Artificial intelligence and its ability to research and benchmark has long been in development but, more recently, its strides have been borderline brain-like, particularly in its ability to process and integrate diverse data sources. No longer limited by traditional methods—i.e., those that struggle with unstructured information—, AI excels at deciphering data from multiple sources, from social media posts to open-ended survey responses. This capability is both advanced and reshaping how we approach complex problems to uncover new insights. 

This capability is only further enhanced by AI’s ability to pull information from a wide array of public and proprietary sources. So, what does this more holistic approach to data gathering and analysis mean? Well, it means more robust benchmarking but, more importantly, it offers more profound insights into industry trends, often revealing connections that might have been missed by more siloed approaches. 

For ourselves here at Bond—and something we recommend to all organizations—, we’ve standardized the use of AI to efficiently collect and summarize industry data on a large scale. Instead of expending significant resources on manual data-gathering through research teams, we deploy AI agents to systematically extract, clean, and synthesize data from various web sources. By fusing Langchain with search engines, we’ve developed an internal tool that transforms the laborious process of understanding industry trends into one that takes just seconds. 

Notable as well is the use of AI for social media listening and trend/theme analysis, which is a capability that has been significantly enhanced by generative AI. At Bond, we rely on generative AI because it excels at synthesizing and evaluating customer opinions. This capability is only further augmented by traditional natural language processing (NLP) techniques. 

Case Study

In healthcare, an example of this transformative power is Novartis' 2019 partnership with Microsoft to leverage AI in drug discovery and development. By analyzing diverse data sources—including research papers, clinical trial results, and patient data—, Novartis significantly accelerated its drug discovery timeline. Some projects progressed from concept to human trials in under 18 months, a stark comparison to the typical 3-5 years. AI's robust analysis of unstructured data led to the identification of novel biomarkers and patient subgroups, enabling more targeted clinical trials. 

As AI technology only continues to progress, its impact on research methodologies is also expected to grow. The future of research and benchmarking likely lies in the synergy between human expertise and AI's data-processing capabilities, promising a new era of discovery and innovation driven by unprecedented access to integrated, comprehensive information.

Image by: Microsoft

Interested in learning more about how Bond can help you leverage AI to synthesize data and reach more profound customer insights? Reach out to info@bondbl.com for all the details.