For ecommerce teams using AI agents, what's the biggest pain with cart recovery/ customer support?
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Specifically curious about ecommerce because the patterns are different from B2B.
What we're hearing:
- Cart recovery flows convert 2 to 3x better with AI but still feel generic
- Support agents handle tier-1 well, fall apart on returns/exchanges
- Personalisation engines and AI agents don't talk to each other cleanly
- Multilingual customers are a known weak spot
For ecommerce operators, what's the pain you'd most want solved in 2026?
Share your opinion below
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the returns/exchanges one is the real pain for me. tier-1 "where's my order" is basically solved at this point, but the second a customer wants to swap a size or split a refund across two items, the agent either loops them back to a human anyway or makes a judgment call it shouldn't be making alone. and returns are exactly where tone matters most - a customer returning something is already a little annoyed, so a generic-sounding bot response reads as "the company doesn't care" way faster than it would on a simple shipping question. feels like the fix isn't a smarter model, it's giving the agent real policy data (return windows, exceptions, restock rules) instead of a generic FAQ to work from.
@galdayan Totally agree with this. Tier-1 WISMO stuff is basically a solved problem at this point, but returns? That's where it gets messy fast. The moment a customer needs to split a refund or swap sizes, most agents just punt to a human anyway, so what's the point.
The bit about tone is underrated too. A customer already annoyed about a return doesn't need a generic "here's our policy" response, they need the agent to actually know the exceptions, the restock rules, the edge cases. Generic FAQ as the knowledge base just doesn't cut it there.
I think the bigger challenge is that AI agents often don't understand why a policy exists—they just execute it.
A return isn't always a straightforward rules problem. It involves customer history, business impact, inventory, fraud risk, and sometimes simple goodwill. That's where human judgment still matters.
For me, the ideal approach is an AI agent that gathers all the relevant context, suggests the best resolution with reasoning, and escalates edge cases to a human instead of making irreversible decisions on its own.
That keeps the speed of automation without sacrificing customer trust.
@kartikbatchu2003Â Yeah this hits on something I've been thinking about too. The "why" behind a policy is basically invisible to most agents, and that's where things fall apart on edge cases.
The context-gathering + suggest + escalate model makes a lot of sense. It keeps speed up while making sure irreversible calls (refunds, bans, fraud flags) still have a human in the loop. Honestly that's probably the right frame for most of CX automation right now, not full autonomy but better-informed humans.