Customers aren’t asking for AI in support. A Gartner survey of nearly 5,800 customers found that 64 percent would prefer companies didn’t use AI for customer service, and over half would consider switching to a competitor over it. The top fear: it’ll get harder to reach a human.
That survey is from 2024 and the tools have improved, but the lesson holds. A support bot works when it resolves things and fails loudly when it traps people in a loop. Here are six companies worth a look, covering both the customer-facing side and the tools that help your agents.
What an AI support chatbot actually does
At its simplest, it answers common questions from your help center. At its best, it also takes action: checking an order, changing a booking, issuing a refund within set limits.
When it gets stuck, it hands the conversation to a person with the full history attached. That handoff is where most bad experiences happen.
Why the stakes are higher than they look
Support is often the only time a customer talks to your company directly. A bad bot experience colors how they see the whole brand.
That’s why testing and a clear exit to a human matter more here than in most other bot projects.
Two halves of support AI
- Customer-facing agents: Bots that talk to customers directly.
- Agent-facing tools: Assistants that help your human team find answers faster.
Most teams need both, which is why this list includes each kind.
What to look for
Resolution, not deflection. A bot that sends people away isn’t the same as one that solves the problem.
Also check helpdesk integrations, channel coverage (email, chat, voice, messaging), handoff quality, reporting and how pricing behaves when volume spikes.
Quick comparison
| Company | Side | Best for |
| Glean | Agent-facing | Giving support reps fast, cited answers |
| Intercom Fin | Customer-facing | Teams wanting pay-per-outcome pricing |
| Zendesk AI | Both | Teams already on Zendesk |
| Ada | Customer-facing | Large consumer brands with repetitive volume |
| Decagon | Customer-facing | Teams that want to write workflows in plain language |
| Sierra | Customer-facing | Larger teams wanting a managed rollout |
1. Glean
Glean Assistant isn’t a customer-facing widget. It helps your reps by searching the helpdesk, policy docs and team chats at once and returning one cited answer.
New agents benefit most, since they don’t have to memorize the refund matrix in their first month. Check that your helpdesk and knowledge base are on its connector list.
Best for: support teams whose agents lose time hunting for answers.
2. Intercom Fin
Fin is a customer-facing AI agent priced per outcome, so you pay when it resolves a conversation, not when it fails. It works in Intercom and with other helpdesks like Zendesk, Salesforce and HubSpot.
Read how “outcome” is defined, and model costs against your past ticket volume.
Best for: teams that like pay-for-results pricing.
3. Zendesk AI
Zendesk’s AI agents resolve multi-step requests across channels and act in connected systems, with a copilot that suggests next steps to human agents. It also absorbed Forethought, known for ticket triage.
The upside is one vendor and a mature ecosystem.
Best for: companies already running support on Zendesk.
4. Ada
Ada focuses on automated resolution across voice, messaging and email for big consumer brands. It doesn’t publish pricing, so expect a demo and a scoped pilot.
Its own case studies show a wide range of automation rates, so plan around the modest ones.
Best for: high volumes of repetitive inquiries.
5. Decagon
Decagon lets support teams write workflows in plain language, then test and monitor them with built-in QA tools. Voice, chat and email run on one layer.
It’s a newer name than Zendesk or Intercom, which can mean more flexibility and less track record.
Best for: teams that want hands-on control over how the agent behaves.
6. Sierra
Sierra builds branded customer-experience agents and prices on outcomes. Agents run across chat, SMS, WhatsApp, email and voice from one deployment.
It’s a sales-led purchase, with a high-touch rollout.
Best for: larger teams that want a managed setup.
Pricing models to know
Vendors charge in different ways: per resolved conversation, per outcome, per seat or by usage. Per-outcome pricing ties cost to results, but definitions vary.
Ask for a worked example using your own monthly volume. It’s the quickest way to compare quotes fairly.
Questions to ask every vendor
Can I see real transcripts, including failures? What happens when the bot isn’t sure? How do you handle personal data, and where is it stored?
A vendor that answers plainly is usually one that has seen real production traffic.
Common mistakes
Chasing a headline automation rate is the big one. Vendors’ numbers come from their best customers, and your questions may be harder.
Another is hiding the human option. Make the route to a person obvious, and tell customers when they’re talking to a bot.
What a typical rollout looks like
Most teams start with one channel and a short list of question types. They run the bot beside their agents for a few weeks, review transcripts, then widen the scope.
Going live everywhere on day one rarely goes well.
What to measure once it’s live
Track repeat contacts within a week, because a “resolved” conversation that returns as a ticket two days later wasn’t resolved.
Watch the handoff rate and what happens after it. Read a handful of failed conversations every week. Nothing shows you where your documentation has gaps faster.
Getting your content ready
A bot is only as good as the articles it reads. Before launch, fix your top 20 help pages: remove duplicates, update old policies and write answers in plain language.
That work pays off for human agents too.
How to choose
Pull 200 recent tickets and sort them into three piles: simple and repeatable, complex but answerable from documents, and needs a person.
Consumer audio and streaming services show how predictable the first pile can be. Questions about downloads, playback, ads and payments repeat endlessly, and a plain-language features page like the one on SCloudDownTech is the kind of source a bot should be grounded on first.
The first pile suits Intercom, Zendesk, Ada, Decagon or Sierra. The second is where Glean helps your agents. The third is why you never remove the escape hatch to a human.