The Unrealized Potential of GenAI: A Call for Data Modernization

Generative Artificial Intelligence (GenAI) has been heralded as the next frontier in technological advancement, promising to revolutionize industries and transform the way we live and work. 

Many organizations are still grappling with realizing the full return on these investments. According to a recent Gartner study, at least 30% of GenAI projects will be abandoned after proof of concept by the end of 2025 due to poor data quality, inadequate risk controls, escalating costs or unclear business value. This discrepancy between investment and outcome raises a critical question: why is GenAI/AI not meeting expectations?

The answer lies in the data.

Many organizations believe that simply layering AI on top of existing systems will yield transformative insights. However, this approach overlooks a fundamental truth: AI is only as good as the data it processes. Poor data quality, fragmented data sources, and outdated data management practices are significant barriers to realizing AI's full potential.

High-quality classified and labeled data, combined with expertly crafted, contextually relevant prompts by domain experts, can substantially elevate the performance and reliability of AI models. This optimization is particularly beneficial in Retrieval-Augmented Generation (RAG) tasks, where deep integration with trusted data sources ensures that AI models operate with the most relevant and accurate information. Agency-specific prompts further tailor AI responses to meet unique organizational needs, driving better outcomes and higher satisfaction.

Data architects and domain experts play a crucial role in integrating data from various sources, providing a unified dataset for AI models. Effective data management and modernization practices, combined with robust data architecture, enable more accurate and insightful AI performance.

Through our experience, CGI understands that effective AI implementation requires more than just advanced algorithms; it necessitates a robust data foundation. Our Virtual Data Environment (VDE) is designed to address these challenges by modernizing and integrating data across platforms. While not a standalone product, the VDE serves as an accelerator in our engagements, enabling agencies to rapidly achieve data modernization and, consequently, more effective AI outcomes.

VDE advantages

Data Governance: With API-driven services, the VDE helps establish a structured framework for data governance, ensuring consistent data access, control, and compliance across platforms. This safeguards data quality, improves security, and aligns with regulatory requirements, enhancing trust in AI outcomes.

• Data Governance: With API-driven services, the VDE helps establish a structured framework for data governance, ensuring consistent data access, control, and compliance across platforms. This safeguards data quality, improves security, and aligns with regulatory requirements, enhancing trust in AI outcomes.
• Data Integration: It consolidates disparate data sources into a unified environment, ensuring that AI models have access to comprehensive and high-quality data.
• Data Modernization: By updating legacy data systems, the VDE enhances data accuracy and relevance, which are crucial for AI applications.

A recent IDC survey found that a significant portion of organizations are not seeing the expected value from their AI investments. This aligns with our observations and underscores the importance of a solid data strategy. Without it, AI initiatives are likely to fall into the “trough of disillusionment,” a phase identified by Gartner where inflated expectations give way to disappointment.

The path to successful AI implementation is paved with high-quality, well-managed data. CGI’s expertise in data management and AI positions us as a thought leader in this space. CGI has established the Innovation Lab and developed POCs with the latest technologies, including GenAI. We invite agencies to collaborate with us to unlock the true potential of their AI investments. By focusing on data modernization, unrealized expectations can be turned into tangible results.