• 4 Outstanding Deep Learning Applications in Business

    In recent years, artificial intelligence solutions have proven to be an asset to all types of businesses. Computers are becoming powerful to the point they can perform complex tasks accurately and quickly, sometimes without supervision.

    In this article, we will learn how several different industries all use deep learning applications to their advantage.

    What Is Deep Learning?

    Deep learning is an artificial intelligence (AI) function that utilizes human-like learning capabilities to perform tasks. Also known as deep neural network or deep neural learning, this learning technique can achieve state-of-the-art accuracy without human assistance.

    Artificial Neural Network vs. Deep Learning

    What is the difference between deep learning and artificial neural networks? Deep learning and neural networks are often used interchangeably, however, there are many differences between the two.

    Artificial Neural Network

    Artificial neural networks (ANNs) mimic the connected networks of neurons that make up the human brain. ANNs act like these interconnected brain cells to learn and perform in a humanlike manner.

    The human brain is comprised of many different parts that all process different kinds of information, flowing from one level of neurons to the next, gathering more insight along the way. This cycle is imitated in ANNs, but instead, a machine sends information from layer to layer until a decision is made. Examples of different layers of an ANN are data input, thought, decision making, memory, reasoning, and action.

    Deep Learning

    Without ANNs, deep learning wouldn't be possible. Rather than teaching a computer how to process and learn from data, deep learning works by the computer training itself to process and learn from data. This self-teaching system is made possible by filtering information through ANNs different layers, like how the human brain functions.

    In short, neural networks and deep learning are closely connected, as deep learning is only made possible through ANNs.

    How Does Deep Learning Work?

    Deep learning is a type of machine learning that uses artificial neural networks (ANN), algorithms inspired by the structure and functions of the human brain.

    During this process, a machine gathers information from images, text, and sounds to achieve the same results that come naturally to humans. Like the human brain, deep learning models process data and identify patterns to effectively make decisions.

    This advanced technology is responsible for voice control in devices like smartphones and Bluetooth speakers, as well as driverless cars that detect stop signs and pedestrians.

    4 Deep Learning Applications

    Practical applications of deep learning can be found in countless industries today as the technology has become more affordable to implement. The following sectors have recently benefited from application areas of deep learning.

    1. Banking Industry
    2. Manufacturing Industry
    3. Pharmaceutical Industry
    4. Oil and Gas industry

    Consider the corresponding examples of deep learning applications to understand the upside of implementing this technology in your business.

    1. Deep Learning in Finance and Banking

    Deep learning technology plays many roles in the finance and banking industries, from detecting high-level fraud to improving customer experience. Here are a few popular deep learning use cases in banking and finance.

    Fraud detection

    Of all the reasons for financial institutions to implement machine learning technology, fraud protection is one of the biggest.

    In 2019, 3.2-million cases of fraud were reported to the Federal Trade Commission (FTC).

    Fortunately, deep learning in investment banking can be used to combat fraudulent financial transactions. With deep learning, systems can quickly scan through vast amounts of electronic data, detecting unusual activities and flagging them instantly.


    Exchanges that can be frustrating for humans are a breeze for machines. Deep learning in finance is responsible for improved chatbot solutions, resulting in better customer service.

    Deep learning allows chatbots to quickly learn from previous interactions and therefore efficiently resolve customer inquiries. This advanced technology is designed to adapt to every customer based on behavioral changes and patterns.

    Document Analysis

    Advancements in deep learning have improved image recognition accuracy far beyond human capabilities. These systems scan and analyze legal documents at phenomenal speeds, a process known as document understanding, allowing banks to significantly increase accuracy and productivity.

    Major companies across financial and banking industries are using deep learning applications to their advantage. JP Morgan Chase & Co. has heavily invested in AI, with a technology budget of $9.6 billion. In 2017, the company implemented a new machine learning program that managed to complete 360,000 hours of finance work in a matter of seconds.

    2. Deep Learning Applications in Manufacturing

    The use of various technologies and solutions in a manufacturing ecosystem, also called smart manufacturing, has been growing in popularity over the years. Deep learning applications can learn and resolve manufacturing challenges without guidance or supervision, making them a valuable addition to any team. Consider the most common applications.

    Predictive Maintenance

    Maintenance issues and the associated problems can be expensive and time-consuming, which is why it is a common goal for manufacturers to make predictions with deep learning technology.

    In predictive maintenance, algorithms are used to predict upcoming failures of a machine, component, or system. Workers are then alerted of the problem, allowing them to perform focused maintenance to prevent the failure in a timely fashion.

    Deep learning processes consider a machine's intricate behavioral patterns, as well as the complex data relating to the overall manufacturing operations.

    Predictive Quality and Yield

    Reducing production losses and inefficiencies is a challenge for manufacturers, making industrial AI extremely beneficial.

    With predictive quality and yield technology, deep learning algorithms are used to understand individual production processes and identify causes of production losses using multivariate analysis. With deep learning, computers consider various factors such as yield, quality, throughput, emission, waste, and energy efficiency.

    If the computer identifies a problem, automatic alerts are generated, informing the production team of the issue. This technology provides workers with knowledge and recommendations on how to prevent losses before they even happen.

    3. Deep Learning in Pharmaceuticals

    Deep learning technology is increasingly finding its way into the healthcare sector. In the US health-care system, AI is used to optimize innovation, improve research and clinical trials, and build new tools for physicians, consumers, and regulators. Here are a few examples of pharmaceutical companies using deep learning to their advantage.

    Disease Identification

    Disease identification is a top priority for machine learning in medicine. Data is plentiful in the pharmaceutical industry but finding resources to work with said data presents a challenge. Analyzing data is time-consuming, costly, and requires a lot of brainpower. So, when computers are advanced to the point where they can complete these tasks on their own, results are achieved much faster.

    BERG, an AI-powered biopharma company, uses modern technology to research and develop diagnostics and treatments in multiple areas, including oncology, neurology, and rare disease.

    Epidemic Outbreak Prediction

    Machine learning and AI are also used to monitor and predict epidemic outbreaks around the globe in real-time. Deep learning utilizes satellite data, social media content, historical information, and other resources.

    Predicting outbreak severity is very useful in third-world countries, where medical infrastructure and access to treatment are often limited. This technology has been used to predict malaria outbreaks by analyzing data such as positive case numbers, historical statistics, temperature, rainfall, and more.

    The use of AI during the COVID-19 pandemic helped target the origin of the virus and predict its spread to help slow transmission. Artificial intelligence was also used to monitor misleading information that began to spread in the early months of the pandemic.

    4. Deep Learning in Oil and Gas

    The Oil and Gas Industry is becoming increasingly technology-driven. AI applications help to streamline the workforce, deliver accurate models, optimize extraction. The following applications are commonly used in the oil and gas industry today.

    Subsurface Characterization

    Deep learning in oil and gas extraction processes is beneficial for improving subsurface characterization. Computer systems are used to predict a formation's reaction to certain drilling techniques, as well as determining a formation's pore size distribution. This allows engineers to pinpoint optimal routes through rock formations.

    Predictive Maintenance

    The oil and gas industry also uses machine learning for predictive maintenance in the field. When equipment isn't well maintained or working to the best of its ability, work will be delayed, and money is lost. From exploration to delivery, deep learning technology ensures that machines are working and delivering.

    O&G processes have several opportunities for technological improvement. Learn how Intelligent Automation in Oil & Gas can help your organization evolve today.

    NITCO Can Help Your Business Benefit from Digital Automation Today

    Our talented and continually trained team can help mine your business processes for untapped potential. Through project-based implementation and ongoing support, we can satisfy most of your digital technology needs. Some of our strengths include Artificial Intelligence Solutions, Intelligent Automation, and Robotic Process Automation Solutions.

    Contact us today to schedule a free consultation regarding the upcoming technological improvements at your business.

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  • What Is Machine Learning & What Can ML Do for Your Business?

    Have you ever wondered how Twitter curates your timeline? Or how Netflix knows exactly which show to recommend for you? Well, the answer is a type of AI solution called machine learning. In this article, we'll explain how machine learning works and what impact it has on the business world.


    What Is Machine Learning (ML)?

    Machine learning (ML) is the study of algorithms that allow software applications to systematically improve through experience.

    What Does Machine Learning Do?

    Machine learning gives a machine the capability to make data-driven decisions on its own, rather than being manually programmed to carry out assigned tasks. These systems are designed to automatically learn and evolve over time when exposed to new data.

    How Does Machine Learning Work?

    Data scientists must first train a machine with practice data for the system to create a model. Once the machine learning model training process is complete and the machine is fully capable of making accurate predictions on its own. New data is introduced to the machine learning algorithm. At this point, the machine should be able to process massive amounts of data, extract useful information and predict outcomes all by itself.


    Deep Learning vs. Machine Learning

    Both deep learning and machine learning fall under the category of artificial intelligence, but the two are very different concepts.

    Machine learning models use algorithms to interpret data, learn from it, and make informed decisions based on its findings. Machine learning technology is often faster and more efficient, although it does require human guidance to some extent. If an inaccurate prediction is made, an engineer will then have to step in and make corrections.

    On the other hand, deep learning is a subset of machine learning and works by structuring algorithms into layers to create artificial neural networks (ANNs). Deep learning is behind the most human-like artificial intelligence technology, as it requires zero training or supervision. ANNs allow algorithms to autonomously determine the accuracy of a prediction.


    AI vs. Machine Learning

    Is there a difference between AI and machine learning? Artificial intelligence (AI) is the science of making computers behave in an intelligent, human-like manner. There are various subsets of AI, including deep learning, robotics, natural language processing, speech recognition, and more. Machine learning is also a subset of AI, focusing primarily on developing computer programs that use data to teach themselves. These models are designed to automatically learn and evolve from experiences without being deliberately programmed to do so.


    Machine Learning vs. Neural Networks

    Machine learning and artificial neural networks (ANNs) both fall under the AI category. Neural network is a subcategory of machine learning, which is a subcategory of AI. ANNs arrange layers of algorithms that can learn and make intelligent decisions on their own. Data passes through interconnected layers of neurons, identifying characteristics and information of each layer before continuing to the next. In machine learning, decisions can only be made based on what the system has already been taught. There are many types of machine learning algorithms that can be used, such as regression, logistic regression, classification, decision trees, and more.

    How Many Machine Learning Models Are There?

    Machine learning can be divided into categories based on how an algorithm learns and performs. The three major recognized categories of machine learning are supervised learning, unsupervised learning and reinforcement learning. Knowing the differences between these models will help you choose the best algorithm for your business. So, how do machine learning models work? Keep reading to find out.

    1. Supervised Machine Learning

    Supervised machine learning occurs when a human teaches the machine by presenting it with training datasets. In these datasets are example inputs and their corresponding outputs. In this learning method, humans are responsible for labeling data to train the machine’s algorithms. After a sufficient amount of training, the machine should have a strong understanding of the relationships between parameters and how the test data works. Once the supervised machine learning system is deployed, it will continue to use new data to train itself, continuously learning new relationships and using machine learning to find patterns. Furthermore, supervised machine learning can be split into two subcategories: regression and classification. A classification model’s output is a category, such as “black or white,” or “male or female.” A regression model's output would be a real value, such as “height” or “minutes.”

    2. Unsupervised Machine Learning

    In unsupervised learning, the system is trained with unlabeled data, as opposed to supervised learning where humans are responsible for tagging data. Unsupervised learning algorithms search datasets for any significant connections and use self-organization to identify patterns. This method forces the system to create an internal representation of its world. In addition, unsupervised learning can be divided into two subcategories: parametric and non-parametric unsupervised learning.

    • Parametric unsupervised learning – algorithms make assumptions about the mapping of the input-to-output variables and have a fixed number of parameters. This method requires less data and the training process is shorter, however, the results may not be as powerful.
    • Non-parametric unsupervised learning – algorithms make few or no assumptions about the target function. This method requires a significant amount of data, making the training process longer, but often results in more powerful models.

    The unsupervised learning method is advantageous in cases where manually annotating large datasets is too expensive or time-consuming.

    3. Reinforcement Learning

    In reinforcement learning, algorithms learn from errors. This method works by teaching a machine to accomplish a multi-step task with a clearly defined set of rules. Data scientists program an algorithm to complete a specific task, giving it positive or negative feedback as it works through task. However, it is up to the algorithm to figure out what steps to take along the way. A prime example of reinforcement learning is when a computer advances to the point where it can defeat humans in online games.

    How to Get Data for Machine Learning

    Now that you have a better understanding of the machine learning process, let's find out what resources you can use to collect data. There are many places to find open data, but here are a few of the most popular machine learning public datasets to help get you started:

    Importance of Datasets in Machine Learning

    To completely understand machine learning, you need to first understand the data that supports it. Machine learning models rely on four main types of data: 1. Numerical data – any data that can be measured 2. Categorical data – data that is sorted by defining characteristics 3. Text data – words, sentences, or paragraphs that offer insight to the machine 4. Time series data – data points that are indexed at certain points in time

    Without these datasets, there would be no machine learning. Data is a major component to machine learning. Teaching a machine to perform an action takes a few steps, all of which depend heavily on datasets.

    What Role Do Datasets Play in Machine Learning?

    First, training datasets are fed into the ML algorithm, followed by testing datasets to make sure that the model is accurately deciphering the data. Once both datasets have been fed into the system, you can use additional datasets to further sculpt your machine learning. The more data you supply a system with, the faster it will learn and advance. The quality of the data will affect the efficiency of the system, so it's important to beware of bad data.

    How Can Machine Learning Be Used?

    So, what are machine learning models used for? There are countless machine learning applications that play key roles in several industries such as healthcare, transportation, finance, government, manufacturing, and more. The following are real-world examples of businesses utilizing machine learning technology.

    Customer Churn Modeling

    Churn modeling is a machine learning application that helps you identify customers that are likely to stop engaging with your business and why. This feedback can provide insight and allow you to take necessary actions, such as implementing discount offers, email campaigns, and other marketing strategies to keep your customers interested.

    Recommendation Engines

    Recommendation engines analyze vast quantities of data to make predictions about customers' interests or actions. If the algorithm determines that a customer is likely to purchase an item or appreciate a particular piece of content, it will make corresponding suggestions. This machine learning application results in an improved customer experience and better engagement.

    Customer Segmentation

    Data scientists use classification algorithms to cluster customers into groups that match their characteristics. Algorithms consider various factors such as demographics, affinity, and browsing behavior. Making the connection between personal traits and patterns of purchasing behavior allows companies to implement personalized campaigns as opposed to less effective, generalized campaigns.

    NITCO Can Help Your Business Harness the Power of Machine Learning

    NITCO has experience with several technologies used in machine learning and can help your organization implement ML to enhance your processes. This learning can be used to power Artificial Intelligence Solutions, AI Chatbots, and Robotic Process Automation for your business.

    Contact the NITCO team today to schedule a free consultation regarding the impact ML can have for your company.

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  • Using Chatbots for Logistics, Transportation and Supply Chain Management

    Logistics keeps things ticking. When you really think about it, Logistics, Transportation, and Supply Chain Management is all about how best to keep physical goods flowing from source to destination with the least cost and friction at the largest scale. In many ways, it is the perfect complement to the IT sector who are really interested in the same thing but with digital data.

    Some of the key factors in the success of a Logistics operation are:

    • Harmonizing an operation of a lot of moving parts(all of which both produce a lot of data)
    • Consuming a lot of perfectly up-to-date, real-time information on the status of other parts.

    As such, it is no surprise that a strong informational backbone is one of the most important competitive advantages that a Logistics company can invest in.

    In some ways, it is unintuitive to suggest that Chatbots could have a powerful role to play in this sort of informational backbone, but when you look at it from the perspective of integrating a lot of systems and components, of providing convenient access to real-time information, of facilitating communication, of accuracy, of availability then it is clear they have a lot to offer.

    At present, when most people think of Chatbots and what kinds of areas they perform best in, marketing and customer service seem to be the ones that come up most often. And in fairness, in Logistics as elsewhere they do have a lot to offer.

    Are Logistics Chatbots Currently Being Leveraged?

    There are very few of those lucky companies who never have to market themselves. In an environment when online and web-based marketing become more and more important, while at the same time seem to be stagnating and not providing the expected ROI, Chatbots can be an important differentiator. They also have the advantage of access to the messaging apps where many users spend most of their time online, but which have so far been an exceedingly difficult arena to crack from a marketing perspective.

    Likewise, and probably more importantly where Logistics are concerned, Chatbots excel in customer service roles. Logistics companies themselves are not the only party who expect to have real-time information on tracking their goods. Increasingly, customers expect this information to be up-to-date and accessible 24/7. Online trackers are important and valuable, but they can be kind of clunky compared with the chatbot experience, where accessing this information can be as simple as sending a text or message.

    Logistics Companies the Leverage Chatbots

    Notably, UPS seems to be leading this space with not only a web app, Facebook messenger, and Skype-based chatbot, but "skills" for Google Home and Amazon Alexa too. Using UPS’ chatbot interface, users can track packages, reschedule delivery, or find UPS locations.

    There are a few key takeaways here. If you look at the kinds of tasks that the Chatbot is designed to take care of then a pattern emerges. As a rule, these are some of the simplest, most basic interactions that users can have with a company. For more complicated tasks or those that may involve more uncertainty or higher stakes in the users’ subjective experience (e.g., troubleshooting a lost package, or certain billing actions), it is likely that most users would prefer to speak with a human agent.

    On the other hand, when dealing with simple, straight-forward tasks then the situation is most often reversed. When users must jump through hoops to access basic information like locations, package information, or simple FAQ questions, even well-meaning customer-service systems that are meant to facilitate this information transfer, or perhaps a poorly designed web-interface that takes too many steps to navigate, can all make users feel like they are being “gate-kept” from information to which they feel entitled to have quick access.

    Chatbots buck this pattern because of their availability and convenience. They are self-service, and by situating themselves on the messaging apps that are already used day-to-day, they bring the information to the user, not the other way around. These qualities, taken together, restore a user's sense of agency, while still being helpful and friendly. Users get quick access to the information that they need with little fuss.

    Business Benefits of Logistics and Supply Chain Management Chatbots

    On the business side, there are huge gains to be had. These simple queries often make up 80-90% of the overall volume of requests for assistance and take up an inordinate amount of time and effort to address. This is bad enough in itself, but it is arguably worse that these types of small repetitive requests introduce a lot of "noise" into any system designed to deal with them.

    It can be extremely difficult to prioritize and manage requests for assistance when a system or team is constantly dealing with a massive backlog of requests. It can also be difficult to be “present” for consumers and get to the heart of their serious problems when your representatives know that while they are dealing with one customer, the backlog of small issues is always growing. These types of interactions add little in the way of value to companies and managing them has a way of sucking up attention and resources.

    In the customer service arena, this is already well-understood, and Chatbots are already a battle-tested tool in reducing these kinds of issues. What is sometimes missed is how useful this principle is when applied to other internal operations as well. As a developer with experience putting these types of solutions in place, I have seen first-hand how powerful this can be, and I don't think there are many fields where this has the power to be quite as transformative as in Logistics.

    Logistics Chatbot Example

    To take our most recent example, we recently implemented a pilot program for creating a "Virtual Operator Assistant" for helping shipping operators file Importer Security Filings (ISF) with U.S. Customs in an automated fashion. The process of filling out the forms for this filing is time-consuming and repetitive. The costs related to any errors are also extremely high. At times, the resolution of information can be quite complicated and require expert attention. For most cases, however, the whole process is as simple as taking the necessary information out of one system and manually inputting it into another. This kind of manual reproduction of data is rife for mistakes and errors, and while there are exceptions, by and large this type of task simply steals the operators' time and attention and gives little back to the business.

    Our solution involved a Chatbot based in MS Teams, integrated with both the company's existing systems, and with a Robotic Process Automation (RPA) component which could handle a lot of the heavy lifting. The “Virtual Assistant” could work with the user to get the most easily accessible and basic information about a shipment, it would search for the rest of the information in the company's systems on its own, then validate it, and if it had enough information to complete the filing by itself it would pass this off to an RPA robot which could fill out the form on the operator’s behalf. All the operator had to do was wait for an email confirming that the process completed successfully.

    By basing this on Teams, we were able to make it so that operators didn't have to download another application or learn another system. With a "Conversational User-Interface", users can engage in normal human conversation over messaging apps to communicate effectively with the new solution without extra training.

    Moreover, it is convenient. Imagine this scenario: you are an operator chatting over teams with your manager who asks you whether a particular ISF was filed. You aren't sure but sending a message to the Chatbot to check is only a click away. You find that it hadn’t been filed yet, so you send the bot the shipping number and in less than a minute, it has been submitted for filing. You then message your boss telling them that the filing is in process. And the best part… it was all done from the same messaging application.

    By making things this seamless, you make sure they get done. By making sure users don't have to switch applications or contexts in order to accomplish their goals, they can accomplish them quickly, and then move on to the things that really deserve their time and attention. This is just one example, but similar ones can be found everywhere, from shipping, to trucking, to warehouse management, administrative work, and others. There are numerous instances where the pathways for the flow of valuable information doesn't align quite right, and higher cost and increased liability is the consequence.

    Let NITCO Implement Chatbot Solutions for Your Supply Chain Management Processes

    Logistics and Transportation stands out as a field in which friction and misalignment, whether in transporting physical goods or digital information, incurs great expense. Chatbots can be a powerful tool in reducing that cost because there is often no greater disconnect than the one between human and IT assets. Chatbots are one of the most powerful and seamless tools for bridging that divide.

    For more information about Chatbot solutions, or to learn how NITCO, Inc. can help achieve your goals through better technology, contact us today at

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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)


    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 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.


    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 that 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 with 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 requests 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

    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 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.

    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
    • 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
    • Employee Onboarding
    • Resume Screening
    • HR Records Processing
    • Time Card Processing
    Supply Chain Management
    • Order Scheduling & Tracking of Shipments
    • Bill of Lading
    • Transport Notes
    • 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 a 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 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 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 so we can help you on your digital automation journey.

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