Author: Jess McCuan

Before and after doctor visits, artificial intelligence, augmented intelligence and machine learning can save overworked humans hours of valuable time.

May 10, 2021 by Jess McCuan

Few industries are in more desperate need of a technology overhaul than healthcare. In a scathing post on the American Medical Association site last year, Dr. James Madara, the AMA president, wrote that if a year of dealing with COVID-19 proved anything, it’s that the US healthcare system (both before and after the pandemic) is “a hodgepodge of ideas, programs and regulations that is both extraordinarily expensive and highly inefficient.” His recommendations for fixing the situation include eliminating needless paperwork and boosting human intelligence with technology — training physicians to use technology from the 21st century, not the 20th.

So what’s the first step in taming healthcare bureaucracy? In fact, one of the easiest ways to help healthcare practitioners is to combine human skill with augmented intelligence to eliminate all the nonclinical manual tasks associated with their jobs. 

Automate appointment scheduling

Take appointment scheduling, for example. The American Association of Family Physicians found that a sizable chunk of healthcare inefficiencies, which cost hospital systems billions each year, include scheduling and missed appointments. The AAFP noted that up to 30 percent of appointments resulted in no-shows, a phenomenon that another study found costs American companies $150 billion annually. 

Artificial intelligence and augmented intelligence can help streamline every aspect of appointment scheduling from end to end. For example, let’s say a patient sends an email to a clinic, hoping to set up an appointment in the coming weeks. That email would normally need to be read by a human, a process that could take up to 5 minutes. But Automation Hero’s platform can detect the intent of that incoming message, automate the scheduling of an appointment based on it, and perform appropriate next steps. (It can also do this for other common requests, which helps eliminate human error in these low-level processes).

Communicate with patients — about the important things

It can help with other aspects of patient communication too. Let’s say a patient is puzzled about some aspect of a monthly bill. He or she emails the healthcare provider with several questions and includes attached documents. If the healthcare group is using Automation Hero, it can instantly use an artificial intelligence model to detect the email’s basic content and intent, then route the email automatically to the appropriate department. It also uses natural language processing and machine learning to extract relevant information from the attached documents, the same way a human might. The info might include patient or medical record numbers, visit dates, and other details. Extracting this automatically combines human capabilities with machine intelligence and saves administrative staff valuable time for higher-value tasks. It also gives IT staff actionable insights based on real-time practice data.

Has your lab testing facility seen an uptick in volume due to COVID-19? Augmented intelligence, machine learning and natural language processing can help with all the nonclinical functions, from processing identification documents during patient intake to interpreting handwritten forms to scheduling appointments,  processing invoices, and delivering lab results. This means human staffers can get back to what they do best, which is interacting with patients and weighing in on higher-level decisions that involve strategy or judgment.

Humans + machines = happier outcomes

To be sure, healthcare’s problems run deep, including plenty of problems data scientists can’t fix. But long before and after a patient sees a doctor, there are plenty of tasks that augmented intelligence and machine learning can streamline, giving actionable insights to administrative staff and giving the healthcare industry’s overworked humans precious hours of their time back. 

Sep 23, 2020 by Jess McCuan

In tech circles, the “monolithic versus microservices” debate may have been hot a few years ago. But now, the best general approach to architecting infrastructure is mostly a settled question, and microservices won.  

Why microservices? 

What makes microservices so much better? The monolithic approach to building software or apps just means building it all in one unified piece. The microservices approach means building everything so that it can be broken up into smaller pieces, or services. A microservice is a tiny program that comes with an API, so it can interact in various ways with other services. Think of it as a small reusable piece that can be built in, around, or on top of another microservice.

Reduce, reuse, reuse again

For automation projects, a microservices architecture is the most future-proof. Business processes that require automation happen everywhere in a company, across business units and divisions. Automations should be built in a way that makes them reusable between teams.

Let’s say you’re a financial institution that runs a Know Your Customer (KYC) process in various areas of the business. A customer wants to open a checking account. You need to verify that person’s identity first, and run a series of KYC risk checks. The same KYC process applies when the customer wants to open a savings account, or apply for a home loan. 

In a perfect world, the way the bank does a KYC check should be standardized across units and divisions. This is not just important for reasons of efficiency and scale, but also a way for the bank to stay in legal compliance. When one team — let’s say the home mortgage team — identifies a way to speed up KYC, the technology involved should be completely reusable, so that it’s easy to apply that KYC innovation to all parts of the bank. 

How microservices work in Automation Hero

You can build an entire automation using our drag-and-drop functions in Flow Studio. With that automation, or Flow, in hand, you can run it in a variety of ways. For example, you might set it to run as a batch. This means the automation is connected to a data source such as a CRM or database, and it can be designed to run on a schedule — once a day, every 30 minutes, or whatever makes sense. You might also run it as a stream, syncing up with a data stream like an email server, for example. The automation can be set to run the moment a message arrives.

You can also set the automation to run as a small standalone application with a REST (REpresentational State Transfer) API, which can be done in our platform with just a few clicks. The rest API can be called from a website or mobile application, or integrated into any kind of application such as a CRM. The automation will be triggered programmatically. This could be part of, say, a signup process through a mobile app or web app.

Weave your company’s intelligence fabric

To be sure, a microservices architecture makes our automations highly reusable. But it also gives companies the unique ability to build an intelligence fabric — an automation fabric, if you will —  around core business processes. Once you start automating parts of your company, let’s say it’s the account signup, or claims processing, you can weave these together as component parts of larger business processes. This collection of automations, then, gives your IT directors and strategists the ability to set standards across the company, knowing that each one is safe, compliant and reusable in new markets. A microservices approach gives your company a new agility around acquisitions and the ability to react more quickly to a fast-changing market. 

Entities, OCR and algorithms, oh my! A glossary of key phrases and concepts.

Sep 14, 2020 by Jess McCuan

Artificial intelligence is everywhere. Whether you understand a lot about it or a little, it never hurts to brush up. 

Beyond personal enrichment, understanding artificial intelligence is now critical for business. Futurists are calling artificial intelligence the fourth industrial revolution, on par in importance with electricity and steam for the business world. Savvy executives are gearing up for both its negative impacts and its revenue-generating potential: MIT and the Boston Consulting Group interviewed more than 3,000 global business executives, and nearly half said their workforces would be reduced because of AI. In the same study, 91% said they expect AI to generate new business value in the next five years. 

Company leaders know AI is changing the way people do business. So how much do you know about it? Below, see our concise definitions of everything from unstructured data to insurance automation.

Common business-world AI terms

Artificial intelligence (AI): When machines learn from data and can perform tasks that normally require human intelligence. These include tasks like visual perception, speech recognition, decision-making and language translations.

Augmented intelligence: Tools and technology designed to elevate human workers and aid them in working smarter. This is seen as a complement to humans, rather than a replacement. Often referred to as intelligence augmentation (IA).

Machine learning: A subset of artificial intelligence in which a machine uses an algorithm to solve a problem or do a certain task. Machine learning tools learn by finding patterns in datasets that they can then use to create bigger-picture outcomes over time. This is also called data mining.

Deep learning: An area of machine learning that essentially stacks neural networks on top of each other to achieve much higher accuracy than any other machine learning algorithm has before.

Information extraction: When a machine mines for interesting or relevant pieces of data found in natural language text (for example, names, companies, telephone numbers, etc.).

Intent detection: When a system uses natural language processing (see below) to predict the intention inside a human message. This can be used to assist in getting the message to the right department or helping respond to it.

Knowledge-based AI: Humans assemble a handcrafted set of rules that are then used to make decision graphs. These graphs often take a very long time to manually create by subject matter experts.

Optical Character Recognition (OCR): Technology that converts images of handwritten text into machine-coded text to make interpretation and data entry go more smoothly.

Natural Language Processing (NLP): Using software or other technology to understand, interpret and manipulate human language. This is also called text mining.

Neural network: This is an artificial network that essentially simulates how the human brain works. A network of firing “neurons” interprets data, making decisions and learning from the input over time.

Reinforcement learning: Systems that learn based on a reward. They create outcomes that are then rewarded or punished, based on whether the outcome is correct or not. Once the correct output is achieved, it will optimize for maximum reward.

Supervised learning: Machine learning models that learn by comparing their output to the “correct” output. If the system is incorrect, it adjusts the algorithm accordingly.

Unsupervised learning: Machine learning models that are trained without receiving the correct “answer” to the problem they might be solving, meaning they learn through a process of trial and error.

Phrases that will help you talk to data scientists

Algorithm: In math and computer science, an algorithm is an equation or set of repeatable steps that a machine goes through to solve a problem, complete a task or perform a certain computation.

Entity: Any object inside a data model. This could be a name, address, or another attribute or bit of information. Entities aren’t the data points themselves but are instead containers for those attributes and relationships between objects. 

Structured data: Data that’s organized into a format or fields, as it is in a spreadsheet or database.

Unstructured data: Data that is not organized into any particular format. Examples of unstructured data might include photos, videos, emails, books, social media posts, or health records.

Semi-structured data: Data that does not live in a database or spreadsheet but may have some attributes that makes it easier to organize. Examples might include XML documents and NoSQL databases.

Predictive analytics: When a machine can make predictions about the future using current and historical data. 

Regression analysis: A statistical method that lets you see patterns and trends in data. There are many types of regression analysis, but all use math to understand the influence of one or more independent variables on a dependent variable.

Automation: the key ideas

Automation: When a machine or tool performs a function that normally requires human involvement.

Hyperautomation: Defined by Gartner, hyperautomation means rapidly identifying and automating as many business processes as possible, using software, robotic process automation and machine learning.

Autonomous business processes: When a series of business tasks can all be fully automated with little human interaction or interference.

Business Process Automation (BPA): Automation of business processes and workflows as a whole, rather than one step or process, with the goal of making the organization as efficient and productive as possible.

Robotic Process Automation (RPA): Software that automates tasks and processes usually done by humans. This can be tasks like processing information, manipulating data, and triggering responses. Essentially this is software automating the existing tools in your tech stack.

Sales automation: Using technology to automate sales processes through static roles. For example, converting leads into the next stage in the CRM based on triggers that occur elsewhere, like sending out certain documents through email.

Insurance automation: Tools that use artificial intelligence and automation to improve any aspect of the insurance business, from customer outreach to claims processing. This can range from simple tasks like automating a reply or scheduling a meeting, or using AI in long-running, complex tasks like document classification, data extraction for claim forms, reducing bias in coverage decisions, or making predictions about new markets.

Intelligent automation: Automation that combines artificial intelligence, robotic process automation and mass amounts of data to automate complex tasks and perform more adaptable workflows. It can perform tasks that require cognitive processing and complexity, making it ideal for tasks too complicated for RPA, but too boring for humans.  

Process mining: Software that helps a company understand its current business processes, find any variations or problems across the organization, and gauge whether it’s worth investing in improvements.

Decision intelligence: Decision intelligence is a practical discipline framing a wide range of decision-making techniques; it encompasses practical applications in the field of complex adaptive systems. Decision intelligence provides a framework that brings multiple traditional and advanced techniques together to design, model, align, execute, monitor and tune decision models.

Plus: 4 ways to kickstart the process, from simplified KYC to intelligent OCR.

Sep 02, 2020 by Jess McCuan

Automation and digital transformation have never taken bigger leaps forward than in 2020. Businesses across industries contemplating touchless or robotic solutions have gone full bore toward those options. At a recent MIT Sloan event, a panel of CIOs predicted that digital transformation will pick up speed post-pandemic, since staying relevant means adapting technology like AI and IoT to perform once-physical processes. 

Still, digital transformation moves a bit more slowly in at least one industry: banking. Why? Banks are highly regulated, and any tech move that might threaten security would also threaten one of the bank’s biggest assets: credibility. After all, a bank’s secure vaults and stone columns — plus the fact that they’ve been around since 1865 — are a big part of why the public trusts them to look after its money. What’s the latest fintech fad or innovation? Banks often view such trends skeptically, as they’re keen to ensure that they cover their security bases and stay in compliance. 

A jumble of legacy systems

The trouble is, this conservative approach has left many banks with a pile of outdated technology. Almost half of banks in the U.K. don’t upgrade outdated IT systems when they should, according to Britain’s Financial Conduct Authority, which found that tech outages more than doubled in 2018. In America, an Ernst & Young report last year found that some 43% of banks still use COBOL, a programming language from the 1950s. 

This means strategists inside banks jump through more hoops than those in other industries. In the outside world, customer demands are heating up as the world goes mobile and consumers want ultra-convenient options. But rolling out a new customer banking feature might involve something of an internal archaeological dig, as IT teams sift through layers of technology that make underwriting and anti-money laundering checks possible.  

Lucky for banks, it’s not too late to automate. And several recent tech advances make it easier than ever to integrate modern tools.

Quick wins with automation

The automation use cases below offer quick wins for financial institutions, giving them new capabilities to speed up document processing, for example, or ways to easily integrate automation and AI with mainframes and legacy on-premise systems. 

1. Use AI to classify loan documents  

In the early phases of loan origination, we can help banks build a document classification model that makes quick work of the intake and application processes. For example: What type of document is attached to a potential borrower’s email? Automation Hero’s platform uses AI to sort a huge range of unstructured data, such as proof-of-identity documents, trade licenses, and founding or tax certificates.

2. Process information faster with intelligent OCR 

Once the document is identified, we establish rules for extracting data from it. Most out-of-the-box OCR (optical character recognition) software can process documents accurately — as long as those documents only include machine-typed text. Our platform has proved particularly versatile at intelligent OCR, an AI-powered approach to processing documents containing a combination of machine-typed text and human handwriting. 

We automatically normalize imperfect documents such as upside-down scans or out-of-focus photos. Is a tax ID number always in the upper-right corner of the document? Deep learning  guides the platform. Then each OCR solution can be clicked together in a custom way that is continuously improved with domain-specific data. The practice dataset would likely include industry-specific words and their context. In the end, this highly specialized training data leads to much more accurate interpretations of forms, and a platform better equipped for handwriting recognition, including messy problem cases.

3. Simplify KYC

KYC (Know Your Customer) and CDD (Customer Due Diligence) standards just got higher at banks, especially those doing business with Europe. And the two processes are still painfully manual at financial institutions around the globe, where employees do plenty of “chair-swiveling” between systems. 

Automation can help on numerous fronts, including outreach to customers to request documentation, a smoother system for keeping humans in the loop to check documents, faster initial fraud and risk assessment screenings, and instant updating of customer records and document transfer to a central repository. 

4. Apply automation to mainframe computing

A surprising number of insurers, airlines, healthcare institutions and finance companies still run on mainframes. But those companies especially struggle to incorporate AI, automation and process analytics into their core systems. 

Automation Hero allows for easy connection to mainframe software and the integration of legacy code such as COBOL. The Automation Hero platform can be installed on Intel or PowerPC (mainframe) hardware and can therefore be co-located with other mainframe software, while simultaneously running AI and other capabilities native on these servers. A number of point-and-click connectors such as Adabas, DB2, Copybooks and even customizable telnet terminal 3270 parsers, make an integration to legacy software a breeze. Legacy code such as Pascal, Fortran, COBOL, PL/I, or C can be easily integrated through our containerized functions. These containerized functions are fully managed by the Automation Hero platform and turn your legacy code into a highly distributed, fail-tolerant, high-performance point-and-click function inside the Automation Hero Flow Studio, making them one building block of your bigger automation flow. We can do this with the highest level of security needed.

Why change now?

The stakes have never been higher for banks. A post-pandemic era is set to bring not only a wave of digital transformation but also further consolidation in banking, along with fiercer sharks in the proverbial waters. Because of their security concerns, plenty of bankers skipped out on the shift to cloud computing. They shouldn’t skip out on automation, which can be designed to enhance whatever technology you’re working with, whether that’s on-prem or in the cloud. No need to rip and replace systems. Automation can bring significant ROI in both the short and long term, and can help a bank streamline processes, gaining critical efficiencies as it charts a course through a faster digital future.

Humans can do better. So can our algorithms.

Jun 12, 2020 by Jess McCuan

We know humans can be biased. But what about machines? Shouldn’t automated decision-making help us steer clear of discrimination? 

Dutch scholar Mireille Hildebrandt argued in a recent paper that bias-free machine learning doesn’t really exist, since bias is fundamental to inductive learning systems. Still, she says, we can design our algorithms to be less outright discriminatory. 

Fairer by design

The algorithms themselves may not have morals, but people designing them do. And though AI can be designed on a single laptop, it’s unleashed into a complex world where it has significant impact on the trajectory of people’s lives.

If you’re stumped about the difference between “ethics,” “discrimination” and “bias,” a good place to start is the first part of our course on Ethics & AI, which you can access below.

In the tutorial, Automation Hero data scientists explain how bias can be designed into all aspects of artificial intelligence, from raw data to classification systems to the complex algorithms that run on top of it all. They also outline ways to be more aware of this bias and steps you can take to mitigate discriminatory outcomes. 

Battling bias

Bias can creep into any project, automated or not. But it can be particularly dangerous in these 5 real-world scenarios: 

1. Recruiting

Companies have used AI for years to speed up manual tasks involved in recruiting and hiring. In some cases, that’s led to unfair breaks for certain demographic groups, including women and minorities. Starting in 2014, Amazon used a machine learning tool that systematically discriminated against women by penalizing words like “women’s” on resumes. And Carnegie Mellon researchers found that Google was much more likely to show ads for high-paying jobs to groups of male job-seekers.   

2. Banking and credit

Classical formulas for credit scoring are designed to leave out variables like age and gender. But plenty of companies now use algorithms to factor in social media activity, granular purchasing data and other information to determine a person’s credit-worthiness. In some cases, algorithmic scoring reinforces older, discriminatory patterns. In others it leads to odd conclusions, like the notion that people who buy birdseed are less likely to default on a loan.   

3. Legal, judicial and policing decisions

Courtrooms and police departments are using big data to make better strategy decisions about where to deploy staff and resources. But algorithms can, for example, overestimate how likely a person is to commit a second or third crime. Or, human bias gets introduced into the datasets, and then the AI can compound those discriminatory decisions. Fair policing has never been more controversial than in the U.S. in recent weeks, after a series of brutal police incidents and the death of George Floyd. Now, large companies have sworn off facial recognition technology over concerns about privacy and bias. 

4. Social services

Few employees are as overloaded as social workers, and AI solutions help them churn through burgeoning caseloads and identify or reduce risks. But things can go awry when, for example, an algorithm can’t distinguish between fraud and innocent mistakes. 

5. Insurance

In this highly regulated industry, AI has helped speed up every aspect of claims processing and payouts, but insurers must walk a fine line when, for example, an algorithm might inadvertently reward those in less need of a payout. 

Why use AI?

When AI has been thoughtfully developed, it can be used to overcome discrimination in human decision making. Ethical AI has the potential to make the world more just by augmenting some of our human limitations. It can also improve the world, in critical areas like mapping wildfires and  detecting cancer. But used poorly, AI can have disastrous consequences, especially for minority groups in the ways mentioned above, in the arenas of employment and the law.

The next best step: Learn all you can about bias and discrimination, and about how to use automation and AI well.

Continue with the second part of our Ethics & AI course, below. 

6 questions for skeptical buyers

May 07, 2020 by Jess McCuan

Why invest in automation? One way to calculate the upside is to start with classical efficiency — being productive with less effort, usually by eliminating tasks, cutting costs, and saving time. But the automation savings are richer than just the time and money won back. Augmented human decisions, increased employee happiness, and more novel opportunities are just some of the added benefits.

Below are some of the most common questions about the ROI of automation, including cost savings, employee impact, and long-term benefits for a company.  

Keep in touch

Intelligent automation: The basics 

First, automation comes in many flavors. Companies have been automating tasks for more than a century, but only during the past few have they figured out robotic process automation, which simply means software automates the tasks usually done by humans. Add in artificial intelligence, and you get intelligent automation, or intelligent process automation. So, RPA + AI = IPA. You may also hear intelligent automation referred to as cognitive automation, smart RPA, or RPA+. 

Intelligent automation, in a nutshell, combines artificial intelligence, robotic process automation, and mass amounts of data captured through next-gen technologies such as intelligent document processing (IDP) to automate complex tasks and perform more adaptable workflows. 

RPA gets you partway

RPA is terrific for certain kinds of tasks. For example, it can perform one action repeatedly, as long as that action has no nuances or exceptions. Because RPA automations must be pre-programmed, they’re best suited for simple, rules-based processes like data entry or invoice processing.

When you add AI to RPA, a whole new world opens up. Intelligent automation can still handle the same routine tasks of business work, but now it can do more heavy lifting — for example, analyzing unstructured datasets and input, such as text, audio, and video. While this type of data may not map perfectly, it often holds valuable insights. Intelligent automation can also handle much more complex rules using natural language processing and predictive examples.

While unstructured data, like audio and video, may not map perfectly, it often holds the most valuable insights.

Intelligent document processing (IDP) gets you all the way

Intelligent document processing (IDP) has emerged as a critical component 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, organizations can optimize processes involving a large volume of documents to reduce manual effort and enhance precision.

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

1. What are my potential automation savings?

No matter which flavor of automation is your favorite, each offers huge benefits — many quantifiable, some qualitative. Let’s start with the cost savings. 

A Deloitte report shared that intelligent automation has been a godsend for businesses looking to reduce operating costs. By streamlining business processes and improving productivity, automation savings were between 25% and 40% on average for those implementing the technology.

At Automation Hero, we worked with a leading German health insurance company that handled approximately 130,000 closed claims per year, which took a team of 450 sales reps between 15 to 30 minutes a day to manually handle them. To close the claims, reps pulled data from multiple systems (e.g. SAP and a CRM) and pasted it into a Word document. After data extraction, the document had to be printed and signed. 

We used a simple automation to compile all the relevant information into a PDF for e-signature, resulting in $1.2. million in ROI. The automation savings for the employees was equal to 18 years worth of work.

2. What does automation mean for customers?

Used well, automation leads to higher-quality customer service. Everyone has had maddening experiences with customer-service phone trees or chatbots. But, automation has more sophisticated uses. Find the sweet spot for offering appropriate customer experiences for each interaction, and automation will be a win-win. 

Turn over rote tasks — like scheduling meetings, CRM data entry, and repetitive aspects of call center work — to automation in order to still offer personal interactions for customers when they want or need it. At critical moments, such as making a sale or handling a complaint, sending your customer to a robot is unlikely to lead to sales or loyalty. Use the predictive analytics capabilities of intelligent automation to make personalized recommendations for customers who will then be much more likely to buy products and give higher NPS scores. 

Knowing which tool is right for the job is equally important when deciding whether to tackle a customer service problem with RPA or IDP. For example, robotic process automation can only handle back-office functions, whereas a combination of intelligent automation technologies that leverage intelligent document processing to captures volumes of data can take on more complex, customer-facing interactions in the front office. 

Automation Hero uses intelligent document processing to help companies that are drowning in customer requests. Some companies can’t handle the volume, which can result in long wait cycles for customers, and plenty of unanswered calls and emails. Not only does this create a negative customer experience, but it can also lead to missed business opportunities entirely.  

We helped a company that was struggling with this issue to build a two-step AI model that could automate responses to 60% of its incoming inquiries. This led to an 80% workload reduction, mere seconds in response time, and overall higher customer satisfaction.

3. What does it mean for employees?

While it’s less obvious, one qualitative benefit of automation is making knowledge workers happy. Intelligent automation helps you eliminate repetitive tasks, meaning your staff gets reassigned to more high-level, high-value activities. Ultimately, they’ll be more engaged with their jobs and stick around. 

Automation can also augment employee decision making. The AI built into a good intelligent document processing platform can provide critical insights into data patterns. The added wisdom, through process mining or other data analysis, leads employees to more success in their roles — and thus increased job satisfaction. Intelligent automation does nothing short of letting employees achieve career goals and benefit your company.

Some companies find that implementing RPA and IDP also lets them shift human capital costs. While automation can mean replacing full-time employees, a more likely outcome is the  company reorganizes staff, optimizes, and eliminates tasks in order to increase overall production.

4. How does it impact the company?

Productivity increases lead to enhanced organizational efficiency. Since labor productivity is the total output of your enterprise divided by the total input, eliminating inefficient processes with automation can lead to higher throughput with the same resources. 

Increased effectiveness also stems from fewer errors. Your brightest workers can then also use AI-enabled predictive analytics to make better decisions about projects large and small. Suddenly, a whole crowd of people at your company are freed up from mind-numbing work and have more time to explore new markets and opportunities for themselves and their teams. These are revenue-producing activities that will directly impact the bottom line. 

5. Why make it an end-to-end platform, instead of a best-in-class product for certain kinds of problems? 

Even if you’re sold on the concept of automation, one further consideration is whether you need an end-to-end platform versus a best-of-breed tool, like data analytics or decision modeling software. Automation includes a whole ecosystem of tools and companies offering different solutions. Vendor selection for any project is important, and Automation Hero may not be the platform for the task. 

Before you jump in, take stock of where you might land. An end-to-end platform helps a company scale faster, and you will see a quicker impact to the bottom line through scaling with an end-to-end platform that comes with built-in intelligent document processing (with AI) versus RPA alone. For example, you’ll get products to market faster and run feedback loops faster on software development with test automation. An end-to-end platform can be used to map processes to run trials, report data, and then iterate, iterate, iterate.

An end-to-end solution also extends the “life” of legacy systems, and is built for the future of automation — an approach called hyperautomation. The constant churn in ripping and replacing legacy systems bottlenecks IT, with some massive IT solutions becoming obsolete before they’re even implemented. When all your best-in-class products need to be duct-taped together, and your infrastructure is fragile at best. 

6. Why now?

It pays to be ahead of the curve on automation. In the stalwart insurance business, for example, startup Lemonade sent shivers through the industry when its technology-forward approach meant it could process a claim in just three seconds. 

With the advent of the COVID-19 pandemic, the universal adoption of analytics and AI only hastened. PwC found 56% of companies accelerated their adoption plans during the past year, and 86% now recognize it as a mainstream technology. The moment has clarified the importance and impact of AI, and CEOs are jumping on board.

Global managing director at Accenture Arnab Chakraborty commented on the trend like this: 

“Since Covid hit, CEOs are now leaning in, asking how they can take advantage of data. They want to understand how to get a better sense of their customers. They want to create more agility in their supply chains and distribution networks. They want to start creating new business models powered by data. They know they need to build a data foundation, taking all of the data sets, putting them into an insights engine using all the algorithms, and powering insights solutions that can help them optimize their businesses, create more agility in business processes, know their customers, and activate new revenue channels.” 

This year, as we continue to reckon with the public health crisis sending shockwaves through the economy, the brightest minds in tech have looked into their crystal balls to find that all-things automated will surge ahead, and digital transformation is accelerating as companies scramble to make better use of slim resources. 

The cost of doing business

When it comes to sizing up an automation project, broad cost reduction is important. Plenty of companies see quick wins from isolated implementations of RPA, but analysts at the Everest Group warned a few years back that cost-trimming isn’t the only thing companies should consider as they get ready for a faster digital future. 

“Enterprises tend to identify and prioritize cost savings as the primary objective of their automation initiatives,” wrote the Everest Group’s Sarah Burnett and Anil Vijayan. “However, this strategy can result in myopic focus and siloed implementation approach, which may significantly limit the potential of automation to deliver strategic benefits beyond immediate cost reduction.” 

Rather than seeing automation as a short-term fix with potentially stalled initiatives, calculating automation savings through true ROI means stepping back to see how it can help your organization achieve its long-term, big-picture goals.

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!

6 questions for skeptical buyers

May 07, 2020 by Jess McCuan

Why invest in automation? One way to calculate the upside is to start with classical efficiency — being productive with less effort, usually by eliminating tasks, cutting costs, and saving time. But the automation savings are richer than just the time and money won back. Augmented human decisions, increased employee happiness, and more novel opportunities are just some of the added benefits.

Below are some of the most common questions about the ROI of automation, including cost savings, employee impact, and long-term benefits for a company.  

Intelligent automation: The basics 

First, automation comes in many flavors. Companies have been automating tasks for more than a century, but only during the past few have they figured out robotic process automation, which simply means software automates the tasks usually done by humans. Add in artificial intelligence, and you get intelligent automation, or intelligent process automation. So, RPA + AI = IPA. You may also hear intelligent automation referred to as cognitive automation, smart RPA, or RPA+. 

Intelligent automation, in a nutshell, combines artificial intelligence, robotic automation, and mass amounts of data to automate complex tasks and perform more adaptable workflows. 

RPA gets you partway

RPA is terrific for certain kinds of tasks. For example, it can perform one action repeatedly, as long as that action has no nuances or exceptions. Because RPA automations must be pre-programmed, they’re best suited for simple, rules-based processes like data entry or invoice processing.

When you add AI to RPA, a whole new world opens up. Intelligent automation can still handle the same routine tasks of business work, but now it can do more heavy lifting — for example, analyzing unstructured datasets and input, such as text, audio, and video. While this type of data may not map perfectly, it often holds valuable insights. Intelligent automation can also handle much more complex rules using natural language processing and predictive examples.

While unstructured data, like audio and video, may not map perfectly, it often holds the most valuable insights.

1. What are my potential automation savings?

No matter which flavor of automation is your favorite, each offers huge benefits — many quantifiable, some qualitative. Let’s start with the cost savings. 

A Deloitte report shared that intelligent automation has been a godsend for businesses looking to reduce operating costs. By streamlining business processes and improving productivity, automation savings were between 25% and 40% on average for those implementing the technology.

At Automation Hero, we worked with a leading German health insurance company that handled approximately 130,000 closed claims per year, which took a team of 450 sales reps between 15 to 30 minutes a day to manually handle them. To close the claims, reps pulled data from multiple systems (e.g. SAP and a CRM) and pasted it into a Word document. After data extraction, the document had to be printed and signed. 

We used a simple automation to compile all the relevant information into a PDF for e-signature, resulting in $1.2. million in ROI. The automation savings for the employees was equal to 18 years worth of work.

2. What does automation mean for customers?

Used well, automation leads to higher-quality customer service. Everyone has had maddening experiences with customer-service phone trees or chatbots. But, automation has more sophisticated uses. Find the sweet spot for offering appropriate customer experiences for each interaction, and automation will be a win-win. 

Turn over rote tasks — like scheduling meetings, CRM data entry, and repetitive aspects of call center work — to automation in order to still offer personal interactions for customers when they want or need it. At critical moments, such as making a sale or handling a complaint, sending your customer to a robot is unlikely to lead to sales or loyalty. Use the predictive analytics capabilities of intelligent automation to make personalized recommendations for customers who will then be much more likely to buy products and give higher NPS scores. 

Knowing which tool is right for the job is equally important when deciding whether to tackle a customer service problem with RPA or IPA. For example, robotic process automation can only handle back-office functions, whereas intelligent automation can take on more complex, customer-facing interactions in the front office. 

Automation Hero uses intelligent automation to help companies that are drowning in customer requests. Some companies can’t handle the volume, which can result in long wait cycles for customers, and plenty of unanswered calls and emails. Not only does this create a negative customer experience, but it can also lead to missed business opportunities entirely.  

We helped a company that was struggling with this issue to build a two-step AI model that could automate responses to 60% of its incoming inquiries. This led to an 80% workload reduction, mere seconds in response time, and overall higher customer satisfaction.

3. What does it mean for employees?

While it’s less obvious, one qualitative benefit of automation is making knowledge workers happy. Intelligent automation helps you eliminate repetitive tasks, meaning your staff gets reassigned to more high-level, high-value activities. Ultimately, they’ll be more engaged with their jobs and stick around. 

Automation can also augment employee decision making. The AI built into a good intelligent automation platform can provide critical insights into data patterns. The added wisdom, through process mining or other data analysis, leads employees to more success in their roles — and thus increased job satisfaction. Intelligent automation does nothing short of letting employees achieve career goals and benefit your company.

Some companies find that implementing RPA or IPA also lets them shift human capital costs. While automation can mean replacing full-time employees, a more likely outcome is the  company reorganizes staff, optimizes, and eliminates tasks in order to increase overall production.

4. How does it impact the company?

Productivity increases lead to enhanced organizational efficiency. Since labor productivity is the total output of your enterprise divided by the total input, eliminating inefficient processes with automation can lead to higher throughput with the same resources. 

Increased effectiveness also stems from fewer errors. Your brightest workers can then also use AI-enabled predictive analytics to make better decisions about projects large and small. Suddenly, a whole crowd of people at your company are freed up from mind-numbing work and have more time to explore new markets and opportunities for themselves and their teams. These are revenue-producing activities that will directly impact the bottom line. 

5. Why make it an end-to-end platform, instead of a best-in-class product for certain kinds of problems? 

Even if you’re sold on the concept of automation, one further consideration is whether you need an end-to-end platform versus a best-of-breed tool, like data analytics or decision modeling software. Automation includes a whole ecosystem of tools and companies offering different solutions. Vendor selection for any project is important, and Automation Hero may not be the platform for the task. 

Before you jump in, take stock of where you might land. An end-to-end platform helps a company scale faster, and you will see a quicker impact to the bottom line through scaling with an end-to-end platform that comes with built-in intelligent automation (with AI) versus RPA alone. For example, you’ll get products to market faster and run feedback loops faster on software development with test automation. An end-to-end platform can be used to map processes to run trials, report data, and then iterate, iterate, iterate.

An end-to-end solution also extends the “life” of legacy systems, and is built for the future of automation — an approach called hyperautomation. The constant churn in ripping and replacing legacy systems bottlenecks IT, with some massive IT solutions becoming obsolete before they’re even implemented. When all your best-in-class products need to be duct-taped together, and your infrastructure is fragile at best. 

6. Why now?

It pays to be ahead of the curve on automation. In the stalwart insurance business, for example, startup Lemonade sent shivers through the industry when its technology-forward approach meant it could process a claim in just three seconds. 

With the advent of the COVID-19 pandemic, the universal adoption of analytics and AI only hastened. PwC found 56% of companies accelerated their adoption plans during the past year, and 86% now recognize it as a mainstream technology. The moment has clarified the importance and impact of AI, and CEOs are jumping on board.

Global managing director at Accenture Arnab Chakraborty commented on the trend like this: 

“Since Covid hit, CEOs are now leaning in, asking how they can take advantage of data. They want to understand how to get a better sense of their customers. They want to create more agility in their supply chains and distribution networks. They want to start creating new business models powered by data. They know they need to build a data foundation, taking all of the data sets, putting them into an insights engine using all the algorithms, and powering insights solutions that can help them optimize their businesses, create more agility in business processes, know their customers, and activate new revenue channels.” 

This year, as we continue to reckon with the public health crisis sending shockwaves through the economy, the brightest minds in tech have looked into their crystal balls to find that all-things automated will surge ahead, and digital transformation is accelerating as companies scramble to make better use of slim resources. 

The cost of doing business

When it comes to sizing up an automation project, broad cost reduction is important. Plenty of companies see quick wins from isolated implementations of RPA, but analysts at the Everest Group warned a few years back that cost-trimming isn’t the only thing companies should consider as they get ready for a faster digital future. 

“Enterprises tend to identify and prioritize cost savings as the primary objective of their automation initiatives,” wrote the Everest Group’s Sarah Burnett and Anil Vijayan. “However, this strategy can result in myopic focus and siloed implementation approach, which may significantly limit the potential of automation to deliver strategic benefits beyond immediate cost reduction.” 

Rather than seeing automation as a short-term fix with potentially stalled initiatives, calculating automation savings through true ROI means stepping back to see how it can help your organization achieve its long-term, big-picture goals.