Virtual 3-day intensive
DevOps Transformation with AI
Build a practical, enterprise-safe approach to AI for platform delivery and operations. Help your teams reduce toil, improve incident response, and standardise platform work without bypassing controls, compliance, or engineering judgement.
Transform
AI for delivery, operations, and support — not just code generation
Modular
Adopt capabilities incrementally with human approval and policy-aware guardrails
Controlled
Use human approval, policy-aware guardrails, and traceable outputs by default
ISO 27001:2022GDPRWhat this programme is
DevOps and platform engineering teams are under constant strain. Output expectations remain high, headcount is constrained, and compliance requirements are not optional.
Much of the work is repetitive but complex: translating requirements into configuration, delivery controls, processes, and operational responses. This is exactly the kind of work where AI can help — provided it operates with context, guardrails, and clear limits.
Most teams are still using AI for isolated generative tasks. The bigger opportunity is using it to support how platforms are built, operated, and improved.
Where AI creates real leverage
This workshop focuses on the practical use of AI in platform and service environments, where reliability, traceability, and secure defaults matter.
You will learn how to use AI to:
produce spec-driven configuration faster and more consistently
reduce operational load with stronger incident triage and first response
create an intelligent support layer that evolves with the platform
improve standardisation without forcing teams into rigid process change
This is not about handing control to autonomous systems. It is about giving teams structured, measurable leverage through specifications, processes, and tools that can validate both human input and AI output.
Tool-agnostic by design
We teach the operating model and AI capability patterns first, then show you how to adapt them to your stack.
Whether you run Kubernetes or serverless workloads, and whether your teams use AWS, Azure, or Google Cloud, the focus is the same: practical AI capabilities that reduce toil, improve support, and fit inside existing engineering controls.
Who this is for
This course is designed for:
platform engineers
DevOps leaders
SRE and operations teams
engineering managers responsible for delivery controls
organisations adopting AI under compliance or governance constraints
Best fit for teams that:
operate production platforms under security or compliance requirements
want to reduce operational toil without weakening control
need practical AI patterns that fit existing workflows and approval models
Workshop agenda
Day 1 — Identify where AI creates real leverage
Understand where platform teams lose time to repetitive but complex work
Identify the best opportunities for AI in delivery, operations, and support
Separate meaningful use cases from novelty-driven experiments
Define where AI can reduce toil without weakening control
Day 2 — Define a safe operating model
Design an enterprise-safe model for adopting AI in platform teams
Work through human approval, least privilege, provenance, and policy-aware constraints
Choose modular capabilities that can be adopted incrementally
Avoid forcing teams into a disruptive process rewrite
Workshop 1 — Improve support and incident response
Turn scattered platform signals into structured triage outputs
Reduce noise and improve first response with better context and escalation
Design support workflows that evolve with the platform over time
Use platform events, operational data, and recent changes more effectively
Workshop 2 — Improve support and incident response
Turn scattered platform signals into structured triage output
Reduce noise and improve first response with better context and escalation
Design support workflows that evolve with the platform over time
Use platform events, operational data, and recent changes more effectively
Workshop 3 — Plan rollout, measurement, and governance
Define what success looks like and what to measure
Plan safe adoption sequencing for teams operating under risk or compliance constraints
Tune the model without creating new operational risk
Create review loops so engineers remain pilots, not passengers
What you’ll be able to do after the programme
This is not theory. You will leave with a clearer adoption model, practical capability patterns, and a plan you can apply in your own environment.
Design practical capability patterns
Shape spec-driven assistance, incident triage, and support workflows that fit the way your teams already work.
Apply enterprise-safe controls
Use human approval, least privilege, provenance, and policy-aware guardrails to keep adoption practical and governed.
Plan rollout with confidence
Take back a modular approach your team can introduce incrementally, measure properly, and improve over time.
Identify the best AI opportunities in platform work
Recognise where AI can reduce toil, improve consistency, and strengthen support without creating unnecessary risk.
What you get
You will leave with practical assets and a clear next-step plan, not just notes.
Capability design templates
Reusable templates for spec-driven assistance, incident triage, significant event monitoring, and support workflow design.
Operating model and guardrails framework
A practical model for human approval, least privilege, provenance, auditability, and policy-aware adoption.
Workflow patterns and working examples
Structured examples for triage reports, support flows, review points, and AI-assisted operational workflows.
Recordings and workshop materials
Session recordings and supporting materials for recap, internal sharing, and follow-on planning.
A practical next-step plan
A clear 30/60/90-day plan for introducing useful capabilities incrementally in your own environment.
Bring your stack
We teach the patterns and operating model first. You implement them using your preferred tools.
- Use your existing delivery and platform tooling
Whether your teams use Jenkins, GitHub Actions, GitLab CI, internal platforms, or a mix of tools, the focus is on patterns that can be adapted without forcing a full toolchain change.
- Choose your cloud and runtime environment
AWS, Azure, and Google Cloud are all in scope, along with Kubernetes, serverless, and mixed platform environments.
- Use the signals you already have
We focus on practical operational inputs such as logs, metrics, traces, change events, tickets, runbooks, and incident workflows.
- AI inside your controls
AI capabilities should fit your existing approval models, permissions, policies, and audit requirements — not bypass them.
Talk to a training specialist
Customer proof