IBM product teams were receiving tens of thousands of feature requests, categorizing many of them manually, and using inconsistent local tools that made prioritization slow, duplicative, and hard to compare across products.
Reported impact
The pilot helped product managers make more consistent, data-driven prioritization decisions, reduce manual review work, and build a common framework for roadmap planning across IBM software teams.
The IBM Chief Analytics Office piloted a watsonx.ai-based prioritization workflow to help product managers process feature ideas across IBM teams. Instead of relying on an optional user-entered theme field and inconsistent local tooling, the solution reads each idea description, applies prompt engineering in watsonx.ai, assigns the request to one or more of eight predefined ISO-aligned categories, and calculates a recommendation using the RICE scoring framework. The workflow then logs prioritization results into the group's roadmap tool so teams can collaborate, track progress, visualize trends, and compare ideas using a shared language rather than isolated spreadsheets and directories. During the six-month pilot, 128 product managers across 10 software products used the approach to reduce manual categorization work and make roadmap decisions faster. The design turns dispersed request data into a repeatable AI pipeline for software planning and portfolio governance.
Feature requests enter the workflow from existing idea-submission processes, watsonx.ai categorizes and scores them with RICE, and the results are logged into the product roadmap tool for collaboration, tracking, and visualization.
"128 product managers focused on 10 software products"
"Across IBM, there are more than 500 product managers"
"The model utilizes the RICE (Reach - Impact - Confidence - Effort) scoring methodology"
"Watsonx.ai reads the idea description and assigns it to a category through prompt engineering."
"received over 30,000 new ideas and feature requests from users during the past year"
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
Related
IBM Sample Sep 28, 2026 OnticWorks.io OnticWorks.io needed its Elmer agentic platform to reason within the physical and operational context where work happens instead of relying on disconnected dashboards, emails, sensor feeds, and simulations that users… 12-month acceleration IBM Bob · IBM watsonx.ai · Red Hat OpenShift IBM Sample Sep 21, 2026 A.I. Tech A.I. Tech needed an enterprise-grade foundation for taking AI solutions such as forecasting and document intelligence from validation to production across complex customer environments that included existing ERP… 39% more inferencing per watt IBM Expert Labs · IBM Fusion · IBM Power IBM Sample Sep 2, 2026 IBM Systems Assurance Project leads in IBM Systems Assurance were spending hours each week preparing for status meetings and consolidating updates from spreadsheets, slides, boards and tracking tools, yet leaders still lacked timely,… 24/7 near real-time visibility into project status IBM Cloud · IBM watsonx Orchestrate · Slack 