Guide to Business Process Automation: Types, Use Cases & Best Practices

ByPaul Christiano Last Update on

Business process automation (BPA) is one of the most impactful applications of technology for improving how companies operate. This guide provides an in-depth look at what business process automation is, types of automation, common use cases, tips for successful implementation, and metrics to gauge ROI.

Whether you‘re new to business process automation or looking to get started with implementing it in your organization, this guide aims to give you a full understanding of how to leverage it to save time and money.

What is Business Process Automation?

Business process automation refers to using technology like artificial intelligence (AI), robotic process automation (RPA), and workflow management to automate routine, repetitive tasks previously done manually.

The goal is to free up employee time from mundane work so they can focus on higher value strategic initiatives. Automation increases efficiency, reduces costs, and improves accuracy by eliminating human errors.

According to Grand View Research, the global BPA market is expected to reach $19.6 billion by 2030. The rapid growth demonstrates how core BPA is becoming for streamlining operations.

Types of Business Process Automation

There are three main types of business process automation:

1. Robotic Process Automation (RPA)

RPA uses software bots to automate simple, repetitive tasks just as human workers would do. RPA bots interact with software user interfaces to complete tasks like:

  • Data entry or data transfer between systems
  • Extracting structured data from documents
  • Web scraping
  • Filling in forms
  • Copying and pasting
  • Reading and writing to databases

RPA is best suited for high volume, rules-based tasks with little variation or need for decision making. It‘s commonly used in finance, IT, operations, and HR.

According to UI Path, over 70% of organizations have adopted RPA, with top drivers being increased efficiency, improved compliance, and cost savings.

2. Intelligent Process Automation

Intelligent process automation incorporates artificial intelligence and machine learning to automate more complex processes. It goes beyond rigid rule-based RPA bots to handle dynamic unstructured data.

Intelligent automation provides capabilities like:

  • Natural language processing to interpret text data like customer inquiries
  • Machine learning to make predictions and recommendations
  • Computer vision to classify images and extract text from documents
  • Speech recognition for conversational interfaces

It‘s well suited for processes involving substantial manual judgement, analysis, or subjective decision making. Common use cases are in customer service, legal/contract work, and finance.

According to IDC, over 30% of medium to large organizations are actively scaling intelligent automation initiatives to transform white collar work.

3. Workflow Automation

Workflow automation focuses on streamlining cross-functional business processes from end-to-end. It connects disparate systems, data, employees involved according to a defined workflow.

Workflow automation provides:

  • Modeling and visualization of processes
  • Rules-based routing of work
  • Dashboards to track and manage tasks
  • Integration bridges between departments and systems

It‘s tailored towards automating collaborative human-involved processes like onboarding, approvals, and case management.

According to Forrester, the workflow automation market will reach nearly $17 billion by 2023 as companies coordinate work across siloed systems.

Why Automate Business Processes?

Automating manual business processes provides major benefits:

Increased Efficiency

By reducing repetitive human tasks, process automation enables employees to focus their time on higher value strategic work.

According to a McKinsey study, 45% of current work activities can be automated which would boost productivity by 30-50%.

Automation also executes routine tasks significantly faster than human workers. One study by IBM found automation led to:

  • 50% reduction in cycle times
  • 35% improvement in throughput
  • 30% increase in staff productivity

Taken together, dramatic efficiency gains are possible from freeing staff from repetitive tasks and speeding up process execution.

Cost Reduction

Automating a process requires some upfront software implementation costs. However over the long run it results in lower ongoing operating costs.

According to KPMG, RPA reduces the cost of process delivery by 25-50% on average.

One financial services company experienced over $2 million in cost savings from finance process automation through reduced labor.

Improved Quality

Humans completing repetitive manual tasks are prone to boredom and fatigue which increases errors. Automation minimizes this by performing tasks consistently without variation.

Boston Consulting Group found that process automation reduces human generated errors by 15-25%.

The structured, predictable nature of automated tasks also facilitates detailed auditing for compliance purposes.

Enhanced Analytics

End-to-end process automation generates rich data on task handling, bottlenecks, and outcomes. This provides visibility unfeasible with disjointed manual processes.

Automation data enables continuous process improvement by pinpointing inefficiencies. It also powers predictive analytics to model likely outcomes.

Common Use Cases for Business Process Automation

Let‘s explore some of the top use cases where implementing automation delivers major value:

Human Resources

HR departments deal with many repetitive tasks and manual data entry including:

  • New hire onboarding – paperwork, system access provisioning, training enrollments
  • Employee offboarding – access revocation, document collection, equipment return
  • Applicant tracking – screening resumes, scheduling interviews, email outreach
  • Payroll processing – timekeeping, tax calculations, paycheck issuance
  • Performance reviews – templates, workflows, 360 feedback collection

According to IBM, automating HR process can reduce costs by 10-30%.

Information Technology

IT teams handle many routine tasks ripe for automation:

  • User provisioning/deprovisioning – managing identities as employees join and leave
  • Incident remediation – diagnosing and resolving support tickets for common issues
  • System monitoring – scanning systems and applications for availability and performance
  • Build and release management – continuous integration, testing, and deployment of code changes

Per Gartner, 60% of IT service desk requests can be automated to boost productivity and user satisfaction.

Customer Service

Customer inquiries often deal with routine questions that intelligent automation can handle:

  • FAQ searches – quickly querying knowledge bases for common issues
  • Purchase order status – looking up order details and shipping tracking
  • Address change – updating customer address profiles
  • Payment inquiries – providing card details, payment confirmation, receipts

IBM estimates that automation can resolve 80% of repetitive customer service requests.

Financial Services

Many accounting and finance tasks involve high volume repetitive work:

  • Accounts payable – invoice receipt, approvals, GL coding, payment
  • Accounts receivable – issuing customer invoices, tracking payments
  • Reporting – pulling data, compiling reports, standard formatting
  • Reconciliations – matching transactions, looking up differences

According to Ernst & Young, over 50% of finance roles have activities suitable for automation.

Legal Services

Law firms and legal departments have applied automation to tasks like:

  • Contract review – extracting key terms, detecting non-standard clauses
  • Lease abstraction – pulling relevant details from lengthy documents
  • Case law research – rapidly scanning records to find precedent setting cases
  • Document discovery – machine learning to classify records for relevance to cases

Per Deloitte, machine learning techniques can reduce document review time up to 75%.

Keys for Successful Implementation

When rolling out business process automation, keep these best practices in mind:

  • Start small – Pick a straightforward 1-2 processes to pilot automation on before wider rollout. Learn lessons on smaller scale first.
  • Measure metrics – Identify KPIs early that align to business goals like cost reduction, faster process times, or improved quality. Continuously track metrics before and after automation to quantify benefits.
  • Get user feedback – Involve employees that interface with the processes as early as possible. Address their concerns and ideas before going live. Strong user buy-in creates smoother adoption.
  • Take an iterative approach – View the initial automation as version 1.0 then incrementally improve it over time as you have more data on bottlenecks and optimization areas.
  • Double down on exceptions – While automation handles high volume repetitive tasks well, it will not cover 100% of cases, especially exceptions. Put extra focus on designing efficient human exception handling for edge cases.

Start Your Automation Journey

Business process automation presents immense potential to save money and free up employees for more impactful work. From routine back office tasks to customer facing processes, a wide range of use cases exist across industries.

This guide provided a comprehensive overview of types of automation, use cases, tips for implementation, and measurable benefits.

Hopefully it gives you a clear understanding of automation possibilities within your organization. Feel free to reach out if you need any help identifying automation opportunities or selecting the right automation platform to produce real business results.

I am Paul Christiano, a fervent explorer at the intersection of artificial intelligence, machine learning, and their broader implications for society. Renowned as a leading figure in AI safety research, my passion lies in ensuring that the exponential powers of AI are harnessed for the greater good. Throughout my career, I've grappled with the challenges of aligning machine learning systems with human ethics and values. My work is driven by a belief that as AI becomes an even more integral part of our world, it's imperative to build systems that are transparent, trustworthy, and beneficial. I'm honored to be a part of the global effort to guide AI towards a future that prioritizes safety and the betterment of humanity.

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