InnoSale – Innovating Sales and Planning of Complex Industrial Products Exploiting Artificial Intelligence: Final report

The InnoSale project, running from 2021 to 2025, set out to transform the sales processes for complex industrial products in B2B environments by harnessing the power of artificial intelligence. The project brought together a diverse international consortium of industry leaders and research organizations, all united by the goal of making sales processes more efficient, accurate, and customer-centric. At the heart of InnoSale was the recognition that traditional B2B sales, especially for highly configurable products, relied heavily on manual expertise, fragmented data, and intuition, which often led to inefficiencies and inconsistent outcomes.

To address these challenges, the project focused on developing and piloting AI-driven tools that could automate routine sales tasks, provide data-driven insights, and support decision-making throughout the sales funnel. VTT and its partners co-created use cases that reflected real-world needs, such as semantic search tools to help sales and support teams quickly find relevant information from historical support tickets, machine learning models to predict the outcomes of sales calls and offers, and intelligent recommendation systems to streamline the configuration of complex products. These solutions were built on a foundation of integrating data from multiple enterprise systems (ERP, CRM, support tickets, and pricing tools) highlighting the critical importance of data quality, completeness, and integration. Experiments conducted during the project demonstrated that AI models could achieve promising accuracy in tasks like offer outcome prediction and customer segmentation, even when working with anonymized or B2C datasets due to the scarcity of real B2B data. The project also underscored the importance of trust and user acceptance: while management tended to be optimistic about AI’s potential, experienced sales professionals were often skeptical, emphasizing the need for transparent, explainable models and user-centered design. Feedback from these users will be invaluable in refining the tools and identifying areas for further development.

Ultimately, InnoSale showed that while advanced AI can deliver significant value, even basic automation and digitalization can lead to substantial improvements in sales processes. The project concluded that the most successful AI solutions are those that augment human expertise, automate repetitive work, and provide actionable insights freeing up sales experts to focus on higher-value activities. Looking ahead, the report recommends further work to secure richer B2B datasets, pilot AI tools in real business settings, and invest in robust data governance and user training. InnoSale thus lays a strong foundation for the continued evolution of AI-driven sales in complex industrial domains, balancing technological innovation with practical, user-focused implementation.