What is process automation?
Process automation is the use of technology to replace manual, repetitive tasks done by humans. It is normally applied to processes that require lots of employee time, but little thought. Types of process automation include business process automation, intelligent process automation and workflow automation.
Early process automation looked like Henry Ford’s famous assembly line and used massive machinery. Today’s process automation involves electronic workflows, document routing, intelligent capture and even content analysis and decision-making. Modern process automation technology uses software, bots and — most recently — artificial intelligence (AI) to improve business efficiency.
Read more: The Forrester Wave™: Digital Process Automation Software, Q4 2023 report
Why is process automation relevant now?
The question is no longer: "Is process efficiency technology worth the investment?"
It's: "Can an organization keep up without it?"
“Due to economic conditions, process optimization as a means to reduce cost or improve efficiencies has become a key focus for a large number of enterprises,” Gartner wrote in its Market Guide for Business Process Automation Tools.
The same report goes on to say: “Forty-two percent of the respondents to the 2022 Gartner Next-Generation Cost Savings in General and Administrative Function Survey indicated that cost optimization via improved resources automation is a top-three technique used successfully by IT leaders for 2023.”
Benefits of process automation
Process automation benefits organizations by driving speed and accuracy. It can also create a better work environment and customer service atmosphere.
More benefits include:
- Optimized processes: When set up correctly, process automation provides an efficient and well-organized workflow. In manual processes, employees often create quick fixes to keep the workflow moving. These Band-Aids typically end up creating process challenges as they scale.
- Reduction of manual effort: If your workflow requires someone to read documents and enter data, process automation will save you time. It is possible AI can even make decisions about the next steps.
- Boosted productivity and innovation: The automation of rote tasks enables your team to focus on more business-critical tasks. This could include innovating or providing high-touch value to customers.
- Improved accuracy: Manual processes often introduce errors, and even small errors in data can prove costly. Automated processes follow rules and flag anything that doesn’t fit parameters. When you pair your process automation solution with AI, the AI will learn to problem-solve these issues itself.
- Reduced costs: A fast, accurate and fine-tuned process eliminates inefficiencies. This leads to cost savings that you can reinvest in priority areas.
- Stricter compliance: Standardized, consistently performed processes mean better compliance. A solution that provides automated audit trails saves time and helps prove compliance.
- Improved scalability: For enterprises with seasonal peaks or those experiencing rapid periods of growth, automated processes can easily compensate for the increased workload.
- Great entry point for bringing AI into your workplace: From intelligent capture to the machine learning that helps optimize your processes and outcomes, AI allows you to continually improve while keeping all the benefits of automation.
Process automation examples
Process automation can be used in essentially any industry and across many departments, and the application of the technology is expansive. Here are just a few process automation use cases to give you an idea of how organizations make use of it:
Healthcare
Large healthcare organizations receive millions of patient documents and medical records a year. These files need to be quickly classified to enable optimal care decisions.
For one major provider, automating the medical records classification process with intelligent capture, extraction and validation technology changed lives, for both patients and providers. Data was accurately brought into the system early, then moved by bots to the correct places and put in front of the right people. Consequently, records became accessible faster than ever possible when processed manually.
Read the case study: How an academic health system decreased turnaround times
Financial services
Bots, like those used in robotic process automation (RPA), can easily identify data on mortgage lending documentation, replicate it to the appropriate places and advance it in the loan origination system, underwriting and any other systems that require it.
Read more: Hyland expert on the power of intelligent automation in banking
Government
A county auditor office had six staffers manually complete 90,000 personal property tax returns. They did this for nearly half the year, every year.
They then brought in a system-agnostic RPA system. This automated the repetitive, tedious task of entering tax return data into their system. Using conditional business rules, RPA even helped flag erroneous or fraudulent data that was then sent to a human auditor for review. In year one of this system, the county processed one-third of its returns with zero human touchpoints.
Read the case study: Horry County’s process automation success story
Education
The University of Texas at Dallas (UTD), a fast-growing public university and Research I institution, implemented process automation across 25 departments. The initiative tallied a lot of wins, but perhaps the most meaningful was its admissions success. By automating the application process, UTD eased administrative burdens and delivered not just top-tier service to college applicants, but also positively impacted decision-making.
Now UTD makes offers to best-fit students faster than nearly any other institution — a strategy that research shows factors into students’ acceptance decisions.
Read the case study: How UTD makes admission decisions less than 48 hours after receiving applications
Accounts payable department
A real-estate investment firm used a paper-based process across 50 office locations. The invoice turnaround times took up to 30 days. This content chaos was negatively impacting employees, customers and the bottom line.
In response, the firm adopted an AP process automation solution, augmented with AI and an integration to its line-of-business system JD Edwards. The firm trimmed weeks off its payment cycle. This meant employees could capture an invoice in one location in the morning and cut a check that afternoon. On top of this, data quality improved and the idea of a lost invoice was eliminated.
Read the case study: Intelligent process automation leads to $1 million in savings a year
Human resources department
A retail company's HR team found it hard to manage operations with paper-based processes and multiple offices. This changed when it onboarded a process automation component with its content services platform. The new technology digitized content, automated document retention and broke down workflow silos. Documents that used to get stuck on desks for days were now visible to the whole team and better able to keep moving.
Read the case study: HR team automates document retention
If I have seen further than others, it is by standing upon the shoulders of giants.
— Sir Isaac Newton
What are the types of process automation?
As automation solutions evolve, it’s important for organizations to remember they are standing on the shoulders of giants.
Each step in the automation process builds on the last, forming a chain of advanced technologies.
Business process automation (BPA)
Business process automation (BPA) helps unify and automate repeatable business activities and services. It improves the accuracy, efficiency, visibility and compliance of core business tasks on a day-to-day basis.
Intelligent process automation (IPA)
Intelligent process automation (IPA) revolutionizes organizational processes and eliminates users' daily robotic tasks. Instead, AI-infused bots complete that rote work. IPA speeds up work, decreases manual errors and simplifies interactions.
Intelligent document processing
Intelligent document processing (IDP) is an AI-powered digital solution that goes beyond the fixed capabilities of RPA and optical character recognition (OCR). IDP uses AI to read and understand the text and formatting within semistructured and unstructured content. This ability enables forms and documents to be processed automatically.
Machine learning (ML) technology “teaches" the IDP software how to make sense of documents, so the more it works, the smarter and more effective it becomes.
Workflow automation
Workflow automation is the process of automating manual tasks, documentation and data flows using rules and logic. A workflow itself is a sequence of tasks that must be completed in a specific order to achieve an objective.
Learn more: A guide to digital asset management workflows
Robotic process automation
RPA uses a software with bots to drive business efficiency and accuracy by automating and standardizing repeatable business tasks. It mimics predictable human behavior within a system, such as completing the clicks a worker would make in a standardized workflow).
Learn more: 50+ RPA use cases
Application integration
Direct integration between two or more applications can also be a great way to automate data-centric tasks without requiring any human touch.
Investing in artificial intelligence for growth, efficiency and competitiveness isn't a leap of faith anymore, but a strategic necessity for businesses.
— Tiago Cardoso, Principal Product Manager, Hyland
The future of process automation is tied to AI
Early generations of process automation solutions lack the capabilities of today’s AI-powered options. Simply put: Artificial intelligence changes everything.
With the maturation of AI, organizations can expand their horizons on what is automatable.
Process automation opportunities used to be limited to repeatable, programmable pathways. With AI, it can now be applied to unstructured, dynamic processes. Where it once required human review and intervention, today, accurate processing can be taught to machines. These machines can then learn to be even more human-like as they work, while still delivering higher accuracy at a greater speed.
The fusion of AI with process automation drastically increases the value and impact organizations gain from their efforts to expand automation.
Processes that depend on a historical knowledgebase — that alarming situation that happens when institutional knowledge is locked up with one person, for example — can be dramatically improved with AI. An AI automation solution can not only access and comprehend vast, intricate data from previous situations, but it can also draw insight and conclusions from that data.
“Investing in artificial intelligence for growth, efficiency and competitiveness isn't a leap of faith anymore, but a strategic necessity for businesses,” said Tiago Cardoso, principal product manager at Hyland. “Rapidly evolving AI technologies can pose significant risk to organizations that fail to adopt them. Being late to the AI game may mean not just missing out on opportunities, but could also risk businesses becoming obsolete, unable to keep pace with AI-enabled competitors.”
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Which AI is most likely to pay off, and soonest? Review this Gartner report for insight.
Intelligent process automation in your workplace
AI isn’t coming for your job, but it is coming to your workplace.
Hyland predicted years ago that the evolution of content management would be driven by AI.
“Document identification and categorization is going to be a completely automated, straight-through process,” former Hyland CEO Bill Priemer said at CommunityLIVE, the organization’s annual user event.
“Natural language searching will become a common and trusted method for retrieving exactly the files your users need," Priemer said. "Your users will be able to ask questions of the system, and the system will use generative AI to formulate answers based on information in your archived content. Your workflows will be increasingly automated as the system learns which information and what circumstances are required in order to approve, deny and route work.”
While Priemer was painting the picture of the future, much of the AI is available right now at Hyland with Hyland Intelligent Document Processing (IDP).
What is Hyland Intelligent Document Processing?
Hyland IDP is a scalable AI solution that captures large amounts of content and extracts data precisely. It provides better automation than legacy systems, minimizes errors and speeds up business cycles with the help of AI and ML.
Hyland was recently named a Leader in the IDC MarketScape for Worldwide Intelligent Document Processing (IDP) Software Assessment 2023-2024 Vendor Assessment. Hyland’s flagship IDP product features:
- Optical character recognition (OCR)
- Intelligent classification
- Intelligent automated separation
- Intelligent extraction
- Intelligent validation
- A document processing platform
- Continuous online learning for the solution, which collects user input for correction results and allows it to improve its results with every correction
Hyland IDP provides human-like intelligence to the process automation workflow. By removing the slower, less accurate human touch points in those traditional processes, Hyland IDP can improve efficiencies, accuracy and the speed of document processing.
Start your process automation journey intelligently. Chat with Hyland about IDP.
Related technologies and terms
- Low-code automation: Automation enabled by application development systems that require limited coding or development skills
- No-code automation: Automation enabled by application development systems that require zero coding or development skills
- Case management: Case management is a collaborative approach to managing cases or projects that involves multiple stakeholders and requires extensive documentation and tracking.
- Hyperautomation: A streamlined approach to automating and orchestrating business and IT processes organization-wide with more accurate, accelerated workflows. If the process can be automated, it will be automated.
- Machine learning: The process of using data algorithms to help a computer learn without direct input. It is a subfield of AI that gives computers the ability to automatically learn and improve from the data it is fed.
- Natural language processing (NLP): Another subfield of AI, NLP teaches machines to learn language much as humans learn it.
> Read more | What's the difference between AI and ML?
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