Kristen Smith 
Organizations accelerating artificial intelligence deployments are failing to see meaningful returns because their underlying data environments remain fragmented and unreliable, according to CGI executives.
CGI’s findings reinforce how foundational data quality remains central to achieving real value from AI. The 2026 Artificial Intelligence Summit on March 19 will feature government and industry experts examining similar challenges around data readiness, AI deployment and mission integration. Register now to join the significant AI-focused conversation.
Why Is Poor Data Quality Limiting AI ROI?
Writing on the necessity of cultivating the “data estate,” Josh Rachner, the national AI strategy and alliances lead within CGI’s commercial and state government unit, and Victor Foulk, vice president of emerging technologies at CGI Federal, said in a recent blog post that focusing on foundational data quality is the key differentiator for AI success, noting that organizations prioritizing building high-fidelity, trusted data platforms will outpace those that do not.
Which Data Areas Should Agencies Address First?
CGI outlined six areas organizations should address to strengthen their data foundations and increase the value of AI investments:
- Strengthen governance: Establish clear ownership and enforce enterprise-wide quality and consistency.
- Modernize with intent: Tie data upgrades directly to high-value mission or business outcomes. Agencies and companies are urged to align data estate improvements with specific, high-impact AI use cases—such as mission delivery, fraud detection or customer services—rather than pursue broad governance overhauls with no operational focus.
- Automate data quality: Use AI tools to detect inconsistencies and accelerate remediation at scale.
- Invest in people: Build a culture that supports continuous learning and strong data stewardship.
- Prioritize strategic domains: Focus first on data most critical to operations, such as citizen, customer or asset data.
- Accelerate cleanup: Deploy AI-enabled tools to validate, align and remediate datasets quickly.
What Differentiates High-Performing AI Adopters?
The executives conclude that organizations that treat their data with discipline achieve returns on AI investments faster. The groundwork—trusted data sources and integrated systems—is what enables private and public sector entities to scale AI and improve operations and services.
