// AI ACROSS THE SDLC · MASTERCLASS
Adopt AI in engineering efficiently and safely.
A free, expert-led one-day masterclass on using AI across the software development lifecycle — the opportunities, the real risks, and the safeguards that let your teams move quickly without compromising quality, security or compliance.
// WHY IS THIS RELEVANT TO ME?
Strong engineering gets faster. Weak engineering breaks faster.
AI coding tools are mainstream and the landscape is moving fast. The question is no longer whether to adopt them, but how to do it efficiently and safely.
The evidence is clear: AI amplifies the foundations already in place. Knowing where the opportunities, risks and safeguards sit across your SDLC is now the differentiator.
We spend most of the day where the impact and the risk are highest — in engineering — while covering the rest of the lifecycle so the picture is complete.
// WHY YOU SHOULD ACT NOW
Adoption is ahead of the safeguards.
Teams are already shipping AI-assisted code. The gains are real, and so is the rework that follows when the foundations underneath are not ready for it.
Of developers are using or planning to use AI tools in their workflow, up from 76% the year before.
Faster task completion observed in controlled studies when developers pair with AI coding assistants.
More issues observed on AI-authored pull requests than on human-authored ones.
Trust gap — only 29% of developers trust AI output for accuracy, and 46% actively distrust it.
SOURCES: STACK OVERFLOW 2025 DEVELOPER SURVEY · GITHUB RESEARCH STUDY · CODERABBIT 2025 STUDY
// TOPICS WE WILL EXPLORE
Opportunities, risks and safeguards.
Expert-led discussion and practical demos across the full lifecycle, from requirements and design through build, test, deploy and operate.
AI across the full SDLC
Map opportunities at each stage, from requirements and design through to build, test, deploy and operate.
Tooling, models and agents
Review the tools, models and agentic patterns shaping 2026 — Copilot, Claude, Cursor and others — and how to govern access, currency and cost.
Codebase AI-readiness
Assess how well your code, patterns and context support effective AI assistance and agentic workflows in real engineering environments.
Speed, quality and safety
The AI velocity paradox, real production incidents, prompt injection, the trust gap, and the rework rising on AI-touched changes.
Foundations and quality gates
SAST, SCA, secret scanning, licence scanning and test coverage — the controls that must precede scaling AI in any regulated context.
Governance, policy and data security
Align engineering, security and leadership on a shared AI risk posture, with controls that scale with capability and stand up to audit.
Measurement, DevEx and enablement
Use DORA metrics with rework rate as a leading indicator of AI quality cost, attribute AI value, and treat rollout as change management.
// MASTERCLASS AGENDA
One day, three movements.
Before the masterclass we send a short questionnaire covering your current tooling, engineering practices, governance posture and goals — so the day starts where you are.
The AI landscape across the SDLC
State-of-the-art tooling and patterns from requirements to operations, grounded in 2026 evidence. We frame the day around the three questions every engineering organisation is asking: where are the opportunities, what are the risks, which safeguards work.
Opportunities and live demos
Stage-by-stage exploration with short live demos to ground the conversation. We capture the highest-value opportunities for your teams, with the engineering core given most of the time and the wider lifecycle covered for completeness.
Risks, safeguards and roadmap
Real incidents, the AI velocity paradox, the trust gap and regulated-environment considerations, paired with safeguards and foundations that scale with capability. We close with a tailored set of next steps for your context.
// WHAT YOU WILL GET
A shared view, and a place to start.
WHO SHOULD ATTEND
This is a focused day to set direction. It is not a substitute for a full AI engineering benchmark and review, which goes deeper across 17 maturity dimensions, with evidence-based scoring, stakeholder interviews and a phased implementation plan.
// LET'S TALK
Set direction on AI in engineering.
A free day with your engineering leaders, shaped around where you are now. Start with a conversation.