AI Chatbots for Business: The Honest 2026 Review

Everyone wants an AI chatbot. Few have one that works well.

We tested 12 platforms over six months. Here’s the honest review.

What We Tested

Customer Support Chatbots

  • Intercom Fin
  • Zendesk Answer Bot
  • Freshdesk Freddy AI
  • Drift AI
  • Custom builds on OpenAI/Anthropic

Internal Knowledge Bots

  • Glean
  • Guru AI
  • Notion AI
  • Custom RAG implementations

Use Cases

  • Customer FAQ handling
  • Pre-sales qualification
  • Internal IT help desk
  • Knowledge base search

The Winners

Intercom Fin (Customer Support)

What it does: AI-powered customer support that answers from your help docs.

What worked:

  • Easy setup (connected to existing help center)
  • Good at understanding question intent
  • Knows when to escalate
  • Clear attribution to source docs

What didn’t:

  • Expensive ($0.99 per resolution at scale)
  • Sometimes over-confident on edge cases
  • Customization is limited

Verdict: Best-in-class for companies already on Intercom. Genuinely reduces support load.

Resolution rate we saw: 35-40% of incoming questions handled without human intervention.

Glean (Internal Knowledge)

What it does: Searches across all your company tools (Slack, Notion, Google Drive, etc.) with AI understanding.

What worked:

  • Unified search across tools
  • Understands questions, not just keywords
  • Permissions respected
  • Learns from company context

What didn’t:

  • Expensive ($15-30/user/month)
  • Setup takes time
  • Requires adoption to show value

Verdict: If you have knowledge scattered across tools, this is genuinely useful. ROI depends on company size and chaos level.

Custom RAG on Claude (Specific Use Cases)

What it does: Custom chatbot built on Claude API with your documents as context.

What worked:

  • Complete customization
  • Lower marginal cost at scale
  • Can handle domain-specific needs

What didn’t:

  • Requires engineering time to build
  • Ongoing maintenance needed
  • No out-of-box features (escalation, analytics, etc.)

Verdict: For AI consultants Brisbane or teams with engineering capacity, this offers the most flexibility. Not for teams without technical resources.

The Disappointments

Most Website Chat Widgets

The “AI chatbot” that lives on marketing websites? Usually terrible.

What happened: Tested three. All produced low-quality conversations that frustrated users more than helped.

Why it failed:

  • Trained on generic data, not your product
  • Users have complex questions widget can’t handle
  • Creates friction before humans can help

Verdict: Skip unless you have significant volume and good implementation. Bad chatbot is worse than no chatbot.

Pre-Sales Qualification Bots

Promise: Qualify leads 24/7, book meetings automatically.

Reality: Most lead “qualification” is just collecting email addresses. Real qualification requires understanding context that bots miss.

What happened: Conversion rates dropped when we replaced human chat with AI qualification. Leads that “qualified” often weren’t actually qualified.

Verdict: Human chat for high-value leads. Bots for information collection on low-value traffic.

IT Help Desk Bots

Promise: Automatically resolve common IT issues.

Reality: The issues people ask about are usually the non-common ones. Common issues they solve themselves already.

What happened: Bot resolved 15% of tickets. But the tickets it resolved were already easy. Net time savings: minimal.

Verdict: Only valuable at scale (1000+ tickets/month) with well-documented procedures.

The Implementation Lessons

Lesson 1: Narrow Scope Works Better

Bots that do one thing well beat bots that try to do everything.

What we changed: Instead of “general support bot,” we built “billing questions bot.” Narrower scope, higher accuracy.

Lesson 2: Escape Hatches Are Essential

Easy path to human help. Visible, one click, no friction.

Users forgive bot limitations if they can get help. They don’t forgive being trapped.

Lesson 3: Training Data Quality Matters Most

Garbage documentation in, garbage answers out.

Before launching a knowledge-based bot:

  • Audit your docs
  • Fill gaps
  • Update outdated content
  • Standardize format

Lesson 4: Monitor and Iterate

Launch is the beginning, not the end.

Track:

  • What questions the bot can’t answer
  • Where users abandon conversations
  • False positive rates (wrong answers given confidently)

Improve based on data.

The ROI Math

Customer Support Bot

Costs:

  • Platform: $500-2,000/month
  • Implementation: 20-40 hours
  • Ongoing maintenance: 5-10 hours/month

Savings:

  • At 40% resolution rate: 40% of volume handled automatically
  • If volume is 1,000 tickets/month at $5/ticket handling cost: $2,000/month savings

Verdict: Positive ROI at ~500+ tickets/month.

Internal Knowledge Bot

Costs:

  • Platform: $15-30/user/month
  • Implementation: Varies
  • Adoption effort: Significant

Savings:

  • Reduced search time: Maybe 30 minutes/week/person
  • At $50/hour: $100/month/person in time saved

Verdict: Positive ROI if people actually use it. Adoption is the hard part.

What to Ask Before Implementing

  1. Do we have enough volume? Under 500 interactions/month, probably not worth it.

  2. Is our knowledge base good enough? Bots amplify knowledge quality issues.

  3. What’s the failure mode? How do users get help when bot fails?

  4. Do we have capacity to maintain it? Bots need ongoing attention.

  5. What’s the real goal? Cost reduction? Better experience? 24/7 coverage? Be specific.

The Prediction for 2026-2027

AI chatbots will get better. Reliability will improve. Costs will drop.

But: They’re still best for well-defined, repetitive interactions. The dream of “AI that handles everything” remains distant.

Plan for augmentation. Build bots that make humans more effective. Don’t plan for replacement.

The companies getting chatbots right use them for specific, bounded use cases with clear escalation paths. That’s the model to follow.