# AI Enablement Services

*Digizoo helps delivery teams ship faster with AI, with quality you can prove.*

We embed a small AI squad in your delivery teams for six months. Together we take real backlog items to production through an AI-enabled lifecycle, build the pipeline and guardrails your risk function can sign, and hand it all to your people to run.

## Quality first, then speed

Most delivery teams already have AI tools. In most of them, output rises,
review clogs and trust falls, because the way of working never changed.

We embed a small squad for six months, agree the quality bar before speed is
asked for, build the pipeline that produces evidence as it runs, and take real
backlog items to production with your people. Then they run it without us.

## Three things decide whether AI adoption works

We organise the whole engagement around them.

- **People, roles and culture**: In our experience AI roughly doubles what reaches review, so the scarce work moves upstream to shaping inputs and designing tests. We reshape roles with the work and transfer capability by pairing, measured against checkpoints rather than assumed.
- **Operating model and cadence**: Chapters own the standard, squads pass the gates in their existing cadence. You get one baseline rather than many, and we facilitate rather than own it.
- **Tooling and enabling capabilities**: We build an agentic pipeline to the standard your regulator describes: one objective per agent, structured hand-offs, lane isolation and audit logging. It runs inside your enterprise AI environment and your CI/CD.

## Six gates from idea to production

Each backlog item climbs them in order, and a named person owns each gate.

- **Discovery and feasibility**: We run fast, reversible experiments before anything is committed. The baseline is measured here, in your own numbers.
- **Problem framing and value**: Each item gets a problem statement, a value hypothesis and a risk class. That decides whether the expensive work happens at all.
- **MVP build**: The agentic pipeline does the routine work and your developers direct it. Each step produces its evidence as it runs.
- **Quality, risk and trust**: We run evals against golden sets and regression suites, and build the controls your risk function asked for, mapped to their requirements.
- **Hardening and release**: We write the playbooks, set up the monitoring and run the incident process. The pipeline never becomes a second path to production.
- **Measure and iterate**: We measure cycle time, defect rate, change failure rate and eval pass rates. The result earns the next item.

## Three ways to bring AI into delivery

|   | An embedded squad | Roll out the tools | Add people to the project |
| --- | --- | --- | --- |
| Real backlog items reach production during the engagement | Yes | No | Yes |
| A measured baseline before any gain is claimed | Yes | No | No |
| Quality gates run on evidence, not status reports | Yes | No | No |
| Review is redesigned as the work shifts to judgement | Yes | No | No |
| Guardrails your risk function signs off as gates | Yes | No | No |
| Your own people run it afterwards | Yes | Yes | No |

## Six months, in three phases

We run it alongside your people and hand capability over as we go rather than at the end.


1. **Discovery and mobilisation**: In the first six weeks we scope your first items with your portfolio owners, engage your risk function by name, stand up the pipeline and measure the baseline. The lifecycle is drafted with your people, not handed to them.
2. **Co-build and deliver**: At least two backlog items go all the way to production. Capability and assets are handed over as we go, in demonstrations and retrospectives, rather than at the end.
3. **Handover**: Your squad takes the next item through the lifecycle with us out of the room. You keep the pipeline, the versioned skills, the risk and controls register, the runbooks and the blueprint the next squad works from.

## Measured against your own numbers

- **6** months, embedded. Discovery, co-build and handover, run alongside your own people.
- **2+** backlog items in production. Taken through the full lifecycle during the engagement, then a third by your squad alone.
- **6** gates with named owners. Each one run on recorded evidence rather than narrative status.

## FAQ

**Where has this run in production?**

The pipeline runs today on our own product, Ark, which ships into a regulated mutual bank's production estate, and the quality approach ran on a core banking replacement at a New Zealand bank.


**Our estate is brownfield. Where does the gain actually sit?**

It does not sit in deploying more often, because release authority stays yours and your cadence does not change. It sits earlier: in requirements and specification preparation, test design, the evidence assembled around it, and the review and approval step itself once evals make it fast.


**How does this fit our chapters and squads?**

Chapters own the standard and squads pass the gates. The lifecycle becomes a chapter craft standard, one baseline rather than many, and the gates are passed in each squad's existing cadence by whoever already holds the accountability. We facilitate, hand it to the chapter, and step back.


**How does this meet our risk function and the regulator?**

We build guardrails as mechanism and sign them off as gates. Each agent gets one objective and structured hand-offs. Lane isolation stops a testing agent reaching development secrets, stopping conditions halt a run that goes wrong, and the audit log records who approved what and in what context. We engage your second line by name in week one and map the controls to their requirements.


**How is capability transferred, and how do we know it worked?**

We transfer it by pairing on real work, against competency checkpoints we agree at the start. The handover pack stands on its own: an ownership matrix, runbooks and an offboarding pack built as we go. The test is that your squad takes the next production item through the lifecycle without us.


**What tools do you use?**

We use yours, wherever you have them. The pipeline runs inside your enterprise AI environment on your managed infrastructure, integrates with your existing CI/CD, and its skills reach the systems your teams already work in. It is designed to keep working without our harness.

## Ready to make AI part of how you deliver?

Talk to the Digizoo team through the contact form: https://www.digizoo.com.au/#contact