A life sciences research and development organization was interested in assessing the effectiveness of their current product evaluation tool and exploring the feasibility of using artificial intelligence (AI). The organization was using a third-party out-of-box solution to quantify product performance, and they were unsure of the accuracy of the results. Leadership needed help to understand current gaps, explore whether existing data could support a custom machine learning (ML) model, and evaluate the utility of updating their methods.
Unify worked with business stakeholders to identify analytic objectives and set goals for quantitative analysis. Unify designed and implemented a feasibility study framework and delivered a proof of concept (POC) for an artificial intelligence-based (AI) analytics model. Unify assessed the feasibility of whether an ML model could outperform the out-of-the-box solution by applying a prototype to one specific use case. An evaluation framework was provided to help understand the performance of the existing solution and enable quantitative comparisons to the ML prototype.
Unify evaluated the efficacy of the current tool and determined it performed poorly. Conversely, the ML prototype Unify created performed better and produced detailed reports containing advanced analysis and visualizations. A roadmap was provided for solving critical business challenges using the sample reports. The upfront investment needed to build an ML model was quantified and leadership was provided with a framework for evaluating the marginal utility of making the change. The organization was ultimately able to assess the feasibility of implementing an artificial intelligence-backed approach as well as the likelihood of improved analysis and decision making.