Category: Uncategorized

  • RPA and intelligent automation challenges | Automation Hero

    Aug 22, 2019 by Maggie Orion

    49-rpa-and-intelligent-automation-challenges

    Change is never easy. There are general challenges associated with adopting new technology and specific challenges that come with adopting RPA and intelligent automation. Let’s discuss the five challenges you can expect to face as well as the opportunities to overcome them.

    1. Lack of strategy

    It’s difficult to develop a strategy for adopting any new technology — and the newer it is, the harder it can be to plan for it as there are so many unknowns. Most companies are making important decisions with a variety of unknowns. For the best results, pull together something that can serve as a starting point. A less-than-perfect strategy is fine (actually, it’s better than fine) because analysis paralysis at this stage is deadly in a situation where the “first automator advantage” will determine long-term winners and losers. 

    It’s critical to know that when it comes to intelligent process automation, there are certain strategic considerations to face early on, namely:

    • Schedule of adoption: How fast will you move compared to your competitors, considering that whoever gets enhanced productivity through automation first has a competitive edge (e.g. “first automator advantage”)? 
    • Scope of adoption: Companies must decide whether to start small with pilot projects or develop an enterprise-wide adoption plan to apply RPA and intelligent automation in all departments. The latter has a track record of failure as companies find themselves mired in complexity and uninformed enterprise-scale decisions. Most companies find success with proof points in a pilot, iterating as they go with agile development and applying what they learn to successive enterprise-wide rollouts. 
    • Governance: Determining a governing structure early won’t immediately solve for the unknowns, but it can ensure success by determining a decision-maker and process for solving future unknowns. Ideally, bureaucratic barriers to decision making should be low and a single decision-maker empowered with the highest authority should make quick and important decisions to keep projects moving. 

    Building a team to inform decision making and carry out execution is also critical and a center of excellence (CoE) for process automation has proven to be very successful, but companies without a CoE can begin by bringing together an ad hoc group through the first proof of concept project. 

    Who needs to be involved? A mix of technical and business experts are needed. Business process managers should be considered for inclusion and ground-level end users — experts in the real day-to-day processes — should be consulted along the way. Depending on your industry, compliance and legal expertise may be required as well. It’s best to bring legal advisors in early rather than trying to explain software robots to a group of lawyers right before deployment! 

    This group will also evaluate success measures and provide recommendations for expanding intelligent automation solutions over time. 

    Each challenge holds an opportunity. Best practices suggest starting small, starting early, and staying agile with a clear decision-maker and potentially a center of excellence to serve as an advisory group. Smaller scope lighthouse projects generate both knowledge and enthusiasm for further expansion.

    2. ROI analysis

    Running the numbers is more complicated than it may initially seem. This can lead to unexpected analysis paralysis as analysts try to perfect the ROI model. Companies run into challenges predicting and clarifying costs as different vendors charge in dramatically different ways: some for the amount of information processed by automations, some for the automation itself, some for the software robot that performs the automation, and some for the platform that manages the robots. Of course, all of this is differentiated by subscription versus one-time upfront costs and where it’s hosted. 

    In terms of calculating ROI based on costs saved versus revenue generated, running the numbers on ROI based solely on the number of full-time employees that might be replaced is short-sighted and generally overlooks some of the more powerful benefits of RPA and intelligent automation. 

    For example, customers receiving more personalized and efficient service will lead to higher net promoter scores and increased purchasing, but how will you put that in your calculator? Different use cases impact the calculations as well as the type of automation technology being applied. RPA tends to see a shorter time to ROI, but intelligent automation has more enduring savings over time. 

    Set realistic expectations by expanding your analysis beyond reducing headcount and accept that initial ROI models are simply best guesses. Don’t waste time trying to predict every number with 100% accuracy. You will be much better served by calculating ROI after your first proof of concept use case and then continuing to measure ROI at regular intervals over time to decide whether to scale intelligent automation up or down, or apply it in a different direction. 

    3. Lack of talent

    Lack of talent is not a challenge specific to the RPA and intelligent automation market. When adopting new technology, finding the people with matching skills and knowledge to support your business is critical. This includes the technical skill, understanding of your business processes, and change management expertise to guide your company to the future. 

    In automation integration, this challenge is a common hurdle. In fact, a primary reason for the lack of democratized automation programs in many companies has to do with the fact that senior leaders have little faith in their employees’ abilities to learn new technologies. Bringing on or upskilling talent to develop automations is usually top-of-mind, but assigning people to ongoing maintenance, support, and troubleshooting is also important. 

    Depending on the talent and hiring capabilities of your company, consider a product without a steep learning curve. Many environments offered by vendors do not require coding. In the case of intelligent automation, that means democratizing machine learning and AI, and creating AI models that are practical for your business. 

    Upskilling, contracting, partnering, and in-house and external training support should all be considered. Also important to consider is early recruitment of RPA and AI experts in both technical and business concerns to appropriately build out your team.

    4. Vendor and use case selection in intelligent automation

    Automation is a $190 billion global market, with new vendor options for curious businesses to explore every day. Selecting your vendor, choosing your use case, and deciding which data is most critical for developing organizational capacity can quickly become overwhelming.

    Choosing the right vendor or vendors to begin your process automation journey can be a challenge. It may seem like every vendor is promising the same thing, and much of the technical nuts and bolts that different platforms offer can be obscure for business decision-makers. Companies may also choose to work with a consulting partner or several vendors at once to compare the best fit. 

    In the past, a tremendous challenge in implementing process automation was dealing with unstructured data. Many processes incorporate some level of semi-structured or unstructured data, and this data simply wasn’t accessible for process automation. With RPA and intelligent automation, this barrier is effectively removed as AI capabilities unlock access to unstructured data. 

    However, the truth remains that RPA cannot solve every inefficient process in your organization, and even with added AI, there are processes that may not need automation — some processes may need to be revised or dismantled altogether. 

    Deciding how best to deploy intelligent automation and which processes will see the most return is challenging and critical to success. Start small, target the “lowest hanging fruit” in your processes, and over time build a library of processes to automate.

    After selecting and executing initial use cases, setting up maintenance and oversight is also important. If automations are programmed incorrectly, then they become efficient at performing processes but in an error-ridden way that can pose many risks for your company. Prevent this by building responsibilities for maintaining, reviewing, and updating your automated processes.

    Determine needed and nice-to-have features, such as machine learning, back-end security, cloud deployment, and a pay-as-you-go model for easily increasing and decreasing your automations. Host a use case discovery workshop with your vendor or partner, purchase a proof of concept with single or multiple vendors, and evaluate the application of RPA and intelligent automation. 

    5. Change management, culture readiness

    The final challenge companies face when adopting process automation technology should be addressed from the beginning to ensure sustained success. Frankly, people are afraid of robots and AI. They are afraid of losing their jobs, and they are afraid of change. 

    Each company has a culture determining the company’s resistance or acceptance of change. While some embrace new technologies enthusiastically, others can be resistant. Without a tailored change management plan and people-centered approach to intelligent process automation, bringing on this type of technology could cause resistance, non-compliance, and upheaval. 

    Successful organizational change nearly always requires leadership sponsorship and high-quality communication regarding the purpose and outcomes of adopting intelligent process automation and should be started early. 

    Consider the impact process automation will have on people early and the messaging around it. Human Resource Executive reports that 70% of office workers wish they had more time to spend on high-value assignments rather than repetitive tasks, marking a clear desire for the benefits that automation can provide.

    High-level leadership sponsorship and ground-level contributor buy-in will ensure success. Host a use case discovery workshop and ask your employees what their pain points are. Start there and the benefits of intelligent automation will be obvious to everyone. 

    Consider a platform with a user-friendly interface that will integrate with your employees’ current working habits in a helpful, non-threatening way. 

    Plan to educate management in RPA and intelligent automation to effectively usher in change or partner with change management specialists. Down the line, you may need to collaborate with HR to revise job duties to accurately reflect a shift from mundane tasks to higher-value job activities — keep HR in the loop on intelligent automation developments. 

    One often overlooked aspect of technology initiatives is making sure to explain and highlight the benefits of intelligent process automation for everyone in accessible and understandable terms rather than technical jargon. You’re already on the right path to overcoming the challenges for RPA and intelligent automation — research fundamentals, clarify benefits, and minimize the myths surrounding process automation.

  • The AI-IA and how it will impact the AI market | Automation Hero

    Jun 11, 2019 by Jessica Munday

    There’s a new bill that’s up for discussion in the Senate involving artificial intelligence. It’s called the Artificial Intelligent Initiative Act (AI-IA). It was introduced on May 21 and is considered a “bipartisan” bill as three senators from across the aisle brought it to life.

    The AI-IA bill would put $2.2 billion federal funds to work organizing a national AI strategy and fund new developments in the space. This would be distributed over the next five years to build an AI-ready workforce for the next 10 years.

    The money would establish committees of federal and nonfederal leaders to develop a national plan for AI research and development. And between 2020 and 2024, $20 million per year would fund the creation of five AI research centers, one of which would focus solely on K-12 education around AI.

    This follows other trends in government participation around the AI industry. The most notable is President Donald Trump’s executive order in February. This order asked for federal agencies to allocate existing funds for AI projects and dedicated a White House committee to using AI to expand opportunities for American workers.

    The AI-IA bill is being proposed specifically to help America keep up with the innovative pace of competing global economies and prevent the future of AI from being decided by forces outside of U.S influence.

    So how will this impact the AI industry? Let’s go through some rough pros and cons of how this bill and overall government influence in AI development will impact the market.

    Pros

    One of the hot topics up for discussion for these committees will be the ethics involved with developing AI. Likely these will be discussions around how to keep humans in the loop as AI enters the workforce. The AI-IA requires the National Science Institute to perform research around data bias, privacy and the societal and ethical implications of AI.

    Because AI will be so involved in politics, it’s likely that it will be applied to old and inefficient government processes. This will put taxpayer dollars toward more productive tasks and create faster-moving government processes.

    The main focus for these government initiatives will be sustainability and job creation, which is an extremely positive goal for future generations of workers. This will allow AI to help people, rather than hinder their success. And with one of the funded research centers focused solely on educating students about AI, these children will grow up with the right skills and knowledge set to work in tandem with this new technology.

    Cons

    With government involvement, one of the inevitable downsides will be the decreased pace of innovation.  The committees established by the AI-IA will likely create regulatory and compliance standards, which can prevent new technologies from innovating at their current pace.

    Another criticism is that the bill doesn’t put money toward vocational education or training programs. While in theory, the main focus is future generations, the bill doesn’t help with the change management that companies will face in order to remain competitive.

    This national strategy also comes a bit late. About 30 other countries already have a national AI strategy, and nearly every other global economic power has launched these plans over the past four years.

    More and more government involvement in AI is on the horizon, especially as AI becomes more visible, democratized and impacts public safety and privacy. We see examples at the local level too, such as San Francisco banning the use of facial recognition software and 29 states enacting legislation related to autonomous vehicles.

    It’s unclear if the AI-IA bill will affect the market positively or negatively, should it pass. But it is certain that governments will increase their involvement with this technology as it becomes more commonplace and makes an impact on the economy.

  • Call Center Automation: Saving Your Customers | Automation Hero

    May 23, 2019 by Jessica Munday

    Ever heard of the term “serial switchers?” If you’re a call center manager you may want to become familiar with it. It’s a new breed of customers who aren’t afraid to take their business elsewhere if a company gives them a bad customer experience and will switch multiple times until they find the right fit.

    Seventy-six percent of customers say it’s easier than ever to switch service providers based on their experience. According to a NewVoice Media report, bad customer service costs U.S. businesses more than $75 billion a year — all from loss of customers.

    Call centers are the primary way customers contact businesses – so it’s extremely critical that they provide a positive experience.

    Most call centers see productivity problems resulting in long wait times, high call abandonment rates and poor average handle times. This has less to do with the productivity of the agents but more the inefficiency of their processes.

    Learn How to Improve your Customer Experience with Call Center Automation

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    Here are three ways to apply call center automation:

    1. Reduce application switching and clicks

    Call center agents work with five or more tools depending on the industry the call center serves. Shifting between applications to copy and paste data requires several clicks and window switches, which lengthens the customer call.

    Automation allows agents to easily transfer data between systems without having to perform these tasks manually.

    For example, an incoming request for a lost credit card at a bank call center would require an agent to work with the banks online portal, a CRM system and a third-party application to order the new card. With call center automation integrations, the data needed (such as the reason for card replacement, card number, address) can easily be pulled and auto-populated into missing fields.

    2. Repeat requests

    Lots of call center agents are stuck in boring, repetitive processes due to repeat call requests that waste tons of time when automation could get them done much faster and more efficiently.

    Call center automation tools can easily integrate with current systems and assist in completing repeat processes. For example, automation can pull data from one system to another with just the click of a button.  

    For example, service provider call center agents get calls about scheduling cable or internet repairs all the time, which requires manually scheduling the appointment and describing the technical issue for the repair person. Automation could create an appointment and transfer appointment details into a secondary system easily without making the agents do it manually.

    3. Post-call work

    After call work (ACW) lengthens wait times as it prevents newly available agents from picking up a new call right away. Some of these tasks include inputting customer interactions into a CRM system or finishing up the final details of a customer request. Many of these ACW tasks can be completed through automation.

    One example is automating the generation of a confirmation letter. Often when someone renews or cancels their service, agents create a letter confirming the change manually. Automation can pull the information from the call centers CRM system and autofill it within a template.

    These are some of the general tasks that automation can perform in call centers, more specific use cases depend on the industry served and types of requests received.

  • What is intelligent automation? | Automation Hero

    start new section

    May 15, 2019 by Jessica Munday

    At Automation Hero, we hear the question “what is intelligent automation?” pretty often. So, as an introduction to the term, here’s an overview on what intelligent automation is and just what this technology can help your enterprise accomplish.

    Before IA, there was RPA

    Before intelligent automation was possible, there was robotic process automation (RPA). RPA is a software that automates repetitive computer tasks and processes usually done by humans like processing, manipulating data, and triggering responses. Most traditional RPA automation tools must be manually programmed to complete a task on a user’s computer screen, in the same way a human would.

    RPA has been around since the late 2000s and has been making repetitive click tasks much easier for business workers. It can be applied to various manual tasks to increase efficiency and accuracy. Some use cases include: screen scraping data collection, document generation, process mapping, and other basic workflows. 

    However, RPA is vastly limited in its capabilities. It performs one action repeatedly without considering exceptions. For example, if an RPA system was programmed to sort red and blue balls, it would be unable to react in the case of a yellow ball. Another downside is that every step within an RPA automation must be programmed, making changes to the automation difficult. While there are areas in which RPA can be advantageous, RPA alone is not intelligent enough to adapt and learn from tasks in real time.

    What is intelligent automation? RPA + AI

    Now is the time to revamp traditional RPA into the more capable technology that is intelligent automation. Intelligent automation combines artificial intelligence, robotic automation, and mass amounts of data to automate complex tasks and perform more adaptable workflows.

    Intelligent automation goes beyond automating simple, repetitive click work. It can perform tasks that require cognitive processing and complexity, making it ideal for the tasks that are too intricate for RPA but too boring and time consuming for humans. This technology is finally intelligent enough to assist in performing sophisticated human tasks.

    Curious about the impact of this technology? Think about how much time you spend at your computer. If you’re an executive, you spend much of your time writing emails, scheduling meetings, creating slide decks, and analyzing reports. Intelligent automation reduces the time you spend on all those items you “need” to do so you can spend more time on tasks that drive your company forward.

    Now, think about this on a wider scale. Employees in the U.S. spend as much as 39% of their time on repetitive work. Taking away their important but most repetitive and time-consuming tasks will allow them to be more productive.

    It frees employees from their mundane computer tasks by handing off the robotic processes to the robots, allowing the human workforce to focus on tasks that drive value for the business.

    Advanced use cases

    As humans waste less time, the benefits of using this technology pile up. Overall productivity improves, operating costs are reduced and tasks are done more efficiently. Below are some examples of intelligent process automation in action:

    Natural language processing (NLP)

    The ability for a computer to understand, interpret and manipulate human language as it is spoken or written. This can be used to understand human speech in channels like email, phone conversations, and documents.

    Intent detection

    When a system predicts the intention of a human message using NLP. This can be used to assist in automating a customer request, routing the message to the right department or responding to a message.

    Predictive analytics

    When a machine can make predictions about the future using current and historical data. This can assist with sales or other types of forecasting models, or when making important business decisions.

    It’s a hot market

    The demand for automation continues to heighten as companies grow wise to the benefits. Gartner calculated global RPA revenue on this technology would hit nearly $2 billion in 2021, with expectations that the market will continue to grow at double-digit rates through 2024. 

    By 2025, the economic impact of implementing RPA into organizations is expected to reach $55 billion, with 35 million employees interacting with the technology regularly. This makes the intelligent automation market even more promising, with enterprise spend on automation expected to grow exponentially to nearly $120 billion by 2026.

    Now that we’ve addressed the question of “what is intelligent automation,” it’s time to explore how your company can start implementing it. As spending increases, this will mean more and more companies are applying this technology to their processes, including your competitors.

    It’s important to move quickly; whichever company enhances its productivity through automation first will have a competitive edge — we call this the “first automator advantage.” Don’t miss your chance to be the first in your industry to intelligently automate.

  • Decoding Intelligent Automation & Document Processing

    May 15, 2019 by Jessica Munday

    At Automation Hero, we hear the question “what is intelligent automation?” pretty often. So, as an introduction to the term, here’s an overview on what intelligent automation is and just what this technology can help your enterprise accomplish.

    Keep in touch

    Before IA, there was RPA

    Before intelligent automation was possible, there was robotic process automation (RPA). RPA is a software that automates repetitive computer tasks and processes usually done by humans like processing, manipulating data, and triggering responses. Most traditional RPA automation tools must be manually programmed to complete a task on a user’s computer screen, in the same way a human would.

    RPA has been around since the late 2000s and has been making repetitive click tasks much easier for business workers. It can be applied to various manual tasks to increase efficiency and accuracy. Some use cases include: screen scraping data collection, document generation, process mapping, and other basic workflows. 

    However, RPA is vastly limited in its capabilities. It performs one action repeatedly without considering exceptions. For example, if an RPA system was programmed to sort red and blue balls, it would be unable to react in the case of a yellow ball. Another downside is that every step within an RPA automation must be programmed, making changes to the automation difficult. While there are areas in which RPA can be advantageous, RPA alone is not intelligent enough to adapt and learn from tasks in real time.

    What is intelligent automation? RPA + AI

    Now is the time to revamp traditional RPA into the more capable technology that is intelligent automation. Intelligent automation combines artificial intelligence, robotic automation, and mass amounts of data to automate complex tasks and perform more adaptable workflows.

    Intelligent automation goes beyond automating simple, repetitive click work. It can perform tasks that require cognitive processing and complexity, making it ideal for the tasks that are too intricate for RPA but too boring and time consuming for humans. This technology is finally intelligent enough to assist in performing sophisticated human tasks.

    Curious about the impact of this technology? Think about how much time you spend at your computer. If you’re an executive, you spend much of your time writing emails, scheduling meetings, creating slide decks, and analyzing reports. Intelligent automation reduces the time you spend on all those items you “need” to do so you can spend more time on tasks that drive your company forward.

    Now, think about this on a wider scale. Employees in the U.S. spend as much as 39% of their time on repetitive work. Taking away their important but most repetitive and time-consuming tasks will allow them to be more productive.

    It frees employees from their mundane computer tasks by handing off the robotic processes to the robots, allowing the human workforce to focus on tasks that drive value for the business.

    Advanced use cases

    As humans waste less time, the benefits of using this technology pile up. Overall productivity improves, operating costs are reduced and tasks are done more efficiently. Below are some examples of intelligent process automation in action:

    Natural language processing (NLP)

    The ability for a computer to understand, interpret and manipulate human language as it is spoken or written. This can be used to understand human speech in channels like email, phone conversations, and documents.

    Intent detection

    When a system predicts the intention of a human message using NLP. This can be used to assist in automating a customer request, routing the message to the right department or responding to a message.

    Predictive analytics

    When a machine can make predictions about the future using current and historical data. This can assist with sales or other types of forecasting models, or when making important business decisions.

    What role does intelligent document processing (IDP) play?

    Intelligent document processing (IDP) has emerged as a critical component in the wider automation landscape. IDP technology was born out of the need for organizations to accurately extract data from documents. In essence, IDP leverages artificial intelligence (AI) and natural language processing (NLP) to effectively handle and oversee document-centric business processes.

    Through the adoption of IDP, organizations can optimize heritage document processes involving a large volume of documents to reduce manual effort, enhance precision, and reach their digital transformation goals.

    A notable advantage of IDP is its seamless integration capability with other automation tools like robotic process automation (RPA). This compatibility with existing technologies enables companies to enhance their current automation strategies, leading to improved overall efficiency and expanded operational scope.

    Optical character recognition (OCR)

    Another advantage of intelligent document processing is its integration with OCR (optical character recognition). Optical character recognition is the technology that can read text within documents the same way humans do.

    However, unlike humans, OCR can recognize characters, numbers, letters, and words — in any font or language. As you might expect, the most significant impact of OCR is on manual data entry tasks. OCR speeds up this essential business process and makes it convenient to turn words from scanned PDFs or images into text that can be edited or copied without manual retyping.

    Standalone OCR technology is not good at deciphering handwriting. However, even with the limitations of legacy OCR technology, it can help organizations streamline the digitalization of structured documents. According to performance benchmarks, Automation Hero’s patent-pending Context-aware OCR is 68% more accurate than ABBY’s Flexicapture and delivers 281% greater accuracy in terms of handwriting recognition.

    It’s a hot market

    The demand for automation continues to heighten as companies grow wise to the benefits. Gartner calculated global RPA revenue on this technology would hit nearly $2 billion in 2021, with expectations that the market will continue to grow at double-digit rates through 2024. 

    By 2025, the economic impact of implementing RPA into organizations is expected to reach $55 billion, with 35 million employees interacting with the technology regularly. This makes the intelligent automation market even more promising, with enterprise spend on automation expected to grow exponentially to nearly $120 billion by 2026.

    Now that we’ve addressed the question of “what is intelligent automation,” it’s time to explore how your company can start implementing it. As spending increases, this will mean more and more companies are applying this technology to their processes, including your competitors.

    It’s important to move quickly; whichever company enhances its productivity through automation first will have a competitive edge — we call this the “first automator advantage.” Don’t miss your chance to be the first in your industry to intelligently automate.

    Unlock the intelligence in your documents with our AI-driven automation today

    Learn how we helped Markerstudy reduce its claims processing time by 40%. Additionally, learn how we reduced total claim processing time by 80% for another multinational insurance partner — cutting down manual tasks from 10 minutes to just two minutes per claim.

    • Speak with an expert — tell us about your specific use case.
    • Get a personalized demo — schedule a demo, and our Heroes will get in touch!

  • Security and Compliance Automation with IDP | Automation Hero

    May 09, 2019 by Jessica Munday

    Data has been coined as the new oil during recent years — and just like oil, it’s essential to keep this unrefined, valuable material safe. Intelligent automation (IA) can help keep your enterprise’s sensitive data under tight lock-and-key.

    The impacts of data breaches hit the U.S. harder than any other country with average customer losses hitting an all-time high in 2021 at $4.24 million per company. When companies fail to keep data safe, they lose customers, revenue, and public trust.

    Nearly every large enterprise manages sensitive data, regardless of the industry. When it comes to properly handling this data, businesses must protect themselves from security threats as well as comply with regulatory protocols.

    Intelligent automation further ensures data security and compliance through automating many of the manual tasks that are part of security and compliance processes.

    Keep in touch

    What is intelligent document processing (IDP)?

    Intelligent document processing (IDP) has emerged as a crucial element within the wider automation landscape. IDP technology was born out of the need for organizations to accurately extract data from documents. In essence, IDP leverages artificial intelligence (AI) and natural language processing (NLP) to effectively handle and oversee document-centric business processes.

    Through the adoption of IDP, banking institutions, governments or any industry adhering to strict ESG reporting regulations can optimize heritage processes involving a large volume of documents to reduce manual effort, enhance precision, and reach their digital transformation goals.

    A notable advantage of IDP is its seamless integration capability with other automation tools like robotic process automation (RPA). This compatibility with existing technologies enables companies to enhance their current automation strategies, leading to improved overall efficiency and expanded operational scope.

    Security automation

    Security automation can increase the safety of your enterprise’s confidential data by preventing data breaches.

    Some of the most sensitive data gets routed through complex processes, which can pose a high security risk. Intelligent process automation solutions perform tasks within your security applications that typically are performed by a human; minimizing human handling and viewing of your sensitive user, customer, or client data. IA cloud solutions add security through user authentication that only allows verified users to see sensitive data.

    IA also reduces the potential for human error — which in this field can be enormous. As many as 43% of employees have acknowledged making mistakes at work that compromised cybersecurity. IPA has much more precise and accurate data handling capabilities, making enterprise data much safer in the face of error-prone tasks.

    For your most important security needs, consider backend or server process automation. Data processed in the backend is isolated, centralized and in a highly controlled environment ensuring your data, your processes, and your business are protected.

    In the case of a potential threat, automation leads to faster response times, better predictions and analyses of potential threats and more accurate predictions with machine learning technology.

    Intelligent document processing for compliance

    Government regulations place heavy burdens on industries to meet legal guidelines and government agencies themselves have complex regulatory mandates as well. Automation can ensure your organization, whether it be public, private or for-profit, is complying with all regulations without pulling your human workers away from other tasks.

    Intelligent automation does this in a few ways:

    • Generates audit trails for changes made during product development or with legal documentation.
    • Ensures that role-based access measures are met by allowing limited access to certain data to authorized groups.
    • Creates documentation about how a company or machine made a decision.

    These are tasks typically performed by a compliance officer. When handled by an automation system, these tasks are done more efficiently and with higher accuracy than when performed by a human.

    Let’s use mortgage lending as an example. Banks must ensure that bankers are not discriminating against applicants but also must reduce their loan risk. IPA can create an audit trail of events that happened with a customer’s loan application, ensure that only authorized users can access that person’s sensitive data and create a document that shows how the bank came to its non-discriminatory decision approving or denying the loan.

    The mortgage industry and other financial services are already reacting and preparing for the use of AI with nearly half expecting to use the technology for fraud prevention within the next three years.

    Security and compliance in AI is paramount to its implementation and use in your organization.

    Unlock the intelligence in your documents with our AI-driven automation today

    Learn how we helped Markerstudy reduce its claims processing time by 40%. Additionally, learn how we reduced total claim processing time by 80% for another multinational insurance partner — cutting down manual tasks from 10 minutes to just two minutes per claim.

    • Speak with an expert — tell us about your specific use case.

    Get a personalized demo — schedule a demo, and our Heroes will get in touch!

  • Security and Compliance Automation with IPA | Automation Hero

    May 09, 2019 by Jessica Munday

    In a world where data is as valuable as oil, it’s becoming ever more important to keep that data safe. Intelligent automation (IA) can help keep your enterprise’s sensitive data under tight lock-and-key.

    The impact of data breaches hits the U.S. harder than any other country. With average customer losses adding to $4.13 million per company for American businesses. When companies fail to keep data safe, they lose customers, revenue and public trust.

    Nearly every large enterprise manages sensitive data, regardless of industry. When it comes to properly handling this data, businesses must protect themselves from security threats as well as comply with regulatory protocols.

    Intelligent automation further ensures data security and compliance through automating many of the manual tasks that are part of security and compliance processes.

    Security automation

    Security automation can increase the safety of your enterprise’s confidential data by preventing data breaches.

    Some of the most sensitive data gets routed through complex processes which can pose a high security risk. Intelligent process automation solutions perform tasks within your security applications that typically are performed by a human; minimizing human handling and viewing of your sensitive user, customer or client data. IA cloud solutions add security through user authentication that only allows verified users to see sensitive data.

    IA also reduces the potential for human error. At least 25 percent of data breaches are caused by a human mistake. IPA has much more precise and accurate data handling capabilities making enterprise data much safer in the face of error-prone tasks.

    For your most important security needs, consider backend or server process automation. Data processed in the backend is isolated, centralized and in a highly controlled environment ensuring your data, your processes and your business are protected.

    In the case of a potential threat, automation leads to faster response times, better predictions and analyses of potential threats and more accurate predictions with machine learning technology.

    Compliance automation

    Government regulations place heavy burdens on industries to meet legal guidelines and government agencies themselves have complex regulatory mandates as well. Automation can ensure your organization, whether it be public, private or for profit is complying with all regulations without pulling your human workers away from other tasks.

    Intelligent automation does this in a few ways:

    • Generates audit trails for changes made during product development or with legal documentation.
    • Ensures that role-based access measures are met by allowing limited access to certain data to authorized groups.
    • Creates documentation about how a company or machine made a decision.

    These are tasks typically performed by a compliance officer. When handled by an automation system, these tasks are done more efficiently and with higher accuracy than when performed by a human.

    Let’s use mortgage lending as an example. Banks must ensure that bankers are not discriminating against applicants but also must reduce their loan risk. IPA can create an audit trail of events that happened with a customer’s loan application, ensure that only authorized users can access that person’s sensitive data and create a document that shows how the bank came to its non-discriminatory decision approving or denying the loan.

    According to a 2016 survey by Accenture, 73% of the surveyed compliance officers believed that automation could be a key enabler in compliance within the next three years.

  • Why insurance automation will define the industry | Automation Hero

    May 08, 2019 by Jessica Munday

    insurance-automation

    Whether you work at a highly innovative company or one that errs on the conservative side, it’s hard to ignore the chatter and the changes brought about by insurance automation.

    It’s a topic that’s quickly gaining traction in the insurance industry. As technological innovations around artificial intelligence (AI) and intelligent automation rapidly accelerate and make their way into the workforce, the pressure is on for the insurance sector to adopt.

    Currently, four of the top ten insurance companies in the U.S. are using some form of machine learning. Over the next three years, more than 75 percent of insurance companies plan to implement some type of insurance automation to correct outdated processes.

    Insurance employees spend 10 to 15 percent of their time on repetitive computer tasks, which wastes several hours of their time each week (e.g. manual underwriting and claims processing, customer database updates, scheduling meetings). These types of tasks don’t help them better serve their customers or earn the company new ones.

    Insurance faces another issue that critically affects productivity: a tightening job market. Of all financial service industries, insurance is arguably one of the least glamorous to work in, and as unemployment continues to trend downward, many insurance roles are left unfilled. This causes work to continuously pile up for their employees, yet there’s never enough people or time to get it all done.

    Insurance automation solutions enabled with AI is a beacon of hope for insurance companies and their employees as it eliminates repetitive busy work.

    For the business, this means that more work gets done. For the employee, this technology offers some relief from the neverending piles of paperwork they face on a daily basis.

    If you’re a traditionalist that doesn’t see the value of AI and insurance automation, hear me out:

    • AI represents a potential cost savings of $1 trillion to U.S. companies across banking, investment management and insurance.
    • Automation ca reduce the cost of a claims journey by up to 30 percent.
    • By 2030, manual underwriting will cease to exist for most personal and small-business products across life and property and casualty insurance.

    The cost savings alone is enough to spark curiosity in insurance automation. So, how can it save companies so much money? When it comes to automation, it’s all about optimizing workforce processes to accelerate productivity.

    Some use cases include (but are far from limited to):

    • Automating customer requests. Automatically updating a customer’s address in a database or forwarding a service request to the proper department.
    • Document generation. Having data pulled from emails, spreadsheets, applications, etc. and populate a closing form or claim document.
    • Scheduling meetings. Scheduling a meeting in an employee’s calendar based on an email interaction.
    • Claim processing. Porting information from a database to an application or vice versa to reduce the amount of “copy-and-paste” tasks a claims officer must perform.

    These tasks take insurance employees away from their customers. Eliminating, or at least alleviating, the workload for your team allows them to focus on the more productive elements of their job and get more work done.

    About 66 percent of insurers believe AI will improve workforce productivity and 98 percent intend to use this technology to enhance the capabilities of workers, which will ultimately increase revenue.

    Let’s break down the cost saving expected from insurance automation:

    The insurance industry will see $400 billion total cost reduction, which is a 14 percent reduction of the traditional cost base.

    • $168 billion of insurance industry costs will be saved by targeting insurance sales staff, customer service agents and commissions.
    • $99 billion will be saved by targeting compliance, information services, workflow and accounting systems and other data processing.
    • And $125 billion will be saved by reducing claims due to higher underwriting accuracy.

    Insurance is typically a late-adopter of new technologies, due to the nature of the work and the vast complexity of the industry. When it comes to AI and automation, they will not have the same luxury.

    And because this innovative gap is so apparent, insurtechs have popped up to fill the market void. Venture capitalists globally invested $2.6 billion in insurtechs in 2015, and nearly $1.7 billion in 2016. Tech-driven startups like Lemonade, a rental insurance company, and Metromile, a pay-by-the-mile car insurance company, are pushing traditional insurance companies to rethink their strategies or risk losing customers.

    By 2021, insurer spending on this type of technology will reach $1.4 billion. Sooner, rather than later, insurance providers should change their digital strategies and get on board with technological innovations.

  • AI Implementation: How to Prepare Your Organization | Automation Hero

    May 08, 2019 by Jessica Munday

    The AI revolution is coming. One where automation will take over repetitive tasks and augmentation will enhance our decisions. During this revolution, we’ll save time, loads of money and business teams will be more efficient and productive. But you can’t participate in this digital transformation if you don’t properly prepare your organization for AI implementation.

    AI isn’t just another tool to add to the tech stack. It will bring lasting impact to your organization. So in order to see optimal ROI from this technology, you need to be sure all of your ducks are in a row.

    To do this, management and executive teams need to analyze the business pains that an AI solution could solve and start researching the right tools for the job.

    Once you’ve generated a short list, you’ll need to create an AI implementation plan, review your data quality, evaluate integration needs and build a change management strategy.

    Plan

    With so much hype building around AI, the sentiment is often implement quickly and be done with it. However, this type of implementation will not bring favorable results. AI systems are complex and require time, effort and strategy to be successful.

    The next industrial revolution (what we call “Business 5.0”) will be an overhaul of business process changes that will improve efficiency and productivity for years to come. So don’t think of sales AI as a “quick fix” as these technologies will bring lasting and impactful change.

    Only one in three enterprise projects succeed – it’s up to managerial teams to guarantee that this doesn’t happen to sales AI.

    When writing out a sales AI implementation strategy it’s important to consider the following:

    • Short and long-term objectives
    • Current KPIs and if those will need to change
    • Who will own this technology (sales ops, CRM administrator, IT, sales engineer, etc.)
    • A rough timeline of AI implementation milestones
    • Obstacles that may prevent or delay AI implementation
    • Installation and onboarding
    • Training for end-users
    • Review sessions to continue optimization

    Implementing a complex AI platform may seem daunting, but taking the right steps in small increments will lead to the desired end result.

    Data

    Having robust, accurate data is essential prior to implementing sales AI. In order for any sales AI or ML system to produce results, it must learn from the mountain of customer data that exists in an organization’s CRM (or other sales database).

    First, perform an audit of your data and consider these three things: data quality, data collection and storage and lastly, security.

    When it comes to data quality, it’s vital that data is clean. AI uses data to make predictions, therefore if the sales data is inaccurate it will produce inaccurate results.

    Assess how much data is missing or inaccurate. For example, if lead title or phone number fields are often left blank or if there are too many variations of a company name. Consider whether the AI system could function properly and bring the desired results with the current state of the sales data.

    Cleanliness of data in itself can be a major business problem that AI can solve. If this is the case, consider implementing an AI solution with a data cleanliness component to increase the accuracy of sales data.

    Second, examine the data storage and collection process. Unfortunately, in many organizations, customer and sales data exists in several siloed locations. Perhaps some information exists in the CRM or other database, others only in spreadsheets, or emails. This is a problem as it prevents your organization from seeing a complete view of the sales process and pipeline.

    Locate all the data related to the sales organization and decide where that data should be centralized.

    And finally, consider security. Customer and business data should be kept under tight lock and key, both for the benefit of the company and its customers. No system should ever remove or have access to any of your sensitive data. Only integrate an AI platform that strongly encrypts data both during storage and transport. This promises that no human will be able to access, view or use any classified information.

    Integration

    Sales teams work with a lot of tools. On average, an SDR uses six tools, each one adding its own step to the sales process and often bogging reps down.

    It’s important not to implement an AI system that will just be another tool in the tech stack. The goal is to help with sales rep productivity, not get in their way and cause more confusion and work.

    Make sure that the AI technology you select can easily integrate with the current tools in use. In your research, you’ll find that some AI platforms easily fit into any sales process while others are complex and have many requirements to get started.

    Take inventory of the tools and processes the sales team has in place and how the new technology will fit. Consider if this is a third party dashboard that the sales reps will need to sign into? Is it an encrypted layer that works in the background? Does the CRM, email or other system need to sync?

    Then, create a checklist of all the items needed before integrating and the people who will help. Ownership will be key.

    People

    And finally, make sure the entire sales organization is ready and aligned before the AI system is in place. Create a change management plan that gets everyone on board with this initiative.

    Begin by aligning all stakeholders. Consider the different “owners” of information and technical implementation. Does IT need to be involved to install the software? Does legal need to analyze the security measures? Does a Salesforce admin need to be sure that the product can be customized? List out all stakeholders that will need to sign off in order to move the project forward. Doing this ahead of time will help you see where there might be potential roadblocks and to ensure you have the support you’ll need within your organization.

    The sales reps (or another end-user) needs to have an understanding of this project and how it will bring value to their role. Otherwise, they will be less motivated to use the technology correctly and consistently.

    Be sure they understand what is required of them through each AI implementation phase. For example, there will likely be test users that will need to provide feedback so make sure they understand why feedback is important and what kind of feedback is needed.

    And once the AI platform is fully purchased and implemented, be sure to train all individuals on how to properly use it. This will need to be ongoing and built into your onboarding process. Included in this training should be what the KPIs are to measure success and if the KPIs of the sales reps will change after AI implementation.

    More than half of early digital transformation efforts were stalled in 2018, which Forrester attributes to a lack of organizational readiness. Ensure your company’s time, effort and money aren’t wasted on stalled initiatives by properly preparing your organization ahead of implementation.

  • What Are The Best RPA Alternatives On The Market?

    May 08, 2019 by Jessica Munday

    Robotic process automation (RPA) is not a new term, but its standing in the business lexicon has exploded during the last five to 10 years. Just looking at Google Trends, you can see searches for this topic have skyrocketed since 2015, with significant pickup in 2019 and 2020. However, a new term is now surfacing that’s proving to be a superior RPA alternative: intelligent process automation (IPA).

    Currently, many businesses are starting to depend on RPA for their daily operations, with even more looking to expand their relationship with the technology. During the last 12 months, 66% of companies increased their RPA spend by an average of 5%, citing a growing interest in efficient use of tech.

    Gartner had predicted global RPA revenue to reach 20% growth by the end of 2021, showing little friction from the COVID-19 pandemic. And while the market is booming, many enterprises don’t realize that most RPA platforms are built with decade-old technology, vastly limited and outdated, which is why next-gen automation strategies that leverage AI, such as intelligent document processing (IDP), are becoming its successor.

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    What is intelligent document processing (IDP)?

    Intelligent document processing (IDP) has emerged as a key component within an intelligent automation strategy. IDP technology was born out of the need for organizations to accurately extract data from documents. In essence, IDP leverages artificial intelligence (AI) and natural language processing (NLP) to effectively handle and oversee document-centric business processes.

    Through the adoption of IDP, organizations can optimize business processes involving large volumes of documents to reduce manual effort, enhance precision, and reach their digital transformation goals. A notable advantage of IDP is its seamless integration capability with other automation tools like robotic process automation (RPA). This compatibility with existing technologies enables companies to enhance their current automation strategies, leading to improved overall efficiency and expanded operational scope.

    RPA vs. IPA vs. IDP

    RPA alone has restricted capabilities. It performs one action repeatedly without considering nuances or exceptions. For example, if an RPA system was programmed to sort red and blue balls, it would be unable to react in the case of a yellow ball. It’s only able to perform automations within its predefined process.

    RPA automations must have these processes programmed. They don’t learn or adapt to different workflows, making it impossible to perform complex, human-like tasks on their own.

    However, when you add AI to RPA, the optimization possibilities are endless; this powerful combination is intelligent process automation and a much more capable alternative to RPA.

    Intelligent process automation platforms can learn, making them flexible in the face of complex processes. IPA’s intelligence and adaptability make it capable of handling complicated, tedious human processes. By giving these robotic processes to the robots, IPA enables employees to be more productive.

    The market for intelligent process automation is expected to explode during the coming years. Enterprise investment in intelligent process automation and similar technology is expected to grow exponentially to nearly $120 billion by 2026, with large-scale adoption expected across several industries.

    So what can intelligent document processing do?

    A vast majority of businesses see automation as the way out for addressing customer satisfaction goals. In fact, 92% have pointed to process automation and digital transformation as the key to taking their business to the next level.

    Companies across dozens of industries are implementing intelligent process automation with impactful results. A report on the current IPA landscape from the World Economic Forum showed:

    • Rising numbers of companies around the world adopting some form of IPA
    • Increased diagnostic capability in healthcare
    • Increased customer satisfaction capability in D2C businesses
    • Improvements in transportation analysis for local and federal governments

    While the biggest benefit enterprise leaders focus on is cost savings, there are also several more benefits that come with implementing intelligent process automation.

    Unlock the intelligence in your documents with our AI-driven automation today

    Learn how we helped Markerstudy reduce its claims processing time by 40%. Additionally, learn how we reduced total claim processing time by 80% for another multinational insurance partner — cutting down manual tasks from 10 minutes to just two minutes per claim.

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