Mike Hyzy, Vice President of Strategy and Innovation at CGI.
I never intended to spend my early career tangled up in bureaucracy. As a product manager, I felt like my days were a relentless cycle of meetings discussing “feasibility,” while my evenings were a solo grind of writing unit tests, documenting defects and wrangling QA. It was deeply frustrating. All of that time spent on process was time stolen from the one thing that truly matters: connecting with customers and creating with them, not just for them.
Today, we’re witnessing a profound transformation in how we conceive, create and deliver digital products. Welcome to the age of agentic AI across the software development life cycle (SDLC), and we’re just getting started. In just a few short years, we’ll look back on today’s methods of building software in the same way we now view vinyl records in the era of digital music: nostalgic, perhaps charming, but unquestionably obsolete.
Why Traditional SDLC Is Under Strain
In many organizations, technical debt quietly consumes up to 42% of developers’ time, according to Swedish researchers’ quantitative analysis of production codebases—work diverted from innovation and customer-centric outcomes. Picture a large bank that rushes a feature release to satisfy quarterly targets; months later, developers spend their days untangling fragile legacy code rather than building new capabilities, eroding both speed and morale.
Modern development teams now face a double bind: complex, distributed systems demand rapid delivery, yet budgets and hiring capacity remain fixed. Agile and DevOps practices help, but persistent handoffs, siloed QA and manual prioritization continue to derail flow and bloat backlogs.
The imperative now is to go beyond incremental tweaks. The transformation must include AI as an embedded force across the SDLC—not just for writing code but for fixing the underlying process inefficiencies that have wasted time and energy for years.
AI From Design To Deployment
AI is reshaping testing with a “shift-left” approach that pulls quality checks to the start of the development cycle instead of saving them for last.
In the design and requirements phase, generative tools scan project documents, stakeholder conversations and user feedback to produce user stories with acceptance criteria already baked in. They even sketch out UI layouts or architecture diagrams. New methods like “vibe coding” let teams describe interfaces in plain prompts and instantly generate working front-end code that drops straight into GitHub.
During development, AI code copilots like GitHub Copilot drastically cut down repetitive coding tasks, allowing developers to complete tasks more than 50% faster. They suggest boilerplate functions, autocomplete segments, flag errors on the fly and create supporting material like documents and unit tests. With the routine covered, developers focus on creative engineering and hard problems.
Teams leveraging these capabilities have reported major efficiency gains. An April 2025 study published in IEEE Software researched AI-assisted testing systems and found that they showed a 31% increase in bug‑detection accuracy, a 12.6% expansion in critical test coverage and a 10.5% improvement in user acceptance rates.
AI In Deployment, Operations And Maintenance
AI is quietly rewiring the guts of software operations. DevOps used to run on scripts, dashboards and late-night firefights. AIOps runs with machine learning built into the workflow itself. Instead of waiting for servers to melt down or memory leaks to spiral, these systems watch, learn and predict.
During a release, the AI studies usage patterns, picks the best time to push updates and rolls back broken deployments before they cause real damage. Each release becomes more than code shipping; it’s training data, sharpening the system’s instincts about risky components and which tests matter most.
Maintenance is nonstop. The AI scans code for vulnerabilities, performance sinks and leftover technical debt. It keeps documentation aligned with reality so development teams don’t end up working from stale notes.
The payoff is direct: faster delivery without sacrificing reliability. Features land in production sooner, quality holds steady and the system runs cleaner than manual DevOps ever managed.
Preparing For The AI Transformation
Here’s the hard truth: Most organizations aren’t ready for what’s coming. They’re treating AI adoption like they treated cloud migration a decade ago, as a technical upgrade rather than a fundamental reimagining of how work gets done.
Take an honest AI maturity assessment. What are you actually using AI models for today? Code completion? Test generation? Map out where agents could replace manual handoffs: requirements to code, code to tests, tests to deployment. Then benchmark yourself against your industry.
Next, redesign your workflows around AI augmentation. Your current sprint ceremonies, code reviews and QA gates were built for human-only teams. With AI handling routine tasks, you need new checkpoints focused on prompt engineering, output validation and AI-human handoffs. Create dedicated roles for AI tool governance. Someone needs to own prompt libraries, model selection and output quality standards.
The Change Management Mountain
The biggest blocker is the 25 years of muscle memory your teams have built around traditional SDLC. You’re asking developers who’ve spent entire careers perfecting their craft to step back and let the computer write the code. You’re telling product managers who’ve mastered the art of writing user stories to hand that over to AI and then fundamentally reinvent their role as orchestrators of AI agents that are now part of their team.
Successful adoption requires deliberate change orchestration. Run proof-of-concept sprints where teams use AI tools on low-risk features. Let them experience the acceleration firsthand before mandating adoption. Create internal champions who can translate AI capabilities into terms that resonate with different roles.
Training can’t be an afterthought. Budget for continuous upskilling—not one-off workshops but ongoing programs that evolve as AI capabilities expand. Focus on prompt engineering, AI output evaluation and human-AI collaboration patterns. Your best developers will become AI directors orchestrating multiple models to solve complex problems faster than they ever could alone.
Most critically, accept that some people won’t make the transition. Just as some COBOL programmers never adapted to object-oriented design, some of your team will resist this shift. Plan for it. Build redundancy, create mentorship programs and be prepared to hire for AI-native skills.
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