Most AI support implementations I see get stuck at the same stage: the AI answers questions, but it doesn't actually resolve tickets.

There's a meaningful difference, and it's worth understanding exactly where the gap is.

Answering vs. Resolving

An AI that answers a question gives the customer information. An AI that resolves a ticket takes action. The distinction sounds simple, but it changes everything about how you build, train, and measure your AI agent.

When a customer asks "how do I cancel my subscription?", answering looks like: "You can cancel by going to Settings > Billing > Cancel Plan." Resolving looks like: "I've flagged your cancellation request—here's what happens next, and here's a confirmation number." Or better: actually processing the cancellation.

Most teams I work with are building answering machines. They're good at surfacing knowledge base content. They score well on containment. But their customers are still frustrated, because the conversation ended without anything changing.

Why the Gap Exists

The gap between answering and resolving usually comes down to three things:

1. Knowledge base focus

Teams spend months building and optimizing their knowledge base for the AI. That's the right foundation. But a knowledge base is designed to answer questions, not take actions. At some point, you have to go beyond it.

2. No integration with your systems

Resolution requires access. If your AI can't look up order status, trigger a refund, update an account, or escalate with context—it can only give directions. Most AI support tools offer some integrations out of the box, but the real leverage comes when you connect them to your actual product data.

3. Measuring containment, not resolution

Containment rate measures whether a customer stopped talking to the AI. Resolution rate measures whether their problem was actually solved. If you're optimizing for containment, you'll build a containment machine. The metrics you track shape what you build.

How to Close the Gap

Here's the progression I see in teams that make it from answering to resolving:

  1. Map your top ticket types. Look at your last 3 months of tickets. Categorize them by type. You'll find that 60–80% of volume is a small number of repeat issues.
  2. Identify which types require action vs. information. Some issues just need an answer. Others need a transaction. Separate these—they require different solutions.
  3. Start with self-serve resolution. For issues that require action, what can the customer do themselves? Build flows that guide them through it. This is resolution without requiring system integration.
  4. Add one integration at a time. Pick the highest-volume actionable ticket type. Build the integration. Measure resolution rate specifically for that type. Iterate.
  5. Track what matters. Add resolution rate as a KPI alongside containment. Define what "resolved" means for each ticket type so your measurement is consistent.

What This Looks Like in Practice

At RB2B, we didn't get to 75% resolution overnight. We started with a knowledge base. We measured containment. We thought we were doing well.

Then we started tracking whether customers came back after an AI interaction. Turns out, a meaningful percentage of "contained" conversations were just people who gave up. That wasn't resolution. That was abandonment.

When we shifted focus to actual resolution—did the customer's problem get solved?—we started building differently. We built flows that ended with confirmation. We connected to our product data. We defined escalation criteria so the AI knew exactly when to hand off with full context.

Resolution rate went from something we couldn't measure to something we track daily.

The Mindset Shift

If you're building an AI support agent, ask yourself this: at the end of every conversation, what actually changed for the customer?

If the answer is usually "they have more information," you're building an answering machine. That's a starting point, not an endpoint.

The teams getting the best results are the ones who treat their AI like a support agent—accountable for resolution, not just conversation.