How to Automate Business Processes – Complete Guide

Who is this article for?

• Companies looking to digitally transform their operations and streamline processes
• Businesses seeking to boost efficiency, reduce costs and improve employee satisfaction
• C-level executives (CEO, CIO, COO)
• Department heads (HR, Finance, IT, Operations)
• Business analysts and process improvement professionals

Automating business processes is critical for organizations aiming to stay competitive in today’s rapidly evolving digital landscape. By leveraging the power of technology to streamline workflows, companies can unlock new levels of efficiency, agility and innovation.

What is business process automation?

Business process automation (BPA) is the use of technology to automate complex business processes. It involves designing, executing, and automating workflows to minimize manual effort, reduce errors, and boost overall efficiency. BPA leverages tools like robotic process automation (RPA), artificial intelligence (AI), and business process management software (BPMS) to streamline operations.
The goal of BPA is to simplify and optimize processes, allowing employees to focus on higher-value tasks that require creativity, critical thinking, and human judgment. By automating repetitive, rule-based tasks, businesses can improve productivity, cut costs, and enhance customer satisfaction.

Why automate business processes?

There are several compelling reasons to automate your business processes:

1. Increased efficiency: Automating manual, time-consuming tasks can significantly boost productivity and reduce cycle times (Paschek et al., 2017).
2. Cost savings: BPA helps cut operational costs by minimizing manual effort, reducing errors, and optimizing resource allocation (Gupta, 2019).
3. Improved accuracy: Automated processes are less prone to human error, ensuring consistent and reliable outcomes (Alotaibi, 2020).
4. Enhanced compliance: BPA enables better tracking and monitoring of processes, making it easier to adhere to regulatory requirements and industry standards (Marrella, 2018).
5. Increased agility: Automated processes can be quickly adapted to changing business needs, allowing organizations to respond swiftly to market dynamics and customer demands (García‐Bañuelos et al., 2014).
6. Better employee satisfaction: By relieving employees of mundane, repetitive tasks, BPA allows them to focus on more engaging and value-added work, leading to higher job satisfaction and motivation.

Which business processes should you automate?

Not all business processes are suitable for automation. Ideal candidates for BPA typically have the following characteristics:

• High volume and repetitive
• Rule-based and predictable
• Time-sensitive and require quick turnaround
• Prone to human error
• Involve multiple systems or departments

Some common examples of processes that can be automated include:

• Employee onboarding and offboarding
• Expense reporting and approval
• Invoice processing and accounts payable
• Customer service and support
• Sales order processing and fulfillment
• Data entry and validation

How to automate your business processes

Implementing BPA involves several key steps:

1. Identify and prioritize processes: Conduct a thorough analysis of your business processes to identify automation opportunities. Prioritize based on impact, feasibility, and alignment with strategic goals.
2. Map and optimize processes: Create detailed process maps to visualize workflows, identify bottlenecks, and streamline processes before automation (Chambers et al., 2020).
3. Choose the right automation tools: Select BPA tools that best fit your organization’s needs, considering factors like scalability, integration capabilities, and user-friendliness. Tallyfy’s if-this-then-that rules make it easy to set up automated workflows without coding.
4. Implement and test: Develop and deploy automated processes incrementally, testing thoroughly to ensure smooth operation and identify any issues.
5. Monitor and optimize: Continuously monitor automated processes using real-time tracking to measure performance, identify improvement opportunities, and make data-driven optimizations.

Risks and challenges of business process automation

While BPA offers numerous benefits, there are also some risks and challenges to consider:

• Overreliance on technology and potential for system failures
• Resistance to change from employees and the need for effective change management
• Initial investment costs and the need to demonstrate ROI
• Cybersecurity risks and the need for robust data protection measures
• Difficulty in automating complex, non-standardized processes

How is AI Transforming Business Process Automation?

Artificial intelligence (AI) and machine learning (ML) are revolutionizing the way businesses automate their processes. Traditional business process management (BPM) relied on manually modeling processes, which was time-consuming and prone to errors. But AI is enabling a new level of automation and optimization for BPM.

One key way AI is being applied is for automated process discovery. Rather than having humans manually map out processes, AI algorithms can analyze data from IT systems and automatically generate process models. For example, researchers have developed techniques to automatically extract processes from unstructured data sources like emails and chats (Chambers et al., 2020). This greatly accelerates process discovery and ensures models reflect the real process execution.

AI is also being used to automatically analyze process models to identify optimization opportunities. By simulating process execution and analyzing bottlenecks and inefficiencies, AI can recommend changes to streamline processes (Marrella, 2018). Some tools even use AI planning techniques to automatically synthesize new process variants to meet changing requirements.

What are the Benefits of AI-Powered Business Process Automation?

Adopting AI and ML technologies for BPM delivers significant benefits for organizations:

• Accelerated process improvement: AI enables faster discovery, modeling and optimization of processes compared to manual approaches. This allows businesses to continuously improve efficiency.
• Increased process flexibility: AI-based process management solutions can automatically adapt to changing conditions and generate new process variants on-the-fly (Paschek et al., 2017). This provides the agility needed in today’s dynamic business environment.
• Better process insights: By analyzing large volumes of process data, AI provides visibility into how processes are really being executed. This helps identify hidden inefficiencies and compliance issues.
• Reduced costs: Automating process management with AI lowers the cost and effort of process modeling, simulation and improvement. It also minimizes costs stemming from inefficient and non-compliant processes.

What Does the Future Hold for AI-Driven Business Processes?

As AI and ML technologies mature, we can expect to see even more advanced automation capabilities for BPM. Some potential future developments include:

• Fully autonomous business processes that can self-optimize with minimal human involvement
• Predictive process monitoring that proactively identifies potential issues and automatically takes corrective action
• Intelligent resource allocation that optimizes utilization of both human and IT resources across processes
• Seamless integration of AI-powered BPM with robotic process automation (RPA) and other automation technologies

By harnessing AI to automate the discovery, modeling and optimization of business processes, organizations will be able to achieve unprecedented levels of efficiency, agility and insight. While there is still much to be done to realize the full potential of AI for BPM, the transformative impact is already being felt across industries (Alotaibi, 2020).

Tallyfy Tango – A cheerful and alternative take

Meet Tina and Tom, two ambitious entrepreneurs with a knack for efficiency and a love for all things automated. One sunny afternoon, they found themselves engaged in a lively discussion about their favorite topic: streamlining business processes.

Tina: “Hey Tom, I’ve been thinking about how we can automate business processes to save time and boost productivity. Any ideas?”

Tom: “Absolutely! I’ve been exploring some fantastic workflow automation tools lately. It’s like having a team of tireless, super-efficient robots working for us 24/7!”

Tina: “Robots? That sounds both exciting and a little bit scary. Tell me more!”

Tom: “Well, not actual robots, but software that can handle repetitive tasks and complex workflows without breaking a sweat. Imagine having your own digital assistant that never gets tired, never complains, and always follows the rules.”

Tina: “That sounds like a dream come true! But wait, won’t that make our jobs obsolete?”

Tom: “Not at all! By automating the mundane stuff, we’ll have more time to focus on the creative and strategic aspects of our business. It’s like having a trusty sidekick that handles the grunt work while we save the day with our brilliant ideas!”

Tina: “I love it! So, where do we start? I’m ready to dive into this automation adventure with you!”

Tom: “Let’s start by identifying the processes that are slowing us down and causing headaches. Then, we’ll find the perfect automation tools to transform our business into a well-oiled, efficiency machine!”

And with that, Tina and Tom embarked on their quest to automate business processes, one workflow at a time, proving that even the most tedious tasks can be conquered with a smile and a little bit of digital magic.

References and Editorial Perspectives

Paschek, D., Luminosu, C., T., & Drăghici, A. (2017). Automated Business Process Management – in Times of Digital Transformation Using Machine Learning or Artificial Intelligence. MATEC Web of Conferences, 121, 04007 – 04007. https://doi.org/10.1051/matecconf/201712104007

Summary of this study

This study explores how digital transformation impacts Business Process Management (BPM) when using machine learning or artificial intelligence techniques. It aims to provide recommendations for optimizing and managing processes via these technologies, helping companies decide which tools are best for automating BPM in the face of changing business conditions and customer expectations.

Editor perspectives

At Tallyfy, we’re always interested in how emerging technologies like AI and machine learning can help streamline and automate business processes. This study provides valuable insights into leveraging these tools for more adaptive, optimized BPM in the digital age.

Chambers, A., Stringfellow, A., M., Luo, B., B., Underwood, S., J., Allard, T., Johnston, I., A., Brockman, S., Shing, L., Wollaber, A., B., & VanDam, C. (2020). Automated Business Process Discovery From Unstructured Natural-Language Documents. Lecture Notes in Business Information Processing, null, 232 – 243. https://doi.org/10.1007/978-3-030-66498-5_18

Summary of this study

This research presents an algorithm for automatically extracting business processes from unstructured natural language documents, such as emails and chat logs. By using action and topic analysis to generate event logs, from which process models can be mined, it enables process discovery even when dedicated process management systems are not in place.

Editor perspectives

Uncovering business processes that occur outside of formal systems is a challenge many organizations face. We find this automated approach to mining processes from unstructured data very promising for gaining a more complete picture of how work actually gets done.

Alotaibi, Y. (2020). Automated Business Process Modelling for Analyzing Sustainable System Requirements Engineering. , null, null – null. https://doi.org/10.1109/icim49319.2020.244690

Summary of this study

This paper proposes a new automatic modeling methodology to derive system requirements from business goal models. It translates business goals into UML diagrams to explore detailed process information, generates XMI formats to examine and map the data, and converts the diagrams to clarify system requirements for developers – helping to align business and IT.

Editor perspectives

Bridging the gap between business goals and system requirements is key for successful process automation. This methodology shows real potential for helping developers implement systems that truly meet organizational needs by providing clarity around process goals.

Marrella, A. (2018). Automated Planning for Business Process Management. Journal on Data Semantics, 8, 79 – 98. https://doi.org/10.1007/s13740-018-0096-0

Summary of this study

This paper discusses how automated planning techniques from the field of Artificial Intelligence can enable new levels of automation and flexibility in Business Process Management. It proposes a methodology for encoding BPM problems as planning problems and outlines the steps to integrate planning technology into BPM environments.

Editor perspectives

Automated planning has great potential to make business processes more adaptive and autonomous. At Tallyfy, we’re excited by the prospect of leveraging AI planning techniques to provide more intelligent process management capabilities for our users.

Gupta, R. (2019). Automating Business Processes. Apress eBooks, null, 145 – 188. https://doi.org/10.1007/978-1-4842-5479-0_6

Summary of this study

This book chapter covers various tools and techniques for automating and improving business processes, including record types, lookup filters, formula fields, rollup summary fields, and validation rules. It provides real-life examples to illustrate the application of these tools.

Editor perspectives

Having the right tools in your process automation toolbox is crucial. This chapter highlights some valuable techniques we leverage at Tallyfy, like data validation and rollup fields, to help our customers streamline their processes and enforce business rules.

García‐Bañuelos, L., Dumas, M., Rosa, M., L., Weerdt, J., D., & Ekanayake, C., C. (2014). Controlled Automated Discovery of Collections of Business Process Models. Information Systems, 46, 85 – 101. https://doi.org/10.1016/j.is.2014.04.006

Summary of this study

This research presents a divide-and-conquer process discovery technique that extracts collections of process models from logs in a controlled manner. By splitting logs into variants and hierarchically using subprocess extraction, it produces smaller model collections with user-controlled complexity and fitness compared to existing trace clustering techniques.

Editor perspectives

Discovering clean, understandable process models from messy real-world logs is a major challenge in process mining. We appreciate this study’s novel approach to extracting collections of models in a more targeted, user-guided way to avoid the pitfalls of one-size-fits-all techniques.

Glossary of terms

Business Process Management (BPM)

The discipline of modeling, automating, executing, monitoring and optimizing business processes to increase efficiency and achieve organizational goals.

Process Mining

A set of techniques used to analyze business processes based on event logs. The goal is to extract process-related information from these logs to discover, monitor and improve processes.

Automated Planning

An area of artificial intelligence that involves the realization of strategies or action sequences to achieve a desired goal state from an initial state, often using techniques like heuristic search and dynamic programming.

Process Model

An abstract representation of a business process, consisting of a set of activity models and execution constraints between them. It defines how a process should or does operate.

Event Log

A collection of events that occurred during the execution of a business process, with each event referring to a case, an activity, and a point in time. Event logs serve as the input for process mining techniques.