Insights
Practical articles on evaluating RAG systems and tool-using AI agents
Building an AI Governance Operating Model: From Policy to Practice
Most organisations have an AI strategy but lack the governance operating model to execute it. A practical guide to building the structure, processes, and accountability mechanisms for sustainable AI governance.
AI Vendor Due Diligence: A Practical Evaluation Framework
AI procurement demands due diligence across five risk dimensions: technical, operational, financial, legal, and ethical. A structured evaluation framework for organisations deploying third-party AI systems.
Model Drift: The Silent Threat to AI Systems in Production
Ninety-one percent of production AI models experience drift. Without proactive monitoring, error rates can increase by 35 percent within six months. A comprehensive guide to detecting and managing model degradation.
Agentic AI: Why Autonomous Systems Need a New Assurance Playbook
Agentic AI systems can plan, reason, and act autonomously — but their failure modes cascade in ways traditional testing cannot catch. A practical guide to assurance for autonomous AI.
When the Words Matter: Quality Assurance for AI Translation in High-Stakes Settings
Machine translation is fast, cheap and remarkably good at everyday text. It is also confidently wrong in exactly the situations where a mistranslation can cost someone their health, their case or their rights. Here is how to tell the two apart and assure the difference.
Assuring AI in Education: Fairness and Accuracy When Algorithms Grade, Detect and Adapt
Education is not classified as a maximum-risk sector by most people, yet AI already decides which students are flagged for cheating, how work is graded and what each learner sees next. When those systems are wrong, the consequences follow a young person for years. Here is how to assure them.
Hallucinated Citations: Why AI Legal Drafting Needs Assurance, Not Trust
Courts around the world have sanctioned lawyers for filing briefs containing case citations that AI simply invented. The legal profession is a case study in why fluent, authoritative output is the most dangerous kind when it is wrong.
Assuring AI-Generated Marketing Content: A Quality Framework for Brands
Generative AI can produce marketing copy at unprecedented speed — and publish factual errors, off-brand claims and invented statistics just as fast. Here is how to build assurance into content generation before it reaches your audience.
When the Chatbot Speaks for You: Assuring Conversational AI in Customer Service
Customer service chatbots now handle millions of interactions daily, making promises, processing refunds, and explaining policies. But courts have ruled that companies are legally liable for what their chatbots say. This deep analysis examines the hallucination problem, the legal and reputational risks, and the assurance architecture needed to make conversational AI safe for customer-facing deployment.
Beyond the Recommendation: Assuring AI in Retail and E-commerce
Retail AI does far more than suggest products. It sets prices, personalises experiences, screens returns for fraud, and determines credit eligibility. When these systems are biased or opaque, they can discriminate against consumers and expose retailers to regulatory action. This deep analysis explores the risks, the evolving regulatory landscape, and how to build retail AI that is both effective and fair.
Fairness by Design: Testing AI in Insurance Underwriting and Claims
AI is transforming how insurers assess risk, price policies, and process claims. But when algorithms determine who gets coverage and at what price, the potential for discrimination is enormous. This deep analysis examines the regulatory framework, the tension between actuarial accuracy and fairness, and the assurance practices that can make insurance AI both effective and equitable.
Algorithmic Accountability in Government: Assuring AI in the Public Sector
Governments deploy AI to allocate welfare benefits, predict crime, assess tax fraud risk, and screen immigration applications. These systems exercise state power over citizens. When they fail, the consequences can be devastating. This in-depth analysis explores the accountability gap, emerging regulatory frameworks, and the assurance practices needed to make public sector AI trustworthy.
Clinical AI Under Scrutiny: Assuring Safety, Bias, and Reliability in Healthcare
AI is diagnosing diseases, triaging patients, and recommending treatments. But clinical AI carries risks that no other sector can match: when it fails, people can be harmed or killed. This deep analysis examines the unique failure modes, regulatory shifts, and assurance disciplines required to make healthcare AI safe.
The EU AI Act Beyond Recruitment: What High-Risk AI in Every Sector Must Prove
Recruitment was the headline example, but the EU AI Act classifies high-risk AI across credit, insurance, healthcare, education, essential services and public administration. Here is what every high-risk system must actually demonstrate — and why the burden falls on the organisation deploying it.
Validating AI in Financial Services: Credit, Fraud, and the Cost of Getting It Wrong
Banks and fintechs let AI decide who gets a loan, which transactions are blocked, and where fraud hides. Each decision carries regulatory, financial, and human weight. This deep dive examines the evolving regulatory landscape, common failure modes, and the assurance disciplines that separate trustworthy financial AI from dangerous black boxes.
Measuring AI Hallucinations: From Anecdote to Metric
Everyone knows AI systems make things up. Far fewer organisations can say how often, how badly, or whether last month's fix actually helped. Turning hallucination from a scary story into a number you track is what separates managed risk from wishful thinking.
Why RAG Systems Fail — and How to Evaluate Them Properly
Retrieval-augmented generation is the most popular way to make a language model answer from your own documents. It is also quietly one of the easiest architectures to get wrong. Here is where these systems break, and how to test them honestly.
LLM Red Teaming Explained: How AI Systems Get Broken on Purpose
Red teaming is the practice of attacking your own AI system before someone else does. For large language models, it is the difference between discovering a dangerous flaw in a controlled test and reading about it in the news.
What AI Assurance Actually Means — and Why Every Sector Needs It
AI assurance is not a certificate or a one-off audit. It is the ongoing discipline of proving that an AI system does what it claims, keeps doing it after deployment, and fails safely when it does not. Here is what that looks like in practice.
The Future of Recruitment AI: Trends, Innovations, and What to Expect
Recruitment AI is moving from keyword-matching toward generative, conversational and agentic systems. This is where the technology is genuinely heading — and why every advance raises the assurance bar rather than lowering it.
GDPR and Recruitment AI: Your Complete Compliance Checklist
Recruitment AI does not sit outside data protection law — it sits at its sharpest edge. This checklist walks through the GDPR obligations that apply the moment you automate any part of hiring, and how they now interlock with the EU AI Act.
When AI Fails: Lessons from High-Profile Recruitment AI Disasters
The most valuable lessons in recruitment AI come from the systems that failed in public. Each of these well-documented cases points to the same conclusion: the failure was foreseeable, and testing would have caught it.
Putting AI to the Test: A Practical Guide to Validating Recruitment Systems
A recruitment AI system is only as trustworthy as the evidence behind it. This guide walks through the validation disciplines — bias testing, validity, adverse-impact analysis, human oversight and ongoing monitoring — that turn a black box into a defensible decision tool.
The Bias in the Machine: How CV-Parsing Tools Discriminate
CV-parsing tools promise objective, high-speed screening. But because they learn from historical hiring, they can absorb and amplify the very prejudices they were meant to remove. This is how that happens — and what to do about it.
The End of Emotion Recognition in Hiring: Understanding the Ban
The EU AI Act now prohibits inferring emotions from candidates in the workplace. This is why emotion-recognition hiring tools were built on shaky science, what exactly is banned, and how recruiters should respond.
The Hidden Pitfalls: How Recruitment AI Fails and What It Costs
Recruitment AI rarely fails with a dramatic crash. It fails quietly — screening out good candidates, entrenching bias, breaching rules nobody checked. Here are the common failure modes and the real cost when they surface.
The EU AI Act: A Recruiter's Guide to Deadlines and Compliance
Recruitment is one of the most heavily regulated uses of AI under the EU AI Act. This guide translates the law into what recruiters actually need to know: the deadlines, the obligations, and the steps to prepare.