The cost of waiting has changed

Across Canada, delivery velocity is constrained by legacy drag, compliance overhead, sovereignty uncertainty, and talent scarcity. These are not only tooling challenges. They are operating model, governance, and modernization challenges.

 

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AI now makes it possible to address the concerns that once made modernization slow and risky - because governance, security, traceability, and human oversight can be embedded directly into how AI is used. CGI’s current page already positions AI as a force across research, ideation, analysis, build, and learning; this rewrite makes that transformation more concrete and outcome-led. With the right model, organizations can:

  • Compress delivery cycles without bypassing controls
  • Modernize critical legacy systems with less disruption
  • Improve quality, test coverage, and release confidence
  • Generate audit-ready evidence as work happens
  • Scale AI usage consistently across teams and portfolios
  • Free scarce engineering capacity for higher-value work

The opportunity is no longer faster code alone. It is faster value, stronger governance, and a more adaptive delivery organization.

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From AI to ROI: Beyond the hype – Reimagining software delivery for real business value

In this kickoff episode for Season 2 of CGI's From AI to ROI podcast series, host Dave Henderson, Chief Technology Officer at CGI, is joined by John Davis and Victor Foulk to explore how AI is transforming software delivery and why this shift is a board-level conversation, not just a technical one.

 

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Our Agent-Native Human-Centric SDLC: AI across delivery, human accountability by design

CGI’s Agent-Native SDLC embeds AI across the software delivery lifecycle while keeping human accountability explicit at the moments that matter: intent validation, code review, release authorization, exception handling, and continuous learning.

Our approach moves teams from manual execution to AI-enabled orchestration. AI assists with requirements, design options, code generation, refactoring, test optimization, release evidence, incident analysis, and drift detection, while people remain responsible for business intent, quality thresholds, approvals, and outcomes.

Plan and design

AI helps teams clarify business intent, analyze options, surface risks, draft requirements, and prepare architecture decisions. Humans validate priorities, constraints, and the target outcome.

Build

Persona-based agents and AI-assisted engineering accelerate coding, refactoring, documentation, and code review. Developers approve changes and maintain accountability for quality and maintainability.

Test

AI supports test generation, coverage expansion, regression analysis, and defect triage. QA teams validate results, coverage thresholds, and release readiness.

Release

Policy-as-code gates, deployment risk scoring, and release evidence generation help teams move faster with stronger traceability. Production authorization remains explicit.

Operate

AIOps, anomaly detection, incident analysis, and auto-remediation playbooks help teams move from reactive support to continuous improvement, with escalation and override paths built in.

A practical model for every software estate

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1. Greenfield: AI-native build

For net-new development, we embed AI-first architecture, agentic workflows, DevSecOps, testing, and governance from inception.

 

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2. Brownfield: AI-augmented legacy enhancement

For existing systems, we use AI to accelerate refactoring, test generation, documentation, incremental modernization, and quality improvement.

 

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3. Transform-field: AI-led modernization and migration

For large-scale modernization, we use AI-enabled analysis, requirements reconstruction, code transformation, target architecture alignment, and controlled migration patterns to reduce risk and improve predictability.