Process automation and optimization have evolved drastically since the late 18th century, beginning with systematic methods in industrial processes that revolutionized manufacturing. A pivotal moment in this evolution came with Michael Hammer’s influential 1990 article, “Reengineering Work: Don’t Automate, Obliterate.” Hammer argued that simply automating existing processes was insufficient. Instead, he advocated for reengineering—completely redesigning processes to achieve dramatic improvements in performance. This concept has driven many organizations to fundamentally rethink their workflows and adopt more innovative approaches.
Today, we are on the verge of a new revolution that could transform traditional business processes, this time driven by Artificial Intelligence (AI). Generative AI (GenAI) has the power to revolutionize process automation. With AI, businesses can automate complex tasks, make better decisions with data-driven insights, and greatly improve efficiency and accuracy.
Generative AI can handle vast amounts of data, identify patterns, and optimize processes in real-time, making it a game-changer for industries looking to innovate and stay competitive. The integration of AI into process automation not only builds on centuries of progress but also opens new possibilities for innovation and efficiency that were previously unimaginable.
In this blog, we’ll explore how AI can drive process innovation. We’ll cover its benefits, provide industry-specific examples, and outline steps to get started. By understanding AI’s history and potential, organizations can use this technology to achieve greater efficiency, accuracy, and scalability in their workflows.
Defining Process Automation
Process automation involves using technology to perform repetitive tasks or processes in a business where manual effort can be replaced. This can include anything from data entry and invoice processing to customer service and supply chain management. The goal is to improve efficiency, reduce errors, and free up human workers to focus on more strategic tasks.
The Benefits of AI for Process Automation
Integrating AI into process automation brings several advantages:
- Increased Efficiency: AI can handle large volumes of tasks quickly and accurately, significantly speeding up processes.
- Cost Savings: Automating repetitive tasks reduces the need for manual labor, leading to cost savings.
- Improved Accuracy: AI systems can reduce errors that are common in manual processes.
- Scalability: AI can easily scale to handle increased workloads without a proportional increase in costs, especially when combined with robotic process automation.
- Enhanced Decision Making: AI can analyze data and provide insights that help in making informed decisions.
Industry-Specific Use Cases
AI and GenAI are particularly well-suited for knowledge work, such as summarizing large volumes of data and identifying trends. Here are some industry-specific use cases:
- Healthcare: Automating patient data entry, summarizing medical records, and assisting in diagnostic processes.
- Finance: Streamlining fraud detection, automating report generation, and summarizing financial data for analysis.
- Legal: Automating contract review, summarizing legal documents, and conducting legal research.
- Customer Service: Using AI chatbots to handle customer inquiries, summarizing customer feedback, and automating ticketing systems.
- Marketing: Generating content for marketing campaigns, analyzing market trends, and automating customer segmentation.
Human-in-the-Loop Workflow Processes
A workflow process is particularly well-suited for a human-in-the-loop (HITL) framework when it involves tasks that require a combination of automation and human judgment. In such a framework, Generative AI can handle repetitive and data-intensive tasks, while humans provide oversight, make critical decisions, handle exceptions, and ultimately make workflow approvals.
For example, in a customer service workflow, GenAI can automate the initial response to customer inquiries, summarize previous interactions, and suggest possible solutions. However, complex or sensitive issues can be escalated to human agents who can provide personalized assistance and make nuanced decisions. This approach ensures efficiency and accuracy while maintaining a high level of service quality. Such solutions are well suited for stepwise workflow automation.
By integrating a HITL framework, organizations can leverage the strengths of both AI and human expertise, leading to more robust and reliable workflow processes.
Process Automation Tools
Using a Business Process Management System (BPMS) tool can be highly beneficial for implementing AI-driven process automation. BPMS tools help in identifying and documenting existing processes, which is crucial for understanding where AI can be applied effectively. They often come with built-in capabilities to integrate with various systems and APIs, making it easier to incorporate data and AI into your existing workflows. Additionally, BPMS tools provide a platform to design, execute, and monitor automated processes, ensuring that AI-driven tasks are integrated into your business operations and systems. They offer dashboards and analytics to monitor the performance of automated processes, helping you make data-driven decisions and improvements.
Additionally, BPMS vendors are building GenAI capabilities into their software. For example, Appian’s GenAI skills (prebuilt capabilities) include text classification, document summarization, data extraction, and sensitive information detection. GenAI models can generate new content, summarize complex information, and identify patterns in data. Appian’s platform allows users to integrate these AI skills into their workflow models, enhancing efficiency and decision-making.
However, if you choose not to use a BPMS tool, you can still implement AI-driven process automation by building custom solutions tailored to your specific needs using AI frameworks and APIs. Another option is to use Integration Platform as a Services (iPaaS) to connect AI models with your existing systems to automate end-to-end processes. Additionally, you can leverage software embedded AI tools and platforms, for example in ERP and CRM systems with specific functionalities, such as natural language processing or machine learning, to automate tasks. While a BPMS tool can streamline the implementation of AI-driven process automation by providing a comprehensive platform for process management, it is not strictly necessary. Depending on your organization’s needs and resources, you can choose the approach that best fits your requirements.
Implementation Steps
To get started with process automating using AI consider the following steps:
- Identify Processes for Automation: Start by identifying which processes in your organization can benefit the most from automation.
- Gather Data: Collect the data needed to train your AI models. This might involve cleaning and organizing existing data or generating new data.
- Choose the Right Tools: Select the AI tools and platforms that best meet your needs. This might include GenAI models, machine learning frameworks, commercial software embedded AI, potentially BPMS, and integration tools like iPaaS and LangChain.
- Develop and Train Models: Develop your AI models and train them using your data. This step might include industry domain knowledge in the AI model. Retrieval-Augmented Generation (RAG) is also an option to interject corporate data with the GenAI model, enhancing its capabilities with specific organizational knowledge.
- Integrate with Existing Systems: Use APIs and other integration tools to connect your AI models with your existing systems and facilitate automation with generative AI.
- Test and Iterate: Test your AI system thoroughly to ensure it works as expected. Make any necessary adjustments and continue to iterate on your models to improve performance.
- Monitor and Maintain: Once your AI system is in production, monitor its performance and make any necessary updates to keep it running smoothly.
Getting Started
Getting started with Generative AI for process automation can seem daunting, but by following these steps, you can quickly begin to see the benefits. Start by identifying the processes that will benefit most from automation and gather the necessary data. Choose the right tools and develop your AI models, integrating them with your existing systems. Test thoroughly and iterate to improve performance, and ensure you have a plan for ongoing monitoring and maintenance.
To identify the benefits, track key performance indicators (KPIs) such as efficiency gains, cost savings, error reduction, and scalability improvements. By continuously monitoring these metrics, you can measure the impact of AI and RPA on your processes and make data-driven decisions to optimize further.
By leveraging the power of Generative AI and a human-in-the-loop framework, you can transform your workflow processes, achieving greater efficiency, accuracy, and scalability.
