AI SDLC Advisory
Move from scattered AI coding use to a governed, measurable AI-enabled SDLC.
I help established engineering organisations redesign how software is specified, built, reviewed, tested, released, and operated when AI becomes part of the delivery system—not merely another developer tool.
The shift
Individual assistance is not yet an organisational capability.
Copilots and coding agents can make implementation faster. But software delivery is a larger system. As output rises, constraints often move to problem framing, product intent, context, architecture, review capacity, testing, release controls, and operational ownership.
The challenge is especially visible in internal IT, mature SaaS, and brownfield environments, where important knowledge is tacit, dependencies are real, and teams remain accountable for production consequences.
AI-enabled SDLC here means using AI across the software lifecycle while keeping human accountability, quality evidence, and business outcomes explicit.
Where tool rollout stalls
AI amplifies the engineering system already in place.
Intent and context
Agents cannot reliably infer missing goals, constraints, domain knowledge, architecture decisions, or a useful definition of done.
Verification capacity
Generated output grows faster than the organisation’s ability to review, test, understand, approve, and own it.
Workflow design
Adding AI to every existing step can preserve the old process while increasing coordination and review load.
Platform readiness
Safe delegation depends on repositories, environments, permissions, tests, observability, sandboxes, and recovery paths.
Roles and decisions
Teams need clarity on what agents may execute, what humans must judge, and who remains accountable for consequences.
Measurement
More code, prompts, or agent sessions are activity signals—not proof of faster, safer, or more valuable delivery.
AI SDLC Diagnostic
Assess how work is changing—and what prevents the next useful transition.
Working mode
Map where teams or workflow types sit today: gated, assisted, parallel, supervised autonomy, or AI-native. The aim is not maximum autonomy everywhere. It is the right working mode for the risk and context.
Capability profile
- Strategy, leadership, and value
- People, roles, and culture
- AI use and workflow autonomy
- Product development and delivery
- Context, platform, and data readiness
- Governance, risk, and trust
Delivery-system evidence
Examine where work waits or fails, how quality is verified, which controls are risk-based, and whether metrics pair throughput with quality, reliability, maintainability, adoption, and developer experience.
Target and next moves
Choose a deliberate target profile, identify the main bottleneck, and define three to five priority actions, owners, pilot boundaries, and evidence needed before scaling.
Typical output
A decision-ready view—not a generic maturity badge.
- Current working modes by team or workflow type
- Six-dimension capability heatmap
- Current and deliberately chosen target states
- The main constraint blocking useful progress
- Priority actions, owners, and governance decisions
- A bounded pilot or 90-day improvement roadmap
- Paired delivery, quality, business, and developer-experience measures
The diagnostic can stand alone or become the first step in a focused advisory engagement.
Good fit
Designed for established engineering environments—not AI theatre.
This is relevant when
- AI use is already happening but varies widely across teams.
- Leadership cannot connect adoption to delivery outcomes.
- Security, legal, or risk concerns block wider use.
- Brownfield context and dependencies limit generic playbooks.
- Review and verification are becoming bottlenecks.
- You need a practical path beyond a Copilot rollout.
This is not
- A developer-tool procurement exercise
- A promise of universal 2× or 3× productivity
- A generic prompt-engineering course
- A requirement to become AI-native everywhere
- An outsourced delivery or platform implementation service
- A one-size-fits-all governance framework
From tools to capability
Find the smallest credible step toward an AI-enabled SDLC.
Start with the context: where AI is already used, which outcomes matter, and where leaders no longer trust the current picture.