The Delivery Levels describe how a team really works with AI, not which tools it has bought. One thing separates each level from the next: how much of the work the AI starts on its own, and how much you still have to start for it. Below you will find each level in plain terms, what it looks like in every phase of your delivery, and how we decide where a team sits.
One thing separates each level from the next. Not the number of licences, not the size of the tool budget. Just this: who starts the work.
| Level | What the AI does | What the people do | In one word |
|---|---|---|---|
| DL1 | Nothing | Everything | Manual |
| DL2 | Reacts to keystrokes | Drive everything, AI assists per task | Reactive |
| DL3 | Understands project context, contributes proactively | Drive each phase, AI contributes within it | Contextual |
| DL4 | Takes initiative across the full SDLC from a business input | Review and approve the AI’s proactive output | Collaborative |
| DL5 | Operates autonomously at organisation scale | Handle exceptions and approve at gates | Autonomous |
All code is human-written. All tests are human-designed. All documentation is human-authored. All reviews are human-conducted. The traditional SDLC operates exactly as it has for decades.
1.0× baseline. Delivery speed scales only with headcount.
Engineers use AI tools (ChatGPT, Copilot, Cursor, Claude) on their own terms. AI provides autocomplete, code snippets, chat-based Q&A, and basic suggestions. Each task is handled independently. AI doesn’t understand your project as a whole. It reacts to what you type, file by file, prompt by prompt.
The developer adjusts the prompt (“fix this, use this framework, do not use that class”) instead of building a feedback loop that can be shared with the rest of the team.
Developer asks AI “write a function to validate email.” AI produces a generic validation function using a library the project doesn’t use, with naming conventions that don’t match the project’s style. Developer manually fixes it.
Productivity: ~1.25× (individual gain, unstructured).
AI is deeply aware of your architecture, project standards, service connections and testing approach. It doesn’t just react to prompts, it proactively contributes. When a feature involves three services, AI recognises the connections between them, generates unit and integration tests that cover cross-service interactions, flags potential conflicts before code review, and drafts requirement summaries from stakeholder input.
The team has a shared AI methodology: rules files, architecture documentation as AI context, structured review practices, and a documented standard for how AI is used across the project.
“Implement the password reset flow.” The AI knows the project uses Express with a specific auth middleware pattern, Prisma and Resend, and produces the full implementation matching all project conventions on the first try. Review finds it 90% correct; one follow-up prompt fixes the edge case.
Productivity: ~2–5× (team-wide, structured).
Given a business requirement or product conversation, AI proactively generates everything: user stories, functional blocks, architecture proposals, implementation, test suites with coverage optimisation, deployment configurations, and documentation. The engineer doesn’t need to direct AI phase by phase. AI takes initiative across the entire lifecycle.
The engineer’s primary deliverable shifts from code to judgment. They review AI’s proposals and make the calls: approve, adjust, or override. The spec is what AI drafts; the human validates and enriches it rather than writing it from scratch.
PM describes a feature in a conversation: “Users need to be able to reset their passwords.” AI generates a structured spec covering all flows, edge cases, security requirements and rate limiting; an architecture proposal showing which services are affected; complete implementation; a test suite with 15 tests covering happy path, error cases and security; API documentation updates; and a migration script. Engineer reviews the spec (“add session invalidation on reset”), approves the rest. Ships.
Productivity: ~10× (team output).
Multiple AI agents orchestrate the full SDLC. One agent refines requirements, another architects, another implements, another tests, another reviews. The workflow runs itself. Humans sit at gates between major phases: approving transitions, catching what agents missed, making judgment calls on strategic decisions.
The distinction from DL4 is not the technology, it is the scope and the autonomy. At DL4, AI is a proactive collaborator on individual projects with human oversight per phase. At DL5, AI operates across the entire organisation, routine tasks run autonomously, and humans focus only on exceptions and strategic decisions.
PM creates a ticket: “Add password reset.” The requirements agent generates the spec: all flows, edge cases, rate limiting, session handling. Gate 1: engineer reviews in 2 minutes, adds “CAPTCHA after 3 failed attempts.” Architecture agent plans the changes. Implementation agent writes the code. Testing agent runs 18 tests, one fails. Implementation agent fixes it, all pass. Review agent checks implementation against spec. Gate 2: engineer reviews the final PR. Approves. Ships.
Productivity: ~10–100×, depending on execution quality.
Almost no team sits at the same level everywhere. You can be at DL3 when writing code and DL1 when testing it. So we look at every phase on its own. Pick one and see how it changes from level to level.
In 30 minutes we go through your six phases with you, place your team on the scale, name the one thing holding the rest back, and show you what the next level is worth. You get it in writing within a week.