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Challenges

Banks, insurers and super funds are investing in engineering but structural problems are limiting the returns.

How work is organised. Project-based delivery resets ownership at the end of every initiative, breaking accountability for resilience, cost efficiency and long-term performance.

How AI is adopted. Most organisations are adding AI as point tools rather than building it as a governed capability. Individual productivity gains don't aggregate into enterprise improvement.

How reviews operate. Risk, compliance and quality checks are applied after delivery, not as a structural control, creating tension between speed and regulatory confidence.

“Banks and insurers deploying agents and AI tooling into project-centric delivery are not solving the problem—they are scaling it. An agent built for one initiative and disbanded with the team is not a capability. It is another form of technical debt.”
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What you’ll find inside

Engineering at Scale in the Age of AI paper covers three shifts that define how leading finance organisations are building engineering for the next decade.

In Chapter 1—Outcome-Driven Engineering—you'll see how high performing banks and insurers replace project-based delivery with persistent, cross-functional teams aligned to products and customer journeys.

In Chapter 2—AI-Augmented Engineering (ADLC)—you'll explore the three-stage maturity model from traditional SDLC to fully governed agentic delivery, and how a major Australian bank has already achieved 30–50% reduction in repeatable delivery effort with 80%+ regression test automation coverage. Live, in production, under APRA regulation.

In Chapter 3—Risk-Embedded Golden Pathways—you'll learn how governance moves from a phase-gate applied after delivery to a structural control embedded throughout the pipeline.

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