Making a global innovation portfolio easier to understand

The company managed a global portfolio of more than 150 supply-chain technology products and initiatives. Teams tracked investment, operating costs, return on investment, deployments, product reviews, vendor performance, and stakeholder feedback across multiple Power BI dashboards and data sources.

The information existed, but answering a new question was often slow and difficult. Users had to navigate predefined reports, apply complex filters, combine information manually, or rely on analysts with knowledge of the underlying data.

The portfolio also included stakeholder feedback from different regions and languages, making it harder to compare sentiment, identify common themes, and understand how product perception related to investment, deployment, and vendor performance.

The company needed a more intuitive way to explore the information it already had. The solution needed to support natural-language questions, connect multiple dimensions of portfolio data, analyze multilingual feedback, preserve enterprise security, and provide answers grounded in the underlying information.

EES designed and built a secure, standalone AI application that extended the company's existing reporting environment rather than replacing it.

We began by understanding how portfolio information was structured, how stakeholders used the existing Power BI dashboards, and which questions were difficult, important, and/or time-consuming to answer.

The application created an AI-native intelligence layer across:

• CAPEX and OPEX data
• Return-on-investment information
• Product and initiative records
• Deployment activity and locations
• Product reviews and stakeholder feedback
• Vendor performance information
• Multilingual feedback and sentiment

EES used secure data pipelines, retrieval-augmented generation, natural-language processing, and specialized AI agents to retrieve, classify, summarize, and analyze the portfolio information.

The application allowed users to ask questions in plain English, including:

• Which products have strong financial performance but weak stakeholder sentiment?
• Where are multiple initiatives solving similar problems?
• Which vendors are receiving recurring negative feedback?
• How does product sentiment compare with investment and deployment levels?
• Which initiatives may require leadership attention?
• What themes are emerging across regions and languages?

The system returned contextually relevant responses grounded in the available enterprise data, while reducing the need for complex queries, repeated filtering, and manual analysis.

The company moved from relying primarily on predefined dashboards and manual analysis toward a more conversational way to explore its global innovation portfolio.

Users could ask direct questions across financial, operational, product, vendor, deployment, and stakeholder information without navigating multiple reports or requiring a new query to be prepared for each question.

The application provided:

• 95% faster query response time
• Faster visibility across more than 150 products and initiatives
• A single interface for exploring multiple dimensions of portfolio data
• Multilingual analysis of stakeholder feedback
• Earlier identification of negative sentiment and recurring issues
• Clearer comparison of products, initiatives, and vendors
• Better visibility into overlap across the innovation portfolio
• A reusable foundation for additional enterprise AI use cases

The value did not come from adding a generic chatbot to an existing dashboard. It came from understanding the questions stakeholders needed to answer, preparing the underlying information, and designing the application around the company's terminology, security requirements, and decision-making process. The result was a more accessible intelligence layer connecting the company's existing data to the decisions that data was intended to support.