"90% ticket deflection!"
I cringe every time I see this metric in a sales pitch. Sure, it's possible, but if your AI is handling that many support tickets, you're probably getting too many tickets in the first place.
That's the costly blind spot in how we think about AI support. We've become so obsessed with deflecting tickets that we've forgotten what actually makes support teams effective.
Let me share what I've learned from watching this play out in companies everywhere.
Picture this scene playing out in B2B companies everywhere: A CX leader closes their laptop after yet another AI demo, that familiar mix of frustration and skepticism washing over them. The sales deck was filled with impressive automation stats and ROI calculations.
It's a story we've seen countless times. Support leaders sitting through demos that treat B2B customer support as if it's the same as helping consumers track a package or find the right shirt size. But enterprise customers aren't just looking for quick half-baked answers – they're trying to get important work done that impacts their entire business.
One CX leader said it perfectly: "Deflection isn't resolution."
The Hidden Cost of Measuring the Wrong Things Here's what fascinates me: When you dig into companies with the highest customer satisfaction scores, you often find something counterintuitive. Their most successful customers don't have low ticket volumes because they never need help – they're actually power users. They've just figured out how to get value from the product naturally.
This reveals something crucial about how we should be using AI in support. Instead of building walls to deflect tickets, what if we used AI to understand and replicate what makes these customers successful?
The best support professionals already think this way. As Jim Smith, a veteran Customer Success Leader who's spent years studying this, points out: "The best support reps don't take the customer's question at face value, they take a step back and understand the why."
Rethinking What "Good" Looks Like "We've been measuring the wrong things for years," Kristi Faltorusso, CCO at ClientSuccess, mentioned recently. "Ticket volume, Average Response Rate, Average Resolution Time, even CSAT – they only tell part of the story."
When support teams shift their focus from deflection to success, they start seeing different patterns. Take Smokeball's experience: they achieved an 83% self-service rate not by aggressively deflecting tickets, but by making their product naturally easier to use and giving their support team tools to spot and solve systemic issues.
The metrics that actually matter?
How quickly users can complete tasks on their own How deeply they adopt key features How easily they can move from basic to advanced usage How much effort it takes to get value from the product What Natural Support Actually Looks Like Think about the last time you used a well-designed product. You probably didn't even notice how it guided you, adjusted to your needs, and helped you discover new features naturally. No chatbot walls, no frustrating deflection attempts – just smooth, intuitive progress.
This is what modern AI support should feel like. As James Pavlovich from Straumann Group puts it: "These solutions go in and you put up walls to get to a person... nobody thinks about the customer's perspective – they've spent 10 minutes trying to get past this wall of chat bots, getting more frustrated with each interaction."
The best implementations focus on three things...
Seeing the Whole Picture : Instead of treating every question as an isolated ticket to deflect, AI can spot patterns and connect dots across your entire user base. Another CX leader put it perfectly: "This is where AI can help. Give support reps some historical support context and relevant usage data on the spot so they're armed with the intelligence where they don't have to rehash most of the past."
Prevention Over Deflection : Jenny Eggimann, Head of Customer Success, discovered this firsthand: "We chose this approach because the analytics and user journey reporting showed us exactly where users might struggle before they ever needed to ask for help."
Natural Learning : Yaniv Bernstein, a startup COO & Co-Founder, found that the right approach can transform team efficiency: "With other options, we would have spent countless hours familiarizing ourselves with the tool. Instead, we were able to tap into our existing knowledge base and boost our customer service efficiency with almost no initial setup."
The Future Isn't About Fewer Tickets The next wave of B2B customer experience leaders won't be bragging about their ticket deflection rates. They'll be the ones whose users barely think about needing support at all – because their experience just makes sense.
They'll be the ones whose support teams spend less time answering basic questions and more time helping customers innovate and grow. Because at the end of the day, the best support interaction isn't one that gets deflected – it's one that never needs to happen in the first place.
Not because we've built better walls, but because we've created an experience that feels as natural as asking a knowledgeable colleague for help.
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