Enterprise leaders are racing to deploy artificial intelligence across their organizations, but they're building on quicksand. A major new study of more than 1,800 business and technology executives reveals a troubling gap between AI ambition and organizational readiness. While 62% of companies are now applying AI to core business and operational processes, only 20% have extended their AI strategy across their broader ecosystem, and just 51% can actually quantify the results of their investments.

The findings, released by CGI, one of the world's largest independent IT and business consulting firms, paint a picture of enterprises caught between momentum and execution. GenAI implementation has jumped by 30 percentage points over the past two years, signaling genuine organizational commitment. Yet this rapid adoption is outpacing the foundational work needed to make AI truly transformative.

Why Are Companies Struggling to Scale AI Beyond Isolated Projects?

The core problem is structural, not technological. Many organizations are applying AI to fragmented data, legacy systems, and outdated operating models. Without modernizing these foundations first, adding AI often increases complexity rather than delivering measurable value. Only 40% of organizations have developed an enterprise-wide AI strategy, and even fewer have the infrastructure to support it.

Cost pressures and talent shortages are compounding the challenge. Nearly 70% of executives report difficulty recruiting IT talent, and 52% say talent shortages materially impact their programs and execution capacity. Meanwhile, 45% of executives cite legacy systems as a significant barrier to their data and AI strategies. These constraints force difficult trade-offs: organizations must choose between investing in new AI capabilities or modernizing the systems that would allow those capabilities to scale.

"Executives are navigating an environment defined by rising complexity, from regulatory pressures to fragmented systems, while still being expected to deliver measurable outcomes. Our 2026 Voice of Our Clients insights show a clear evolution toward digital engineering and reengineering initiatives, as organizations build new capabilities and modernize legacy environments to scale AI and achieve their digital transformation outcomes," said Tim Hurlebaus, President and CEO of CGI.

Tim Hurlebaus, President and CEO, CGI

What Are the Three Critical Barriers to AI Success?

• Foundational System Gaps: Organizations applying AI to fragmented data and legacy systems often create more problems than they solve. Without modernized infrastructure, AI becomes another layer of complexity rather than a source of competitive advantage.
• Talent and Execution Capacity Shortages: Nearly 70% of executives struggle to recruit IT talent, and 52% report that talent shortages materially impact their ability to execute AI programs at scale. This forces organizations to prioritize hiring and training over innovation.
• Measurement and Accountability Challenges: Only 51% of organizations quantify the results of their AI adoption, making it difficult to justify continued investment or identify which initiatives are actually delivering business value.

The research also reveals broader organizational pressures shaping AI strategy. Tech and digital acceleration remain the most impactful macro trend, cited by 70% of executives. At the same time, 52% are prioritizing data sovereignty and local cloud strategies in response to geopolitical complexity. Yet only 25% rate their operating models as highly agile, suggesting that many organizations lack the organizational flexibility to adapt quickly to these shifting priorities.

How Can Organizations Move Beyond Isolated AI Pilots to Enterprise-Scale Deployment?

• Modernize Foundational Systems First: Before deploying AI widely, invest in digital reengineering of core data infrastructure, legacy systems, and operating models. This creates the clean, integrated foundation that AI needs to deliver measurable value rather than adding complexity.
• Consolidate Around Fewer, Trusted Partners: Rather than managing multiple point solutions, organizations are increasingly consolidating toward fewer partners capable of combining business consulting, systems integration, and digital reengineering. This end-to-end approach helps align technology, people, and processes toward measurable outcomes.
• Shift Toward Managed Services and Selective Outsourcing: C-level executives are increasingly turning to managed services models to strengthen delivery capacity and support scalable AI-enabled transformation. This allows organizations to access specialized talent and execution capacity without bearing the full cost of permanent headcount.
• Establish Clear Measurement and Accountability Frameworks: Define what success looks like before deploying AI, and build in mechanisms to quantify results. This helps organizations justify continued investment and identify which initiatives are truly delivering business value.

"With AI adoption accelerating, the priority is now execution and value realization. The opportunity lies in helping organizations move beyond isolated AI use cases toward embedding AI into complex enterprise environments to deliver tangible results and sustainable competitive advantage," explained Dave Henderson, Chief Technology Officer at CGI.

Dave Henderson, Chief Technology Officer, CGI

The shift toward managed services reflects a pragmatic recognition that talent and execution capacity are the real bottlenecks in AI transformation. Rather than trying to build all capabilities in-house, organizations are increasingly partnering with external firms that can provide specialized expertise, proven methodologies, and access to talent pools that would be difficult to recruit independently.

The research underscores a critical insight: AI success is not primarily a technology problem. It is an organizational and execution problem. Companies that succeed in scaling AI will be those that modernize their foundational systems, build or access the talent and execution capacity needed to implement at scale, and establish clear frameworks for measuring and demonstrating business value. Those that treat AI as a technology add-on to existing fragmented systems will likely find that their investments deliver complexity rather than competitive advantage.