Failed Automation Projects? It’s Not the Tools - It’s the Data

Enterprises often face challenges in scaling automation due to the prevalence of unstructured data, which accounts for about 80% of all enterprise data, making it difficult for tools like Robotic Process Automation (RPA) and AI agents to function effectively. Traditional methods such as OCR, ICR, and ETL struggle to convert unstructured data into a structured form due to limitations in accuracy and adaptability, resulting in a reliance on manual interventions. While large language models (LLMs) offer some potential, they are not a complete solution because they are not specifically designed for parsing vast amounts of enterprise data with the required accuracy and integration. The shift towards fine-tuned, purpose-built LLMs for data extraction tasks is proving more successful, as these models can better interpret complex documents and output structured data, reducing the need for human intervention in automation processes. Successful real-world applications, such as those by companies like Asian Paints and SaltPay, illustrate the benefits of using AI-driven data extraction to enhance efficiency and accuracy in processing unstructured data. Ultimately, the key to achieving full automation lies in establishing clean, structured data pipelines, which serve as the foundation for effective use of automation technologies.