Your employees often spend a ton of time on repetitive mundane tasks thanks to non-integrated systems or silos of information that you think can only be bridged manually. Think about some of your data input tasks, you need to manually enter information from one document or system into the next document or system, or information must be parsed out to various reports or departments. Repetitive work like this causes employees to become distracted, bored or tired \u2013 leading to mistakes and less work completed. This not only diminishes individual productivity, but it creates a downstream ripple effect in the rest of your business processes. Even with entry-level positions, mundane tasks can leave employees frustrated, leading to turnover and increased hiring and training costs. You may think this type of work doesn\u2019t exist in your company, but we can almost guarantee that it does. And it can be fixed.<\/p>\n
This is where robotic process automation (RPA) comes into play. Advances in RPA have made it easier, quicker and cheaper to roll out with reduced risk. It\u2019s quick \u2013 often going from \u201cideation to operationalization\u201d in three to four weeks. RPA can run repetitive, mundane tasks 24 hours a day, quickly, with an extremely low error rate. Once programmed, it never needs a refresher course, and updates or changes are implemented easily \u2013 without additional training costs. RPA is not intended to replace employees \u2013 even lower-level workers. It\u2019s meant to free up those employees for more important work. They can become \u201cknowledge workers.\u201d<\/p>\n
Okay, so what\u2019s hyper-automation or hyperautomation?<\/strong><\/h4>\nIn \u201cGartner Top 10 Strategic Technology Trends for 2020,\u201d they define hyperautomation as \u201cdeal(ing) with the application of advanced technologies, including artificial intelligence (AI) and machine learning (ML), to increasingly automate processes and augment humans.\u201d So, it\u2019s often used interchangeably with the terms and concepts discussed below.<\/p>\n
Intelligent automation \/ hyperautomation<\/a> \u2013 getting by with a little help from my friends<\/strong><\/h4>\nLet\u2019s move up a bit to \u201cexception\u201d work, like claims processing in healthcare insurance. While most claims processing never needs human intervention (because of RPA), your claims process specialist \u2013 a knowledge worker \u2013 must review certain claims for such things as over-threshold amounts, suspected fraud, audit, and other exceptions. Often there are tedious, repetitive tasks associated with these exceptions, like compiling facts, figures and information out of different data sources, business applications and\/or policy documents. Your knowledge worker needs to get this specific information, based on whatever the exception kick-out is, to make a decision. But compiling the info (even assuming no mistakes are made) slows down the worker, the decision, and the process \u2013 resulting in inefficiencies and higher costs.<\/p>\n
Intelligent automation \/ hyperautomation<\/a> is the answer here. This type of bot learns where to get the information needed to process the exception \u2013 quickly, accurately and completely. Imagine the knowledge worker getting the exception report along with all the information needed to make the decision.<\/p>\nIntelligent automation \/ hyperautomation<\/a> is especially valuable when volume is considered. Claim filings can vary widely based on events and time of year. And when something like COVID-19 hits, with the potential for an explosion in claims volume with specific claim exceptions \u2013 for example, no co-pays \u2013 volume and workforce size are difficult to match. Having a stable workforce while scaling up or down for claims volume is difficult. There\u2019s a lot of time and cost involved in hiring and training knowledge workers. Hire too many and you may end up with layoffs; hire too few and you\u2019re scrambling to get the job done. IA bots, also known as digital worker assistants or simply digital workers, can be easily added or removed to scale up or down based on volume, helping to stabilize your knowledge-worker hiring.<\/p>\nIntelligent automation \/ hyperautomation<\/a> is not just for claims exceptions. It can be used across multiple industries to actually make decisions based on the information it gathers. Examples include:<\/p>\n\n- Healthcare<\/a>: Check on provider data to determine \u201cin-network\u201d or \u201cout-of-network\u201d status.<\/li>\n
- Banking and financial services<\/a>: Reconcile accounts and post journal entries.<\/li>\n
- Mortgages\/loans<\/a>: Process documentation and related decisions for loan applications.<\/li>\n<\/ul>\n
Like straight RPA, intelligent automation is not meant to replace workers. It gives them the information they need to do their jobs quicker, more efficiently and more accurately.<\/p>\n
Artificial intelligence\/machine learning \u2013 \u201cI\u2019ve got this\u201d<\/strong><\/h4>\n\n![\"\"](\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" "\\"\\"")
<\/figure>\n<\/div>\nArtificial intelligence\/machine learning (AI\/ML) <\/a>capabilities are the top capabilities of digital workers. A digital worker that includes an AI\/ML solution can complete complex decision making on its own. This is because new AI\/ML techniques look at large amounts of data, make connections, and generate insights better and quicker than a knowledge worker ever could.<\/p>\nFor example, basic automation can make a decision on a loan by collecting data and aligning it with the parameters set by the financial institution. An organization using AI\/ML, by contrast, can analyze the entire historical loan portfolio on an ongoing basis, and recognize, for example, that a certain combination of factors leads to a much higher risk of default. If the loan application has those factors, the AI\/ML digital worker will deny the loan.<\/p>\n
AI\/ML is for \u201cwhat\u2019s next\u201d questions \u2013 such as who\u2019s likely to default on a loan, what\u2019s your customer\u2019s next purchase, or when might your equipment break down. You would use artificial intelligence and machine learning tools to automatically detect sophisticated data patterns to predict potential outcomes, giving you better decision-making insight. Does AI\/ML replace workers? Again, we would say no \u2013 AI\/ML gives your company opportunities for sophisticated, strategic work that generates competitive advantages.<\/p>\n
How can we help?<\/strong><\/h4>\nIf you\u2019re interested in any of the bot concepts discussed here, we can sit down with you for a free discovery workshop. If we believe one of our digital workers can help, then we\u2019ll recommend a solution from our catalog of digital workers. If you want to go further, we\u2019ll jump-start the automation solution, including deploying and customizing as needed for your environment. If an AI\/ML solution is involved, we\u2019ll also install and set up that platform. With Prolifics digital workers, we offer a quantified outcome expressed in improved time, increased volume and better quality.<\/p>\n
To learn more, email us at solutions@prolifics.com<\/a>.<\/p>\nMeet Our Team of Digital Workers<\/h2>\n
Let\u2019s move up a bit to \u201cexception\u201d work, like claims processing in healthcare insurance. While most claims processing never needs human intervention (because of RPA), your claims process specialist \u2013 a knowledge worker \u2013 must review certain claims for such things as over-threshold amounts, suspected fraud, audit, and other exceptions. Often there are tedious, repetitive tasks associated with these exceptions, like compiling facts, figures and information out of different data sources, business applications and\/or policy documents. Your knowledge worker needs to get this specific information, based on whatever the exception kick-out is, to make a decision. But compiling the info (even assuming no mistakes are made) slows down the worker, the decision, and the process \u2013 resulting in inefficiencies and higher costs.<\/p>\n
Intelligent automation \/ hyperautomation<\/a> is the answer here. This type of bot learns where to get the information needed to process the exception \u2013 quickly, accurately and completely. Imagine the knowledge worker getting the exception report along with all the information needed to make the decision.<\/p>\n Intelligent automation \/ hyperautomation<\/a> is especially valuable when volume is considered. Claim filings can vary widely based on events and time of year. And when something like COVID-19 hits, with the potential for an explosion in claims volume with specific claim exceptions \u2013 for example, no co-pays \u2013 volume and workforce size are difficult to match. Having a stable workforce while scaling up or down for claims volume is difficult. There\u2019s a lot of time and cost involved in hiring and training knowledge workers. Hire too many and you may end up with layoffs; hire too few and you\u2019re scrambling to get the job done. IA bots, also known as digital worker assistants or simply digital workers, can be easily added or removed to scale up or down based on volume, helping to stabilize your knowledge-worker hiring.<\/p>\n Intelligent automation \/ hyperautomation<\/a> is not just for claims exceptions. It can be used across multiple industries to actually make decisions based on the information it gathers. Examples include:<\/p>\n Like straight RPA, intelligent automation is not meant to replace workers. It gives them the information they need to do their jobs quicker, more efficiently and more accurately.<\/p>\n Artificial intelligence\/machine learning (AI\/ML) <\/a>capabilities are the top capabilities of digital workers. A digital worker that includes an AI\/ML solution can complete complex decision making on its own. This is because new AI\/ML techniques look at large amounts of data, make connections, and generate insights better and quicker than a knowledge worker ever could.<\/p>\n For example, basic automation can make a decision on a loan by collecting data and aligning it with the parameters set by the financial institution. An organization using AI\/ML, by contrast, can analyze the entire historical loan portfolio on an ongoing basis, and recognize, for example, that a certain combination of factors leads to a much higher risk of default. If the loan application has those factors, the AI\/ML digital worker will deny the loan.<\/p>\n AI\/ML is for \u201cwhat\u2019s next\u201d questions \u2013 such as who\u2019s likely to default on a loan, what\u2019s your customer\u2019s next purchase, or when might your equipment break down. You would use artificial intelligence and machine learning tools to automatically detect sophisticated data patterns to predict potential outcomes, giving you better decision-making insight. Does AI\/ML replace workers? Again, we would say no \u2013 AI\/ML gives your company opportunities for sophisticated, strategic work that generates competitive advantages.<\/p>\n If you\u2019re interested in any of the bot concepts discussed here, we can sit down with you for a free discovery workshop. If we believe one of our digital workers can help, then we\u2019ll recommend a solution from our catalog of digital workers. If you want to go further, we\u2019ll jump-start the automation solution, including deploying and customizing as needed for your environment. If an AI\/ML solution is involved, we\u2019ll also install and set up that platform. With Prolifics digital workers, we offer a quantified outcome expressed in improved time, increased volume and better quality.<\/p>\n To learn more, email us at solutions@prolifics.com<\/a>.<\/p>\n\n
Artificial intelligence\/machine learning \u2013 \u201cI\u2019ve got this\u201d<\/strong><\/h4>\n
![\"\"](\"https:\/\/prolifics.com\/usa\/wp-content\/uploads\/2020\/05\/AdobeStock_166288016-scaled.jpeg\" "\\"\\"")
<\/figure>\n<\/div>\nHow can we help?<\/strong><\/h4>\n
Meet Our Team of Digital Workers<\/h2>\n