Data Platform Delivery

Enterprise Data & Analytics

Western Australia

Context

An enterprise data technology team responsible for building and maintaining production data pipelines faced growing demand without a matching increase in capacity.

Delivery time was absorbed by repetitive work: writing and debugging pipeline code, chasing incomplete business requirements for new and changed datasets, searching fragmented internal technical documentation, and writing documentation after the fact when it was written at all.

Rather than adopting a single tool, the team embedded AI assistance at five points in the delivery lifecycle: requirements interpretation, pipeline development and debugging, documentation discovery, documentation authoring, and internal tooling. The same capability was then turned inward.

AI agents built over internal technical documentation, and repeatable engineering tasks packaged into a shared development-environment extension, so individual productivity gains became team-wide capability.

5

Workflows Uplifted

60%

Faster Build & Debug

100%

Scripts Documented

Minutes

To Find Answers

4

Team Tools Shipped

Zero

Extra Headcount

  • 200+ pages of business case, cyber strategy, risk registers, architecture standards, and audit findings reviewed manually by one BA — taking 3–5 days with inconsistent results.
  • Critical regulated requirements routinely missed buried inside risk register appendices, latest regulations, and previous audit findings.
  • No traceability: project teams could not always link scope items back to the originating business case, risk, or regulatory obligation.
  • Scope boundary between PAM, MFA, SIEM, and future Zero Trust workstreams was undefined creating downstream creep and rework risk.
  • AI agent deployed with StepChange’s Scope Development Method embedded; ingesting all source documents simultaneously: business case, strategy, risk registers, architecture standards, audit findings, and workshop scripts.
  • Structured extraction prompts applied systematically across all documents — identifying in-scope items, exclusions, assumptions, dependencies, and regulatory obligations.
  • Every extracted scope item automatically linked to its source document, section, paragraph, risk register entry, and applicable regulatory obligation — building a complete traceability matrix.
  • Governance-ready PAM project scope document delivered in under 4 hours, versus 3–5 days manually.
  • Complete traceability matrix produced with every scope item linked to its originating business case section, risk item, or regulatory obligation.
  • 14 previously overlooked requirements surfaced
  • Automated compliance mapping to SOCI Act 2018, Essential Eight, and ACSC guidance, with gap analysis against each framework.
  • Formal out-of-scope register produced across all four workstreams preventing scope creep before the project commenced.

The win wasn’t any single tool — it was putting assistance at every step: what the business actually needs, the code, the documentation, and now the tooling the whole team shares.

Data Engineer, Enterprise Data & Analytics