These criteria typically include: (i) being computationally or analytically intensive (i.e. where there are large amounts of data to consider) and/or highly repeatable; (ii) of a type such that there may be subjective elements to the analysis (e.g. where the data to be analysed can be presented in a variety of different ways); and (iii) of a type where the analysis would be hard to achieve manually, but the output is easy to verify for accuracy.

Our latest study explores the application of gen-AI in a range of general areas related to the analysis of data pertaining to brand monitoring services. The experiments make use of a proprietary business workflow automation system (the AI ‘tool’), incorporating AI and natural language capabilities to generate agentic functionality, with which interaction is possible in a ‘chatbot’ style.

The analysis considers two broad areas of analysis. The first area is related to sets of tasks required to carry out ‘clustering’ analysis (i.e. the establishment of links between related findings, on the basis of shared associated characteristics, which is advantageous for identifying priority targets associated with high-volume or serial infringers, demonstrating bad-faith activity by bad actors, allowing efficient bulk takedowns, and providing data for entity investigations). The specific tasks considered in this area fall into the categories of generalised ‘scraping’ functionality (i.e. the extraction of data points from arbitrary webpages) and the parsing (i.e. interpretation) of free-form data, such as that provided in domain name whois records. The second area of analysis relates to the high-level categorisation of results (i.e. potentially infringing webpages or other content) identified through brand monitoring services.

The analysis shows that gen-AI does have significant potential in assisting with these broad task areas, although the specific use-cases must be carefully selected to ensure that the tools in question are a good fit to the tasks and the potential for undesirable outputs such as ‘hallucinations’ can be minimised. The specific AI tool used in the analysis is particularly applicable, as it incorporates functionality to save the necessary prompts as pre-defined ‘tasks’ (i.e. the creation of agentic systems), which should provide a basis for greater repeatability and efficiency going forward.

You can read the full study here.