Most engineering leaders today feel the same tension: everyone talks about “AI in software delivery,” but few know where to start. Should you launch pilots? Train teams? Complement Jira or some other SDLC tools with some AI copilot plugins? Or just wait until the chaos settles? In reality, the first step is not about tools at all. It’s about clarity. Before jumping into models, prompts, or copilots, it’s critical to understand what kind of project is actually being transformed. Not every project needs the same approach. Step 1: Recognize What You’re Transforming Step 1: Recognize What You’re Transforming Across multiple organizations, I’ve seen dozens of teams experimenting with AI in the SDLC. Some succeed, many stall. The difference usually comes down to one question: Are you improving how you deliver, or redefining what delivery means? Are you improving how you deliver, or redefining what delivery means? This is why I categorize AI SDLC initiatives into three types: **Existing Projects - Efficiency Mode**Teams already use some AI tools but lack structure. The goal is to improve efficiency and reduce waste in specific, measurable areas: faster testing, smarter documentation, or automated reviews. These projects deliver quick wins - a good way to start proving value fast.
**New (Greenfield) Projects - AI-First Mode**When you build something from scratch, you can design the architecture to be AI-native from day one. That means clean codebases, controlled environments, and experienced engineers who know how to use GenAI tools responsibly. It’s high risk, high reward, but also the most scalable model.
**Transformation Projects – Integration Mode**The hardest and most strategic. These involve multiple teams (in-house teams, vendor teams, maybe even partner teams). The task is to unify architecture, process, and governance - to make the whole system AI-ready. This is where true enterprise transformation happens. **Existing Projects - Efficiency Mode**Teams already use some AI tools but lack structure. The goal is to improve efficiency and reduce waste in specific, measurable areas: faster testing, smarter documentation, or automated reviews. These projects deliver quick wins - a good way to start proving value fast. **New (Greenfield) Projects - AI-First Mode**When you build something from scratch, you can design the architecture to be AI-native from day one. That means clean codebases, controlled environments, and experienced engineers who know how to use GenAI tools responsibly. It’s high risk, high reward, but also the most scalable model. **Transformation Projects – Integration Mode**The hardest and most strategic. These involve multiple teams (in-house teams, vendor teams, maybe even partner teams). The task is to unify architecture, process, and governance - to make the whole system AI-ready. This is where true enterprise transformation happens. Understanding which type of project you’re in changes everything: the tools you pick, the metrics you track, even the conversations you have with stakeholders. Step 2: Stop Measuring “Velocity” the Old Way Step 2: Stop Measuring “Velocity” the Old Way The biggest mistake I see during AI adoption is using the same metrics we used ten years ago. Traditional velocity (counting story points, features delivered, or backlog burndown) simply does not reflect transformation effort. Let’s take a real case: A team automates 40% of its regression tests, documentation, and code reviews using GenAI tools. Their sprint velocity drops because they temporarily stop building features. By the old metric, they’re “slower.” In reality, they’ve unlocked future acceleration, a structural gain that compounds over time. A team automates 40% of its regression tests, documentation, and code reviews using GenAI tools. Their sprint velocity drops because they temporarily stop building features. By the old metric, they’re “slower.” In reality, they’ve unlocked future acceleration, a structural gain that compounds over time. So the right question isn’t “How fast did we deliver?”. It’s “How much of our delivery process have we made AI-friendly?” Every transformation should measure both: Feature Velocity: the short-term delivery metric everyone understands.
Transformation Velocity: the long-term improvement in how the system itself produces software. Feature Velocity: the short-term delivery metric everyone understands. Feature Velocity Transformation Velocity: the long-term improvement in how the system itself produces software. Transformation Velocity If you track only the first, you’ll punish innovation. If you track both, you’ll build real capability. Step 3: Start Small, Measure Fast Step 3: Start Small, Measure Fast From my experience, every successful AI SDLC engagement follows a similar rhythm, and the first measurable impact usually comes in 1.5-2 months. Assess & Benchmark: Understand your architecture readiness and team maturity.
Joint Execution: Work hands-on with engineering teams. No slideware, just real integration.
Validate Impact: Use data to confirm progress (velocity, quality, cycle time, coverage).
Transition & Scale: Hand back ownership once the new model runs sustainably. Assess & Benchmark: Understand your architecture readiness and team maturity. Assess & Benchmark Joint Execution: Work hands-on with engineering teams. No slideware, just real integration. Joint Execution Validate Impact: Use data to confirm progress (velocity, quality, cycle time, coverage). Validate Impact Transition & Scale: Hand back ownership once the new model runs sustainably. Transition & Scale This combination of advisory and execution is what makes transformation tangible. It’s not a slideware initiative, it’s a measurable shift in how engineering happens. Step 4: Expect Resistance, and Manage It with Data Step 4: Expect Resistance, and Manage It with Data AI transformation is emotional. Teams fear changes, clients desire fast results, and everyone feels the risk of the unknown. The only antidote is transparency and evidence: Involve delivery champions early.
Use only secure, enterprise-approved AI tools.
Track clear quality gates. For example, 90% AI-augmented review acceptance rate before scaling.
Pair-enable engineers instead of training them in isolation. Involve delivery champions early. Use only secure, enterprise-approved AI tools. Track clear quality gates. For example, 90% AI-augmented review acceptance rate before scaling. Pair-enable engineers instead of training them in isolation. AI doesn’t replace teams, it amplifies them. But only if you create the right structure to prove it works. Step 5: Think Systemically, Not Tactically Step 5: Think Systemically, Not Tactically An AI-driven SDLC isn’t just “DevOps with prompts”. It’s a system where data, code, and operations are intertwined. That demands leaders who can think across boundaries: Architectural Vision: build modular, auditable, AI-friendly systems.
DevOps Mastery: integrate continuous automation and monitoring into your pipelines.
Quality Redefined: move from deterministic to probabilistic validation.
Agile Leadership: lead through uncertainty, manage experiments, and measure outcomes. Architectural Vision: build modular, auditable, AI-friendly systems. Architectural Vision DevOps Mastery: integrate continuous automation and monitoring into your pipelines. DevOps Mastery Quality Redefined: move from deterministic to probabilistic validation. Quality Redefined Agile Leadership: lead through uncertainty, manage experiments, and measure outcomes. Agile Leadership When we master these dimensions, teams stop “doing AI” and start engineering with AI. So, Where to Start? So, Where to Start? Start where impact meets readiness. Pick one project that’s stable enough to measure, small enough to control, and visible enough to matter. Define your baselines, introduce AI carefully, and measure relentlessly. You’ll soon find that transformation is not about velocity spikes, it’s about sustained change. And once you prove it once, the rest of the organization will want it too. Side Note Side Note If you’re interested in transforming not just your SDLC but your own thinking as a technology leader, you may find my book Enterprise Solutions Architect Mindset helpful. You can check it out on amazon or here for more options. amazon amazon here for more options here for more options Orkhan Gasimov is a technology executive and AI transformation strategist helping enterprises modernize software delivery with AI. Orkhan Gasimov is a technology executive and AI transformation strategist helping enterprises modernize software delivery with AI.

AI in the SDLC Starts With Clarity, Not Tools.

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

Beyond the Hype: Why 87% of AI Projects Fail and What the 13% Do Differently

Beyond the Hype: 4 Core Truths About How AI Agents Get Things Done

Prompt vs Feature Engineering: The Hidden Bridge Between Humans and Machines

The Noonification: The Future of DeFi - Emerging Evolution (11/3/2022)

10 Ways PMs Can Earn The Respect and Trust of Their Team

10 Terms Every Engineer Must Know

Beyond the Hype: Why 87% of AI Projects Fail and What the 13% Do Differently

Beyond the Hype: 4 Core Truths About How AI Agents Get Things Done

Prompt vs Feature Engineering: The Hidden Bridge Between Humans and Machines

The Noonification: The Future of DeFi - Emerging Evolution (11/3/2022)

10 Ways PMs Can Earn The Respect and Trust of Their Team

10 Terms Every Engineer Must Know

Light-Mode

Classic

Newspaper

Minty

Dark-Mode

Neon Noir

Minty

HN StartUps