Many industries rely on control processes to ensure operational accuracy, maintain quality, and comply with regulations. Common examples of these processes include deviation control, quality control, compliance checks, fraud detection, and documentation control. These checks often happen at different stages, such as pre-controls, post-controls, or through mapping workflows. Traditionally, these processes have been done manually, which can be time-consuming and prone to errors.

Language models offer a new way to automate control processes without needing to specify or code every detail explicitly. Instead of relying on predefined rules, language models work by identifying patterns. This makes them effective at detecting deviations or irregularities on their own. Specialized versions of these models can be fine-tuned to focus on specific tasks, such as fraud detection or anomaly identification, making them powerful tools for modern automation.

To automate control processes using language models, it’s helpful to take a step-by-step approach. First, identify what needs to be controlled, what data is required, and where this data resides in systems and processes. This involves close collaboration with domain experts such as lawyers, engineers, or healthcare professionals, depending on the field. It’s important to focus on areas with high potential for improvement, where automation can have the greatest impact.

Next, determine which control steps and processes are suitable for automation. Processes where there are large data volumes, significant manual effort, or readily available data are often good candidates. Once areas for automation are identified, the next step is to test with a proof of concept. Starting with simple examples in a secure sandbox environment helps validate the model’s capabilities. Testing different language models is essential to finding the best fit for specific needs.

If the proof of concept shows promise, the next step is to run a limited pilot program. A subset of real-world data can be used to experiment with automated controls while comparing different approaches. The results should be carefully analyzed to assess whether automation delivers measurable improvements. Pilots should function as separate processes to avoid disrupting ongoing workflows while testing scalability and reliability.

When automated controls prove valuable in pilot testing, the final step is scaling up for full production. Successful solutions can be integrated into live systems to streamline workflows and handle larger data volumes. Monitoring and refinement are critical during this stage to ensure continued effectiveness and adaptability.

While automating control processes offers significant advantages, practical challenges need to be addressed. Collaboration with subject matter experts ensures that automation captures all critical requirements. Reliable, accurate datasets are key to achieving good results. Additionally, building trust among stakeholders is crucial to gaining buy-in and ensuring that automated controls are accepted. Finally, successful implementation relies on starting small, testing thoroughly, and scaling gradually.

The potential for automating control processes with language models is immense. By reducing manual workload and improving accuracy, organizations can increase efficiency and build smarter workflows. Starting with smaller tests and scaling gradually provides a clear path to unlocking these benefits while maintaining quality and compliance.