From fragmented data to AI ready operations: a manufacturing company’s transformation journey

A global manufacturing company set out to understand how artificial intelligence could strengthen its commercial and service operations and support long term growth. It wanted clarity on which capabilities to develop, how best to use its existing technology landscape and where AI could create measurable value.

Over three months, the joint team shaped a target state for key business capabilities, prioritised the most relevant AI opportunities and designed two Salesforce based use cases ready for implementation. This created a clear roadmap and a shared direction for building scalable, AI-enabled ways of working.

Customer description

The company is a long-established industrial integrator, serving machine builders and manufacturers worldwide. With decades of experience and operations across three continents, it helps customers improve productivity through advanced manufacturing solutions.

As the market moved towards smarter, data driven operations, the organisation recognised the need for more consistent processes and better insight. Fragmented information and varied practices had begun to slow decision making and make growth harder to sustain.

Setting the direction

The collaboration began by identifying the capabilities that would matter most for future growth. A target state capability map illustrated how value should flow across the organisation, from creating interest to delivering service and learning from customer feedback. This gave teams a shared view of where greater efficiency, repeatability and customer focus would make the strongest impact.

Designing practical AI use cases

With this foundation in place, the team reviewed more than forty ideas to determine where AI could bring the most value. Two use cases were selected for detailed design on Salesforce and validated with business stakeholders. They demonstrated how AI can enhance sales productivity, strengthen data quality and improve forecasting. A wider backlog was also created to guide future development cycles.

Understanding the current state

Interviews with sales, service, operations and IT confirmed a consistent set of challenges. Data issues slowed lead qualification, sales processes differed between teams and installed base information was incomplete. Limited development capacity made careful prioritisation essential. These insights shaped both the capability design and the AI use case selection, ensuring the work matched real needs.

Creating a clear path forward

The final recommendations focused on strengthening sales and service capabilities in a structured way. Suggested actions included improved pipeline management, the introduction of Service Cloud and early deployment of the designed AI use cases.

Installed base data was highlighted as an important source of future sales potential. A roadmap for 2025 to 2026 helped leadership make informed decisions about where to invest next.

Building alignment

Throughout the project, close collaboration between business and technology teams created a shared understanding of how the organisation should evolve. The work balanced quick wins with longer term improvements and showed how AI can support scalable and more customer centric operations. By the end, teams had a clear and unified view of the direction ahead and the practical steps required to reach it.