A guide to Fin AI handovers and their limitations

Let's talk about that critical moment in customer service: when an AI passes the conversation over to a a human. Get it right, and it's a smooth, helpful experience. Get it wrong, and you've got a frustrated customer. It’s where automation’s speed needs to meet human expertise, without any awkward bumps in the road.

The best AI agents aren’t just good at answering questions; they’re smart enough to know when to get out of the way.

Intercom’s Fin AI is a big name in this space, handling millions of conversations. But how does it actually manage that crucial handoff to a human agent? We’re going to walk through how Fin AI Handovers are set up, dig into some of the limitations of their approach, and show you what a more modern, flexible, and predictable alternative can look like.

What are Fin AI Handovers?

A Fin AI handover is simply the process of moving a customer chat from Fin, Intercom’s AI, to a person on your team. The idea is to bring in a human when a question gets too complex, the customer is upset, or the request is something the AI just can’t do on its own.

This is supposed to prevent customers from getting stuck in that dreaded "bot loop" we’ve all experienced. On paper, it’s a simple concept. In reality, setting it up inside Intercom means you have to piece together several different tools and settings, which can get complicated pretty quickly.

How Fin AI Handovers work: Triggers and workflows

You might expect a single, neat dashboard for managing Fin’s handoffs. Instead, the controls are spread across various automation features inside Intercom. This creates a few layers of configuration that you have to connect and keep an eye on.

Using Intercom workflows for basic routing

Your main point of control is the "Let Fin answer" step inside an Intercom Workflow. This is where you give the AI the first crack at answering a customer. If Fin can’t find a good answer or if the customer just types "talk to a human," the conversation is supposed to follow a path you’ve built in the workflow that leads to the right team.

A screenshot of the Intercom Workflow builder, illustrating how Fin AI handovers are configured.

The main headache here is that you have to build and manage these handoff rules in a separate workflow builder. It adds an extra step to what feels like it should be a core part of the AI's setup. A more intuitive system would have these rules built right in. For instance, eesel AI uses a single workflow engine where you can map out all your escalation logic in one place, no need to jump over to a different automation builder.

Configuring escalations with Fin guidance

Intercom also has a feature called "Fin Guidance," which lets you give Fin instructions in plain English. You can write prompts like, "If a customer mentions 'cancel my account' or sounds angry, send them to the retention team right away."

An image showing the Fin Guidance feature where users can input plain English instructions for Fin AI handovers.

This sounds flexible, but the catch is you have to word your instructions perfectly. The AI’s interpretation isn't always consistent, which means you might not get the predictable, rock-solid handoffs you need for important issues.

Advanced handovers with data connectors

Need to do something more complicated, like creating a ticket in an external helpdesk like Zendesk? For that, Fin relies on "Data connectors." These are basically API calls that connect Intercom to other software.

The problem is, setting these up almost always requires a developer. If you’re a support manager who wants to build and tweak your own tools without getting in line for engineering resources, this can be a huge bottleneck. This is exactly the kind of friction a platform like eesel AI is built to remove. With one-click integrations for major helpdesks, you can set up advanced actions and handoffs to other platforms without touching a single line of code.

The limitations of the Fin AI Handovers model

Beyond the tricky setup, there are a few real-world challenges that teams face when they try to scale their support with Fin.

The unpredictable cost of resolution-based pricing

This is a big one. Fin charges $0.99 per resolution. At first glance, that might seem reasonable. But it creates a strange problem: your costs are unpredictable and can actually go up as your AI gets better. The more time you spend improving your knowledge base and training the AI, the more resolutions it gets, and the higher your bill. You're essentially penalized for doing a good job.

And what about the conversations that don't end well? If a customer gets a bad answer and just leaves the chat without asking for a human, it might still be counted as a resolution. That means you could be paying for experiences that leave customers frustrated.

This is a complete reversal of how eesel AI's pricing works. Our plans are based on your overall conversation volume, not on how many tickets the AI closes. This lets you focus on improving your automation rate without worrying about a nasty surprise on your invoice.

Workflow rigidity and tool sprawl

As we've covered, getting handoffs right in Fin means you’re constantly jumping between Workflows, Guidance, and Data Connectors. This fragmented setup makes the whole system a pain to manage, troubleshoot, and grow. It often feels less like you're using a cohesive AI platform and more like you’re trying to bolt an AI feature onto an older, more rigid system.

Limited visibility before going live

All this complexity creates risk. Fin has a preview feature, but it’s really hard to know how all these interconnected rules will actually perform when real customers start throwing curveball questions at it. There isn't a proper sandbox where you can see the whole picture before you launch.

This image shows the testing interface for Fin AI, highlighting the limited visibility before Fin AI handovers go live.

This usually leads to "fixing it live," where teams are scrambling to tweak rules while customers are already in the system, which is never a good look. This is why eesel AI's simulation mode is so useful. You can safely test your entire AI setup, including all your handoff logic, against thousands of your actual historical tickets. It gives you a clear forecast of your resolution rate and cost savings before a single customer interacts with it.

A better approach to Fin AI Handovers: A flexible and transparent engine

So, what does a better AI handoff system look like? It’s really built on three simple ideas: control, context, and confidence.

Start with selective automation and gradual rollout

A great AI system shouldn't force you to go all-in from day one. You should be able to choose exactly which kinds of tickets the AI handles. For example, you could start by automating only "password reset" questions and have the AI pass everything else to a human with 100% certainty.

This kind of granular control is at the heart of eesel AI. Our fully customizable workflow engine lets you define exact rules for what gets automated and what gets handed off, so you can roll out automation at your own pace.

Unify knowledge for smarter handovers

For a truly seamless handoff, context is everything. The AI and the human agent who takes over need to be working from the same playbook. This means pulling from more than just your public help articles; it includes information from past tickets and your internal docs in tools like Confluence or Google Docs.

eesel AI connects to all of your knowledge sources right away. This makes sure that when a handoff happens, the human agent has the full story without making the customer repeat themselves.

A visual of how Intercom connects various knowledge sources, which is crucial for smart Fin AI Handovers.

Test with confidence through robust simulation

Before you go live, you should have solid answers to questions like, "What percentage of our tickets will be handed off?" and "Are those tickets going to the right people?" A powerful simulation tool isn't just a nice-to-have; it's essential for any serious AI support platform. It takes the guesswork out of the equation and lets you launch with total confidence.

Understanding the pricing of Fin AI Handovers in detail

To give you the full picture, here’s a straightforward breakdown of Fin AI’s pricing. That per-resolution model is the key thing to watch when trying to predict your costs.

Component	Cost	Notes
Fin AI Agent	$0.99 / resolution	Minimum of 50 resolutions per month.
Intercom Helpdesk Seat	Starts at $29 / seat / month	Required if you're using the full Intercom suite.
Copilot Add-on	$35 / user / month	For agent-assist features in the inbox.

The bottom line is that as your resolution rate goes up, so does your bill. This can make budgeting a challenge and might even discourage teams from getting the most out of their AI.

Moving beyond rigid Fin AI Handovers for a better customer experience

While Fin AI Handovers can work, they rely on a complicated setup of separate tools within the Intercom ecosystem. This fragmentation, paired with an unpredictable pricing model that can punish you for being successful, creates real challenges for teams that want to build a smooth and scalable support operation.

The future of AI in customer service belongs to platforms built from the ground up to be simple, powerful, and predictable. It’s about giving you fine-grained control over your automation and the confidence to scale it without worrying about hidden costs or complexity.

If you're looking for an AI support solution that you can set up in minutes, test with real data, and scale without surprise bills, it might be time to try a different approach.

Discover how eesel AI's all-in-one platform gives you total control over your AI handoffs and more. Start your free trial today.