Deploying AI in a business: the AI is the easy part
I spent a weekend putting AI into a client's internal processes. The integration was boring. Picking the right processes, writing the guidelines, training the staff, and deciding who owns it afterwards - that's where the actual work was.
I spent last weekend wiring AI into a client’s internal processes. The technical part was unremarkable; I’ve built harder things in an afternoon. Everything that made the project worth doing happened around it.
Before I get to what we actually built, some ground rules, because they’re the reason the build looked the way it did.
It makes mistakes, and that’s not a bug you can file
An LLM is a non-deterministic process. So is OCR, and so is a human reading a scanned invoice. Nobody deploys OCR at scale without a correction step; everybody knows it’ll misread a 6 as an 8 often enough to matter. LLMs get deployed as if that step were optional.
It’s also a moving target. A new model behaves differently and brings its own quirks. Latency is fine on Tuesday and terrible on Thursday. Requests fail. Sometimes a model seems to shift in character between releases and you’re left re-tuning prompts that worked fine for months.
You can shape the thing without training it. If you need a specific voice, or you want the output constrained to a shape, you can get most of the way there with instructions, examples and validation. Treat all of it as best-effort. It’s a strong tendency, not a hard truth, and anything that depends on it being a hard truth needs a check behind it.
If you need better output, one option is to involve more than one model: one drafts, another reviews against the guidelines, and you look at what survives. That’s not a trick, it’s the same thing you’d do with people when the output actually matters. It helps. It doesn’t get you to zero.
And this part I’ll say flat: today, a professional with experience in their field, a reputation at stake, and some care about what leaves the building will beat the model on quality. Not on speed - it’ll lose that badly. On quality. That’s true as of today; I don’t know what tomorrow looks like.
None of this is an anti-AI position. Humans have the same failure modes; I wrote recently about a 1,534-line commit called “runs” that shipped a hardware wallet generating seeds from a non-cryptographic PRNG, and no model was anywhere near it. The difference is that a person can be told, and a person minds.
The calculator problem
Here’s the failure I actually worry about in a company: the output looks good, so people trust it.
My calculator tells me 1+1=2 and I trust it, correctly, forever. The LLM arrives on the same screen, on the same machine, in the same tab strip, and produces fluent, confident, well-formatted text. Everything about the packaging says “computer”, and everybody has thirty years of training that computers are right about this kind of thing. They’re not the same category of thing at all, and nothing in the interface tells you that.
Which makes deployment a human matter before it’s a technical one. Staff need training, and they need reminders, because the trust creeps back in. Don’t treat this like a calculator; treat it like something that makes mistakes.
The odds get worse in the directions you’d expect: the longer the output, and the tighter your expectations of it, the more likely you are to get something wrong. A one-line classification is fairly safe. Three pages that have to be right in every particular is a different proposition.
Outward-facing is where it gets expensive
That’s for internal processes. Point it at customers and the stakes change.
Think about how a call center actually works. People get trained before they take a call. They’re listened in on. They’re coached, corrected, and scored. The script is tight, and someone owns the script. Nobody hires an agent and then never checks on them again.
An LLM doing the same job gets the same treatment, minus every incentive the human had. Its job isn’t on the line. It has no reputation to protect, no interest in a promotion, no bad feeling about a call that went badly. It is, as it comes out. Set it and forget it isn’t a thing here; it isn’t a thing for the humans either, and we’ve known that for decades.
Somebody has to own it
So the question every AI project has to answer early: who watches this?
It’s either someone external, on a maintenance and QC arrangement, or someone internal you designate and give the time to actually do it. There’s no third option where nobody does it and it stays good.
What doesn’t work is the fifty-a-month subscription you sign up for and forget. That only holds if you genuinely don’t care about the outcome, and if you don’t care about the outcome, it’s not clear why you started. You want a measurable result. Does it bring in customers? Does support get better, or just faster? Does the process finish earlier? What’s the error rate, and is it going up or down? If you can’t answer those, you don’t have a project, you have a subscription.
What we actually did
With all that said, the build itself was boring, straight-forward stuff. Roughly:
- Found the processes worth automating. Not all of them; the ones where more throughput, with oversight, genuinely helps. Time-consuming, repetitive, and cheap to check.
- Wrote guidelines. How the system is to be used, and what to look out for. Think of the checklist a flight crew runs before departure: they’ve done it a thousand times and they still read it out.
- Trained the assigned staff. The people who’d own it day to day, on their own workflows, not a generic demo.
- Implemented the guidelines. Writing them down isn’t implementing them. They had to land in the actual process, with the actual checks in the actual places.
Using it is about as hard as using Gmail. The staff picked it up in ten minutes. The interface was never the hard part, and if your AI project’s difficulty is concentrated in the UI, something has gone sideways.
Why nobody pushed back
There was very little resistance, and I don’t think that’s down to change management. It made previously boring, time-consuming work easier, and it didn’t replace anyone. What it means in practice is that by Friday, the work is actually done.
That’s the whole pitch. It isn’t headcount, it’s the backlog that never quite cleared.
What happens next
I’ll check in weekly for a while, then every couple of weeks: collect feedback, look at what’s going wrong, fine-tune the guidelines. At some point it stops being the new thing and becomes how it’s always been done, which is the goal. If it keeps going well, we’ll pick the next process.
That last part is the bit I can’t shortcut and won’t pretend otherwise. AI is fast and it works at any hour, and that means there’s a lot more room for things to go wrong, quietly, at volume.