AI-Enabled SDLC

I work with enterprises that want AI to become a real part of how their software gets built — not just a set of licences and a memo asking developers to use them. The work is led by someone who has designed, validated, and rolled out this exact program across a multi-market regulated enterprise: a structured four-sprint R&D program, a validated adoption framework (Explore → Document → Test → Plan), and a phased metrics approach that starts with signals visible in weeks and matures toward full cycle-time measurement.

The methodology, in short

Before scaling AI across engineering teams, I run a structured R&D program to validate hypotheses, build reusable artifacts, and establish what actually works inside the constraints of a regulated enterprise. The pattern is consistent: a sprint on documentation generation, a sprint on AI-driven unit test generation, a sprint on vulnerability remediation combining LLMs with SAST, and a sprint on integration and database analysis. The output is not a slide deck — it is a working set of artifacts (meta-prompts, fix-pattern catalogues, context layers, a measurement dashboard) that the organization keeps.

Validated findings I bring in

Custom agents with structured context (repo structure, dependencies, coding patterns) produce roughly two to three times better unit tests and documentation than default copilot configurations. LLM-based security scanning catches vulnerabilities traditional SAST tools miss, and vice versa — together they provide materially better coverage than either alone. The four-phase adoption framework is documented and reusable across codebases.

Engagement structures

Fractional AI-SDLC Advisor

Typically around 25% capacity, working alongside the client's own AI engineers (or supporting hiring for them). Responsibilities include pilot design and baselining, AI tooling strategy and model selection, context architecture, enterprise guardrails, executive-level measurement and reporting, and the scaling playbook when the pilot succeeds. Suited to organizations that want senior leadership in the room without standing up a permanent role.

Embedded program lead

Deeper involvement for organizations that want the AI-SDLC rollout treated as a structured program: running the four-sprint R&D, instrumenting baseline metrics, choosing and integrating tooling, and standing up the Center of Excellence. Typically three to nine months, with the explicit aim of handing the program over to internal leadership at the end.

Targeted assessments and workshops

Defined scope, defined deliverable: an AI-readiness assessment of an engineering organization, a portfolio intelligence exercise that maps and profiles a codebase estate before a modernization decision, an executive workshop that builds the business case, or a review of an in-flight AI-SDLC effort that has stalled.

What gets delivered

Pilot design and starter metrics

Selecting the pilot team or teams, defining success criteria, and instrumenting the three starter signals — test coverage, documentation coverage, and vulnerability reduction — that are measurable within weeks and tie directly to AI-assisted SDLC work. Then a graduated path to cycle time, change failure rate, and the full three-pillar maturity model (delivery, quality & trust, people & capability).

Context architecture and tooling strategy

Designing the structured context layer that makes AI agents materially more effective, and guiding tool selection across unit test generation, documentation, vulnerability remediation, and legacy analysis — drawing on validated R&D comparing the major frontier models across each use case.

AI-powered portfolio intelligence

Using LLMs to systematically read and map codebases at portfolio scale — generating standardized profiles of each repository (tech stack, dependencies, API surface, data models, integration points) and building a living dependency graph that makes the invisible coupling between systems visible. This is the precondition for any modernization effort: you cannot plan a transformation if you cannot see what you actually have.

Enterprise guardrails

Quality, security, and model-risk guardrails appropriate for a regulated environment, including AI + SAST combined scanning workflows, fix-pattern catalogues for the common vulnerability classes, and a reusable meta-prompt library production-tested for security review, test generation, and documentation.

Scaling playbook and CoE design

Once the pilot has proven out, the organizational scaling blueprint: Center of Excellence structure, training programs, office hours, internal AI assessments, pod-to-CoE rotations, and the role-evolution guidance that helps an engineering organization adjust its job functions rather than just adding new tools on top of old ones.

Who this is for

Enterprises and large companies that have moved past the "buy some seats and see what happens" phase and want AI to genuinely change how engineering gets done — including organizations under regulatory scrutiny where the standard playbooks for AI rollout do not fit. This is distinct from the advisory work, which is aimed at startups, SMEs, and PE-backed situations.

If you'd like to discuss whether an engagement makes sense, email me or find me on LinkedIn.