GenDiscover: AI-Powered Modernization Intelligence

Asset Profile

Focus: AI-Driven Portfolio Discovery & Modernization Planning Model: Managed Asset (Professional Service by Capgemini)

Overview

GenDiscover — An AI-powered cockpit turning raw portfolio data into actionable modernization intelligence

Enterprises struggle to modernize application portfolios built over decades of technology and organizational change. Fragmented data, unclear transformation paths and limited visibility into business capabilities slow down this progress. Manual analysis is time-consuming and prone to errors — resulting in delaying decisions and increased transformation risks.

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Typical Capabilities

• Automated Data Ingestion & Normalization: Consolidates heterogeneous application portfolio data (applications, servers, databases, usage) into a unified and consistent schema for analysis.
• Application Discovery & Portfolio Transparency: Provides a comprehensive view of the application landscape, including technology distribution, business capabilities, and dependencies.
• AI-Driven Dependency Mapping: Identifies relationships between applications, infrastructure components, and business functions using rule-based and AI-driven analytics.
• Scenario-Based Modernization Recommendations: Maps each application to optimal transformation paths (e.g., Rehost, Refactor, Replatform, Retire) based on technical and business criteria.
• Portfolio Assessment & Prioritization: Generates prioritized insights and dashboards to identify high-value modernization candidates across the portfolio.
• Interactive Dashboards & Reporting: Delivers portfolio-level and application-level visualizations for decision-making and stakeholder alignment.
• Business Case Generation (TCO/ROI): Provides quantified cost, effort, and value estimates to support investment decisions and cloud business cases.
• Strategic Roadmap & Migration Planning: Translates insights into actionable transformation roadmaps and structured migration waves.

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Typical Fit Criteria

The asset is ideally suited and will deliver its greatest impact under the following conditions:

• Data Landscape Complexity: The customer operates a large and fragmented data ecosystem (e.g., multiple data sources, legacy systems, unstructured data) that requires structured discovery and harmonization.
• Early-Stage GenAI Exploration: The customer is in the ideation or exploration phase of Generative AI and needs support to identify, prioritize, and shape high-value use cases.
• Lack of Use Case Transparency: There is limited visibility into existing data assets, processes, and potential GenAI applications across business units.
• Business Value Orientation: The customer requires clear linkage between GenAI use cases and measurable business outcomes (e.g., efficiency gains, revenue uplift, cost reduction).
• Structured Discovery Approach Needed: A standardized, repeatable, and workshop-driven methodology is required to systematically identify and assess GenAI opportunities.
• Cross-Functional Alignment Required: Multiple stakeholders (business and IT) need to be aligned on priorities, feasibility, and roadmap for GenAI adoption.
• Foundation for Roadmap Development: The engagement aims to establish a qualified pipeline of use cases and a prioritized implementation roadmap.
• Data Readiness Assessment Needed: There is a need to evaluate data availability, quality, governance, and accessibility as a foundation for GenAI initiatives.

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