A global automotive data provider offers detailed data across vehicle production, sales, and component-level markets. Its customers use this information to plan supply chains, assess investments, forecast markets, and track technology shifts across regions.
The client’s data products had grown into separate platforms over 15 years, each with its own structure, logic, and user patterns. Customers could access data, but working with it required too many steps.
To answer questions, users often had to search across tools, export data, compare results manually, and interpret large datasets on their own. This made analysis slow and dependent on expert knowledge.
Internally, subscription and entitlement rules had also evolved separately across platforms. Access logic, contracts, and permissions were inconsistent, creating manual work and long turnaround times.
AI was initially considered as a chatbot-style feature. But the real opportunity was bigger: using AI inside the product experience to help users understand reports, charts, terminology, and quantitative data as they worked.
What we did
We started with a four-month discovery phase across regions, teams, and platforms. We mapped how data moved through the ecosystem, how users worked across tools, and how subscription and entitlement logic affected access. This gave stakeholders a shared view of what needed to be combined, simplified, or redesigned.
Instead of treating AI as a chatbot added on top of the platform, we defined where AI could support real moments of work. This included helping users summarize reports, clarify terminology, explain visualizations, and ask questions over quantitative data. The goal was to make AI part of the analysis flow, not a separate destination.
We introduced a computational notebook as a core interaction model inside the platform. Users can pull data from different parts of the system, compare datasets, ask natural-language questions, and explore results without exporting files or switching tools. In parallel, subscription and entitlement workflows were redesigned to create clearer rules for access and administration.
Value delivered
AI Support Where Users Actually Feel Empowered to Explore and Analyze
The redesigned platform gives users one place to explore and analyze automotive data, instead of moving between disconnected tools. The computational notebook creates a working environment where users can combine data, compare results, and investigate questions directly inside the product.
AI is not positioned as a separate feature. It supports the user in context: explaining charts, summarizing reports, clarifying terminology, and helping users navigate quantitative data.
This makes the product easier to use without oversimplifying the depth of the underlying data.
The work also brought structure to subscription and entitlement handling, reducing internal friction and creating a clearer path from discovery into implementation.
