Insights
Product, design, AI, and engineering perspectives from our team.
Designing Technical Assessment Frameworks for AI/ML Hiring
Hiring in-house AI and ML engineers requires a different interview loop to general software hiring — and a different lens to evaluating AI vendors. This guide sets out how data and engineering leaders can structure interview loops and take-home assessments to test ML judgement, not just code.
Measuring AI Feature Adoption: A Framework for SaaS Teams
Usage counts don't tell you whether an AI feature is actually working. This framework covers event design, cohort analysis, and qualitative feedback loops for measuring real adoption of AI features in SaaS products — distinct from financial ROI modelling.
AI Centre of Excellence: Three Models and How to Choose
There are three real operating models for an AI Centre of Excellence — centralised, federated, and hub-and-spoke — and the right one depends on how many AI initiatives you're running, how mature your business units are, and how much central governance you're willing to fund. This article breaks down each model's trade-offs and how to choose.
Engineering Capacity Planning for AI Adoption
Engineering capacity planning for AI adoption means matching team size, skill mix, and engagement model to the real technical workload an AI initiative creates at each stage. This guide covers how to spot the gap early and choose between fractional, contract, pod, and permanent resourcing.
Do Growing Australian Companies Need a Chief AI Officer?
As AI initiatives scale beyond a single pilot team, growing Australian companies face a real organisational design question: create a dedicated Chief AI Officer role, or extend an existing CTO or Head of Data mandate? This piece looks at the trade-offs against the backdrop of Australia's active regulatory environment for AI and data.
Engineering Leadership Succession Planning During a CTO Exit
A CTO exit puts delivery velocity, architectural continuity, and team retention at risk if handled as a recruitment problem alone. Here's how to structure interim leadership, preserve institutional knowledge, and protect momentum during the transition.
Board-Level AI Literacy: A Director's Guide
Board-level AI literacy doesn't require directors to understand machine learning — it requires knowing what questions to ask. This guide sets out a practical framework for AI oversight grounded in existing directors' duties.
DORA Metrics for Mid-Market Engineering Teams
DORA metrics are the clearest read on engineering delivery performance, but most guidance is written for large platform teams. Here's how a 10-50 person engineering team can instrument DORA cheaply, benchmark realistically, and account for AI-assisted development shifting the baseline.
Change Management for AI Adoption: Getting Staff to Actually Use It
AI rollouts stall not because the technology fails, but because the people side is an afterthought. This post breaks down why adoption flatlines and provides a practical eight-step change management playbook to drive genuine, sustained AI usage across your teams.