AI didn't teach me how to renovate a bathroom. It taught me how to buy expertise.

I’ve spent the past week trying to organise a bathroom renovation.

This is the sort of thing I’m spectacularly unqualified to do. I don’t know how long tanking should take, what a reasonable tiling price looks like, or whether a quote should include adhesive, grout, plywood, pipe fittings and waste removal as a matter of course.

And that’s exactly why AI has been so useful. Not because it can replace the bathroom fitter, but because it can dramatically reduce the information disadvantage between me and the bathroom fitter.

From a wishlist to a spec

We started with a family wishlist rather than a specification: the bath out, a shower in, a new toilet and vanity, new tiles, a heated towel rail, a mirrored cabinet, and a Thai-style bidet spray.

AI helped turn that into something I could actually send to contractors, and that alone changed the buying process. Instead of asking several builders to “quote for a new bathroom” and getting back three different interpretations of what that meant, I could get them all pricing roughly the same job.

A good deal of that groundwork happened before I’d spoken to a single contractor, using the Checkatrade app now built into ChatGPT. It came back with a shortlist of ten strong candidates, which I then investigated myself — reading reviews, checking past work, deciding who actually looked right for this job. That was a noticeably better result than going to Checkatrade directly, where I kept being pointed towards builders who felt less relevant, some of whom seemed to be there because they’d paid for sponsored placement rather than because they were the best fit. Asking through ChatGPT got me past that, at least this once, and that’s where most of the preamble happened — while I was still thinking out loud, not once I was already on the phone to somebody who fits bathrooms for a living.

It also helped me work out what to ask. Whether the shower enclosure would actually fit. What waterproofing system they’d use. Whether waste removal was included, and who was supplying the building materials. What would happen to the extractor. How the bidet spray would be installed with proper backflow protection. And what happened if hidden work turned up after strip-out.

A number is only useful once you know what it buys

Then the first proper quote arrived: £6,950.

A few years ago I’d have looked at that and asked whether £7,000 sounds reasonable for a bathroom — which is close to a useless question. Instead I could look at what the number actually bought: roughly £700 for strip-out, £2,100 for plumbing alterations and shower preparation, £2,200 for waterproofing, tiling, grouting, silicone and painting, £1,600 for second-fix installation, and £350 for waste removal, with the underlying pipework, fittings, timber, plywood, waterproofing, adhesive, grout and silicone included in those figures rather than billed separately.

I also had an older quote from the year before: a more comprehensive option at £5,640 labour plus £1,400–£2,400 materials. Against that, £6,950 in 2026 including materials wasn’t just something that “felt okay”. It had a benchmark behind it.

The terms mattered as much as the price

AI also helped with a part I’d probably have skimmed past: the payment terms. The original proposal was 40% up front, 40% when tiling begins and 20% on completion — which meant paying 80% of the job before the final stage. I asked whether that could change, and the contractor agreed to 20% at the start, 50% after first-fix plumbing and waterproofing, and 30% on completion.

I also queried the one-year workmanship warranty. He offered a second year for £450. Working through what that actually covered, my conclusion was to keep the one-year warranty — another year wasn’t attractive enough to pay for.

It’s easier on the contractor too

None of this was really adversarial, and I don’t think it should read that way. A contractor doing honest work benefits from a client who arrives with an actual specification rather than a wishlist, who isn’t renegotiating scope after strip-out because nobody agreed what was in it, and who isn’t asking for 80% up front out of anxiety rather than trust. Knowing what you want and knowing what’s fair isn’t only protection for the buyer. It’s less friction and fewer disputes for the person doing the work.

What it hasn’t done

None of this has made me a plumber. I still wouldn’t know whether someone had tanked a shower correctly by looking at it, and I wouldn’t use AI instead of hiring someone who has spent years learning the trade.

It also hasn’t made me right by default. Asking a sharper question only helps if I can tell a good answer from a confident-sounding wrong one, and AI can’t do that part for me — it can tell me what to ask about backflow protection on a bidet spray, but not whether the answer I get back is actually correct. What changed isn’t my judgement. It’s my vocabulary, and the gap between the two is exactly where I’m still as exposed as I ever was.

I made a version of this argument with a different kind of purchase when choosing a family computer. There the risk was an outdated or incomplete price rather than lopsided expertise, but the same discipline carries over: know which facts deserve the expensive check, and how close to the actual commitment that check needs to happen.

Where this stops being straightforwardly good

I don’t think this is unambiguously a good thing, and I’d rather say so than leave it implied. The same week I was doing this, the BBC reported that AI-assisted complaints are straining councils and schools: documents that used to run to a page of A4 are arriving at twenty pages, sometimes misquoting the legislation they cite, and one body estimated it needed double its current workforce to cope.

That’s the same mechanism I used on my bathroom quote — closing the gap between someone who understands a subject and someone who doesn’t. But there it’s pointed at an adversarial relationship instead of a collaborative one, and the effect isn’t fairness, it’s strain: more confident, more voluminous, and not obviously more accurate. My contractor and I both wanted the same bathroom built properly. A council fielding an AI-drafted complaint and the resident who sent it don’t share that alignment, so the same tool that got me fairer payment terms is, somewhere else, getting institutions buried in longer arguments they still have to check line by line.

The difference wasn’t the technology. It was whether the gap it closed sat inside a relationship where both sides gained, or one where only one side was trying to win. The same underlying pattern — AI reducing the disadvantage of being the less-prepared party — shows up outside transactions too, including in personal conflict rather than purchasing, and the same question applies there: is it being used to find the fair position, or to build the strongest possible case regardless of what’s fair.

That’s one renovation, not a study. I’d want to see the same pattern in somebody else’s solicitor’s letter or garage bill before calling it more than a strong personal case.

Buyers of expertise, not just of bathrooms

We talk constantly about whether AI can replace experts. I’m increasingly interested in a different use case: AI helping non-experts become better buyers of expertise — with builders, mechanics, accountants, solicitors, mortgage brokers, insurers, IT suppliers and consultants, wherever one side of a transaction understands the subject and the other basically doesn’t.

AI doesn’t have to close that gap to be useful. It just has to move someone from “I have no idea what any of this means” to “I understand enough to ask much better questions” — and, on this evidence, that’s worth a good deal more than another first draft.