Redesigning software delivery for the age of AI

AI is changing how organizations plan, build, test, secure and operate software. Applied across the software development lifecycle (SDLC), it can help teams deliver higher-quality software faster, reduce costs and create a more direct path from development effort to business value.

Organizations can realize greater value by applying AI across the lifecycle, connecting people, processes and technology while embedding quality, security and governance throughout.

The software development lifecycle provides a structured approach for planning, designing, developing, testing, deploying and maintaining software. It helps organizations manage complexity and ensure software continues to meet business, user, quality and security requirements.

As organizations sought to deliver software faster and respond more readily to changing requirements, Agile approaches evolved how the traditional lifecycle is managed. An Agile software development lifecycle uses iterative development, continuous feedback and close collaboration between business and technology teams to adapt software as requirements evolve. Implementing an Agile SDLC involves organizing work into iterative cycles and automating integration, testing and delivery where appropriate. DevOps and DevSecOps extend these practices through greater automation and security embedded throughout development.

AI is now advancing this evolution further, not only in how individual development tasks are performed, but how work is coordinated. Generative and agentic AI are driving the next shift by supporting activities across the lifecycle and increasingly connecting work that previously depended on human coordination.

This creates an emerging progression in software delivery:

• Traditional SDLC
  People perform and coordinate most activities, supported by established development and automation tools.
• AI-assisted SDLC
  AI supports individual tasks such as coding, testing, requirements analysis and documentation.
• AI-integrated SDLC
  AI is embedded across connected development workflows, while people direct and validate work.
• AI-driven lifecycle (AI-DLC)
  AI agents plan, coordinate and execute more activities, while people establish intent, apply context and judgment, and provide governance, validation and accountability.

AI adoption and application modernization are increasing the need for software delivery approaches that support faster change while maintaining quality, security and resilience.

CGI Voice of Our Clients research shows:

• 62% of organizations are applying AI to core business and operational processes
• 16% plan to modernize more than 80% of their total application portfolio over the next two years, up 9 percentage points

Connecting AI across the SDLC can help teams increase development capacity, accelerate modernization and turn business needs into production-ready software faster.

AI can accelerate code generation, but faster coding does not automatically shorten the end-to-end software delivery lifecycle. Requirements, architecture, testing, security, integration, deployment and operations can still create bottlenecks across the SDLC.

Requirements must reflect business needs. Architecture needs to support security, scalability and maintainability. Software needs to be tested, integrated, deployed and operated reliably. When these activities progress at different speeds, rework and technical debt can limit gains made during development.

The greater opportunity is to improve the end-to-end flow of software delivery. This can enable:

• Faster time-to-market
• Faster testing and release cycles
• Higher engineering quality with less rework and technical debt
• Stronger security and compliance
• Lower delivery costs
• Better business and user outcomes

For example, AI-enabled quality engineering and testing helped an insurance organization achieve 40–50% faster testing cycles and approximately 35% lower delivery costs through reduced manual testing and rework, while improving release predictability.

AI can create value throughout the SDLC by reducing repetitive work, accelerating decisions and improving consistency.

Across core software delivery activities:

1. Research, ideation and requirements
   Synthesize information, refine requirements and translate business needs into actionable development work.
2. Architecture and design
   Analyze existing environments, support solution design and evaluate technical approaches and dependencies.
3. Development
   Create, explain, refactor and review code, with AI agents increasingly able to execute defined development tasks.
4. Quality engineering and testing
   Generate test cases, automate validation and identify defects and vulnerabilities earlier to reduce downstream rework.
5. Deployment and operations
   Support CI/CD, monitoring, issue detection and remediation to improve deployment speed and application reliability.

Across the lifecycle:

• Modernization and continuous improvement
  Analyze legacy code, generate documentation and specifications, and accelerate the transformation of complex application environments.
• Security and governance
  Embed security, quality and compliance controls into development workflows to support consistent, auditable delivery.

Secure AI-assisted software development requires security, quality and accountability across the SDLC.

A secure software development lifecycle (secure SDLC or SSDLC) embeds security requirements, controls and testing throughout software development. DevSecOps reinforces this approach by integrating development, security and operations and introducing security earlier and continuously throughout delivery.

As AI assumes a greater role in software delivery, these principles become critical. AI-generated outputs can introduce vulnerabilities, technical debt or compliance risks without appropriate controls. Organizations need to understand how AI is being used, protect enterprise data and intellectual property and ensure people remain accountable for AI-generated work.

Key controls include:

• Security and quality by design: Embed security and quality gates, automated code analysis and continuous testing throughout the SDLC.
• Human oversight and accountability: Define where human review is required and who remains accountable for AI-generated work and consequential decisions.
• Responsible AI governance: Establish policies and controls for how AI is selected, used and governed across software development.
• Traceability and auditability: Maintain visibility into AI-generated outputs, development activities and decisions.
• Data and intellectual property protection: Secure enterprise data, source code and intellectual property used within AI-enabled development.

AI agents can increasingly plan, execute and coordinate software development activities, creating a human-led, agent-orchestrated approach to software delivery.

As AI takes on more execution, human expertise is critical to defining intent, applying business and domain context, navigating ambiguity and directing work toward the right outcomes.

In this model:

• AI agents execute and coordinate: Specialized agents can perform complementary roles across the SDLC. One might interpret requirements and develop code while another independently generates tests and validates the result.
• People direct and govern: Human teams define the problem and desired outcomes, apply business and domain context, exercise judgment and orchestrate work across people and AI while retaining oversight and accountability.

Specification-driven development (SDD) connects the two. Before code is written or generated, teams define intent, constraints and success criteria in a specification, including security and compliance requirements. People define the “what” and the “why,” AI agents execute the “how” within those boundaries, and code and tests are validated against the specification so every change remains traceable to business intent.

AI can also help generate these specifications. Using a curated knowledge base of design patterns, agents can turn workshop discussions, transcripts or existing code into user stories, acceptance criteria and initial test cases, which architects and domain experts then review and refine. Within the human-led lifecycle, this enables agent-native development, where people and AI agents work from the same specifications.

CGI applied this approach for a healthcare organization using Databricks. Specialized agents developed, tested and independently validated software changes within governed environments before handing them to developers for final review and deployment. This created an autonomous quality feedback loop while maintaining human accountability, data protection and architectural flexibility.

As these models mature, software professionals can spend more time framing the right problems, applying domain expertise, managing complexity and orchestrating AI-enabled delivery toward business outcomes.

Scaling AI across the SDLC requires organizations to transform how software delivery teams work, how AI is integrated across workflows and how people and agents coordinate to deliver outcomes. This creates a repeatable, governed model that can scale across teams and application portfolios.

Organizations should focus on four priorities:

1. Align AI with business and delivery outcomes
   Identify where AI can improve delivery performance and create measurable value, then use those outcomes to guide investment and measure progress.
2. Assess and redesign the delivery lifecycle
   Evaluate SDLC maturity, engineering practices and sources of delay or rework, then connect AI across requirements, development, quality, security, deployment and operations.
3. Establish governance and a human-AI operating model
   Build AI literacy, define how people and AI work together and embed security, responsible AI, quality controls and human accountability by design.
4. Build for enterprise scale and continuous improvement
   Create reusable platforms, frameworks and engineering practices that can scale across teams and application portfolios, using delivery performance, quality and business outcomes to continuously improve.

“By leveraging CGI’s GenAI-driven coaching and technical enablement, we accelerated our upgrade delivery cycle by 30–40%, benefiting from expert-led implementation across architecture, tooling, and process.”

– VP of IT and Application Development, Large Manufacturing Company

AI-powered software delivery should be measured across the complete lifecycle, connecting development performance with software quality and business outcomes.

This supports a shift toward outcome-based delivery, where teams define the outcomes that matter and use AI-enabled visibility to track progress toward them.

Organizations can measure performance across five areas:

• Speed and productivity: Development and delivery cycle time, lead time to production and testing and release speed
• Quality and reliability: Defect and change failure rates, test coverage, rework, technical debt and application reliability
• Security and governance: Vulnerabilities identified and remediated, compliance with engineering controls and traceability of AI-generated outputs
• Cost and capacity: Delivery cost, manual effort reduced and increased delivery capacity
• Business value: Time-to-market, user and customer outcomes, product adoption and value generated from new or improved capabilities

We help organizations redesign the software development lifecycle across technology, workflows and operating models, combining AI-enabled speed with human expertise, engineering discipline, security and quality required for enterprise-scale delivery.

Our enterprise software delivery approach brings together AI-native engineering, human-AI operating model transformation, DevSecOps, quality engineering, application modernization and intelligent operations.

We help organizations:

• Transform software engineering for AI:
  Define AI-driven SDLC strategies, human-AI operating models, transformation roadmaps and measures of success, supporting the transition from standard Agile approaches toward AI-native development.
• Accelerate software delivery
  Apply AI across requirements, design, task planning, development and iterative delivery while maintaining enterprise governance.
• Orchestrate AI agents with CGI DevAccel
  Apply specialized AI agents across the SDLC, enabling multi-agent collaboration while keeping people in control.
• Embed security and quality earlier
  Integrate DevSecOps, automated testing and shift-left quality engineering throughout the lifecycle, supported by CGI IP such as CGI SimpliTest for automated testing and CGI NAVI for AI-enabled software development and quality assurance.
• Accelerate application modernization
  Combine AI-enabled modernization with application management to understand and transform legacy applications, reduce technical debt and create more maintainable environments.

This approach enables connected, repeatable and governed AI-enabled software delivery designed to improve engineering performance and business outcomes.

CGI combines expertise in cloud-native development, DevSecOps, application modernization and AI-enabled software delivery to help organizations evolve how enterprise software is built and managed.

AI Talk - 7th May 2026

Business Reporter hosts CGI’s Fred Miskawi and other experts on AI’s growing role in software delivery and the shift toward AI-native delivery models.