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AI-native software delivery

Reimagining delivery, from idea to benefit.

AI-native software delivery is a delivery model where AI agents carry the routine work of the software lifecycle, including requirements, code, tests and release evidence, while your people set direction, judge the output and hold the accountability. Transformativ redesigns the lifecycle end to end for Australian enterprises in financial services and other regulated sectors, so the speed AI gives your engineers reaches production and shows up as value.

THE PROBLEM

Why AI coding tools haven't moved your delivery dates

Most enterprises have put AI tools in front of their engineers, and the release calendar looks much the same. AI assistants on their own lift software productivity by 10 to 15 percent. Paired with end-to-end process change, some companies report 25 to 30 percent (Bain & Company, Technology Report 2025).

The gap shows up in three places:

AI assistants alone
10 to 15%
With end-to-end process change
25 to 30%

  1. The bottleneck moves.

    Build time drops from weeks to days, and the gain then waits in the approval queue.

  2. Requirements decide everything downstream.

    AI agents produce what the specification tells them to. Weak requirements mean rework at machine speed.

  3. Roles go undesigned.

    Engineers who joined to build don't want a future spent reviewing AI output, and adoption stalls when nobody answers what each role becomes.

WHAT CHANGES

What changes

AI-native delivery turns the lifecycle into one connected flow from idea to production.

  1. Work moves from idea to production as one connected flow, with AI agents carrying the routine in every stage.

  2. Requirements become the contract the agents work from, so quality is set at the front.

  3. Governance is met inside the flow, so the speed survives approval.

  4. Every role reaches further. Analysts move toward product ownership, developers toward architecture and the direction of agents, and testers toward evaluation.

  5. Value is provable at every release, measured against a baseline.

THE METHODS

The methods behind it

AI-native delivery draws on all four of our capabilities.

HOW IT RUNS

How it runs inside Catalyst+

AI-native software delivery runs through Catalyst+, our AI transformation product, in three stages.

See how Catalyst+ works →
  1. 01 · DEFINESIX WEEKS

    Define

    We baseline your delivery lifecycle, design the target flow, roles, agent architecture and governance, and build the costed value case. Where the scope is one product team, AI agents are live on that team's delivery by week 6.

  2. 02 · ACTIVATETYPICALLY 8 TO 12 WEEKS

    Activate

    The redesigned lifecycle goes live on a first segment of your delivery.

  3. 03 · SCALE

    Scale

    It extends across teams in waves, with value measured at every release.

What happens in the six weeks

WEEK 1WEEK 6: AI DELIVERING VALUE
  1. WEEKS 1 TO 2ALIGN

    WEEKS 1 TO 2 | ALIGN

    Agree the product team or delivery area in scope. Baseline your delivery lifecycle, from idea to production, and the AI spend today.

    BY WEEK 2

    The scope agreed, with today's delivery lifecycle and AI spend baselined.

  2. WEEKS 3 TO 4DISCOVER

    WEEKS 3 TO 4 | DISCOVER

    Work directly with your analysts, developers and testers. Give every task in the lifecycle a verdict: automate, augment or preserve. Find where the work waits.

    BY WEEK 4

    Every task in the lifecycle has a verdict, and you know where work waits and why.

  3. WEEKS 5 TO 6DESIGN

    WEEKS 5 TO 6 | DESIGN

    Design the target flow, roles, agent architecture and governance, and build the costed value case. Where the scope is one product team, AI agents go live on that team's delivery.

    BY WEEK 6

    Where the scope is one product team, AI agents are live on that team's delivery, with a costed value case and a board-ready blueprint to scale them.

QUESTIONS

Common questions about AI-native software delivery

It's a way of running software delivery where AI agents do part of the work at every stage of the lifecycle: drafting requirements and acceptance criteria, writing and reviewing code, generating tests, and producing release notes and change evidence. Your analysts, engineers and testers decide what gets built, check what the agents produce and approve what goes to production. The process, roles and controls are redesigned around that split, so faster coding turns into faster releases.

Coding assistants speed up one step. Google's 2025 DORA research, a survey of nearly 5,000 technology professionals, found that AI adoption now lifts delivery throughput but also raises delivery instability, and that AI magnifies whatever an organisation already does well or badly. AI-native delivery also changes the steps around the code: how requirements are written so an agent can build from them, how testing and approvals keep pace, and who is accountable for what an agent produced.

Usually the ones you already have: Atlassian (Jira, Confluence and Rovo), GitHub Copilot or Claude Code for engineers, and ServiceNow for change and release. We design around your existing stack and add agents only where there's a gap. We're not tied to any one vendor, so recommendations follow your environment, contracts and risk settings.

Each role shifts toward work that needs judgement. Analysts spend more time writing specifications precise enough for an agent to build from, and on product decisions. Developers spend more time on architecture, code review and directing agents. Testers move into test strategy and evaluating agent output. We redesign each role with the people in it before agents arrive, and set out the skills each one needs.

For one product team, within six weeks. In Define we baseline that team's lead time, cycle time and release frequency, then put AI agents into its delivery by week 6, with the team reviewing every output. The costed value case for extending to more teams is ready at the same point, and Activate typically takes 8 to 12 weeks.