Regal’s Chatbot Playbook: Win Trust, Not Eye-Rolls

Most companies get chatbots wrong because they treat them like a cost-cutting project instead of a service product.

That’s why a stat like this lands so hard: in a recent Gartner survey, 64% of consumers said they’d prefer companies didn’t use AI of any kind in customer service—including chatbots. And 53% said they’d consider switching to a competitor because of it. Those numbers don’t mean “customers hate automation.” They mean customers hate bad automation.

Regal (a company building AI for customer interactions) is betting it can beat that skepticism by building customer service chatbots that are “better than most.” Whether you buy the claim or not, the interesting part is the blueprint: what it takes for an AI customer support bot to feel helpful, not dismissive—and how contact centers can roll out AI without torching CSAT.

Why customers distrust customer service chatbots

Customers don’t dislike the idea of fast help—they dislike feeling trapped. When a chatbot becomes a maze, it signals the company doesn’t want to talk to you.

If you work in a contact center, you’ve probably seen the pattern: customers arrive already irritated because their last “chat with a bot” was a loop of canned replies. They weren’t mad about AI. They were mad about wasted time.

Here are the three most common drivers of chatbot resistance:

1) The bot can’t do the thing the customer came for

A lot of chatbots are built around FAQ deflection. That’s fine for “What are your hours?” but useless for:

• Order exceptions (partial shipments, backorders)
• Billing disputes
• Account access issues
• Policy edge cases (returns outside the standard window)

When a chatbot can’t complete tasks, it becomes a speed bump before the real support.

2) The handoff to a human is hidden or punitive

If escalation is buried, customers assume the company is trying to avoid them. A chatbot that performs well still needs an obvious, respectful path to an agent—especially when emotions are high.

3) The bot acts confident while being wrong

This is where many AI chatbots fail hardest. A polite, confident response that’s incorrect feels like gaslighting. Customers will forgive “I don’t know”—they won’t forgive confident nonsense.

A customer service chatbot earns trust by knowing its limits and saying so clearly.

Regal as a case study: what “better than most” should mean

A good customer service chatbot is measured by outcomes, not presence. “We have a bot” isn’t the milestone. “Customers get help faster with fewer repeats” is.

Regal’s claim matters mainly because it frames the right competition: not “human vs. bot,” but great support vs. frustrating support. When teams evaluate AI in customer service, they should ask: does this reduce customer effort?

Here’s what I’d look for in any Regal-style approach (and what you can copy even if you’re using a different platform).

Build for resolution, not deflection

Deflection is tempting because it’s easy to count. Resolution is harder—but it’s what customers feel.

A “better than most” chatbot should:

• Complete real workflows (refund status, address changes, cancellations)
• Pull from systems of record (CRM, order management, billing)
• Confirm actions with clear summaries (“I’ve submitted X; you’ll receive Y within Z hours”)

If your bot can’t take action, it should at least gather context and pass it to an agent so the customer doesn’t have to repeat themselves.

Make escalation a feature, not a failure

High-performing AI in contact centers treats escalation as a designed experience:

• Offer escalation early when confidence is low
• Escalate immediately when the customer asks
• Escalate when signals show risk (refund threats, chargebacks, “cancel my account”)

This is also how you protect your brand when customers are already skeptical. You’re showing them: “We’re not hiding the humans.”

Treat accuracy like a product requirement

If you want customers to accept AI customer support, you need guardrails:

• “Answer with citations” internally (even if you don’t show citations to users)
• Restricted knowledge sources (approved policies, current pricing, valid SOPs)
• Clear fallbacks when the model isn’t confident

In practice, that often means retrieval-based responses and strict prompting that prevents the model from freelancing.

The metrics that actually prove an AI chatbot is working

If you can’t measure trust, you can’t improve it. Most chatbot dashboards obsess over containment rate. Containment matters, but it’s not the goal. The goal is customer outcomes.

For AI in customer service & contact centers, I’ve found these metrics give a much clearer picture:

1) Customer Effort Score (CES) for bot interactions

Ask a simple post-chat question: “How easy was it to resolve your issue?” Compare bot-only, bot-to-agent, and agent-only.

2) Time-to-resolution (TTR), not time-to-first-response

Bots can respond instantly and still waste ten minutes. Track end-to-end resolution time.

3) Repeat contact rate within 7 days

If customers come back for the same issue, the chatbot didn’t resolve it. This is where “confident but wrong” shows up.

4) Escalation quality score

When the bot hands off, did it capture:

• Issue category
• Customer intent
• Account/order identifiers
• Steps already attempted

A strong handoff reduces handle time and frustration.

5) Sentiment delta during the conversation

Many contact centers now run sentiment analysis on transcripts. Look at whether sentiment improves from start to finish—especially in bot-to-agent cases.

Containment without satisfaction is just hiding the problem.

How to design chatbots that customers don’t hate

The reality? It’s simpler than you think: customers want speed, clarity, and a fair shot at a human. Here’s a practical playbook you can run in a quarter.

Step 1: Pick “high-intent, high-volume” use cases

Start where automation actually helps customers:

• Order status with proactive issue detection (“Your package is delayed; here are options”)
• Appointment rescheduling
• Password reset/account unlock
• Plan changes with clear pricing confirmation

Avoid starting with emotionally loaded edge cases (fraud claims, medical billing disputes, escalations). Put those behind a fast handoff.

Step 2: Write the bot’s rules of engagement

This is not fluff. It’s how you prevent brand damage.

Include:

• When to escalate (confidence thresholds + keywords)
• What the bot is allowed to do (actions) vs. describe (info)
• Tone rules (short, direct, no fake empathy)
• Compliance constraints (PCI, PII handling)

Step 3: Make “I can connect you” visible

Don’t bury “talk to an agent” behind three failed attempts.

A pattern that works:

• Offer two clear choices after the first response: “Try steps” or “Connect me”
• If the customer rejects the first solution, offer escalation again

Customers who want self-serve will self-serve. Customers who don’t will punish you in CSAT.

Step 4: Feed the bot with the same content agents trust

If your knowledge base is outdated, the bot will be too.

Operationally, this means:

• One owner for support content (not 12 scattered editors)
• A change log tied to product/policy releases
• A monthly “top 20 failure intents” review with support leadership

Step 5: Train with real transcripts, then keep training

The fastest way to improve chatbot performance is to mine transcripts for:

• Unrecognized intents
• Missing integrations (bot knows the answer but can’t execute)
• Confusing language (“refund” vs. “chargeback” vs. “credit”)

If you’re running AI at scale, plan on weekly iteration early on. That’s normal.

People also ask: what’s the right role for AI in contact centers?

AI shouldn’t replace the contact center—it should reshape the work. The best deployments move humans up the value chain.

Will AI chatbots reduce headcount?

Sometimes, but the healthier target is reducing backlog and improving service levels. Many teams reinvest saved capacity into proactive outreach, retention, and complex case handling.

How do you prevent an AI chatbot from hallucinating?

You reduce the model’s freedom:

• Limit answers to approved knowledge sources
• Force structured outputs for certain tasks
• Add confidence scoring and safe fallbacks
• Route uncertain cases to humans quickly

What’s the best first channel for AI customer support?

Chat is usually the easiest start because it’s asynchronous, transcript-rich, and easier to supervise than voice. Then expand to email and agent assist before full voice automation.

A practical stance: skepticism is earned, so trust must be engineered

Regal’s claim that its customer service chatbots are “better than most” is believable only if “better” means something specific: higher resolution, lower effort, fewer repeat contacts, and faster human handoff when needed. That’s the standard customers are using, whether they say it out loud or not.

If you’re leading AI in customer service & contact centers, the opportunity in 2026 isn’t to plaster “AI” across your help widget. It’s to design automation that respects the customer’s time and doesn’t pretend it’s smarter than it is.

Your next step: audit your current chatbot (or planned rollout) against three questions—Can it resolve? Can it escalate gracefully? Can it admit uncertainty? If the answer isn’t “yes” across the board, you’re not fighting skepticism—you’re feeding it.

Where do you think your customers lose patience first: the bot’s accuracy, the lack of human handoff, or the time it takes to get a real outcome?