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At Octopus Deploy, AI did not start with a big rollout or a strict adoption plan. It began with individual developers trying things out. Sometimes it was curiosity. Sometimes it was to solve a specific pain. Some teams were skeptical. Others had concerns about trust, privacy, or whether the tools were mature enough to use in production. Instead of forcing adoption, we allowed people to experiment and figure out what helped them ship faster or work smarter.

That approach gave us room to learn. Teams ran small tests, shared what worked, and slowly built confidence. Over time, AI shifted from something a few people played with to something teams relied on daily. Eventually, we brought that experience into the product itself. We added AI features where they made sense, not as flashy add-ons, but to improve the deployment experience in simple, practical ways.

You should attend this session if you are a developer who wants a realistic view of what AI looks like inside a software team. If you have tested tools but struggled to get lasting value or are unsure how to introduce AI to your team without it becoming a distraction, this talk will help. You will hear what worked for us, what did not, and how we kept the focus on practical outcomes. Whether you are curious, cautious, or somewhere in between, you will leave with ideas you can take back to your team and use right away.

NDC Conferences

A practitioner talk from an Octopus Deploy developer relations engineer sharing how the company adopted AI tools without top-down mandates. Key themes include bottom-up AI adoption driven by developer curiosity, using a diverse set of tools (Claude, Copilot, Cursor, Codex) without mandating any single one, and the importance of skills/instruction files (claude.md, rule sync) for sharing AI context across teams. The talk covers what worked (scaffolding, documentation generation, debugging, YAML/Terraform authoring), what failed (hallucinations, vague prompts, oversized PRs), and how Octopus integrated AI into its product via a Chrome extension AI assistant, a deployment recovery agent, and an MCP server. The speaker argues code review is becoming theater as AI-generated PRs grow too large to review meaningfully, and that tests and behavioral guardrails matter more than code review going forward. He also warns against 'vibe deployments' and advocates for deterministic deployment pipelines.

One Team’s Journey to Using AI Without the Hype - John Bristowe - NDC Sydney 2026