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  • Let's Chat About Chatbots! - AI vs. Rules-Based Chatbots & Applications

    We have been receiving a lot of interest lately from our clients regarding Chatbots, so this blog is written just for you.

    Chatbots are on the rise enjoying their renaissance. For a long time, they were considered a niche or specialist subject, but now they are mainstream. Many of the world's top brands and companies are using them to power their services and as part of their outreach and marketing campaigns to engage their customers. Part of what makes the story of this technology's rise fascinating is that it does not begin and end with the technology itself, although the proliferation of Machine Learning and Natural Language Processing techniques is certainly interesting in their own right. Rather, Chatbots are situated right at the center of the trends that are changing the technological and business landscape as we know it.

    What Are Chatbots?

    A Chatbot is an application that automatically responds to messages from users. Chatbots can be rules-based or AI-based depending on their design and application. Cloud technologies and increasingly accessible artificial intelligence and machine learning techniques have led to a spike in Chatbot's interest from businesses.

    Rules-based Chatbots take messages from humans and decide how to respond according to a pre-set, limited set of rules. This type of Chatbot tech has been around for years and is still popular today. Are Chatbots AI programs as well? Let’s see.

    Conversational AI vs. Chatbot

    Do Chatbots need to use AI and Machine Learning to be effective? The answer is that it depends, as not all solutions rely on AI or ML to power their Chatbots, but the idea of using Natural Language Processing (NLP) to be able to understand text or speech from users is the dominant paradigm. While Chatbot solutions that do not include NLP are certainly still viable for many use cases, the gulf between solutions that include it and those that don't are only widening in capabilities.

    Artificial intelligence solutions have allowed Chatbots to evolve into more useful tools over the past few years.

    What is an AI Chatbot?

    At a basic level, an AI Chatbot is a Chatbot using artificial intelligence to be able to process the text given to it by a user. One thing to remember is that although they can be extremely engaging, Chatbots aren't able to converse like humans. IBM's Deep Blue supercomputer was able to beat World Chess Champion Gary Kasparov at chess in 1996, so computers can "do" chess and be very successful at it. But, whether a computer can play chess depends very much of the definition of "play." Similarly, Chatbots can "do" or "perform" conversation, even "perform" understanding by noticing patterns in speech.

    What is generally understood by AI is some type of computer or program that can perform actions, like playing or conversing. Humans do this naturally, but it is tough for computers to grasp given that their "thinking" is extremely literal and linear.

    Modern AI-powered Chatbots can execute these kinds of human performances by using statistics and probability as an "input", to apply the "meanings" given to it by its human designers to the text given to it by its human user.

    Natural Language Programming and Machine Learning Algorithms Used in Chatbots

    The key to allowing computers to understand "Natural Language" is taking text which the computer is not able to understand and first processing it into something it can work with. Computers are best at dealing with numbers, so effectively that is exactly what happens, AI Chatbot algorithms literally transform a series of words into a series of numbers. This is called "Vectorization" because we end up with a series or "vector" of numbers. To give an example, the most popular way of accomplishing this is to start with a dictionary, which can represent the set of all words that our Machine Learning algorithm knows about, or is relevant to us. Let's say our dictionary looks a little like this:

    ["hello", "I", "who", "what", "great", "ok"]

    and we want to vectorize some text that looks like this: "Hello, there I am doing great, really great."

    Our vector would come out looking something like this:

    (1, 1, 0, 0, 2, 0)

    DictionaryVector
    hello1
    I1
    who0
    what0
    great2

    What this gives us is a pattern that we can then analyze and compare to other patterns made with the same process. Let's say that we want to decide or "classify" this text is positive or negative, having two greats in there might tip us off, but it's not enough. So, what we do is collect as many quality samples as we can get our hands on that a human has marked positive, or that we are very confident about and use those as the basis for comparison. We can then build a statistical model where, although the computer still doesn't know what a particular piece of text "means", it can apply a label to it with a quantifiable degree of confidence depending on how similar or different it is to all of the vectors it has seen in the past. When the vectors are longer, they contain more information to compare and predictions can then be more detailed. But the most important part is having enough information to make statistically relevant comparisons. In this case, we only have two very broad labels, but what if it has to be much more subtle than this? There may be more labels, and they overlap in the type of language that they use. In this case, more information would be needed to make relatively small differences in the vector patterns to stand out.

    How to Make an AI Chatbot

    When it comes to making an intelligent Chatbot using deep learning, the first step is to take a step back and think carefully about its use case and audience, as this helps make any number of decisions further on down the line. There are quite a few solutions available, and in a lot of cases Chatbots are a company's first experience with AI and Machine Learning, resulting in choices being made, even before sitting down to build your bot. So, having a clear vision of what you are trying to accomplish will be invaluable in helping to navigate these decisions. With that said, it is also difficult to know what you are looking for unless you are already experienced with what's possible and available! But here are a few questions to help you out:

    • What kind of Chatbot do you intend to build? Is it Business-to-Business or Business-to-Consumer? Or possibly internal i.e. Business-to-Employee? This will make it easier to decide what platform is the best suited for your Chatbot. If you are targeting consumers then you may want your Chatbot to operate on a platform like Facebook messenger, for internal it may be installed on an internal website portal, or use Slack or MS Teams to have members communicate with it.

    • Will there be a voice component? What about visual components? Voice interfaces are amazing for certain use cases, for example, virtual assistant roles like Alexa or Siri. Having a hands-free way to accomplish tasks is amazing when you are on the move, or in the home or office. However, if we look at Chatbots on a spectrum between those that are all text-based and those which blend conversational and visual components, those that lean on the far text-based side of this spectrum will lean on NLP that much more, and more time and care will have to be taken in designing the dialog than those which can fall back on images and buttons to supplement their design. Chatbots which feature minimal NLP usage is almost always on the far-right hand of the spectrum while Voice-based are on the left. When executed well they can be a liberating experience for users, but they can also constrain design decisions.

    • Do you have a favored cloud-service provider? There are other solutions on the market that are not cloud-based, especially in the Digital Marketing space. But even these normally operate on a PaaS or SaaS model. Often they will not be as sophisticated as those offered by the major cloud service providers, and are usually very constrained in the Platforms or "Channels" that they support; usually, they will support at least Facebook, but few offer much more than that.

    Concerning the major cloud-service providers there is IBM Watson, Dialogflow on Google's GCP, MS Bot Service with LUIS on Azure, and Lex on AWS. All of these are good options, although they have their relative strengths and weaknesses regarding AI Chatbot features. If you are a completely free agent, you may want to shop around and try one or more out before you settle on a platform. But if you are already heavily invested in one of these cloud providers for your other infrastructure it is somewhat unlikely that you will find that it is worth it to integrate another provider into the mix.

    Once you have thought about these questions and conducted a little research, it is a good idea to do one final exercise before committing to a stack or platform. Writing simple example dialogs to refine your Chatbot idea is one of the best ways to refine a good chatbot idea. You can use either document or simply use pen and paper to compose short dialogs kind of like scripts imagining possible interactions between your Chatbot and your intended user.

    You may not have committed to a solution yet but by now you should have a general idea of:

    • what type of messaging platform you may be using?
    • How do you want to interact with your audience?
    • who is the user?
    • what are they trying to accomplish?
    • in broad strokes, you should have a better idea of what is practical and possible using the Chatbot tooling you are thinking about selecting.

    Look at the "happy path," or what you think a successful interaction will look like, but also think about what happens if something different comes up in conversation. Think about how to handle the exceptions, as well as any help messages you might need to communicate all the context that the user might need in an interaction.

    When you start your development, this is very heavily dependent on the actual tooling which is available for your platform or solution. It will be different if you go with LUIS, Lex, Dialogflow, Watson, or another solution. Building a Chatbot will require some amount of investment and experience in learning the ins and outs of the platform and the method used in building a chatbot. However, most solutions have excellent documentation, tutorials, and resources that exist on youtube and elsewhere into how to go about it.

    In our biased opinion – the best way to make an AI Chatbot is to trust your AI Chatbot development to the experts at NITCO.

    Why Are Chatbots Important for Businesses?

    Many of you are already familiar with some of the uses of Chatbots or Virtual Assistants like Siri, Alexa, and Google Assistant which are now common enough to be household names. But these are by no means the only solutions.

    Chatbots are popping up on Websites, answering common questions, offering support, and driving sales leads. They are also a part of every messenger program from Slack, Facebook Messenger, and WhatsApp just to name a few. Did you know that the same Chatbots that talk to people using voice, or on messaging platforms can even communicate over more traditional mediums like SMS or email as well?

    Chatbot Applications in Business

    Besides communicating over a wide range of platforms, Chatbots can communicate with backend applications and services such as Web or Mobile applications and can and perform any action they can do. For example, you can ask a Chatbot the status of an AP invoice without having to log in to an application and conducting a search. You can log or search for the status of a service help ticket or sales order. You can ask a Chatbot to complete a government form for you from a shipping transaction in your ERP system.

    With such a broad range of platforms and nearly endless possibilities in terms of function, the word Chatbot sometimes seems like it evades definition--what do Chatbots do? But asking what Chatbots do is a little like asking: “what does the Internet do?” or “what does mobile do?”

    When we are talking about Chatbots in a broad general way like this, it might be better to think of Chatbots as “Conversational Interfaces.” What an interface does is allow a user a means to easily engage with a service.

    A Desktop Web application and a mobile application might offer users access to the same service, but they have different properties and different strengths. For example, long documents might be harder to read on a phone than in a desktop browser, but phones are more portable and much more convenient.

    One of the main benefits of chatbots is that instead of having to install and open a whole new application, or navigate to a Website in a browser, Chatbots “live” inside the messaging apps and social media platforms that users already spend most of their time using. Since they also operate along with the same principles as people use to communicate with each other, users never have to learn to use an unfamiliar app or interface, they all use the same “universal interface” we have spent our whole lives learning to use effectively.

    We may soon see the design applications for a chat in the same way we design for Web or Mobile right now. In the next few years, we may see companies and products become "Conversational First” or even “Conversational Only”, the way we see “Mobile First” and “Mobile Only” right now.

    AI Chatbot Examples

    The best way to get a sense of what is possible with AI Chatbot technology is through examples.

    text

    Chatbots excel in areas like customer service, onboarding, and FAQ type scenarios. These patterns suit Chatbot usage in government extraordinarily well.

    Other major use cases are in marketing and brand engagement. Major brands like Starbucks, Sephora, and Domino’s pizza use Chatbots as a way of creating a dialog with consumers directly.

    EMMA - Government Bot for Department of Homeland Security

    In the same vein, the Department of Homeland Security has EMMA, which assists the department in answering queries about services offered by the department. Significantly, this bot can operate in English and Spanish and is estimated to handle around one million interactions monthly.

    Sephora - Reservation Assistant and Color Match

    Major makeup retailer Sephora operates more than one chat-based service through Kik, which is a social messaging app popular with their target demographic. One provides a way for consumers to easily book appointments with beauty specialists in-store, while the other operates as a form of augmented reality allowing users to upload photos to find products with matching colors.

    Sephora’s approach is very typical of brands which wish to use Chatbots in the business to consumer role. Chatbots are functional and may guide consumers through a practical workflow, but in addition to providing a service, a great deal of attention is paid to creating an experience with their customers. Customer engagement is key.

    Starbucks - MyBarista

    In the same way, Starbucks also operates a Chatbot both through its native mobile application but also as a "skill" as part of the Alexa platform. The MyBarista bot allows customers to place orders through the app for pickup in-store. Users can place their orders from the comfort of their own home using voice, or hands-free from their vehicle and have their drink waiting for them by the time they arrive.

    There is also an interesting synergy between Chatbots and other kinds of automation. This means that by designing services such as to-go ordering, appointment booking with automation, and efficiency in mind Chatbots are perhaps the most complementary platform available for making an already nearly frictionless system easier to use.

    Uber and Lyft - Seamless Ridesharing

    You can also see this at play in both the largest ride-sharing apps. Already automated and seamless to order through their native mobile applications, Chatbots can use the same API or backend service to perform the same function with even more convenience. In Uber's case, this is restricted to Facebook messenger but extends to Alexa and Slack in the case of Lyft.

    Imagine ordering a ride using voice while getting ready to go out, or in Slack right, before you leave the office: you dash off a quick update to your colleague or boss, and in the same application you order yourself a ride.

    From Our NITCO Experience

    We at NITCO are completely fascinated by finding new uses for this technology and have helped our customers to visualize this in important ways as well. Plus, having built a framework and several examples we can use the experience we have accrued, as well as code, to cut down on development time.

    Logistics - Robotic Process Automation Integration

    Our most recent Chatbot-related project was a Proof of Concept we created for a major logistics company. As part of the shipping process, operators for this company would have to get information about a shipment from an internal source and input these details into another external governmental regulatory Website in advance of the shipment.

    Having to replicate data by hand across multiple systems is not fun and tends to be time-consuming and error-prone. So, we were able to demonstrate that the process could be automated safely and effectively and that the process could be made user-friendly by allowing users to control an RPA Robot process via chat. This is called Intelligent Process Automation, or IPA.

    Accounting - Customer Service and Help Ticketing

    Many accounting processes make terrific candidates for automation, one of the best examples of this is ticketing and support. Having to answer questions about the status of vendor payments can take up a significant portion of accountants’ valuable time, even more so when there are issues and disputes.

    To that end, we outlined a solution that would allow their partners and vendors to query the status of payments and request clarification if payments were delayed, or the status was unclear. Depending on the security requirements of your organization, you can make this external to your vendors for self-service or keep internally to support your employees with ready answers to vendors on their invoices.

    IT - NITCO’s Ticketing Service

    At NITCO, we want to remain agile and responsive by being as flexible as possible, so in addition to our other means of communication, we wanted to have a Chatbot that would be available 24/7 and able to handle any number of requests. With that in mind, we tweaked our ticketing and support process to support the processing of support tickets in JIRA and built a Chatbot to be able to interact with JIRA's REST API.

    In this way, customers can check on the status of a ticket they raise with us at any time. This eliminates waiting for a representative to become available, even outside of regular hours, in addition to raising and having common questions answered.

    On the NITCO Horizon

    We plan to use this same Chatbot at NITCO for our government clients to request software quotes online based on our previous approved pricing sheets.

    We are also going to try it out during one of our project testing iterations as a front end to Jira Xray. Leave it to NITCO to have fun along the way.

    NITCO's AI Services Can Help Your Organization Improve Efficiency with Chatbots

    NITCO, Inc. has the expertise to help with any number of aspects in your digital transformation journey, including Chatbots. We have the expertise necessary to design, build, and deploy useful Chatbots with several application integrations, including Robotic Process Automation.

    Contact us today with any inquiries you might have about this exciting new field.

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  • AI Chatbots in Government - Examples and Opportunities

    What Are Chatbots?

    In one sense, Chatbots are not new. Broadly defined any type of application which can respond to messages from users automatically. From a certain, perhaps overly broad perspective, programs which auto-moderate forum posts are a type of chatbot, even a service which delivers out-of-office messages over email is a type of chatbot.

    Rules-based chatbots which receive or intercept messages from human users and decide how to respond to according to a pre-set limited set of rules have been around for years and are still popular today. But a major explosion in both the interest and utility of Chatbots hasn't come from these, rather, it's has been the rise of cloud technologies and the proliferation of accessible Artificial Intelligence and Machine Learning techniques that have led to Chatbot technologies' recent renaissance.

    Natural Language Processing and the Evolution of Chatbots

    Natural Language Processing (NLP) is the field in Artificial Intelligence which deals with a computer's ability to understand human language as it is written and spoken by flesh and blood human beings. Here, "Natural" languages like English, Spanish, and French are contrasted with programming languages which were explicitly designed for giving instructions to computers.

    Once, not so very long ago, Natural Language Processing techniques were only available to the academic departments which studied them, followed by organizations with specialized Data-Science teams. Today, they are widely available and have already seen action in a huge variety of fields, including in government.

    Unlike rules-based Chatbots, human beings already come equipped powerful tools developed over millennia to interface with the world and solve social problems; it's called conversation. Trying to navigate complex or unfamiliar situations without those tools can be confusing and disarming. However, being forced to rely on human intervention for help can introduce major overhead into an interaction.

    Human intervention is a major bottleneck for how many interactions can be undertaken by an organization, and even how convenient an interaction is on the individual level. What "Conversational UI" excels at is providing users just the right level of guidance and reassurance to take actions which may be unfamiliar to them independently. It does this by putting them back “in their element” so to speak.

    Benefits of Chatbots in Government for Citizens

    What this means for citizens is that they can have convenient, a la carte access to the information and services they require without having to wait for another human being to tell them how, when or if they can get it. Information is accessible and digestible; all they must do is ask.

    Benefits of Chatbots in Government for Agencies

    The benefits for agencies are simple. Chatbots can scale to allow agencies to handle huge numbers of request for services or information. Under normal circumstances and times of crisis, Chatbots allow agencies the flexibility to adjust their bandwidth to tackle more valuable work and save resources in the process.

    A close up of electronics Description automatically
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    AI Chatbot examples in the Public Sector

    To date, most states have already implemented chatbots in some capacity. Chatbots have been a vital component in several states' response to the Coronavirus crisis. These tools have shown themselves capable of helping departments deal with major spikes in demand under the most adverse conditions.

    Here are some examples of Chatbot government services that are helping serve the public.

    Larry the Chat Bot - Texas Workforce Commission

    One such example is Larry, which was developed for the Texas Workforce Commission as they were hit with a completely unprecedented number of unemployment insurance applications. Larry, who has since handled 4.8 million queries from 1.2 million users, took only four days to develop. This by itself is impressive, but the key problem that Larry was able to solve is that during a time of peak demand staff were no longer forced to choose between spending time answering questions from the public and devoting resources to productive work such as processing or adjudicating claims.

    EMMA - Government Bot for Department of Homeland Security

    In the same vein, the Department of Homeland Security has EMMA, which assists the department in answering queries pertaining to services offered by the department. Significantly, this bot is able to operate in English and Spanish and is estimated to handle around one million interactions monthly.

    Mrs. Landingham - Chatbot Government Service for General Services Administration

    The General Services Administration is one agency that plays a prominent role in the promotion of RPA and Chatbot services within Government. They employ Mrs. Landingham, a slack based chatbot able to handle many of the questions and functions involved during the onboarding process of new hires. Mrs. Landingham can help guide new hires through task such as filling forms and answering any employment-related questions they might have.

    NITCO's AI Services Can Help Your Government Agencies Improve Efficiency with Chatbots

    NITCO, Inc. has the expertise to help with any number of aspects in your digital transformation journey, including Chatbots. We have the expertise necessary to design, build and deploy useful chatbots with several integrations, including Robotic Process Automation.

    Contact us at YourDigitalTechnologyPartner@nitcoinc.com with any inquiries you might have about this exciting new field.

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  • Understanding, Document Understanding

    Across many industries and business functions, the automation of the handling of business documents has had numerous major benefits, including:

    • reduction in the time and costs associated with processing the documents
    • reduced risk with the elimination of human error
    • more effective audit trails ensuring compliance
    • free valuable human resources from manual and repetitive data processing activities
    • optimizing processes
    • increasing revenues
    • reducing expenses by taking advantage of better payment terms
    • faster and timelier document processing due to 24/7 run times
    • digitalizing the data to allow it to be indexed, analyzed, and stored more effectively
    • ensuring work is completed, even though human resources are not available

    Successful automation projects in the past have required access to easily digitizable, easily digestible, highly structured files and data which are easy for machines to understand. But the question remains: How well does that describe most of the documents that enterprises have in their pipelines?

    File formats such as Excel, Word, PPT, XML, readable PDFs and others fall into the category of standard readable input, assuming they come in a predictable format. But what about forms that can come in multiple formats, legal agreements and contracts, microfiche, financial reports, invoices, receipts, handwritten documents or resumes? It is rare for these to be easily converted into the kind of structured data that most digital processes require.

    It has been estimated that between 50% and up to 80% of all documents in organizations are unstructured or semi-structured in nature. These types of documents have been excluded from the radical benefits that automation and digital processing can provide. In turn, this means the data is hard to find and the documentation difficult to automate.

    So, what is the solution? Document Understanding.

    What Is Document Understanding?

    There are three document categories within document understanding. Each category has specific challenges and may require a different technical solution.

    DefinitionExamplesChallenges
    StructuredDocuments that are standard in format and can be templatized with a fixed location for specific data sets.
    • Banking forms
    • Tax forms
    • Surveys
    • Licenses
    • Timesheets
    • Passports
    • Easy to Implement
    • Handwriting/poor quality scan remains difficult to support
    Semi-StructuredDocuments with similar sets of information (usually labeled), but with variance in design and placement of data.
    • Invoices
    • Receipts
    • Purchase Orders
    • Medical Bills
    • Bank Statements
    • Utility Bills
    • Difficulty with multiple transaction lines/tables, handwriting, objects, scan quality
    UnstructuredDocuments without a standard structure, with significant variance in data consistency and structure.
    • Contracts
    • Agreements
    • Letters
    • Emails
    • Drug Prescriptions
    • Requires AI - technical difficulty
    • Greatest value potential for highly manual processes

    What are the advantages of Document Understanding?

    Document understanding provides the means to store, index, query and analyze entire categories of documents where these operations were previously impossible (or at least hugely expensive and impractical).

    Examples of Document Understanding Use Cases
    Accounting
    • Accounts Payable & Accounts Receivable
    • Invoices and Receipt Processing
    • Surveys
    • Handwritten field receipts
    • Vendor Onboarding
    • Customer Onboarding
    • Tax Reporting
    • Compliance-related Processes
    Financial Services & Insurance
    • Loan Applications
    • Mortgage Processing
    • Title Search
    • Account Opening & Customer Onboarding
    • Confirmations and Pre/Post Matching
    • Claims Processing
    • Compliance-related Processes
    HR
    • Employee Onboarding
    • Resume Screening
    • HR Records Processing
    • Time Card Processing
    Supply Chain Management
    • Order Scheduling & Tracking of Shipments
    • Bill of Lading
    • Transport Notes
    Manufacturing
    • Sales Order Processing
    • Customer Parts Request
    • Remittance Processing
    Public Sector
    • DMV Title and Registration Applications
    • Driver's License Applications
    • Immigration Applications
    • School Applications
    • Passport Management Applications

    As noted, AI document analysis can produce several benefits, including:

    • Reduction in errors.
    • Better compliance.
    • Free resources from manual and repetitive document processing tasks.
    • Perform analysis and gain insight into your data.
    • Integrate previously underutilized information within your system into other business apps and processes where it can do the most good.
    • Integrate with your Current Cloud Service provider's services for cloud document processing.

    How does Document Understanding work?

    Document understanding AI encompasses a range of techniques, but the fundamental steps are the same. Here is a look at those steps in practice:

    1. Taxonomy

    Defines the files and data for extraction.

    Just as you look in a catalog file to find a book in a library, a taxonomy provides a way to organize the data you need. Not thinking of how to organize the data can also reduce productivity.

    When extracting data for entry into an ERP system, the data fields are already identified. However, research has shown that employees frequently cannot find the data they need or duplicate information that already exists.

    2. Digitization

    Provides text and its location for the technical solution.

    Once a structure is defined, then the document is scanned using OCR (Optical Character Recognition). The document understanding solution will create an image of the document textually, and visually that can be used to perform further analysis.

    The image contains more information than we really need right now, and we haven't yet performed any analysis of the data. In the steps to come this will be the image that the AI for document analysis will "see" when interpreting the meaning of the document's elements.

    3. Classification

    Identifies and classifies the documents from a specified list.

    The next step is classification. We use a machine learning model to tell us exactly what type of document we are dealing with. This may be useful information in itself when sorting files of different types, but it is invaluable when we go to figure out what information we expect to find and extract from the document i.e. what outline or model we should apply to which type of document.

    4. Extraction

    Extracts the data from the document.

    At this step we perform document data extraction. By now we should already have the output from the first step containing all of the raw data, we should know what kind of document we are dealing with, and we should also have a pre-defined definition of the information that we want to get out of the document.

    Using AI understanding text, that is, a machine learning model trained and tested using human-validated information extracted from similar documents in the past, it extracts the information out of the document that will be useful to the business process going forward.

    5. Validation

    If needed, a human will help confirm the extracted data by a human using Machine Learning.

    Finally, for our model to become smarter, and more powerful at performing the desired outcomes we must give it feedback. A human will have to validate a small sample of its output as a trusted source of what the "correct" results should be until we have data sufficient in quantity and quality that our model's predictions are accurate "enough," and that any potential errors are statistically negligible.

    What this means in practice may depend on business requirements, but the most common practice is to send any predictions that do not meet a certain threshold for confidence to be validated manually. Over time the number of predictions that do not meet the threshold will naturally diminish.

    6. Export

    Exports the extracted information for further usage.

    The data can now be entered into an ERP or other data system or placed in a repository for further analysis or reporting. Often this is a Robot that takes the data and places it where trained.

    So, document understanding is about utilizing AI and NLP technologies to expand the range of available types of business documents and files that a Robot can understand. This extends from only the very predictable, highly structured data, to the variable unstructured and semi-structured data that makes up most real business documents in use across many industries and departments.

    What we are seeing with the emergence of document understanding technologies is, in a practical sense part of what makes the application of AI and natural language processing so exciting. By expanding the range of documents the automated document processing systems can handle, businesses can now take the major gains which result from automation, like reduced error rates and costs, better audit trails and faster more efficient processing pipelines, and apply them to problems where it was previously impossible.

    Get Reliable Document Understanding AI Solutions with NITCO

    Ultimately, information or data is very often the most valuable resource that a business can have at its disposal but is only as productive as its ability to process, understand, and get insight and value out of it. To that end, document understanding is a powerful tool to unlock more of the value contained in your documents.

    NITCO, Inc. is partner to some of the most exciting players in the intelligent document processing market, including ABBYY, UiPath and AppZen. We are experts in finding the best and most cost-effective document understanding solutions to meet the business requirements of any organization.

    Contact AISales@nitcoinc.com to get started on your document understanding journey.

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  • How to Plan for Robotic Process Automation ROI

    Enjoy the Benefits of Robotic Process Automation

    One of the classic leadership books of all time is Stephen Covey's, The 7 Habits of Highly Effective People. He identified Habit 2 as, "Begin with the End in Mind". This leadership concept is also critical to the success of any RPA implementation to justify and validate your robot process automation ROI.

    Two things must happen to plan your RPA ROI implementation before you develop an automated process:

    1. First, analyze the process to determine if it satisfies key RPA selection criteria.
    2. Create a business case for implementing the automated process to align with expected business outcomes.

    8 Key RPA Selection Criteria

    There are basic questions you need to ask when assessing a business process for an automation opportunity. These criteria are weighted by varying measures and will have a fundamental impact on the ROI for robotic process automation. The selection criteria determine if the automation will be simple, medium, or highly complex to develop.

    1. Is the process rules-based?

    This means do the activities performed have clear processing instructions (template based), with decision making based on standardized and predictive rules? Setting up a new supplier, customer or employee are good examples of process rules-based processes.

    2. Does the process have standard, readable input?

    Automated processes require standard, readable electronic input with consistent results. Examples include readable input types like Excel, Word, email, XML, PPT, and readable PDFs. Handwritten documentation requires more sophisticated technology such as AI and machine learning.

    3. Does the process have low exception rates?

    Activities with a low number of variation scenarios existing in the process leading to different handling procedures. A non-PO AP invoice might be an example of a standard process with a low exception rate. An AP PO invoice with three-way matching could be an example of a high exception rate. This would require a tool for documentation understanding to extract data from forms, invoices, etc., and machine learning from users.

    4. Will there be any system or process changes in the next 6 to 12 months?

    The processing method cannot be changed. Fundamental changes are not required for the underlying technical architecture of the current system. It should be obvious that if the process or system changes, the trained Robot will not know what to do.

    5. Is this a stable, well-documented process? How many process steps involved?

    The process should be well documented, stable, and predictable.

    6. How many applications are involved?

    More applications can make development more complex.

    7. Are there high volumes or frequency of the process?

    Transactions that occur frequently or with high volumes are invoicing, reading, and assigning emails, attaching documents, and so forth.

    8. Is the process highly manual and repetitive?

    Reports, activities, or transactions that are highly manual and repeatable.

    Business Case Justification

    There are several reasons for automating a process such as cost, productivity, quality, and compliance. It is critical that you capture a business case with key performance indicators to determine ROI for RPA. By properly planning for and reporting on these measurable activities, you can easily quantify the money and time saved by your automation and benchmark the efficiencies and performance over time.

    A simple business case to measure the costs and benefits of an automated process over a period looks something like this:

    • Cost savings can be labor savings on the hours saved through automation (be sure to use your burdened rate with all taxes and benefits), material savings, storage savings, and so forth.
    • Productivity can be creating more sales, hence more revenue by processing more transactions or receiving on open purchase orders faster to take advantage of discounts on payment terms by paying early.
    • Quality can be a reduction in data entry errors.
    • Compliance can be industry regulatory requirements that will have a cost but can be completed faster, more frequently, and with no errors such as claims processing.

    NITCO’s partner UiPath has made capturing this information easy to plan for using Automation Hub which is agnostic of any automation tool and uses algorithms to calculate the ROI based on key selection criteria and business case validation. In addition, they have released Insights to measure the actual outcomes and link them to your business bottom line.

    Contact us at AISales@nitcoinc.com to find out how our digital automation experts can partner with you.

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  • Can RPA Process Mining Make Your Business Recession-proof?

    How COVID-19 Has Affected the Unemployment Rate

    With the introduction of the COVID-19 virus in late 2019, continuing into the middle of 2020, companies and employees have been affected globally. Unexpected events like this can devastate any industry in any economy and are reflected in the employment statistics in the United States this year.

    According to the US Bureau of Labor Statistics, the number of unemployed persons rose by 15.9 million to 23.1 million in April of 2020 alone. These drastic changes in the employment resulted in “the lowest rate and largest over-the-month decline in the history of the series.

    Every industry has been affected by this global situation and has experienced declines in employment, downsizing, and cost-cutting measures as a response. Though companies cannot be blamed for taking these actions, common practices may not be the best solution for preparing for the future.

    Hoping to glean helpful insights, employees and business leaders alike seek out examples of businesses that do well in recessions.

    Businesses That Do Well in Recession Deploy Defense AND Offense

    Companies that find themselves suffering during economic downturns and recessions can be categorized into groups based on their strategic responses during these times. According to the Harvard Business Review, “companies typically combine three defensive approaches—reducing the number of employees, improving operational efficiency, or both—with three offensive ones: developing new markets, investing in new assets, or both.”

    Using these criteria, we can analyze past efforts by companies to categorize them accordingly. Categorizing companies as defensive and progressive based on their actions during and after recessions helps identify effective strategies for emerging successful in prerecession economies.

    Prevention-focused companies, which make primarily defensive moves and are more concerned than their rivals with avoiding losses and minimizing downside risks.

    Promotion-focused companies, which invest more in offensive moves that provide upside benefits than their peers do.

    Pragmatic companies, which combine defensive and offensive moves.

    Progressive companies, which deploy the optimal combination of defense and offense.

    How to Improve Efficiency of Operations

    Taking from the examples presented in the Harvard Business Review’s study, we can discern that improving efficiency in business operations is one of the key factors for success in recession.

    While progressive companies do cut staff during recessions, they do so at a lower rate. The Harvard Business Review study reports only 23% of progressive companies cut staff while 56% of prevention-focused companies cut staff at much higher numbers comparatively. Rather than cutting costs through layoffs, progressive companies focus on improving the efficiency of business operations which can reduce costs permanently. This strategy eliminates the scramble to rehire in post-recession economies as demand begins to rise and emerge with fast-growing profits beating out competitors.

    Many companies are taking advantage of robotic process automation (RPA) to bring more recession-proof automation solutions into their organization. While traditional outsourced work solutions are able to reduce costs and free up local staff, the impact of the recent Coronavirus has highlighted more limitations and inefficiencies.

    Considering RPA and traditionally outsourced work, RPA produces results faster, more efficiently, and at lower costs for companies. Additionally, RPA is a recession-proof solution as it can operate the same functions no matter the economic conditions. Businesses that do well in recession have leadership that holds steady to the path of long-term growth and profits and don’t hurry to downsize their workforces drastically. With automation and technologies like RPA, companies are able to perform better than their strictly defensive-minded competition by downsizing at a lower rate, improving efficiency of their operations, and setting the foundation to flourish in the post-recession economy to follow.

    The 4th Industrial Revolution (4IR): Robotic Process Automation

    Robotic process automation has been coined the 4th Industrial Revolution as early as 2017 and has proven a great enhancement for businesses of all industries. Whether in times of economic downturn or increased volumes of business, RPA can enhance operational efficiency and lower costs to provide more profits and productivity. Changing the way businesses function, RPA improves business operation efficiency and human employee efficiency.

    RPA is to a company, as a tractor is to a farmer. As a tool, RPA and tractors improve worker efficiency, offer new implementations of other technologies to enhance that improvement, and allow the human worker to spend less time on repetitive tasks affording them the time to plan and strategize. In this analogy, the employee, the customer, and the business as a whole benefit largely to the implementation of the new technology solution. RPA offers these benefits to any industry and can revolutionize the way business tasks are performed.

    RPA and COVID-19

    In times of economic uncertainty or recession, such as this year’s Coronavirus pandemic, RPA is a solution that enables companies around the world to fair much better and strategize for future improvements and profits. A large reason RPA is so impactful during economic downturns is it offers companies the ability to continue to perform their automated tasks uninterrupted and adhere to schedules without the impact such situations have the human workforce.

    An implemented RPA solution is available at any time to complete any task assigned to it while being quicker and less error-prone. It does not replace human employees but offers them the ability to focus on strategizing and identifying operational efficiency improvements to improve the business’ future success in a post-recession economy.

    Can Your Business Benefit from RPA in the Face of Recession?

    Facing the impact of the recent COVID-19 outbreak has been a challenge for companies across the world. With profit margins slimming and businesses reducing staff sizes, it is easy to take the approach of downsizing and limiting expenditures across operations. Yet, the studies mentioned previously point at business optimization through automation technologies like RPA.

    RPA brings a permanent improvement to operational efficiency which enables employees and managers to focus on expanding and growing as the post-recession economy is established. Nitco Inc and other leading RPA specialist companies can offer stability and optimization to companies across all industries reducing the hours spent on simple tasks that hold back high-level planning and strategies that can bolster profits in the near future.

    Allow our RPA solutions to improve your business operations and free employees to focus on more impactful work. Contact NITCO Inc today and allow our specialized teams to transform your business into a more efficient and resilient competitor in your industry by implementing RPA solutions into your company.

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  • RPA Use Cases - Benefits of Robotic Process Automation

    Robotic Process Automation (RPA) is a technology enjoying a renaissance across all industries changing the way businesses function and providing substantial savings to operational costs. RPA technology has been shown to improve the efficiency of process completion across a bevy of business departments at a low cost.

    With RPA technology, humans can conduct more impactful and meaningful work while RPA handles the previously time-consuming tasks. Though RPA implementation can be affected by the complexity of a task, this technology solution is easily coupled with other technologies such as machine learning and speech recognition. This coupling enhances RPA and incorporates a cognitive technology solution, intelligent process automation (IPA), that is more malleable to changing situations and business environments.

    This article will explore opportunities for RPA implementation, types of RPA configurations, and Robotic Process Automation examples throughout different industries.

    Characteristics of High-opportunity RPA Scenarios

    At Nitco, we recognize the capability of RPA solutions to improve businesses across all industries. Our team can identify and categorize opportunities for RPA and AI use cases by using our planned implementation methodology. By analyzing processes based on attributes such as complexity, urgency, and effort/time, we classify processes in a simple manner with four categories:

    Low Hanging Fruits

    Low Hanging Fruits are considered processes which are low in effort (complexity) and low in impact. That is, the effort required to integrate RPA to assist in the specific process is low as is the impact that integration will have on the organization.

    Long-term Improvements

    Long-term Improvements are processes which are high in effort (complexity) and lower impact. These projects should be viewed as improvements to tackle later as they will be burdensome due to their complexity. They will also prove more costly and less impactful which can drive internal support for RPA implementation down.

    Must-do Improvements

    Must-Do Improvements are processes that are high in effort (complexity) and high in impact. These processes should be prioritized higher as they will provide the most benefit from RPA implementation.

    Quick Wins

    Quick Wins are processes which are low in effort and high in impact leading them to be the highest prioritized processes for RPA. These quick wins will provide great benefit to the organization and are comparably simpler than most other processes.

    To determine a good process candidate for RPA we look for attributes such as complexity and impact/value to the company. Identifying these quick wins offers quicker benefits from investment and demonstrates the value RPA can provide to the numerous business departments across an organization.

    When evaluating the complexity of a process for automation, ideal candidates for automation usually include characteristics such as:

    Process Is Manual & Repetitive - The same process steps for all cases or transactions. Rule-based - Human users do not rely on experience or judgment to process cases or transactions but use predefined business rules and logic. Standard Input - The content of the input is fixed and in the same location for all cases or transactions. Consistent - Process will not change within the next 3-6 months.

    Types of RPA Projects

    Robotic Process Automation use cases are found throughout different industries and can be adapted to provide value in various environments. The processes that RPA can be applied to with great success vary from customer management processes and payroll processing to compliance checking and employee onboarding/offboarding.

    Because of the variation in type of data and the manner of execution of processes, RPA robots come in two variations to tackle the differing needs. The determination of the type of robot needed is based on the business needs and the environment for which the solution will be developed. Different scenarios call for different solutions.

    1. Unattended RPA

    Unattended robots work on separate virtual workstations with no human interaction. Back office tasks with highly rule-based activities are ideal processes for an unattended RPA solution. In these RPA scenarios, the unattended robot is capable of remote execution, monitoring, scheduling, and providing support for work queues. Unattended robots are favored in situations that involve large amounts of data that must be gathered, sorted, analyzed, and distributed to various members in an organization.

    2. Attended RPA

    RPA scenarios that call for attended RPA robots usually rely on data that needs to be validated by humans, are tasks that cannot be scheduled, and directly assist humans in their tasks. The attended robot performs some tasks and the human will intervene when needed. In these RPA examples, attended robots work on the same workstation as a human and work in tandem with humans to complete a process.

    3. Attended and Unattended - Hybrid RPA

    Hybrid RPA combines the power of RPA robots and human employees in one environment. By having both, virtual robots and human employees, hybrid RPA scenarios can operate efficiently by deploying each actor when they are most efficient. This combines the efficiency and scalability benefits of an RPA robot with the flexibility and creativity benefits of a human actor.

    Examples of Common RPA Use Cases

    Explore some of the most common RPA use case examples below.

    • Supply chain RPA
    • Healthcare RPA
    • Accounting & Finance RPA
    • Customer Service RPA
    • HR RPA
    • IT RPA

    Supply Chain RPA

    Within supply chain management in various industries, organizations put large amounts of effort into understanding consumer markets and meeting changing demands. Combining robotic process automation and artificial intelligence technologies can speed up business processes and improve the accuracy of information such as consumer market projections. This helps suppliers meet demands by accessing the necessary data for human employees.

    Use cases within supply chain management range from inventory management and product quality checking, to logistics administration and customer service. Taking inventory management into consideration, RPA solutions provide a quick and simplified process for retail businesses to prepare item stocks based on consumer estimations. By implementing automation solutions, the bot can provide suggestions for purchase orders in the specified business period speeding up the process substantially and improving the efficiency of resource use.

    Supply Chain RPA Use Cases

    • Sales forecasting and projections
    • Catalog management
    • Purchase order management and shipping tracking
    • Document gathering and data management
    • RMA and receipt validation

    Healthcare RPA

    The healthcare industry has great potential to increase patient services and improve efficiency in back-office tasks through implementing RPA. Technology advances have improved the healthcare industry over decades and RPA offers another opportunity to drastically improve business processes and patient care.

    The benefits of robotic process automation can introduce a new level of efficiency in business processes such as data processing, health interoperability, and cloud computing while improving patient care through RPA and AI solutions like conversational AI and natural language processing. With most healthcare leaders identifying automation as a priority for their businesses, any healthcare organization is able to improve their operational efficiency with the benefits of robotic process automation.

    Healthcare RPA Use Cases

    • Pharmacy order and supply management
    • Patient access management
    • Patient enrollment, billing, and claims
    • Document triage
    • Data management and business analytics

    Accounting & Finance RPA

    In the accounting and finance industry, challenges such as regulatory compliance and risk mitigation are of great importance. Relying on enterprise resource planning (ERP) software and legacy applications is being replaced in this new digital era with more efficient technologies like RPA. RPA plays a crucial in transforming many business operations by reducing costs, improving risk management, and offering Chief Financial Officers and their teams the ability to focus on higher value activities such as advisory roles.

    Areas in this industry such as accounts receivable, general accounting, and tax regulation compliance all benefit from the introduction of RPA with optical/intelligent character recognition (OCR/ICR). Introducing RPA into straightforward processes like invoice data entry can provide a great proof of concept before starting on the digital transformation journey with RPA. Once the benefits of RPA are realized, introducing artificial intelligence components amplifies the capabilities and efficiency of RPA solutions throughout all business processes within the accounting and finance industry.

    Accounting & Finance RPA Use Cases

    • Manage cash flows
    • Execute hedging transactions
    • Process end-of-period adjustments
    • Monitor and track capital projects and budgets
    • Calculate and record depreciation expenses

    Customer Service RPA

    While RPA shows great efficacy being implemented to improve back-office tasks, RPA can be an important component of improving customer service tasks. Introducing RPA as a customer service solution improves customer relationship management through increased internal functionality within an organization. Improving back-office tasks with RPA and AI technologies enhances customer-facing tasks by affording employees more time to interact with customers and turn one-time customers into repeat customers.

    Effective RPA use cases in customer service can remove mundane back-office tasks such as pulling customer data and other helpful data within the context of the interaction to improve response times while providing the necessary information. With quicker customer service engagements, customer service departments can reach larger audiences, improving the company’s outreach.

    Customer Service RPA Use Cases

    • Manage and access customer data securely
    • Customer engagement
    • Compliance and regulation enforcement
    • Customer retention
    • Data entry and changelogs

    HR RPA

    In Human Resource departments across the world, HR employees spend hours completing repetitive tasks such as onboarding, offboarding, screening applicants, and compliance reporting. All these tasks provide great use cases for robotic process automation to be introduced into HR departments across all industries. RPA can enable HR professionals to be more efficient in their tasks and improve the employee experience through automation.

    Implementing RPA into an HR team can prove valuable to all departments throughout an organization, as HR is the link between employees and the organization. With automation solutions, HR employees have more time to identify further process candidates for automation and improve the employee experience throughout an organization.

    HR RPA Use Cases

    • Employee performance management
    • Manage HR helpdesk
    • Manage Pre-employment verification and resume screening
    • Data management and reporting
    • Payroll, benefits, and compensation processing

    Information Technology RPA

    Within IT departments across all industries, there are many transactional processes occurring each day. Many IT teams are unable to fulfill the various day-to-day requests due to the large volume, leading to backed-up departments across an organization. RPA can relieve these issues and helps standardize processes across an IT department. By automating tasks and standardizing workflows, IT teams can complete their work at a much more efficient rate while introducing improved functionality.

    IT RPA Use Cases

    Monitoring and scheduled maintenance of servers and applications

    • Testing and QA for applications and software components
    • Password resets and email processing
    • Managing IT infrastructure operations
    • Database administration and management

    Contact NITCO for Help Implementing Efficient RPA Solutions

    Organizations often employ the services of RPA experts such as Nitco Inc to evaluate which processes can be automated using RPA and identify the best RPA use cases to produce the substantial benefits RPA offers.

    Contact us at AISales@nitcoinc.com so we can help you on your digital automation journey.

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  • Use of AI Throughout the COVID-19 Pandemic

    At the time of this writing, there are over 400,000 confirmed cases, over 18,000 confirmed deaths, and nearly 200 countries with cases of COVID-19, according to the World Health Organization (WHO). So, how did our planet get here? And more importantly, what efforts are being made to restore a sense of normalcy to the world? We shed light on COVID-19, and several examples of artificial intelligence (AI) being used to identify and combat the Coronavirus

    Overview of COVID-19 (Coronavirus)

    COVID-19 (short for Coronavirus Disease 19), is a respiratory disease caused by a new virus called “severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).” Thus, the disease caused by the virus is COVID-19, while the actual virus is identified as SARS-CoV-2, according to the WHO. On March 11, 2020, the outbreak was officially characterized as a pandemic. Worldwide efforts are being made to lessen the spread of the disease, but the infrastructure of many healthcare organizations is being overwhelmed by the volume of COVID-19 cases.

    5 Examples of AI During the COVID-19 Pandemic

    How can AI be used to improve the COVID-19 pandemic? In fact, artificial intelligence has already played a key role in the identification and response to SARS-CoV-2. Discover how machine learning and AI has been harnessed to help improve the human condition through these uncertain times.

    1. Use of AI in COVID-19 Discovery

    On January 9, 2020, the WHO officially announced a “cluster of pneumonia cases in Wuhan, China.” However, nine days before this official announcement, an artificial intelligence platform designed to “track, locate, and conceptualize” the spread of infectious disease flagged the group of “unusual pneumonia” cases happening in Wuhan, according to CNBC. The AI solutions company to thank for this early identification, BlueDot, used machine learning and natural language processing (NLP) to process data from thousands of sources, and present anomalies to a team of experts. We’ve established that artificial intelligence can be used to identify world health threats, but what can it do to slow and stop them?

    2. Use of AI to Predict Spread

    In early February of 2020, another AI tool called Healthmap was being used to create visualizations of COVID-19 spread. This AI program is being used for WHO’s EIOS initiative, a public health collaboration intended to identify global biological threats. This particular example of AI in the COVID-19 pandemic is used to show concentrations of disease manifestation using maps and different-sized colored circles. By visualizing early signs of the Coronavirus disease, public officials should be able to take informed precautions instead of being forced to react after significant loss of life. We now see how artificial intelligence can help inform literal life-or-death decisions, but what about equipping the public to combat the spread of the disease?

    3. Use of AI to Identify Misinformation

    There have been instances of officials from the U.S. State Department blaming foreign entities for the spread of misinformation about COVID-19. In early March, the Office of Science and Technology Policy from the White House met with tech industry leaders, urging their representatives to “coordinate efforts to root our misinformation about the coronavirus." To understand what misinformation is, we need a baseline understanding of quality information. To that end, the White House project aims to make a large amount of research related to the Coronavirus accessible to AI players in order to identify helpful insights. Getting our facts straight is important for preventing the spread, but what about treating those who have already been exposed to SARS-CoV-2?

    4. Use of AI to Streamline Healthcare

    Microsoft's Azure Healthcare Bot framework was used to build an AI-powered bot named Clara to “help the CDC and other frontline organizations respond to these inquiries, freeing up doctors, nurses, administrators and other healthcare professionals to provide critical care to those who need it." The aim is to help healthcare organizations give the public a way to better self-assess their condition if they are experiencing cold or flu-related symptoms. By preventing someone with a common cold from using resources that could be spent on a COVID-19 patient, Clara could have far-reaching life-saving consequences. Artificial intelligence has played a role in the discovery, prevention, and treatment of COVID-19, but can it help inhibit the virus altogether?

    5. Use of AI to Develop Inhibiting Drugs

    There are at least a handful of AI companies currently working on the development of or reapplication of antiviral drugs that could be used to inhibit the ability of SARS-CoV-2 to replicate within hosts. So far, several of these companies have:

    • • Identified FDA-approved antiviral drugs already in existence with a high likelihood of binding and/or blocking the replication proteins of SARS-CoV-2
    • • Discovered new molecules with high potential for blocking the replication proteins of SARS-CoV-2
    • • Making and testing new molecules with high potential for blocking the replication proteins of SARS-CoV-2

    Many of these AI-generated solutions have only resulted in preprint research papers, not yet peer-reviewed by peers. Other solutions have led to communication with existing drug manufacturers about testing their products as potential COVID-19 treatments.

    NITCO Offers AI Solutions for Many Industries

    If you’re interested in harnessing the benefits of AI-powered automation, the AI and RPA solutions available from NITCO can help. We are experts in robotic process automation, natural language processing, artificial intelligence, and cloud platforms, and have worked with several major companies to streamline their operations. Contact us today for more information about how our solutions can benefit your business.

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