A consistent question in business AI adoption is whether to use the general-purpose frontier models — GPT-4o, Claude Sonnet, Gemini Pro — that can handle almost any task, or specialised domain-specific models trained specifically for medicine, law, finance, code, or other focused domains. The intuitive appeal of a specialist model is strong: surely a model … Read more
Context window size — the amount of text a model can read and work with in a single interaction — has become one of the most practically significant differences between AI models for business use. A small context window limits what you can do: you cannot summarise a 100-page report if it does not fit … Read more
Reasoning models — OpenAI’s o-series (o1, o3) and Anthropic’s extended thinking mode — represent a fundamentally different approach to AI problem-solving than standard generation models. Where standard models produce responses by predicting the most likely next token given the context, reasoning models generate an internal chain of thought that works through a problem step by … Read more
The question of when and how to tell clients you used AI in producing deliverables is one of the most practically contested issues facing consultants, agencies, freelancers, and professional services firms in 2026. The legal requirements are evolving, professional norms are unsettled, and client expectations vary enormously. What is consistent is that the businesses that … Read more
As AI-generated content becomes indistinguishable from human-written content in many contexts, the question of how to attribute, track, and establish ownership of AI-generated work is becoming practically important for publishers, businesses, legal teams, and anyone producing AI-assisted content at scale. Watermarking — embedding information in AI-generated content that identifies its origin — is an active … Read more
When you send data to an AI tool, it goes somewhere — to a data centre in a specific country, processed by servers under specific jurisdictions, potentially retained in storage systems with their own data protection obligations. Most businesses never ask where. For the majority of AI uses — generating marketing copy, summarising public documents, … Read more
Buying an AI tool without proper due diligence is one of the most common and costly mistakes businesses make when building their AI stack. The market is crowded with tools that make similar claims, some of which are well-built and genuinely valuable while others are thin wrappers around general-purpose APIs with limited differentiation. A structured … Read more
Every AI system has failure modes — inputs or situations that cause it to produce wrong, harmful, or embarrassing outputs. The question is not whether your AI system has failure modes, but whether you find them first or your customers do. Red-teaming is the practice of deliberately trying to break your AI system before deployment, … Read more
Shadow AI is the AI equivalent of shadow IT — staff using AI tools that have not been approved, evaluated, or even disclosed to their organisation. An employee who pastes customer data into ChatGPT to summarise a support case. A sales rep who uses an unapproved AI outreach tool that accesses the company CRM through … Read more
Voice-activated AI for business has been promised for years — smart meeting assistants, voice-controlled workflows, AI that responds to spoken commands in real time. Some of these capabilities are genuinely available and production-ready in 2026. Others remain more impressive in demos than in practice. Separating the two is valuable before investing significant time evaluating tools … Read more