Let me be direct: I use AI from time to time at the workshop. Not every day, not systematically, in specific situations where it's genuinely useful. To double-check a diagnostic, fix a piece of writing, analyse technical symptoms I haven't seen before, or sometimes just to learn something new quickly.
But I'm also going to be honest about what AI can't do. And in my line of work, the line between what it can and can't do is very sharp.
What AI does for me, when I use it
1. Diagnosing rare faults
Computer repair is often a process of deduction. A customer turns up with a symptom, "my PC shuts down randomly", "this error code appears at boot", "my iPhone won't charge but only with some cables", and my job is to trace back to the cause.
For common faults, I have the experience. For rare or unusual ones, I used to spend a lot of time on specialist forums (iFixit, Reddit r/techsupport, Tom's Hardware) looking for someone who'd had the same problem.
When I get one of those cases, I describe the symptom to Claude or ChatGPT with as much context as possible, the exact model, the operating system, what happened just before, the conditions in which it shows up. And in 30 seconds I get a structured list of possible causes, ranked by likelihood, with the tests to run to discriminate between them.
It's not infallible. AI can be wrong, especially on very recent issues (the models have a cutoff date). But as a starting point and reading grid, it's effective. Especially for Windows error codes or kernel panic messages on Mac, that's where AI is often quicker and more thorough than a Google search.
A concrete example: a customer had a Synology NAS crashing with an obscure error code linked to the RAID. I described the situation to Claude, and it immediately pointed me towards bad sectors on one of the disks silently corrupting write operations. That was exactly it. That diagnostic would have taken 30–45 minutes of manual research. It took me 3 minutes.
2. Finding part references and checking compatibilities
Repair is also about sourcing parts. Each model has its specific connectors, its particular ribbon cable dimensions, its battery versions. On common devices, I know them by heart. On less common models, obscure business laptops, phones from Asian brands less common in France, it's a different story.
I used to spend a lot of time digging through spec sheets on iFixit or supplier sites. Now I just ask directly: "reference for the touch ribbon cable of a Huawei MateBook D14 2022, FPC connector screen side, approximate width", and I get a solid lead in seconds.
I always check the answer against primary sources (manufacturer spec sheet, cross-references at suppliers), but AI works as a first orientation that drastically cuts research time.
3. Writing quotes and customer explanations
The technical part of my job, I love. The admin side, drafting clear quotes, explaining a complex problem to a non-technical customer, writing a delicate email, comes less naturally to me.
Sometimes I ask the AI to help me phrase technical explanations in plain English. When I have to explain to someone why their PC isn't economically worth repairing anymore, or why a repair costs more than expected, finding the right words can take time. I describe the situation to the AI and ask it to help me phrase it clearly and honestly, then I adjust to my own way of speaking.
For quotes too, on complex cases: the structure, the phrasing. I start from what the AI generates and personalise it.
4. Replying to customer emails
Same logic. Some emails need a precise but tactful reply: a customer unhappy about a delay, a sharp technical question, a warranty request. When the wording won't come, AI can help me structure the response and make sure I don't forget anything.
I never copy and paste directly, I always rewrite in my own voice. But having a structured base helps.
5. Automating repetitive tasks
I use this less often, but it's effective when the need arises. For scripts to copy folders, bulk rename, or small automations, I describe what I want to do in plain language and the AI writes the script. I adapt and test it. It saves an hour of looking up the exact syntax.
6. Writing these guides
Yes, I use AI to help me write the articles on this blog, including the one you're reading. It's not a secret and I'd rather say so clearly.
How it works: I provide the outline, the technical information (which I know from experience), the opinions, the concrete examples. The AI helps me structure, phrase, make sure nothing is missing. Then I re-read, correct, adjust the tone, add details that only hands-on experience can bring.
The technical content is mine. The shaping is collaborative. I think it's honest to say so.
If you're a tradesperson, freelancer or independent professional and you spend time writing quotes, customer replies or content: try Claude or ChatGPT for these writing tasks. The gain can be real on cases where the wording sticks, it doesn't replace your expertise, it frees up time for what you do best.
What AI can't do in my work
And here, I want to be just as clear.
It can't see the device
AI works with words and descriptions. It doesn't see the logic board I have in my hand. It can't notice that the capacitor at C34 is slightly bulged, that the corrosion mark on the charging connector is old (from before the fall), or that the screen ribbon was poorly refitted by someone before me.
Visual and tactile experience is irreplaceable. Opening an iPhone is feeling whether the clips are fragile or whether they've already been forced. It's seeing whether the original glue is still there or whether the device has been opened before. It's recognising the smell of a burned component before you've run a single test.
It doesn't solder
Microsoldering, repairing a lifted trace on a logic board, reballing a BGA, replacing a soldered charging connector, requires hands, tools, and thousands of hours of practice. No language model can hold a soldering iron.
That's where the main value of a repair technician lies: in manual know-how, not in document research. AI can help me search, but I'm the one who repairs.
It can be wrong, especially on recent hardware
AI models have a training data cutoff. For an iPhone released 6 months ago or a new-generation chip, AI can give me obsolete or inaccurate information. I know it, I always check against primary sources for recent devices.
More dangerous still: AI sometimes "hallucinates", it invents references that don't exist, cites incorrect procedures with total confidence. In a field where one bad move can fry a 200€ logic board, blind trust in AI answers would be a serious mistake.
It can't tell if a customer is satisfied
The customer relationship, the listening, adapting to what the person in front of you really wants, do they want to repair at all costs, or are they just looking to be reassured before buying new with a clear conscience? that's human feel. I won't delegate that to a model.
If you use AI to diagnose a hardware problem yourself, treat its answers as a working hypothesis, not as a certified diagnostic. AI doesn't know your specific device, its history, its exact state. It can give you a good lead, or send you in the wrong direction. For anything involving opening a device, consult a technician.
AI diagnostic vs technician diagnostic: where one stops and the other begins
To be completely transparent about what AI handles well in my field, and what absolutely needs human intervention.
What AI handles well
- ✓Interpreting a Windows or macOS error code
- ✓Listing the probable causes of a software symptom
- ✓Walking you through a Windows reinstall
- ✓Explaining the differences between device models
- ✓Finding a part reference for a common model
- ✓Suggesting diagnostic tests to run yourself
- ✓Explaining whether a problem is software or hardware
- ✓Writing a clear description of the problem for a technician
What needs a technician
- ✓Seeing and physically touching the device to confirm the diagnostic
- ✓Distinguishing old / recent corrosion, bulged component, impact mark
- ✓Microsoldering, BGA reballing, trace repair on a logic board
- ✓Replacement of screen, battery, soldered connector
- ✓Data recovery from a mechanically damaged drive
- ✓Judging whether a repair is worth it vs replacement
- ✓Diagnosing an intermittent fault that doesn't reproduce when cold
- ✓Guaranteeing the outcome and taking professional responsibility
My advice to you as a customer
AI is an excellent first filter for computer problems.
If your PC shows a strange error, if your phone behaves oddly, if you don't understand a Windows message, ask ChatGPT or Claude. Describe exactly what you see (the full error text, what you were doing when it happened, your device model). You'll often get a useful answer that tells you whether the problem is serious or harmless, and sometimes a simple solution you can apply yourself.
For settings, software or configuration issues, AI can often walk you through step by step without you needing to leave the house.
For anything hardware : logic board, connector, battery, screen, drive, AI can help you understand what's happening, but the repair itself needs a technician, tools, and experience that words don't convey.
That's where I come in.
My view on environmental impact
This is a subject I don't want to duck. These AI models consume a lot of electricity, each request mobilises entire datacentres. It's far more than a classic Google search. At the individual level it's still marginal. At the scale of hundreds of millions of users a day, it's a significant footprint.
My personal approach: I use them when there's a real gain in time or quality. Not by reflex, not to test, not to replace a simple search. If the question is worth 10 seconds on Google, I do the Google search. If the problem is complex and AI can save me 30 minutes, I use AI. It's a question of proportion, like with any tool.
Where AI is, and where it's going
Multimodal models, the ones that can analyse images, are progressing fast. In 2026, ChatGPT (GPT-5.4), Claude (Sonnet 4.6) and Gemini (3.1 Pro) can all analyse a photo. Experiments exist where a technician photographs a logic board to ask the AI to identify a suspect component. The results are promising but still fragile in real-world conditions.
In a few years, AI will probably be an even more useful assistant in visual diagnostics: photo of a component, description of the symptoms, structured diagnostic tree. But that won't change the need for hands capable of doing the repair.
AI is changing my work as a technician at the edges, research, documentation, communication. It isn't changing its core.
A problem that neither you nor the AI can solve? Bring your device to the workshop, I'll take over.
In summary
| Task | AI alone | Technician needed |
|---|---|---|
| Understanding an error code | Yes, very effective | Not required |
| Changing a Windows / iOS setting | Often yes | Rarely |
| Diagnosing a hardware fault | Useful hypothesis | Yes, to confirm |
| Replacing a screen, a battery | No | Yes |
| Microsoldering, logic board repair | No | Yes |
| Recovering data from a dead drive | No | Yes |
| Cleanly reinstalling Windows | AI-guided possible | Better with a technician |
AI has made me more efficient on certain dimensions of my work, research, documentation, communication. It hasn't made the technician redundant. They're two different things, and I think it's important to say so clearly, both ways round.
Frequently asked questions
Frequently asked questions
Can AI diagnose a computer problem remotely?+
Is it better to ask ChatGPT or call a technician?+
Can AI repair my PC?+
How do technicians use AI today?+
Will AI replace computer repairers?+
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