I come from a world where customer support is a significant expense for operations and everyone was SO excited to implement AI for this. It doesn't work particularly well and shows a profound gap between what people think working in customer service is like and how fucking hard it actually is.
Honestly, AI is better at replacing the cost of upper-middle management and executives than it is the customer service problems.
Nicely fitting the pattern where everyone who is bullish on AI seems to think that everyone else's specialty is ripe for AI takeover (but not my specialty! my field is special/unique!)
and these are people are not junior developers working on trivial apps
It couldn't/shouldn't be responsible for the people management aspect but the decisions and planning? Honestly, no problem.
There are legitimate support cases that could be made better with AI but just getting to them is honestly harder than I thought when I was first exposed. It will be a while.
IMO we can augment this criticism by asking which tasks the technology was demoed on that made them so excited in the first place, and how much of their own job is doing those same tasks--even if they don't want to admit it.
__________
1. "To evaluate these tools, I shall apply them to composing meeting memos and skimming lots of incoming e-mails."
2. "Wow! Look at them go! This is the Next Big Thing for the whole industry."
3. "Concerned? Me? Nah, memos and e-mails are things everybody does just as much as I do, right? My real job is Leadership!"
4. "Anyway, this is gonna be huge for replacing staff that have easier jobs like diagnosing customer problems. A dozen of them are a bigger expense than just one of me anyway."
AI, for a lot of support questions works quite well and does solve lots of problems in almost every field that needs support. The issue is this commonly removes the roadblocks from your users being cautious to doing something incredibly stupid that needs support to understand what they hell they've actually done. Kind of a Jeavons Paradox of support resources.
AI/LLMs also seem to be very good at pulling out information on trends in support and what needs to be sent for devs to work on. There are practical tests you can perform on datasets to see if it would be effective for your workloads.
The company I work at did an experiment on looking at past tickets in a quarterly range and predicting which issues would generate the most tickets in the next quarter and which issues should be addressed. In testing the AI did as well or better than the predictions we had made that the time and called out a number of things we deemed less important that had large impacts in the future.
Sure, but when the power of decision making rests with that group of people, you have to market it as "replace your engineers". Imagine engineers trying to convince management to license "AI that will replace large chunks of management"?
With "legacy industries" in particular, their websites are usually so busted with short session timeouts/etc that it's worth spending a few minutes on hold to get somebody else to do it.
These people don't want the thing done, they want to talk to someone on the phone. The monthly payment is an excuse to do so. I know, we did the customer research on it.
Again, this is something my firm studied. Not UX "interviews," actual behavioral studies with observation, different interventions, etc. When you're operating at utility scale there are a non-negligible number of customers who will do more work to talk to a human than to accomplish the task. It isn't about work, ease of use, or anything else - they legitimately just want to talk.
There are also some customers who will do whatever they can to avoid talking to a human, but that's a different problem than we're talking about.
But this is a digression from my main point. Most of the "easy things" AI can do for customer support are things that are already easily solved in other places, people (like you) are choosing not to use those solutions, and adding AI doesn't reduce the number of calls that make it to your customer service team, even when it is an objectively better experience that "does the work."
Every company we talk to has been told "if you just connect openai to a knowledgebase, you can solve 80% of calls." Which is ridiculous.
The amount of work that goes in to getting any sort of automation live is huge. We often burn a billion tokens before ever taking a call for a customer. And as far as we can tell, there are no real frameworks that are tackling the problem in a reasonable way, so everything needs to be built in house.
Then, people treat customer support like everything is an open-and-shut interaction, and ignore the remaining company that operates around the support calls and actually fulfills expectations. Seeing other CX AI launches makes me wonder if the companies are even talking to contact center leaders.
The default we've seen is naive implementations are a wash. Bad AI agents cause more complex support cases to be created, and also make complex support cases the ones that reach reps (by virtue of only solving easy ones). This takes a while to truly play out, because tenured rep attrition magnifies the problem.
We've found that just a "Hey, how can I help?" will get many of these customers to dump every problem they've ever had on you, and if you can make turn two actually productive, then the odds of someone dropping out of the interaction is low.
The difference between "I need to cancel my subscription!" leading to "I can help with that! To find your subscription, what's your phone number?" or "The XYZ subscription you started last year?" is huge.