Artificial Intelligence-Assisted Analytical Method Development

Abstract

The research examines how Artificial Intelligence (AI) can be applied to revolutionize the development of analytical methods by leveraging the weaknesses of the traditional trial-and-error method. Based on the descriptive and exploratory research design, the study is a synthesis of secondary sources in 50 peer-reviewed articles, industry reports, and case-based studies that were published between 2015 and 2025. The results indicate that AI-supported approaches are far much better than traditional ones in a variety of aspects. The optimization cycles were cut by 58 percent, parameter accuracy also improved by 19 percent, and the general efficiency of the process was enhanced by a cut in the development time of 45 percent, a reduction in cost of 38 percent and reduction in human error by 52 percent. Pharmaceutical analysis, environmental chemistry, and food quality testing case studies further attested that the reproducibility always increased above 89 percent in contrast with a common of 73 percent through the conventional techniques. In spite of these developments, problems like the scarcity of data, interpretation problems, and regulatory acceptability were discovered. Nevertheless, potential adoption in the future is high where it is estimated that the integration level is more than 77 percent in all of the categories evaluated. The paper highlights the role of AI in improving efficiency, accuracy and reproducibility and minimizing dependency on resources hence presenting substantial advantages to the laboratories, industries and regulatory authorities. On the whole, AI can be discussed as one of the defining tools that can make an analytical procedure redefined, innovative, and become more reliable in terms of scientific practices.