett, the team at Everest Research Group set out to help enterprise executives and automation practitioners understand the landscape and its options. This result is this recent report: “Intelligent Automation: Accelerating from Short-term Wins to Long-term Strategic Business Outcomes: A Guide to Undertaking Automation-led Business Transformation.” A complimentary licensed copy of the report is available for download here, or read on for key points and our responses:

1. How is Intelligent Automation different from traditional RPA?

Robotic process automation (RPA) follows rules to automate work that has no variation. When you log into your email account, you enter a username and password the same way every time. RPA is great for these types of repetitive tasks, which often string together to form a simple business process, like: log in, click box, move file from Point A to Point B, log out.

Scalability and ROI problems rapidly emerge, however, when variation is introduced. How often does your business process change? How much of the high-volume work that inundates your team involves unstructured data? People adapt, but software bots that only follow rules do not.

This is why AI-driven Intelligent Automation is superior to rules-driven RPA. Intelligent Automation (IA) integrates all the capabilities found in RPA, plus adds capabilities only possible through bots that learn and adapt to data in real time. According to the Everest Group report, these are some critical features of what they call “RPA 4.0” or Intelligent Automation:

• “Machine learning, computer vision, text analytics and NLP” to process unstructured data, automate tasks that require judgment, and detect and adapt to constant change
• “Predictive and prescriptive analytics” help leaders plan resources and set achievable KPIs, plus help ensure optimal operational results
• “Self-managing, self-healing robots” to handle exceptions and reduce bot management

“In order to build a sustainable competitive advantage through digital transformation, it is essential for enterprises to adopt a long-term automation strategy that aims to implement intelligent automation solutions that combine both RPA and AI capabilities.”

2. How many companies are using IA vs. traditional RPA?

Everest Group takes great care to understand technology, and its research team also reports on how the smartest enterprises apply it. “Pinnacle Enterprises” is a term they use to distinguish long-term strategy focused vs. short-term, tactically oriented businesses. (Read more about it here.)

In a comprehensive market survey, Everest Group found that among Pinnacle Enterprises:

• 44% are “in piloting stages for cognitive / AI” vs. only 9% of other enterprises
• 45% are “currently in planning stages for cognitive / AI” vs. 49% of others
• 11% are “currently not considering it” compared to 33% of others

3. How are the benefits different?

Especially in banking, finance and healthcare companies, regulatory compliance has eroded margins, and leaner, fast-moving digital-native competitors have more room for profit variability. Reducing cost is a critical benefit, no matter how strategic an enterprise is.

The difference between IA and RPA in term of cost reduction is simple: AI automates more and automates more efficiently because it processes unstructured data, handles exceptions and continuously learns. In short: IA delivers significantly greater cost reduction.

“Siloed RPA implementations in disparate business processes can provide short-term wins for enterprises around immediate cost reduction (mostly in transactional processes) but may not dramatically improve business outcomes.”

Also: Traditional, rules-driven RPA doesn’t meaningfully reduce human effort. It just changes it. Analysts may eliminate repetitive work, but then have to spend time managing and re-training RPA bots. IA improves employee engagement by reducing both.

Compliance is another concern in these sectors. As Everest explains in the report, “the compliance functions of enterprises from regulation-heavy verticals such as banking, insurance, and healthcare go through multi-step repetitive processes and need to navigate through complex IT architecture.” Navigating this complexity and providing the depth and detail of reporting that regulators mandate is beyond the capability of standalone RPA products. IA helps complex organizations “ensure adherence to quality and compliance.”

And let’s not disregard the customer experience, the factor most responsible for driving revenue. By automating many middle- and back-office tasks connected to customer queries, companies can lower the “average handle time” (AHT), process requests with greater accuracy, and create higher customer satisfaction.

4. Why is an integrated platform superior?

Buyers of automation products who understand the importance of both AI and RPA to delivering superior results have two choices: 1) point solutions that require integration of RPA and AI through APIs and web services, or 2) a single platform that natively integrates all of the capabilities required to automate an end-to-end process or an entire role.

Everest thoughtfully presents the pros and cons of both approaches.

Some of the benefits attributed to the first approach include “reduced single vendor dependency” and “access to a wider partner ecosystem.” In our opinion, these both have their own down sides: more vendor management and more service checks to write.

All the cited challenges of this ecosystem approach are unambiguously negative: “increased complexity in integration,” “potentially higher TCO,” “potentially higher license costs,” and “governance & traceability challenges with multiple vendors,” among others.

On the other hand, Everest Group says the benefits of buying a single integrated platform that provides all critical capabilities are intuitive: “ease of interoperability,” “scalability,” “reduced license cost and TCO,” “native capabilities can be optimized to work well together.” To distill it down, buying a single platform is better, cheaper and faster. It removes from internal IT the archaic burden of integrating disparate applications, which is ironically why RPA was originally conceived. Even more importantly, the data generated from each capability is harmonized into robust analytics that help leaders manage and plan productivity.

Have any questions about the report? Or are you curious how well-suited Intelligent Automation would be for your transformation program? WorkFusion’s Intelligent Automation Cloud offers intelligence, simplicity and scale. We welcome your inquiries at learn@workfusion.com.

If You’re Only Automating with RPA, You’re Playing the Short Game was originally published in WorkFusion on Medium, where people are continuing the conversation by highlighting and responding to this story.

Everest Group is among the first firms to map the market and recognize its leaders, and in their latest assessment of the landscape, they have designated WorkFusion a Leader based on Market Impact and Vision & Capability.

We’ve licensed the report — Everest Group’s Intelligent Document Processing PEAK Matrix™ 2019 — which includes the full vendor evaluation of WorkFusion. Download a complimentary licensed copy here.

Read on for highlights of the report and why WorkFusion was spotlighted.

What is intelligent document processing (IDP)?

According to industry research, about 90% of data within any organization is unstructured, and most of it is locked in documents. Everest defines documents as “email, text, PDF and scanned documents” — which first-generation, rules-based RPA cannot process. IDP has emerged as a critical capability for large organizations because RPA customers have struggled to scale automation programs, most of which require processing a large volume of documents. With IDP, operations are able to automate data capture, classification and extraction.

Why is WorkFusion a Leader out of 16 IDP vendors?

Everest Group defines Leaders as those vendors who “command higher overall satisfaction among their clients than Major Contenders and Aspirants. Leaders’ clients are highly satisfied with their product vision & roadmap, IDP product capabilities, and their ability to help them achieve better business outcomes.” Looking beyond the way our products delight our customers, Everest Group offered these main reasons for assessing WorkFusion at this level:

1. A unified platform combining core automation capabilities

In Everest Group’s words, “WorkFusion has a distinctive vision to enable digital transformation for enterprises by providing innovative AI-driven automation capabilities … within its integrated automation platform that brings together RPA, BPM, OCR, analytics, and AI capabilities.”

Yes, it’s a lot of acronyms, but each one of them is a critical capability in enterprise digitization initiatives, and it’s essential they all work in concert. Put simply:

• IDP automates extraction and structuring of document-based data
• RPA automates pulling and pushing of structured data into disparate systems
• BPM orchestrates the business process that contains many data tasks
• AI makes cognitive decisions based on the data
• Analytics says how a process is performing, how it will perform, and what to do to optimize

WorkFusion has created the best iteration of each capability and built them into a unified software platform so that rather than cobble together multiple point solutions, customers only have to buy and learn one great product.

2. Making AI easy and scalable for business people

Not too long ago, AI was complex, expensive and scarce. WorkFusion was founded to make AI easy for business people — and the success that leading banks, insurance companies and healthcare organizations have seen using our product suggest we’re meeting that goal.

How? The key is a proprietary native capability called AutoML, which automates the expensive, complex data science work that used to require a team of Ph.Ds to spend months cleansing data, selecting and testing different machine learning models. AutoML watches people do their jobs, finds and learns patterns, and selects and trains the right algorithm to automate the work… within days.

3. Pre-built use cases and user-friendly process design tools

One example of a pre-built package: Anti–money laundering (AML) efforts are complex and expensive, but necessary to ensure banking customers are not performing illegal financial activity. Along with two major services partners, WorkFusion decided to create an off-the-shelf AI-driven automation solution for AML.

Everest Group noticed and rewarded this innovation, writing that our platform “has the capability to leverage pre-built use cases such as invoice processing and anti–money laundering [AML] to quick-start automation or configure a new use case through its built-in visual BPM/workflow with a simple drag-and-drop interface. This provides enterprises with the flexibility to enable end-to-end process automation with RPA, OCR, IDP, and system tasks with human-in-the-loop to handle exceptions.”

Versatile, complete enterprise automation solution

RPA, OCR, BPM and AI have each been around for a while, but our breakthrough automation capability has combined and delivered them as a unified product for customers. This is truly versatile intelligence: making many different tasks in a process more accurate, more measurable and more predictable. Entire functions are not only automated, but also perform better, faster and more efficiently as volume and variability of work increases. Our recognition by Everest Group as a leader in IDP, in two significant dimensions (Market Impact and Vision & Capability) is proof that one powerful, versatile platform that creatively combines capabilities can outperform specialized, rigid point tools. Our vision since 2012 has been to for our customers, and we’re grateful to every company who has bet on us and helped us blaze this trail — and those who will continue to leap forward with us.

Intelligent Document Processing, the New Enterprise Technology Superpower was originally published in WorkFusion on Medium, where people are continuing the conversation by highlighting and responding to this story.

Notably, this report — “Robotic Process Automation Game Changers Advance Financial Services Institutions Toward Intelligent Digital Workforce” — takes a firm stand on how to define “intelligence” in automation software. It’s a strong validation for operations who choose AI-driven automation products like WorkFusion, and it’s a great guide for those who are transitioning from first-generation RPA to Intelligent Automation.

Let’s consider IDC’s six defining characteristics of the intelligent digital workforce.

1. Simple, usable and reusable by business users

Before RPA, integrating applications required IT expertise, and lowering the cost of operational work required business process outsourcing (BPO). An intelligent digital workforce gives business people — the people in charge of functions like customer onboarding, trade settlements, and anti–money laundering efforts (AML) — the power to automate, on their own.

Intelligent Automation software with enhanced ML and QC capabilities automates cleansing data and training machine learning models. The IDC report mentions “improving data quality, redesigning processes and automation workflows, interoperability and integration.” A unified platform approach simplifies integration of critical tools such as OCR, BPM and rules engines.

2. Ability to deliver enterprise-wide scale

IDC acknowledges the “rapidly growing demand for an automation solution that can offer a single unified platform with centralized view and management of enterprise-level automation across various IT systems and technologies.” In other words, neither end users nor executive sponsors want rogue projects, cobbled together with disparate tools. To ensure automation success at a business-wide level, the smartest businesses select one primary platform to automate cross-operational functions and generate robust predictive and prescriptive analytics.

3. Security and governance as foundational tenets

IDC highlights the importance of “institutions’ ability to address the issues around data quality, data usability and data governance.” The inability of first-generation RPA products to monitor data quality is one of the biggest reasons complex businesses have struggled to scale their automation programs. Also, no one should overlook the growing challenge of regulatory compliance — essential for banks, insurers and healthcare providers. Intelligent automation products with native OCR, AI and RPA can process documents at massive scale. They also provide audit trails and explainable algorithmic decisions, which let compliance teams give regulators proof of success and comprehensive documentation of process.

4. Availability of real-time operational analytics

To measure ROI and achieve strategic resource planning, it’s critical to know both the performance of individual bots and of the functions they automate. Enterprise customers who choose products with unified platform models — i.e., “all-in-one” products — can see real-time performance and predicted performance of bots, people and functions. The latest generation of intelligent automation platforms also generate prescriptive insights: recommendations toward optimal operational performance.

5. Intelligence powered by cognitive/AI technologies and innovative tools

IDC points out that “progressing to more advanced automation technologies will stand a higher chance of delivering better business value and success.” Though there are many success stories, there are also numerous automation programs that have stalled — and those have first-generation RPA products to blame. Products with native AI make it possible to automate complex, non-standardized, and less repetitive tasks. IDC says: “Most vendors offer these capabilities through third-party partnerships; only a few have proprietary solutions” — and we are proud to say that WorkFusion pioneered native cognitive/AI automation, plus it’s the only product that delivers end-to-end process automation without third-party vendor integrations.

6. Strong support extended by the ecosystem

Despite the increased simplicity of modern products, most large enterprises work with partners to deploy and scale intelligent automation. Products with a unified platform model and native AI eliminate the brute force integration work that partners are often paid to do and allow expert resources to focus on the most valuable work: identifying processes to automate and deploying the product across the business.

In every growing technology category, buyers have many choices. Enterprise leaders with a long-term, strategic view of their business want to reduce cost, drive revenue, and improve the experience of both their customers and employees. Intelligent automation is key — and knowing what to look for when it comes to taking their automation efforts to the next level helps leaders make the best decision for their organizations.

Note: This article responds to only part of IDC’s “Robotic Process Automation Game Changers Advance Financial Services Institutions Toward Intelligent Digital Workforce.” In another article, we discuss the report’s definitions for an “intelligent digital workforce” and “cognitive/AI” solutions. WorkFusion has licensed the report, so if you’re interested in reading more, you can freely download a copy here.

IDC Research: 6 Defining Characteristics of an Intelligent Digital Workforce was originally published in WorkFusion on Medium, where people are continuing the conversation by highlighting and responding to this story.