When businesses want AI that knows their specific products, policies, or processes, two technical approaches come up repeatedly: fine-tuning and retrieval-augmented generation (RAG). Both make AI more relevant to your business context, but they work very differently, have very different costs and complexity profiles, and suit different use cases. Understanding the distinction is essential before … Read more
One of the most requested AI capabilities for small businesses is a system that knows your specific content — your products, your policies, your processes, your brand voice. The good news is that this is genuinely achievable without any coding, without fine-tuning a model, and often for free or at very low cost. The technique … Read more
Every AI model release is accompanied by benchmark scores: MMLU, HumanEval, LMSYS Arena, MT-Bench, and dozens of others. These numbers are used to justify premium pricing, claim leadership positions, and guide purchasing decisions. But the relationship between benchmark performance and performance on your actual business tasks is far weaker than the marketing suggests. Understanding what … Read more
Most business AI use starts with text — writing, summarising, classifying, analysing documents. But a significant portion of business information does not arrive as clean text: it arrives as scanned invoices, product photos, handwritten notes, charts in presentations, screenshots of software errors, and PDF forms. Multimodal AI — models that can process images alongside text … Read more
The question of whether to run AI models locally or use cloud-based AI APIs is becoming increasingly practical for small businesses. A generation ago, running a capable AI model locally required significant hardware investment and technical expertise. In 2026, tools like Ollama make it possible to run useful AI models on a standard business laptop. … Read more
The open-source AI model ecosystem has matured significantly. Models from Meta, Mistral AI, and Microsoft that are freely available and can be run on your own infrastructure now rival commercial API models for many business tasks. For the right use cases — particularly those involving sensitive data, high volume, or specific compliance requirements — open-source … Read more
The AI industry has a marketing problem: every new model release is described as a breakthrough, pushing users toward the newest and most expensive options. The reality for business use is more prosaic. For a substantial majority of real business tasks, models released 12–18 months ago, or the current generation of smaller models, produce output … Read more
Defaulting to the most capable AI model for every task is one of the most common and costly mistakes in business AI adoption. GPT-4o and Claude Sonnet are remarkable models — but they cost 10 to 50 times more than their smaller counterparts, and for the majority of business tasks, that premium buys no meaningful … Read more
Jailbreaking AI tools — finding prompts or techniques that bypass a model’s safety guidelines — is typically discussed in the context of malicious actors seeking harmful content. But there is a second, more common context that is rarely discussed: employees who jailbreak AI tools inadvertently, or who find workarounds to restrictions that are preventing genuinely … Read more
Most businesses have a significant gap between the AI tools their policies permit and the AI tools their employees actually use. The gap is not malicious — it is practical. Employees adopt tools that make their work easier, often faster than IT or management becomes aware of them. The result is a shadow AI landscape: … Read more