Automation That Understands What You Meant … Not Just What You Said

The role of intent recognition in reducing misroutes, repeats, and customer friction

Every support team knows the pain of misrouted tickets.

A customer calls in. They say the issue clearly (at least to them!). But the IVR menu doesn’t have an obvious option. They press 3. The call lands with billing. Billing transfers them to technical support. Technical support can’t help, because it’s really a “usage” question. Now the customer is frustrated, the handle time is climbing, and the agent is starting from scratch.

It happens every day.
And it costs more than just time.

When customers are misrouted, they lose faith in the process.
When agents handle the wrong tickets, productivity drops.
And when repeat contacts rise, so does customer churn.

All of this happens because most automation is built around what customers say, not what they mean.

Intent is what changes everything

When someone says, “I need help with my account,” that could mean a password reset. Or a billing question. Or a contract renewal. The words alone don’t tell you enough.

But a good intent engine doesn’t just hear the phrase. It analyzes:

• The customer’s history

• The words they chose

• The emotion behind the message

• The channel they’re using

• The time of day or point in the cycle (e.g., end of trial)

These signals, when stitched together, give automation the ability to interpret—not just route.

That’s the difference between menu logic and machine learning.

Getting intent right reduces volume and improves quality

When intent is recognized accurately:

• Fewer tickets bounce between agents or departments

• Fewer customers repeat themselves after being transferred

• Agents spend more time resolving, less time redirecting

• CSAT improves even on difficult issues, because the process feels smoother

This doesn’t require complex AI deployments either.
Even lightweight NLU (natural language understanding) models, when trained on real customer conversations, can outperform traditional flow-based logic by a wide margin.

But automation only works if humans trust it

One of the biggest issues with intent-driven automation isn’t accuracy. It’s adoption.

If agents or managers don’t believe in the AI’s ability to triage correctly, they’ll override it. They’ll re-tag cases. They’ll avoid leaning on the system. And all the efficiency gains disappear.

That’s why Nectar treats AI intent models like part of the team. Not a black box.

• We show our agents why a case was tagged a certain way

• We gather agent feedback to refine model outputs weekly

• We A/B test updates to catch regressions early

• We create shared dashboards so CX, product, and ops see the same signals

Trust in the model leads to better adoption. Better adoption leads to better outcomes.