Many companies believe that generative AI models, like ChatGPT, are a one-size-fits-all solution for all business processes. While these models excel in language processing and generating conversational responses, they often fall short when it comes to structured tasks that require precision and cost efficiency.
Generative AI models are undeniably powerful; however, their true value emerges when paired with the valuable knowledge embedded in an organisation’s rich data This is where data orchestration plays a vital role. Data orchestration involves managing data-related tasks such as collecting data, performing quality checks, transferring data across systems and automating workflows. A key example is converting unstructured data into a standardised format for AI processing, such as transforming unstructured data into electronic invoices. This step can be the critical factor that determines the success or failure of Generative AI, especially with fragmented databases and disparate IT systems.
As organisations work to harness the power of AI, particularly generative AI, they often encounter a gap between its potential and the realisation of tangible business value. After all, the quality of AI outputs depends on the quality of the input data. While generalised AI models are excellent for broad, high-level tasks, they struggle with the precision and efficiency required for specialized processes—invoice coding being a prime example.
For example,
Proactis Rego Cai uses cutting-edge technology to revolutionise purchase order and invoice coding. Engineered to enhance the AP Automation process through improved operational efficiency and accuracy, it leverages predictive AI and machine learning to elevate organisations from manual, error-prone coding in their order and invoice processes, to a more sophisticated, efficient and accurate AI-driven approach. This helps decrease the time spent manually coding transactions, reducing the risk of human error, improving the accuracy of first-time coding​ and financial data, and reducing cumbersome downstream adjustment of codes. And, thanks to advanced machine learning, Rego Cai will continue to learn, once implemented, driving further accuracy of coding and improved data quality for reporting.
As Procurement and Finance professionals, it's understandable to feel uncertain about the growing presence of AI in the workplace. While discussions about automation often focus on job displacement, the reality is much more promising – creating new opportunities to boost strategic value and improve efficiency. Instead of fearing AI, employees should see it as a valuable ally – a tool meant to enhance their skills and simplify their work.