From gut feel to smart prioritisation: Building an artificial intelligence opportunity scoring model that sales teams actually use

Abstract

In business-to-business (B2B) sales environments, the use of intuition and manual lead qualification methods over data-driven techniques frequently results in missed revenue opportunities, inefficient use of scarce resources and, ultimately, revenue leakage. This paper presents a hybrid artificial intelligence (AI) approach to predictive opportunity scoring that addresses the technical, organisational and cultural challenges commonly faced by B2B firms, such as low conversion rates and misaligned sales and marketing efforts due to poor data quality and inconsistent lead management. Emphasising interpretability, stakeholder engagement and incremental deployment, the approach integrates machine learning with domain expertise to deliver measurable gains in sales performance, forecasting accuracy and cross-functional alignment. Drawing on a real-world case study in B2B manufacturing, the paper outlines practical strategies for implementing predictive scoring with imperfect data, fostering adoption among sales teams, and aligning marketing and sales departments through a shared, data-driven framework. The results show that even with imperfect data and dispersed global teams, a well-managed predictive scoring initiative can increase win rates and deliver tangible return on investment, offering B2B organisations a scalable and effective path toward transforming lead qualification into a strategic growth engine. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/ business/.