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.

 

Listen to the podcast

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.

 

Man working at a computer

2. Brownfield: AI-augmented legacy enhancement

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

 

Abstract code background

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.

Governance at the speed of AI

Enterprise AI delivery requires more than productivity tools. It requires a controlled autonomy envelope.

CGI’s governance model is built around:

  • Human-in-the-loop approval for high-impact actions 
  • Risk-tiered autonomy, from suggest-only to execute-with-approval 
  • Policy-as-code and auditable decision trails 
  • Agent lifecycle management, including intake, registry, versioning, drift monitoring, and decommissioning 
  • Tool and integration governance with least-privilege access 
  • Observability across usage, cost, latency, policy events, quality, and business outcomes 
  • Regulatory and industry alignment for sectors where trust, sovereignty, privacy, and auditability are essential 

This governance model is designed to answer the questions leaders are asking: Can AI-enabled delivery pass an audit? Will sensitive data or intellectual property be protected? Who is accountable for AI-generated output? Can teams move faster without bypassing enterprise controls?

Governance at the speed of AI

Outcomes we have helped clients achieve

CGI’s accelerated delivery work has already demonstrated measurable results, including $750K–$1M saved on a single legacy application, 100% auditability of AI decisions through mandatory human-in-the-loop review, and 2–4 applications modernized per business quarter versus timelines previously measured in years.

Additional proof points include:

8 weeks
with proof of value demonstrated in less time, helping organizations move quickly from strategy to evidence.
4–6x
faster modernization in public sector forestry, supporting better services, efficiency, risk mitigation, and sustainability.
52%
reduction in effort for one end-to-end remediation application, with other applications achieving 24–50% faster turnaround times depending on complexity.
60%
reduction in modernization effort in an oil and gas IP modernization example involving complex legacy VB6 and PowerBuilder platforms.

 

4 weeks
to deliver a greenfield claims portal originally requiring 3–6 months, achieving 42% cost savings.

 

Why CGI for SDLC

Why CGI

CGI’s AI services in Canada bring together deep application services experience, Canadian delivery capabilities, regulated-industry knowledge, responsible AI governance, modernization expertise and strategic technology alliances. We combine these strengths to help organizations scale AI-enabled software delivery with confidence.

Clients choose CGI because we combine:

  • Local, secure, sovereign delivery options
  • Proven experience in complex public and private sector environments
  • Built-in governance for regulated industries
  • AI accelerators, reusable patterns, prompt libraries, skills, and agents
  • Delivery discipline grounded in CGI’s management and engineering practices
  • Outcome measurement tied to cycle time, quality, adoption, risk, and ROI

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FAQs

What should I look for in an AI consulting partner?

Look for a partner with proven expertise in data, technology and your industry. They should connect AI initiatives to clear business goals, take a practical approach to governance and implementation, and help your teams develop the skills to sustain progress.

At CGI, we work with your teams to integrate AI into your day-to-day operations, helping you move forward with confidence and a clear focus on business value.

What is agent-native software delivery, and how does it differ from AI coding tools?

Agent-native software delivery integrates AI agents across the software development lifecycle, from planning and design to testing, release and operations. Its scope extends beyond the coding tasks supported by individual AI tools.

CGI’s Agent-Native Human-Centric SDLC combines AI-assisted work with defined governance and human accountability. Our approach helps organizations improve delivery speed and quality while keeping people responsible for business priorities, reviews and approvals.

How can AI help modernize legacy applications?

AI can support legacy application modernization by analyzing existing code, reconstructing requirements, generating tests and assisting with code transformation. These capabilities help teams understand complex systems and plan changes with greater confidence.

CGI applies AI to both incremental application improvements and larger modernization and migration initiatives. We combine automated analysis with human review and controlled delivery practices to help reduce effort, manage risk and limit disruption to business operations.

Can AI-enabled software delivery work with our existing delivery methods?

AI-enabled software delivery can work within existing methods, including Waterfall, Agile, Scrum, Kanban, SAFe, DevOps and DevSecOps. The integration of AI should reflect how teams plan, build, test and release software, including their existing controls.

CGI’s AI-enhanced software development lifecycle supports these environments across new development, legacy enhancement and modernization. We help teams incorporate AI into delivery activities while maintaining clear responsibilities, quality expectations and approval processes.

How does CGI maintain accountability and control in AI-enabled software delivery?

CGI maintains human accountability at key decision points, including requirements validation, code review and release authorization. AI agents operate within defined permissions, with autonomy adjusted according to risk and approval requirements.

Our governance approach combines human review, policy-based controls, auditable decision trails and ongoing monitoring. This helps organizations trace AI-assisted actions, manage exceptions and maintain oversight as AI becomes part of everyday software delivery.

How does CGI address data sovereignty and security in AI-enabled delivery for Canadian organizations?

CGI offers local, secure and sovereign delivery options for Canadian organizations adopting AI-enabled software delivery. The appropriate model depends on the organization’s data sensitivity, privacy obligations, industry requirements and operating environment.

We combine Canadian delivery capabilities with governance practices such as least-privilege access, controlled tool integrations and auditable decision trails. These measures help organizations manage access to sensitive information and align AI adoption with their security and sovereignty requirements.

What business results can AI-enabled software delivery achieve, and how should they be measured?

AI-enabled software delivery can shorten delivery cycles, reduce modernization effort and improve software quality. Results should be measured against an agreed baseline, using indicators such as cycle time, cost, quality, adoption and risk.

CGI’s client examples include a 52% effort reduction for one end-to-end remediation application. Outcomes vary by application complexity and scope; we focus on measuring business value from the start and using that evidence to guide broader adoption.