Traditional approaches to business process automation focus on improving efficiency and reducing costs by automating repetitive, rule-based tasks in a predefined (designed) process. BPM-based processes are very effective in structured environments, but can be challenging to apply for dynamic or unstructured processes. As AI technologies evolve, automation approaches that combine AI and process automation are worth exploring to overcome the limitations of traditional systems.
Overview of traditional automation approaches
“Traditional” approaches to automation in business processes (that is, automation without the application of artificial intelligence) have been widely implemented in business. Automation is intended to increase efficiency, reduce costs, and streamline operations in ways that are better than what human-centered, manual processes can do. These methods focus on automating repetitive, rule-based tasks - to minimize the opportunity for human error, faster than humans can do.
Three “traditional” automation approaches include:
- Automation through business rules
- Workflow automation
- Robotic process automation (RPA)
Automation using business rules
Automation using business rules involves automating decisions and processes based on predefined logic or conditions, typically expressed as "if-then" statements. It ensures that repetitive tasks and decisions follow consistent patterns without human intervention, based on these clear, structured rules. This approach is used in BPM-based process automation platforms based on standards such as BPMN 2.0 to define these if-then statements and conditions using graphic symbols.
Workflow automation
Workflow automation uses predefined rules for the flow of tasks, data, and approvals among people and information systems. It automatically routes tasks, sends notifications, and ensures the correct sequence of steps. Process automation platforms define workflows from end to end, beginning with an initiation step and terminating when the defined sequence has completed. Again, the BPMN 2.0 standard helps define human and system tasks in a sequence using graphic symbols, and include a BPM engine that runs the application automatically, pausing for human intervention where it is predefined to require it.
Robotic process automation
RPA uses software bots to mimic human actions in interacting with digital systems, automating repetitive tasks such as data entry, form filling, and transaction processing. These bots follow predefined rules to complete tasks across multiple applications without altering existing systems.
The key challenges AI presents to traditional automation
The integration of artificial intelligence (AI) into business processes is transforming how business and IT teams approach automation. While AI promises advantages over traditional automation methods, the adoption of AI in business process automation also introduces challenges.
AI presents a significant departure from traditional rule-based automation, offering more flexibility and intelligence, but at the cost of increased complexity, hidden costs, and ethical and governance risks. Businesses looking to adopt AI-driven automation must carefully weigh these challenges against the potential benefits to ensure smooth integration and optimal performance.
Increased complexity in business process automation with AI
- AI models are often considered “black boxes,” which makes it difficult to understand how decisions are made. This can be an issue for critical business processes.
- AI models can produce varying results depending on the data used to train them, which means that some types of AI-assists in some processes may not be consistent and repeatable.
Cost and resource usage of AI for automation
There may be both obvious and hidden costs associated with the use of AI in process automation. It’s a good idea to look closely at both.
- AI-driven automation typically requires more substantial upfront investment compared to traditional automation. This includes costs for data infrastructure, skilled personnel, and ongoing model training.
- AI implementation may require specialized expertise, such as data scientists, AI engineers, and machine learning experts - people you may not already have among your IT team.
- AI models require constant monitoring and possibly retraining to maintain performance. This continuous need for tuning is a marked departure from traditional systems, which often operate with minimal maintenance after implementation.
Governance, regulatory and ethical considerations
As AI workflow automation solutions are allowed to make more decisions in business processes, it’s important to pay attention to governance, privacy and data protection, and ethical decision-making.
- In highly regulated industries like finance or healthcare, compliance to regulations has to take precedence and needs to be taken into account in training models.
- AI systems often process sensitive data. Ensuring data protection and confidentiality while training AI models is a critical challenge.
- AI-driven automation must not lead to biased or discriminatory outcomes, particularly in areas like hiring and customer services (for example, in loan or medical decisions).
When AI is making autonomous decisions, determining accountability for errors or malfunctions may be unclear. Oversight and responsibility can’t be abrogated!
AI automation compared with traditional automation technologies
There are a few key areas where traditional automation and AI business process automation should be considered side-by-side.
- Traditional automation processes use predefined data sets which may be sensitive but can be treated confidentially. Training AI models on sensitive data requires care to ensure that data confidentiality is maintained.
- AI models require constant monitoring, tuning, and retraining to maintain performance. Business applications developed and managed with process automation software (traditional automation) generally operate with minimal maintenance after implementation, and can generally be maintained by the same IT team.
- Traditional automation provides a clear control framework, where every step is predetermined by human logic. In AI business process automation, achieving the same level of control can be difficult due to the model's dynamic decision-making process.
Intelligent process automation: opportunities for integrating AI technologies with existing automation
AI business process automation can augment decision-making, data extraction, task automation, and process monitoring, giving a step up to process automation as it is currently done, and contributing to hyperautomation for business processes. AI models can feed their insights directly into processes that are automated end-to-end with process automation software. Here are some places where AI business process automation is already proving to be useful:
- Use AI to aid decisions in workflows. Incorporate AI models into key decision points that determine the “next step” in automated workflows. AI services such as machine learning models hosted on cloud platforms like AWS or Azure can be trained to make predictions based on workflow data for intelligent process automation.
- Use AI for automated document processing. Combine AI-based optical character recognition and natural language processing tools to automatically extract data from unstructured documents such as invoices, contracts, or emails.
- Use AI-powered chatbots to start processes. Integrate AI chatbots to interact with users (customers or employees) to gather information and trigger process applications. The chatbot can ask the user questions, understand the intent, and automatically initiate a specific application.
- Use AI predictive analytics to optimize processes: AI predictive analytics are useful to monitor and predict workflow bottlenecks, such as delays in approval processes or resource shortages. Intelligent process automation can then automatically adjust workflows based on these predictions, rerouting tasks or assigning them to different users.
- Use AI for intelligent task assignment. AI can evaluate factors like workload, employee performance, and task complexity to intelligently assign tasks within traditional process workflows. With an AI model that evaluates task complexity and employee responsibilities, an assignment recommendation can be integrated into specific tasks via a REST API.
- Use AI for sentiment analysis in feedback. AI-driven sentiment analysis can be applied to customer feedback or support tickets, categorizing them based on sentiment (positive, neutral, or negative). Intelligent process automation applications can then escalate negative feedback for further attention or automate responses to positive feedback.
- Use AI for process monitoring and anomaly detection. AI can monitor ongoing processes, analyzing performance metrics to detect anomalies such as process slowdowns, system errors, or unusual behaviors. When an anomaly is detected, the process can automatically trigger remedial actions or send alerts to process managers.
- Use RPA tools automate repetitive, rule-based tasks. AI can enhance RPA by handling tasks requiring cognitive skills (e.g., understanding natural language, making predictions). Integrating AI with workflows can help improve decision-making, or add intelligence to repetitive tasks. Use an RPA tool like UiPath to automate tasks that can be done by software robots instead of humans in an end-to-end automated process.
An example of AI intelligent business process automation
Here is an example of how AI can be implemented with traditional process automation platforms like Bonita for intelligent process automation, provided by Bonitasoft partner Eviden!
Eviden has implemented a virtual conversational assistant that interacts with employees to answer HR-related questions, manage parking for employees who need to come into the office, and access company documents. In one example, an employee notifies the virtual assistant that he has learned that his wife is pregnant (congratulations to the future papa!)
Triggered by this simple announcement from the employee, the virtual assistant - integrated with multiple systems across HR - comes back with information about health insurance benefits, family leave, and other actions that the employee can take to prepare for his wife’s upcoming delivery. And as the employee needs to come physically into the office to access and sign some specific documents, the virtual assistant offers the option to find an open parking spot in the company garage and reserve it for a day that is convenient for the employee. A few clicks to activate an application that runs on the Bonita process automation platform, and the parking place is reserved, the documents are in process, and this employee has had a smooth and helpful user experience!
Concluding thoughts
AI business process automation is revolutionizing the way organizations design, execute, and optimize their business processes, providing greater agility, accuracy, and efficiency. With AI we are seeing advancement towards hyperautomation for business processes.
AI can easily automate repetitive tasks, but beyond that, it can also help manage complex and unstructured processes, such as coordinating data inputs, outputs, and collection across enterprise systems connected to business processes, verifying documents in multiple formats, and even generating BPMN diagrams from natural language. (Like this AI text-to-BPMN generator!)
The use of virtual assistants and intelligent process automation improves customer interaction and satisfaction by offering fast and accurate responses.
AI enables continuous improvement in process performance through predictive and prescriptive analytics, identifying areas for improvement before problems materialize.
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Bonitasoft offers solutions that streamline operations and empower teams to innovate and drive meaningful change. Whether you’re a developer looking for advanced tools, or a business leader aiming to optimize processes, Bonitasoft’s process automation and process intelligence products deliver the perfect balance of speed, flexibility, and insight—giving you everything you need to scale, adapt, and succeed.
Ready to take the next step? See how Bonita can revolutionize your business operations and secure your business future. Contact us to learn more leveraging AI with business process automation.
