This is really damning evidence of AI’s vaporware benefits tbh.
As LLMs key functionality is supposed to be their ability to take a body of work (training data) and then take natural language input to deliver valuable and accurate natural language outputs, automated helpdesk and call centers are supposedly their absolute bread-and-butter low challenge implementation cases.
And yet… Here we are. They still aren’t anywhere near the quality or value of just hiring people.
Ish.
The issue is that it isn’t a straight shot as a lot of people paint. Call Centers work off of User Interfaces, AI can’t see or use those, so those UIs suddenly have to be retooled in a way that the AI understands, which that’s not easy. Additionally there’s business logic that is complex and there’s a lot of siloed knowledge, all of that is hard to extract and put into a model that’s usable.
The thing is that these LLM and AI companies were thinking the rest of the world is as structured as the data models they trained their AIs on and that’s just not the case. The LLMs can absolutely do the task if given the task correctly, it just that it’s near impossible to give the task they need to perform correctly in 100% of the situations. Hell, even humans fail this, people get written up at call centers all the time.
To put it simple, you ever hear the joke, “we don’t have to worry about AI taking the programmers jobs because then the CEO would have to accurately explain the problem they’re trying to solve/sell”? It’s IRL that, that’s holding up a ton of the LLMs in call centers. Like there’s two VERY narrow processes that the company I work for has implemented AI for, and those are really basic situations where explaining the full scope is pretty easy.
But take what I have to say with a grain of salt. I can’t say the company I work for has ever really been that gung-ho about AI to begin with. But I can tell you that it’s WAY, WAY, WAY more work to deploy AI than the tech bros like to paint it. Like you can just hit the button and “go”, but it’s going to crash and burn. Like to get it right is way more work than the AI industry let’s on.
The thing about this though, is that it’s not a new problem at all. LLMs didn’t start to get good enough in the early 20’s and only then did they come up with this idea. I worked for a company out near Seattle back in ~2014 that was already well into trying to tackle this problem.
They ran callcenters with a variety of contracts for different companies and took calls, chats, and emails. The main business model wasn’t the centers themselves but the information gathered by the ticketing system to help build tools like this.
Personally, with that insight and assuming surely there must’ve been other companies moving along that path, I find it quite telling that they still haven’t sufficiently stepped up to the role. There are some hard limits on cost and hallucinations that I think will ultimately fail to deliver a truly long-term, viable product. When you see they can’t maintain the veneer on even that use case, you’ll know the bubble has to be close to popping.
Of course no one can really say for sure, we’ve all been predicting it for some time and when there’s this much money invested they’ll protect that reality ferociously, so who really fucking knows. But still …
Newsflash AI actually sucks
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Even college educated jobs are being off-shored. In my field, some companies are moving to India.
There was at least one “AI” company that was caught passing off the cheap overseas labor as AI.
Two ways this can go. AI enhances cheap labor by allowing lower performing labor to seem better at their jobs or AI allows better performing employees to do their jobs better increasing their burden and creating job scarcity. Either way, sooner or later, we are screwed, better find a way to deal with it.
Or they use the low cost overseas labor as a model to train their AI because they can’t say no.
