I became properly aware of the modern wave of artificial intelligence in November 2022, when the public release of ChatGPT caused the first great stir. Like many others, I was intrigued at once and subscribed to the paid version on the sensible assumption that the free tier was only a sample. But fascination is not the same as use. Through 2023 and 2024 I used it only intermittently. It was only in 2025, when life became a little less crowded, that I had the chance to read more, experiment more freely, and begin to see where AI might fit into my professional life.<\/p>\n\n\n\n
Play matters here. Since ChatGPT appeared, there has been an avalanche of AI applications, some overlapping, some entirely different. The only sensible way to understand them is to try them. A cheap subscription taken out of curiosity for a month can be surprisingly instructive. But the real secret sauce, at least for me, was ChatGPT itself. One of its most useful qualities is not merely that it answers questions, but that it explains things clearly. It can tell you how to write better prompts, how to use other AI tools more effectively, and how to navigate unfamiliar software without forcing you through technical jargon.<\/p>\n\n\n\n
That led me to what I think is the right method. Do not begin with the shiny application and ask what it can do. Begin with the problem. Time is finite. If technology can stretch it, allowing more to be done more quickly without any loss of quality, then it deserves serious attention. So I made a list of the tasks and frustrations that recur in my work, and asked where AI might genuinely help.<\/p>\n\n\n\n
The first way to understand AI is as a universal key. It opens doors to knowledge, instruction and possibility without demanding prior expertise from the person asking the question. The information was always there, scattered through books, manuals, websites and forums. What AI changes is the ease with which that information can be retrieved and presented. It rewrites the thing in plain English, step by step, until even a novice can follow it.<\/p>\n\n\n\n
Take something as ordinary as building a website. Not very long ago you either fought your way through incomprehensible instructions or paid someone else to do it. Now an AI tool can break the task down into manageable stages, tell you which settings to choose, which boxes to tick, and what to do if something goes wrong. The same applies in legal practice. A barrister can use AI to master Teams or Zoom, to transcribe and summarise meetings, or to solve some awkward practical problem without sitting through a training day or badgering an IT department. Knowledge once hoarded by specialists becomes portable and, in effect, democratised.<\/p>\n\n\n\n
That is a considerable gain, but not an unqualified one. A universal key opens more than respectable doors. If a system can explain how to build a website, it can also explain how to make things that ought not to be made, or how to do things best left undone. The danger is not that such knowledge never existed; it always did. The danger is that AI removes the barriers of obscurity, expertise and technical language. It makes the forbidden intelligible. That is why power always needs restraint. The answer is not to ban the hammer because it may be misused, but to accept that useful tools require rules, norms and consequences.<\/p>\n\n\n\n
The second way to think about AI is as an imagination machine. You begin with the seed of an idea and the machine helps it grow. Used properly, it can interrogate the idea, test its weaknesses, expose likely problems, and suggest ways round them. It can tell you, for example, that your proposed project may run into regulatory difficulty, cost more than you suppose, or fail because there is no market for it. A human being might discover that after six expensive months. The machine may tell you in six minutes.<\/p>\n\n\n\n
That can be remarkably useful in legal business development. Consider Direct Access work. A barrister dealing directly with the public needs more than a vague aspiration. There must be an explanation of what Direct Access is, a system for onboarding clients, a secure route for document upload, and a mechanism for deciding whether a case should be accepted. AI can map that structure out, suggest whether an off-the-shelf platform already exists, indicate what a bespoke solution might cost, and even sketch the marketing needed to attract the right people. It can also address the hardest question of all: whether there is any actual demand.<\/p>\n\n\n\n
That same quality has obvious applications in writing. For years I have written on costs and litigation funding. The difficulty has not been a shortage of opinions, but the time needed to turn them into finished prose with a consistent voice. AI can help narrow the gap between the spark of an idea and a usable first draft. I do not use it to write for me. I use it to help me write. Fed enough examples of earlier work, it can produce something that already walks in roughly the right shoes. The draft still needs a human hand. But it is easier to refine something promising than to begin every time with a blank page.<\/p>\n\n\n\n
The same is true of images, and perhaps of video too. A well-chosen image can lighten tone, add character and help tell the story. AI image generation makes this cheaper and quicker, though what it cannot supply is taste. An AI avatar can deliver a lecture to a much wider audience than the people who happened to be in the room on the day. It offers consistency, though not the nuance of a live speaker.<\/p>\n\n\n\n
More immediately, AI offers practical help with the machinery of daily legal work. Client conferences can be transcribed and summarised, preserving detail that might otherwise be lost. Dictation technology has improved to the point where legal vocabulary can be handled competently. Proofreading tools can spot repetition, inconsistent terminology and awkward phrasing. Presentations can be turned more quickly into slides. Bundles can be mined for dates and events to create working chronologies. Draft agreements, client care documents and funding papers can be generated from established precedents and adapted to circumstance. Costs schedules can be analysed and visualised. Authorities can be extracted and indexed. Judgments can be monitored and summarised. Administrative burdens, from travel planning to task lists and receipts, can be reduced.<\/p>\n\n\n\n
In all this, the pattern is clear. AI is at its most useful not where it replaces judgment, but where it reduces friction. It is good at the mechanical, the repetitive, the time-consuming and the structured. It is much less good at the things that remain distinctively human: deciding what truly matters, judging credibility, reading a room, cross-examining a witness, choosing strategy, or writing with genuine individuality. Those are not defects in the machine. They are reminders of what the profession is for.<\/p>\n\n\n\n
So the real question is not whether AI is capable. Plainly it is. The real question is whether it can be used in a way that is reliable, secure and consistent with professional standards. In law, near enough is not good enough. Every document, every submission and every piece of advice carries consequences. AI therefore has to remain a controlled assistant, never an unsupervised substitute. Used with care, it can save time, improve consistency, and extend the reach of human work. Used carelessly, it produces noise, risk and occasionally farce.<\/p>\n\n\n\n
That is why the sensible approach is neither breathless enthusiasm nor elderly panic. It is steady experimentation, informed by purpose. Start with the problem. Test the tool. Keep what genuinely helps. Distrust what merely dazzles. The measure of success will not be how much work is handed over to the machine, but how much better the lawyer is able to do the work that remains.<\/p>\n\n\n\n
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