CGI: Why AI Adoption Faces Gaps Despite Growing Investment

2026-06-19 · Source: AI Magazine · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Corporate Strategy & Leadership, Cloud Computing & IT Infrastructure · Depth: Intermediate, medium

Summary

CGI's 2025 Voice of Our Clients research, based on 1,813 discussions with 1,477 CXOs, reveals that 46% of organizations struggle to scale AI projects from proof-of-concept to production despite rising investment. Global AI Research Lead Diane Gutiw identifies three core barriers: legacy systems and technology constraints affecting 46% of organizations, organizational challenges including a 69% talent shortage and weak governance, and a failure to build adaptive data foundations. The research indicates that organizations with holistic AI strategies show 6.6x higher Gen AI maturity, emphasizing that responsible AI frameworks, built with "governance by design," accelerate rather than hinder innovation by fostering trust and avoiding costly rework.

Key takeaway

For Directors of AI/ML evaluating scaling strategies, recognize that foundational investments in data quality, governance, and talent are critical. Your organization should adopt a "governance by design" approach to responsible AI, integrating ethical principles from the outset to accelerate deployment and build trust. Prioritize strategic partnerships to bridge the severe AI skills gap, focusing on partners with both deep AI expertise and relevant industry knowledge to ensure practical, value-driven implementation.

Key insights

AI scaling is hindered by legacy systems, talent gaps, and weak data foundations, not a lack of investment.

Principles

• Responsible AI accelerates innovation.
• Governance by design prevents costly rework.
• Holistic AI strategies drive maturity.

Method

CGI's Responsible Use of AI framework integrates governance by design across the AI lifecycle: Envision, Explore, Engineer, and Expand, ensuring human oversight.

In practice

• Prioritize foundational data quality.
• Embed governance from day one.
• Balance quick-win and foundational AI investments.

Topics

• AI Adoption
• AI Governance
• Legacy Systems
• Talent Shortage
• Data Strategy
• Responsible AI

Best for: CTO, Executive, AI Product Manager, Director of AI/ML, Consultant, VP of Engineering/Data

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI Magazine.