Beyond Code Completion: The Full Picture Most discussions of LLMs in software engineering focus on code completion tools like GitHub Copilot. These are genuinely useful, but they represent only a fraction of where LLMs are transforming how engineering teams work. The broader impact spans the entire software development lifecycle: requirements analysis, architecture decision-making, code review, … Read more
Why Sales Is a Natural Fit for LLMs Sales work is built on three capabilities that LLMs directly augment: knowing your prospect well, communicating persuasively, and managing a complex pipeline efficiently. Researching a company before a call, drafting personalised outreach, preparing tailored proposals, and writing follow-up communications are all time-intensive text tasks. The average sales … Read more
How LLMs Are Reshaping Financial Analysis Financial analysis is one of the most language-intensive professional functions outside of law. Earnings calls, annual reports, regulatory filings, research notes, credit memos, and board presentations are all primarily text — and large language models are transforming how analysts produce, consume, and synthesise each of them. The productivity gains … Read more
The Privacy Stakes in LLM Applications Every LLM API call is a data transfer. The text sent in your prompts — user queries, document contents, conversation history — travels to a third-party server, is processed by a model, and generates a response. For most types of content this is unremarkable. For content that includes personal … Read more
Why HR Is One of the Most Promising — and Sensitive — Areas for LLMs Human resources sits at the intersection of two things LLMs are exceptionally good at — processing large volumes of text and drafting personalised communications — and one thing that demands extreme care: decisions that affect people’s livelihoods and that carry … Read more
Why Legal Is One of the Best Fits for LLMs Legal work is disproportionately language-intensive. Contracts, briefs, research memos, due diligence reports, regulatory filings, and client communications are all primarily text — the core medium in which LLMs operate at their strongest. Unlike industries where the value is in physical processes, manufacturing, or numerical computation, … Read more
Why Every Organisation Needs an AI Policy Now Employees are already using AI tools — with or without organisational guidance. Studies consistently show that 60–80% of knowledge workers use consumer AI tools at least occasionally for work tasks, and a significant fraction use them regularly. Without a policy, usage is invisible to the organisation, ungoverned … Read more
The Core Tension in LLM Systems Every LLM deployment involves a fundamental tension between two desirable properties that pull in opposite directions. Accuracy — producing responses that are factually correct, well-calibrated, and reliably grounded in evidence — tends to improve when models are more cautious, more willing to express uncertainty, and more likely to decline … Read more
Why LLM ROI Is Hard to Measure — and Why It Matters Most enterprise AI projects fail not because the technology doesn’t work, but because the organisation cannot demonstrate that it worked. Without clear ROI measurement, successful pilots don’t get scaled, budgets don’t get approved, and the business case for continued investment erodes. LLMs present … Read more
What AI Agents Actually Are The term “AI agent” is used loosely enough in 2026 that it is worth being precise. An AI agent is an LLM-powered system that takes actions in the world — calling tools, querying databases, executing code, sending emails, or interacting with software — in pursuit of a goal, rather than … Read more
