AI & The Software Design Lifecycle (SDLC)
Professional Development Training Course
AI & the Software Design Lifecycle (SDLC) equips teams with frameworks and controls to integrate AI across development processes. Building on this foundation, the course helps participants understand how AI reshapes design decisions, documentation standards, and cross‑functional collaboration. Learners explore how AI influences requirements, architecture, testing, and deployment, and how to embed responsible‑use principles into every stage of the lifecycle. The result is a practical, end‑to‑end understanding of how to integrate AI in a way that is scalable, compliant, and aligned with enterprise risk expectations.
Participants learn not just how to use AI tools, but when to use them, how to document AI-assisted decisions, how to maintain traceability, and how to prevent the erosion of technical judgment and oversight that can introduce operational or supervisory risk. The course reinforces the importance of human‑in‑the‑loop controls, model‑risk alignment, and defensible decision‑making practices that withstand audit scrutiny. Through guided exercises, teams practice evaluating AI outputs, identifying failure modes, and applying structured review processes that preserve accountability. Participants also learn how to balance productivity gains with long‑term capability development, ensuring that AI enhances, not replaces, the engineering expertise required to maintain resilient systems. By the end of the program, learners are equipped with the confidence and discipline to integrate AI responsibly across the SDLC while supporting organizational readiness and regulatory expectations.
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Foundational Course: Delivery & Schedule
The foundational course, as outlined below, is delivered on-site in one full day or virtually in two 3.5-hour sessions. Please explore our ALFSM as seen above to learn more about our customization, flexible durations, and delivery modality options.
Course Modules & Learning Objectives
AI Foundations & Requirements Engineering
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Understanding AI in Software Development: distinguish augmentation from automation, identify relevant AI tools, determine where human oversight is required, and understand regulatory impacts.
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AI‑Powered Requirements Gathering: apply AI to synthesize stakeholder and regulatory inputs, surface gaps and conflicts, validate outputs with expert review, and maintain audit‑ready traceability.
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Predictive Planning & Estimation: leverage AI for project forecasting and risk estimation, recognize limitations in regulated environments, and balance predictions with documented human judgment.
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Design & Architecture Decision Support: evaluate AI‑generated recommendations, analyze architectural trade‑offs and secure patterns, and document decisions for audit defensibility.
AI‑Augmented Development & Code Generation
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Generative AI for Code Creation: leverage large language models for secure, compliant code generation, implement AI pair‑programming workflows, and understand the capabilities and limits of AI‑produced code.
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Code Quality, Security & Compliance: identify vulnerabilities in AI‑generated code, apply structured verification and peer‑review standards, ensure documentation meets governance expectations, and address IP and licensing considerations.
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Maintaining Developer Capability: prevent skill atrophy through intentional practice, design balanced oversight workflows, recognize signs of unhealthy AI dependency, and maintain professional accountability in AI‑assisted environments.
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Refactoring & Technical Debt Management: use AI to identify and prioritize technical debt, apply automated refactoring safely within controls, and leverage AI for legacy documentation and risk exposure analysis.
AI‑Driven Testing, Deployment & Operations
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Intelligent Test Automation: implement AI‑driven test generation and optimization, balance automation with human oversight, and document validation processes for compliance review.
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AI in CI/CD Pipelines: integrate AI into controlled CI/CD workflows, apply intelligent coverage analysis, enhance deployments with rollback safeguards, and maintain change‑management controls.
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Intelligent Operations & Monitoring: deploy predictive monitoring responsibly, detect drift or anomalous behavior, and implement structured incident documentation and escalation protocols.
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Production Learning & Continuous Improvement: use AI to analyze operational patterns, drive improvement without bypassing governance gates, and maintain transparency in AI‑enabled environments.
Governance, Ethics & Building AI‑Capable Teams
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AI Governance & Model Risk Management: establish guardrails for AI usage, align SDLC integration with model‑risk practices, meet documentation and audit standards, and define clear oversight roles.
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Ethics, Bias & Responsible AI: detect and mitigate bias, understand fairness implications in financial systems, and apply accountability and transparency frameworks to AI‑assisted decisions.
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Recognizing AI Dependency Risk: identify signs of unhealthy over‑reliance, understand when augmentation shifts toward substitution, and implement practices that preserve professional judgment and oversight.
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Building Sustainable AI‑Capable Teams: design SDLC processes that strengthen resilience, create governance‑aligned norms for AI usage, and develop actionable plans for responsible integration.
How Your Teams Will Benefit
This course is designed for teams integrating AI into the SDLC within regulated financial‑services environments, helping organizations increase development velocity while strengthening governance discipline. Participants learn to boost productivity without eroding core skills, improve code quality through earlier defect detection, and maintain documentation integrity required for audit defensibility. Through structured frameworks and real‑world scenarios, teams establish shared AI standards that enhance collaboration across engineering, risk, compliance, and internal audit. At the enterprise level, organizations reduce time‑to‑market, lower technical debt, strengthen model‑validation posture, and mitigate supervisory, security, and vendor‑dependency risks, often realizing measurable gains within three to six months while advancing regulatory alignment and operational resilience.
How AMS Can Help
We are uniquely positioned to strengthen capability across the entire organization through a structured, modern training approach grounded in our Adaptive Learning Framework℠ (ALF). With senior-level facilitators, highly customizable courses, and multiple delivery modalities, we design learning experiences that meet participants at every level, from executives to emerging professionals, and accelerate real-world application. The ALF℠ ensures knowledge transfer at each stage, enabling individuals and teams to absorb core concepts, practice new skills, and sustain improved performance over time. This integrated approach deepens capability, enhances alignment, and results in learning that is fully embedded, practical, and built to last.
Join the ranks of leading organizations that have partnered with AMS to drive innovation, improve performance, and achieve sustainable success. Let’s transform together, your journey to excellence starts here.