A practical guide to Fin AI escalations in 2025

AI support agents are powerful, but that moment when they hand a conversation off to a human, the escalation, is everything. Get it wrong, and you've got a frustrated customer. Escalate too quickly, and you’re losing the very efficiency you were aiming for. It’s a real tightrope walk.

Intercom's Fin is a big name in this space, so figuring out its approach to escalations is essential for anyone looking to automate support. Let's break down how Fin AI Escalations actually work, the real-world challenges of managing them, and a more straightforward way to get the job done.

Understanding Fin AI and its escalation process

Before you can start tweaking anything, you need to know what’s going on under the hood. Fin can feel complicated, but its logic for handing off conversations boils down to a couple of key ideas.

What is Intercom’s Fin AI?

Fin is Intercom’s AI agent, designed to handle customer questions over chat, email, and other channels. The idea is to let Fin take the first pass at support by training it on your help articles and company info. It's meant to be an all-in-one solution you can set up, track, and hopefully improve over time.

How do Fin AI Escalations work?

An escalation isn't a bug; it's a feature. It's the planned handoff from Fin to a human when the AI is out of its depth. Think of it as a built-in safety net. Fin decides when to pass the conversation along based on two main things:

First, there's its default behavior. Right out of the box, Fin is programmed to escalate when it detects certain signals. It uses sentiment analysis to pick up on frustration, recognizes when someone directly asks to "talk to a person," and knows when a conversation is just going in circles.

Second, you have custom guidance. This is where you get to tell Fin what to do. You can write simple, plain-language rules to give yourself more control. For instance, you could tell Fin, "If a customer mentions a refund, pass this chat to a teammate."

A screenshot showing the custom guidance interface for Fin AI escalations in Intercom, where users can write plain-language rules.

The mechanics of managing Fin AI Escalations

Getting a bit more granular, managing Fin’s escalations means writing instructions, trusting the tech to understand you, and doing a lot of ongoing upkeep.

Configuring custom guidance

"Guidance" is the main tool you have for shaping Fin's behavior. It’s a set of instructions you write in natural language to tell Fin how to act in specific scenarios. According to Intercom's own best practices, good guidance is direct and clear.

But let's be real, getting this right isn't a one-and-done setup. It takes a lot of trial and error and constant tweaking as you see how customers actually talk to the AI. You're basically teaching the AI new rules, one prompt at a time.

The technical side of the escalation model

Behind those simple guidance prompts is some pretty complex tech. A research post from the Fin team explains they built a custom model that makes a three-way decision in real-time for every single interaction: escalate now, offer to escalate, or let Fin continue.

While the model is impressive on paper (they claim over 98% accuracy in their own testing), it’s mostly a black box to you, the user. Your main way to influence it is through the custom guidance you write. You can't just go in and adjust the model’s settings; you have to feed it better instructions and hope it gets the message.

Monitoring and reducing unnecessary escalations

Once Fin is live with your customers, the job switches to optimization. The aim is to cut down on escalations that a better-trained AI or a clearer help article could have solved. This usually turns into a routine cycle:

• Beefing up your knowledge base: Finding and filling the gaps in your support content.

• Tweaking workflows: Adjusting automated paths to give Fin a better shot at answering questions.

• Clarifying your guidance: Rewriting any rules that seem to be confusing Fin and making it escalate too often.

This creates a continuous, manual loop of digging through reports, spotting problems, and adjusting content or prompts. It works, but it's a pretty demanding process for your team.

Challenges and limitations of managing Fin AI Escalations

While Fin is a capable system, the day-to-day reality of managing it brings a few headaches that can slow your team down and mess with your budget.

The headache of manual setup

"Custom Guidance" might sound straightforward, but it’s basically a prompt engineering job. You have to guess all the weird and wonderful ways a customer might ask for something and then write rock-solid rules to cover every possibility. It’s surprisingly easy to write rules that contradict each other, which can make Fin behave in ways you just didn't expect.

This puts a lot of pressure on your team to write, test, and maintain a huge library of rules. That’s time and energy that could be spent on more important things than constantly fine-tuning AI prompts.

No risk-free way to test and simulate escalations

Fin lets you test your setup, but it’s tough to know how a new rule will actually perform at scale before you unleash it on your customers. You can’t easily run a new rule against thousands of past conversations to see how it would have changed things.

This leads to a "launch and pray" approach. A poorly worded rule can easily create bad experiences for live customers. You often only find out a rule is broken after it’s already caused some damage, forcing you to scramble and fix it.

The challenge of unpredictable pricing

Here’s the catch with Fin’s pricing model: it’s $0.99 per resolution. Every time the AI successfully closes a ticket without needing a human, you get charged. So while you want to reduce escalations to make your support more efficient, every escalation you prevent actually costs you money.

This setup creates a weird tension. Your team's goal (let the AI solve more problems) is in direct conflict with your budget's goal (keep costs predictable). It makes it incredibly difficult to forecast your monthly spend and, in a way, penalizes you for building a really effective AI agent.

A more transparent and controllable alternative

What if you could get the benefits of AI without the messy setup, the launch-day anxiety, and the wild-card pricing? That’s the thinking behind eesel AI, which was built to give you more control and predictability from the start.

Go live in minutes with a self-serve platform

Instead of a long, drawn-out setup, eesel AI is designed to be simple and self-serve. You can get up and running in a few minutes without ever needing to schedule a demo or talk to a salesperson. With one-click integrations for help desks like Zendesk and Freshdesk, it plugs right into the tools your team already uses. There's no need to rip out your existing systems.

Test with confidence using powerful simulation

This is where things get really cool. eesel AI’s simulation mode lets you safely test your AI setup on thousands of your own historical tickets. Before a single customer interacts with it, you get a clear forecast of its performance, including how many tickets it's likely to resolve and how much you could save. This takes all the guesswork out of launching a new automation tool.

eesel AI's simulation mode provides a risk-free way to test AI performance on historical tickets before going live.

Get total control with a customizable workflow engine

With eesel AI, you’re in charge. You have fine-grained control to decide exactly which tickets the AI should touch. You can start small by automating just one or two simple topics and have the AI safely pass everything else to your team. As you get more comfortable, you can gradually let it handle more.

Plus, you can connect eesel AI to all of your company knowledge, not just a polished help center. It can learn from past tickets, internal Google Docs, Confluence pages, and more, giving it the full context needed to provide accurate answers.

The eesel AI platform allows for granular control over automation with customizable rules and workflows.

Pricing comparison: Fin AI Escalations vs. a predictable alternative

The difference in philosophy is clearest when you look at the price tags. Fin’s per-resolution model is volatile by design, while eesel AI offers predictable, flat-rate plans.

Fin Pricing:

• $0.99 per resolution.

• Can be bundled with Intercom’s Helpdesk for an additional fee.

• Costs grow as the AI gets better, making your monthly bill a moving target.

eesel AI Pricing:

• Clear, predictable plans based on a set number of AI interactions per month.

• No per-resolution fees. You’re never penalized for automating more conversations. Your bill is the same every month, which makes budgeting a whole lot easier.

Here’s a quick side-by-side:

A view of the eesel AI pricing page, showing transparent, predictable plans as an alternative to per-resolution models for Fin AI Escalations.

Why Fin AI Escalations are a feature, not a failure

Handling Fin AI Escalations is a major part of running a modern support team. While Intercom's tool is powerful, it brings a lot of complexity, hands-on management, and a pricing model that can punish you for being successful.

A better approach should give you full control over your automation, the ability to test without risk, and simple, predictable costs. The goal isn't to get rid of every single escalation. It’s about making sure they happen at the right time, for the right reasons, so the handoff from AI to human feels like a smooth and helpful part of the customer's experience.

Ready for an AI support agent that puts you in control? Try eesel AI's simulation mode and see how many of your tickets you could automate, completely risk-free.