Intelligent automation is the foundation for building up your digital transformation strategy. It’s more than just going paperless or eliminating slack in a process — it’s how organizations can deliver great service, both within their own teams and for their customers. Intelligent automation is the key to connecting today’s workforce to the future of work, and if you don’t have an intelligent automation footing and roadmap for growth, you’re already falling behind.
Intelligent automation (IA) describes a large category of technology solutions that automate processes that were once manual, so businesses can anticipate the needs of users and customers. It helps organizations simplify or eliminate tedious tasks through technology that learns as it operates, meaning the product will work better six months after implementation than it did after the first week, and will surpass the efficiency of the six-month mark 12 months after implementation.
Intelligent automation provides a reduction in human-first touchpoints, a more digitized work environment, enhanced accuracy, a more empowered workforce and organizational resiliency. It allows your organization to process its data faster and more accurately than with a human-only workforce. According to a 2021 Forrester report, Intelligent Automation’s Value Spreads Beyond Cost Savings, “intelligent automation can free up $132 billion in economic value,” in the U.S. alone, including:
What provides intelligent automation? Many organizations capitalize on their intelligence strategy by leveraging the embedded intelligence within their foundational technology platforms. For example, leading content services platforms (CSPs), increasingly provide intelligent content services, which means IA tools like artificial intelligence (AI), machine learning and advanced analytics are native capabilities to the CSP. While still a burgeoning trend, the use cases for embedded intelligence within content services is seen as a transformational opportunity in the industry.
Data is being created in our world at staggering rates: “On average, information volume coming into organizations is expected to grow by 4.5X over the next two years, with nearly 60% of that information to be unstructured (like a contract or a conversation) or semi-structured (like an invoice or a form),” according to AIIM. How — or whether — we harness and use that data is the question of the moment. The same AIIM report discovered a cumulative D+ grade for organizations’ ability to “extract intelligence from information.” A tsunami of data coming into your organization without the ability to quickly and accurately recognize it, extract it and organize it puts your team at a loss, with impacts such as:
Intelligent automation success, no matter which tool is being considered or deployed, is predicated on the prework that went into designing the solution. In previous generations of technology, deployments may have begun with an identified technology, but in today’s fast and fluid business structure, one size doesn’t fit all. The first step in pursuing an intelligent automation project is a comprehensive data-gathering mission centered around the business challenges you want to solve. Ironically, your success with intelligent automation begins with people. Once the preliminary implications and desired outcomes are identified, you need to choose a partner to help guide you to the best-fit solution. Any intelligent automation solution, regardless of which tool is chosen, will need to:
Intelligent automation tools deploy a range of strategies to reduce the amount of human touch required in business processes. With infinite types of job tasks across industries and in various departments, there are many tools that have been designed to automate those processes effectively. The tools in the intelligent automation toolbox are dynamic and diverse, ranging from autonomous (no human interaction required) to semi-autonomous and even human-led, such as a workflow tool that is executed by the user.
Machine learning (ML) is a subcategory of AI where a computer uses algorithms and statistical models to learn how to perform specific tasks without the need for instructions from a human-user. ML uses patterns and inference to complete tasks. Each time an ML process runs, the system can use the results to measure the algorithms’ accuracy and make improvements automatically. Read more about embedded ML. Deep learning Deep learning is a subset of machine learning, and it’s the behind-the-scenes training that teaches computers to learn like a human brain. This is how computers learn to recognize patterns and identify unstructured data, through continued exposure to different content and deep learning feedback.
When data arrives to your organization, intelligent capture and extraction tools automatically ingest that data using pattern recognition (not templates) to recognize and interpret the information on a document. It begins its automation journey by learning what kind of data it should expect by processing just a few sample pages of the forms you’re using and begins to understand the different document types and data locations on each.
Optical character recognition - Optical character recognition (OCR) is an intelligent capture tool that extracts data from a scanned document or image file and converts the text into a machine-readable format for use in data processing. Organizations that employ OCR to convert images and PDFs (typically originating as scanned paper documents) free up human resources and save time that would otherwise be spent managing unsearchable data.
Optical character recognition - Optical character recognition (OCR) is an intelligent capture tool that extracts data from a scanned document or image file and converts the text into a machine-readable format for use in data processing. Organisations that employ OCR to convert images and PDFs (typically originating as scanned paper documents) free up human resources and save time that would otherwise be spent managing unsearchable data.
Optical mark recognition - Optimal mark recognition (OMR) determines a selection from a list of choices, such as check boxes and filled circles.
Bar code recognition - Bar code recognition (BCR) extracts data from bar codes on the document.
Process automation
According to Deep Analysis, business process automation orchestrates multiple complex and parallel activities and is often used as the cornerstone to integrate multiple data stores, applications and people. Implementing a content and process automation use case, like claims processing or new account onboarding, requires strong workflow and content intelligence capabilities that integrate with core line of business applications.
The technology industry is fast-moving with digital transformation trends coming and going, and new solutions entering the market all the time. Sometimes the newest concept takes off, finding its way into organizations quickly and to great success. Other times, exciting new concepts see slow but steady adoption, eventually finding their place into the everyday. And for some concepts, early promise leads nowhere. Here are the intelligent automation trends to keep on your radar.
Embedded intelligence describes components of AI that are native to a platform. For example, a content services platform with embedded intelligence can predict, classify and enrich content based on business-specific needs using its foundational capabilities. Although AI has long been table stakes for CSPs, the market is evolving to demand that intelligent features be embedded in solutions. Market trend: In the 2021 Gartner® Magic Quadrant™ for Content Services Platforms, Gartner noted embedded intelligence as one of four key trends impacting content services, and said: “AI is critical to content services. In the past, it has been an interesting feature looking for a use case. However, it is becoming increasingly embedded with real business solutions from correspondence management to case management.”
The impact scenarios for intelligent automation are endless, but we know real-world applications go a long way in truly seeing how a solution can improve the reality for your people and processes. Here are three intelligent automations examples and how the technology can impact an organization. Looking for more examples? Read 5 examples of what intelligent automation could look like at your organization.
The problem: Firms in the financial service sector see massive amounts of sensitive data entering their post room and mail inbox every day, in many different formats. Manually sorting these large volumes of incoming mail and routing it to the correct departments often puts pressure on the operational efficiency of organisations. The solution: The RPA solution integrated with Optical Character Recognition and a document management software to automatically trigger workflows that will route inbound mail to the correct employee. Instead of devoting valuable worker time to reviewing incoming forms, invoices and other documents to route them to the appropriate destination, this document recognition and routing technology reduced labour costs and delivers accurate results, faster. Read the full case study, Leading Financial Service Firm Digitize Their Mailroom Using Workstream RPA
Montgomery Transport Group reduces processing time by 50% with AP automation
The problem: The company’s accounts payable processes were slow and resource heavy processing up-to 200,000 invoice pages each year and resulted in slow and labor-intensive processes. That kind of lag is bad for the customer experience and bad for team morale. The solution: An AP solution leveraged intelligent capture, extraction intelligence and workflows; all invoices, no matter where they originate in the group, or what format they take, are captured by this system. These captured savings resulted in a 50% reduction in invoice processing time and the freeing up of 2.5 Full Time Employees who are free to focus on higher value activities. Read the AP automation success story.