Your complete Intercom Fin AI readiness checklist for case closure hygiene

Everyone's talking about AI support agents like Intercom Fin, and for good reason. The idea of resolving more tickets faster and keeping customers happy sounds great. But many teams jump in headfirst only to find the results are… underwhelming. The problem usually boils down to a simple, age-old concept: what you put in is what you get out.

An AI agent is only as smart as the data it learns from. If your support history is a tangled mess, your shiny new AI will be just as confused. This guide is a practical Intercom Fin AI readiness checklist for case closure hygiene to make sure your data is ready to go. We'll walk through the cleanup you need to do and also look at a more modern alternative that lets you skip this whole process and get from setup to resolution in minutes.

What is Intercom Fin AI?

So, what exactly is Intercom Fin? Think of it as an AI agent designed to handle customer support conversations automatically. It’s built on powerful models like GPT-4 and works to solve customer problems across live chat, email, and social media. You can use it within the Intercom ecosystem or as an add-on for other helpdesks like Zendesk or Salesforce.

Intercom claims Fin can resolve a huge chunk of incoming questions, with some users seeing over 50% of their tickets handled automatically. It does this by learning from your help center articles and knowledge base to spit out instant, around-the-clock answers. To get those kinds of results, though, Fin needs a clean, well-organized, and up-to-date source of truth. And that’s where the real work for your team begins.

An overview of the Intercom Fin AI interface, which is a key part of the Intercom Fin AI readiness checklist for case closure hygiene.

Why is case closure hygiene crucial for AI readiness?

"Case closure hygiene" is just a formal way of saying you need to make sure every support ticket is closed out with accurate, consistent, and clear information. Basically, it’s about keeping your support history tidy. When a human agent closes a ticket, they might leave behind a messy trail: internal notes mixed in with customer replies, the wrong tags, or a final response that doesn't actually solve the problem. We’ve all seen it.

For an AI like Fin, this history is its textbook. It studies thousands of your past conversations to learn your brand's voice, figure out common issues, and see what a successful solution looks like. If your ticket history is full of mistakes and inconsistencies, the AI will learn all the wrong things. This can lead to some pretty frustrating outcomes:

• Wrong answers from your AI: The AI might confidently give a customer outdated info or an incorrect solution. This is a quick way to frustrate people and lose their trust.

• More work for your team: If the AI can’t find a reliable answer, it just punts the ticket back to a human agent, which kind of defeats the whole point of automation.

• A bigger bill for bad service: Intercom Fin has a pay-per-resolution model. You could end up paying for "resolutions" that are actually just customers giving up in frustration because the AI's answer was useless.

Getting your data in order is a must-do for most AI platforms. But honestly, it’s a huge project. The good news is that some newer tools are being built to handle the reality of messy data from the get-go, which can save teams months of painful manual work.

The Intercom Fin AI readiness checklist

Before you let Intercom Fin loose, you need to get your house in order. This checklist covers the main parts of case closure hygiene you’ll have to tackle to give your AI the best shot at being helpful.

1. Audit and clean historical data

This is the big one. You'll need to do a deep dive into your old support conversations because that's where the AI gets its education. It means someone has to manually go through thousands of tickets to fix wrong categories, delete random internal notes, and double-check that the final answer was actually correct. For most teams, this turns into a massive, multi-month project that pulls your best agents off the front lines.

But what if you didn't have to spend months cleaning up old tickets? A platform like eesel AI is built differently. It's designed to learn from your existing ticket history, mess and all, right from day one. It figures out which past resolutions were the best and uses them to start automating support accurately, with no manual data cleaning needed from you.

eesel AI learning from past ticket history, a step that simplifies the Intercom Fin AI readiness checklist for case closure hygiene.

2. Standardize macros and canned responses

Macros are supposed to be a source of truth, but let's be real, they often turn into a graveyard of outdated, off-brand, and conflicting information. You'll have to go through all of your canned responses, update them for accuracy, make sure the tone of voice is consistent, and get rid of anything that’s no longer relevant.

While clean macros are a good start, your AI could do more. For example, eesel AI’s AI Copilot doesn't just rely on rigid macros. It can draft replies based on what has actually worked best in similar situations in the past, giving your agents relevant, context-aware responses that are proven to solve problems.

The eesel AI Copilot drafting a response in an email, an important tool for the Intercom Fin AI readiness checklist for case closure hygiene.

3. Curate external knowledge bases

Intercom Fin pulls most of its information from your public knowledge sources. This means your Help Center, developer docs, and any other customer-facing resources need to be perfectly organized and up-to-date. A single outdated article or broken link can send the AI down the wrong path and lead to a bad customer experience. This requires a full content audit and a real commitment to keeping it all fresh.

The thing is, company knowledge doesn't live in just one place. What if your AI could tap into everything? eesel AI connects to all your knowledge sources with over 100 one-click integrations. It can pull from Confluence, Google Docs, Notion, and more, so your AI always has the complete picture.

An infographic showing eesel AI's knowledge integration capabilities, relevant to the Intercom Fin AI readiness checklist for case closure hygiene.

4. Define escalation paths and rules

You can't just unleash an AI on all your tickets from day one. You have to decide which types of questions it should handle and which ones still need a human. This means building out some potentially complicated workflows and routing rules in your helpdesk. The tricky part is that these rules are often rigid, and you don't really know what will work until you see the AI in action.

With a tool like eesel AI, you get fine-tuned control through a flexible, no-code workflow engine. You can start small by automating just one simple type of ticket and then expand as you get more comfortable. Even better, its simulation mode lets you test your entire setup on thousands of your own historical tickets before you go live. That gives you a pretty accurate forecast of how it will perform, so there are no surprises.

A view of the eesel AI workflow screen, a crucial component for the Intercom Fin AI readiness checklist for case closure hygiene.

Pricing implications of AI readiness

A huge part of being ready for AI is understanding what it's going to cost. Intercom Fin’s pricing is based on how many tickets it resolves, which sounds simple enough but comes with a few strings attached.

Intercom Fin's pricing model

On the surface, Intercom's pricing for Fin is pretty clear. You pay a fee for each conversation the AI successfully closes. A resolution is counted when a customer says their problem is solved or when they just stop replying after the AI’s last message.

Here’s a quick look based on their pricing page:

Plan Type	Per-Resolution Cost	Minimums & Requirements	Additional Costs
Fin on Intercom Suite	$0.99	Requires at least one paid seat	Starts at $29/seat/mo (Essential Plan)
Fin on existing helpdesk	$0.99	50 resolutions per month minimum	No platform fees

Hidden costs of poor data hygiene

Paying only for what you use sounds good, but this model can cause a few headaches:

1. Surprise bills: Your support volume isn't always predictable. A new product launch, a big marketing campaign, or a temporary outage can send your ticket numbers through the roof, leading to a much larger bill than you expected.

2. Paying for bad resolutions: If the AI gives a wrong or incomplete answer and the customer just leaves the chat out of frustration, that can still be counted as a "resolution." This is where messy data can come back to bite you right in the budget.

3. Focusing on quantity over quality: The model naturally pushes the AI to close tickets as fast as possible. This can sometimes mean you lose the empathetic, thorough customer experience you're known for, as the goal shifts from quality to pure deflection.

An alternative: Simple and predictable pricing

If you'd rather know exactly what you're paying each month, a different model might be a better fit. Platforms like eesel AI offer straightforward subscription plans without any per-resolution fees. You pick a tier based on the features you need, and you get a fixed monthly cost that doesn't go up just because you had a busy month. With flexible month-to-month plans you can cancel anytime, it's a risk-free way to get started with AI without being locked into a long contract.

A screenshot of the eesel AI pricing page, which is part of the Intercom Fin AI readiness checklist for case closure hygiene.

Beyond the readiness checklist: A faster path to AI

Getting your company "AI-ready" for a tool like Intercom Fin is a serious project. It takes a huge upfront investment of time and people to clean up your data, rewrite your content, and build out a bunch of rigid rules. This whole prep phase is often the biggest, most underestimated reason why AI rollouts fail.

But what if a platform made you ready from day one? eesel AI was designed to be completely self-serve, cutting out that entire painful preparation stage. It learns from your existing knowledge and real ticket history, all the messy parts included, so you can get it configured and launched in minutes, not months. You can simulate its performance to see exactly how it will work and get total control over its actions without writing a single line of code.

A simulation of eesel AI's performance, a key consideration in the Intercom Fin AI readiness checklist for case closure hygiene.

Instead of spending the next quarter working for your AI, you could have your AI working for you by the end of the day.

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