Why Zendesk-style support is broken—and what AI does better: A support leader's take
“Everything’s my fault. Wrong color? My fault. Late delivery? My fault. I just walk in and say, ‘Here we go—beat me up some more.’”
That’s how Jeff, a support leader with 25 years in the trenches, sums up the emotional reality of managing customer service today. He’s not bitter, just honest. And like many support managers, he’s juggling a high-volume, high-pressure environment with tools that don’t always make life easier.
We sat down with Jeff to understand what support leaders really care about—and why AI agents might be more than just another buzzword.
8 major support hassles solved with AI agents
What do customer support managers actually want?
Jeff’s team handles thousands of customer interactions each week across voice, email, SMS, and chat. The current system? Zendesk. But while it’s widely adopted, it’s also widely loathed.
“I hate the ticketing system. If someone calls in, emails, and chats the same day, I get three different agents giving three different answers. That’s not support—that’s chaos.”
What Jeff really wants is a CRM-style timeline—one unified customer view with all interactions, across all channels, tied together. He wants a single, contextual conversation history, not a graveyard of fragmented tickets.
And it’s not just about a unified customer view—it’s also about reporting.
“I want to see if we’ve fixed the customer issue or not. Not three pending tickets, and no real sense of whether we’ve helped or not.”
The limits of traditional AI support tools
Despite their investment in Zendesk, Jeff admits their current AI chatbot isn’t doing much.
“I had never worked with AI before. They told me to build it. I did the best I could, but it’s mostly trial and error.”
The current bot lacks depth. There’s no meaningful tracking of what it solves, and it creates zero visibility into resolution impact or cost savings.
What shifted Jeff’s skepticism to confidence in AI for customer service was Sendbird’s AI agent demo—a proactive LLM-based system that merges customer communication records across voice, chat, email, WhatsApp, and SMS. One that supports sentiment detection and integrates with any helpdesk software for live AI to human agent handoffs. One that offers real-time reporting on AI containment rates, breaking away from the headaches caused by traditional ticketing systems.
“You’re telling me I can actually track if the AI solved the issue? And close out the case automatically with a proactive follow-up? That’s what I need.”
Leverage omnichannel AI for customer support
The metrics that matter to customer service leaders
Support managers like Jeff are laser-focused on performance. But not in abstract terms—they care about what their teams can actually deliver:
ASA (Average Speed of Answer): Target = 30 sec.
ART (Average Email Response Time): Target = 48h.
FCR (First Contact Resolution): Target = 87%.
CSAT: Goal = 4.5/5
These numbers aren’t just KPIs—they’re the heartbeat of customer trust.
“I monitor group-level performance: ASA, FCR, ART. But I don’t have a good view of how the bot impacts those. I want that.”
What would support leaders fix first with AI?
If AI could solve even a few high-friction workflows, it would be a game changer. Jeff’s team is drowning in requests like:
“How do I cancel my membership?”
“My screen won’t update.”
“How do I replace my crank arm?”
Many of these are repetitive and follow predictable paths—making them ideal for automation. But other workflows are complex and require hand-holding or a smarter form of AI customer service automation
Jeff’s ideal system would:
Handle simple, repetitive issues automatically.
Offer proactive support follow-ups.
Route in real-time based on expertise and sentiment.
Merge all conversations into one unified customer record.
Auto-close tickets based on business logic, not manual guesswork.
“I'd love to maintain service quality independently of my ability to scale my staff. But that only happens if the AI works.”
Do customers like AI agents?
“I'm old school,” Jeff admits. “I hit zero. I want a human.” But even he knows times are changing, and many customers prefer a self-serve option to a human rep as a first point of contact.
“Most customers don’t mind bots—if they actually solve the problem.”
Here is the real opportunity with AI for customer service: The goal is not to replace human agents but to allow them to focus on high-value work while AI agents manage repetitive and more evolved tasks. It’s to let AI identify when a customer is angry or frustrated and to only involve humans when necessary. It’s not just automation anymore—it’s augmentation.
Reinvent CX with AI agents
So, where do we go from here?
Support managers like Jeff don’t want another tool. They want results:
Fewer tickets.
Faster resolutions.
Clearer reporting.
And fewer fire drills from bad handoffs that lack continuity and context.
AI won’t replace the empathy and experience of an excellent support team, but it can give them the breathing room to improve.
“If we can fix that first-touch chaos & give customers a smoother ride, I’m all in.”
In a world where customers expect more from businesses by the day—and support teams are stretched thin—these fixes aren’t just a wishlist. They’re a necessity, one that’s finally within reach.
Sendbird helps enterprises deploy and scale proactive, omnichannel AI agents that can be tested, evaluated, and continuously improved in a unified dashboard. Our solution integrates AI agents into many support software.
Ready to see what AI agents are really capable of? 👉 Contact us.
