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How to maximise the performance of your existing systems
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Building What Matters – Reducing the 80% Feature Waste in Product Delivery
Half of everything software teams build is wasted. This is a consistent finding across multiple studies and decades of data, and it should be the starting point for any conversation about digital product delivery. This article looks at why technology projects consistently lose focus on user outcomes, what the evidence says about the impact of user-centred delivery, and how engineering leaders can build the discipline of tying every feature to genuine user value.
Enterprise Data Foundations: The Determinant of AI at Scale
Most enterprise AI projects stall not because the model fails, but because the data underneath is incomplete or inaccessible. This article sets out the five dimensions of AI data readiness, examines how data debt compounds across initiatives, and explores the architectural patterns that allow organisations to scale AI.
What AI-Assisted Engineering Means for Software Testing
AI coding tools are now embedded in most development workflows, but AI-generated code introduces more security vulnerabilities, duplication and critical defects than human-written code. This article examines the risks and the testing and governance practices engineering leaders need to capture the productivity benefits without accumulating quality debt.
Audacia shortlisted in British Data Awards 2026
Audacia is shortlisted for AI Company of the Year at the 2026 British Data Awards.
Audacia shortlisted in National Technology Awards 2026
Audacia has been shortlisted at the 2026 National Technology Awards for "AI Innovation of the Year" with the Medicines and Healthcare products Regulatory Agency (MHRA).
Why Team Alignment Determines Whether Technology Projects Succeed
Decades of research across tens of thousands of projects point to team alignment as the root cause of failure. This article examines why alignment failures are so common, how they compound over time, and what engineering leaders can do to build the single-team model that is the strongest predictor of project success.
Non-Functional Testing in the Cloud-Native Era
Cloud-native architectures have changed the landscape of software quality. This article examines the five dimensions of non-functional testing that matter most in cloud-native environments: performance, resilience, security, observability, and accessibility, and what engineering leaders need to consider to address them.
Why AI Projects Fail to Scale: The Five Root Causes
More than 80% of AI projects fail which is twice the rate of non-AI IT projects. This article explores the five reported consistent root causes of this failure: starting with technology instead of a business problem, weak executive sponsorship, poor data readiness, no path to production, and treating AI like a traditional IT project.
Testing AI: How to Effectively Evaluate LLMs
This article examines why traditional software testing falls short for LLM-powered systems and what organisations need to do differently. It covers the scale of the hallucination problem, evaluation approaches for RAG and agentic AI systems, the emerging regulatory requirements around AI testing, and how engineering leaders can build the evaluation capability needed to deploy AI responsibly.
Why AI Governance is Key to Scaling AI
This article explores the role of AI governance in scaling AI beyond pilots and into production. It examines the cost of ungoverned AI, the evolving regulatory landscape including the EU AI Act and the UK's principles-based approach, and outlines three practical governance principles - proportionate, embedded and automated - that enable organisations to scale AI with confidence.
