Insights
Product, design, AI, and engineering perspectives from our team.
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.
Contact Centre AI: Integrating Voice and Chat Automation
A practical look at integrating AI-driven voice and chat automation into existing telephony and CRM systems — covering integration patterns, where automation should and shouldn't replace agents, and how to measure impact honestly.
AI Localisation Architecture for APAC Market Expansion
Expanding into New Zealand and APAC takes more than a translation layer. This guide walks through the architectural decisions — retrieval, data structure, model selection, and locale-specific UX — that determine whether a multi-language AI rollout scales cleanly or needs a rebuild.
Shadow AI Usage Policies: A Guide for Tech Leaders
Unsanctioned AI tools are already inside your engineering and product teams, whether or not anyone has approved them. Here's how technology and business leaders can identify shadow AI use and build governance that reduces risk without killing productivity.
AI Vendor Risk: Contracts, SLAs & Liability for AI Features
Embedding third-party AI into your product introduces risks that standard SaaS contracts and SLAs were never designed to cover. Here's what technology and business leaders need to check before they sign.
AI Vendor Risk Management: Contracts, SLAs & Liability
Embedding a third-party AI model changes your risk exposure in ways standard SaaS contracts don't cover. Here's what actually needs to be in the agreement — from SLAs to indemnity to data handling.
Data Monetisation: Turning Internal Data Into Revenue
A practical look at how growing Australian companies can turn internal data into an external revenue stream — covering commercial models, technical foundations, and the governance considerations that come first.
Enterprise AI Chatbots in 2026: Build vs Buy
Enterprise chatbots in 2026 aren't a simple build-vs-buy call — managed platforms like Google's Gemini Enterprise Agent Platform now handle session state, RAG and governance out of the box. We break down when buying wins, when custom builds win, and the mid-market sweet spot in between.