The 2024 process automation guide: Setting the right priorities

Why is process automation relevant now?

It’s no longer a question of if optimized process efficiencies are worth the investment in the necessary technology, but rather, 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, but it can also create a better work and customer service atmosphere for the people who interact with your company. More benefits include:

• Optimized processes: When onboarded according to best practices, a process automation strategy has the most optimized, refined processes to work from. This is often in contrast with manual processes, where employees often create their own workarounds or stopgaps to keep the flow of work moving. These Band-Aids typically end up creating process challenges as they scale.
• Reducton of manual effort: If any part of your workflow requires someone on your team to manually read documents and enter data, process automation will reduce that time by automating content capture, data entry and, with AI, can even make human-like decisions about what to do with it next.
• Boosted productivity and innovation: With rote tasks automated, your team can work on more expansive business priorities, whether it’s innovating or providing high-touch value to customers or other business priorities.
• Improved accuracy: Manual processes often introduce errors, and when it comes to data, even small errors can become costly. Automated processes follow your rules and flag anything that doesn’t fit your parameters. As your process automation solution increases with intelligence, the AI will even learn as it goes so it can problem-solve common hurdles.
• Reduced costs: A fast, accurate and fine-tuned process eliminates inefficiencies and leads to cost savings that can be reinvested on priorities.
• Stricter compliance: Standardized, consistently performed processes mean better compliance, and automated audit trails help to prove it.
• 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 document and medical records a year, many of which need to be quickly classified so the best care decisions can be made and prescribed. For one major provider, automating the medical records classification process with intelligent capture, extraction and validation technology changed lives, for both patients and providers. By bringing data into the system early — and accurately — then having bots flow it to the right places, for the right people, records became more accessible more quickly than ever possible when done 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 working to complete 90,000 personal property tax returns — manually — for nearly half the year, annually. By bringing in a system-agnostic RPA system, the office automated the repetitive, tedious task for 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 process automation, the county was able to process 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, went all-in with hyperautomation. It implemented process automation across 25 departments and tallied a lot of wins, but perhaps the most meaningful is 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 paper-based process, combined with a distributed workforce across 50 office locations, meant invoice turnaround times of up to 30 days for a real estate investment firm. The content chaos was negatively impacting employees, customers and the bottom line. By adopting an AP process automation solution that included AI and a key integration to its line-of-business system JD Edwards, this firm trimmed weeks off its payment cycle — allowing employees to capture an invoice in one location in the morning and cut a check that afternoon, plus improve data quality and eliminate the idea of a lost invoice.

Read the case study: Intelligent process automation leads to $1 million in savings a year

Human resources department

A retail organization’s HR department struggled to keep operations moving smoothly with its paper-based processes and multiple offices. When it onboarded a process automation component with its content services platform, things changed. In addition to digitizing content, the new technology automated document retention and broke down workflow silos. Documents that used to get stuck on desks for days at a time were now visible to the whole team and better able to keep moving.

Read the case study: HR team automates document retention

What are the types of process automation?

With automation solutions evolving and becoming not only more available, but also smarter, it’s important for organizations to remember that the idea of “standing on the shoulders of giants” applies here. Phases along the automation journey build on each other, making it a spectrum of additive technologies that are connected to what came before.

Business process automation (BPA)

Business process automation (BPA) uses software to create a foundation for unifying and automating the dynamic actions taken across 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) includes a collection of technology tools that revolutionizes organizational processes and enables users to eliminate the robotic work of their daily duties. Instead, AI-infused bots complete that rote work. IPA speeds work, decreases manual error 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, recognize and understand, as a human would, the text and formatting within semistructured and unstructured content, so forms and documents can be processed automatically. This increasingly sophisticated technology uses machine learning (ML) to “teach” the IDP software how to make sense of documents, and the software gets smarter and more effective the more it works.

Workflow automation

Workflow automation is the process of optimizing and automating manual tasks, documentation and data flows using predefined business rules and logic. A workflow itself is a sequence of tasks that must be completed in a specific order to achieve a particular objective. Workflow automation refers to the application of a specific technology to streamline and manage these tasks or processes.

Learn more: A guide to digital asset management workflows

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.

Related technologies and terms

• 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 (essentially completing the clicks a worker would make in a standardized workflow).
• 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?

The future of process automation is tied to AI

Early generations of process automation solutions lack the power of today’s AI-infused possibilities. Simply put: Artificial intelligence changes everything.

With the maturation of AI, organizations can expand their horizons on what is automatable.

While 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, which can learn to be even more human-like while also delivering higher accuracy.

To say AI revolutionizes the way organizations can drive new efficiencies is an understatement. The fusion of AI into 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 enhanced 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.”