Chatbots Customers Don’t Hate: Lessons From Regal

People don’t dislike automation. They dislike being trapped.

A Gartner survey captures the mood bluntly: 64% of consumers say they’d prefer companies didn’t use AI at all in customer service, and 53% say they’d consider switching to a competitor after a bad chatbot experience. That’s not a chatbot problem—it’s a design and operations problem.

Regal (a customer engagement platform) has been making a public claim that its customer service chatbots perform better than most. Whether you buy that claim or not, the underlying story is useful for anyone running a contact center: the bar isn’t “add a bot.” The bar is “solve the customer’s problem quickly, without making them repeat themselves.” This post is part of our AI in Customer Service & Contact Centers series, and it’s focused on one question: what does it take to build chatbots that customers accept—and teams trust.

Why most customer service chatbots fail (and why customers blame you)

Most chatbots fail because they optimize for deflection instead of resolution. The customer can tell within 30 seconds.

The typical failure pattern looks like this:

• The bot greets the customer nicely.
• It asks two or three generic questions.
• It can’t take an action (refund, change, cancel, reschedule, apply credit).
• It offers a help-center article the customer already read.
• It hides the human option.

That’s how you earn the “I’ll just call” response—and in 2025, that often means higher costs, longer wait times, worse CSAT, and angry agents who inherit a mess.

The real issue: “automation” without accountability

When a human agent fails, the interaction is owned by the company. When a bot fails, customers still blame the company—but internally, teams treat it like a side project.

A simple rule fixes this: your chatbot needs an owner with the same accountability you’d assign to a queue manager. That includes metrics, QA, coaching data, and weekly iteration.

What the Gartner numbers are actually telling you

Those stats (64% prefer no AI; 53% consider switching) don’t mean “don’t use AI.” They mean:

• Customers have learned that many AI chatbots are dead ends.
• Customers will punish brands that use bots to block access to help.
• A bad bot experience is now a retention risk, not an “ops annoyance.”

If you treat chatbot performance like a brand metric—because it is—your design decisions change.

Regal’s claim is a reminder: “better than most” is achievable

Regal’s claim matters because it hints at a shift: AI chatbots can be a competitive advantage when they’re built as part of an end-to-end service system. Not as a widget.

Even with limited details from the RSS summary, there are a few likely “ingredients” behind any chatbot that performs above average in a real contact center:

1. Tighter integration with systems of record (CRM, order management, billing, scheduling)
2. A controlled set of high-impact use cases (not “answer everything”)
3. Strong handoff design to agents (with context preserved)
4. Continuous improvement loops driven by real conversation data

If you’ve been burned by chatbots before, here’s my stance: you don’t need a “smarter model” first—you need a smarter operating model.

“Better” means measurable outcomes, not nicer conversations

A bot that sounds human but can’t do anything is still a bad bot.

For customer service automation, “better than most” shows up as measurable improvements like:

• Higher first-contact resolution (FCR) for targeted intents
• Lower average handle time (AHT) after bot-to-agent handoff
• Higher containment without a drop in CSAT
• Lower repeat contact rate (the silent killer)
• Improved conversion for service-to-sales moments (upgrades, add-ons, renewals)

Notice what’s missing: “number of conversations handled.” Volume is not the goal. Resolved outcomes are the goal.

How to design a customer service chatbot people actually like

Design starts with honesty: the bot should clearly state what it can do, then do it fast. Customers will tolerate automation when it behaves like a competent concierge.

Start with 5–7 intents that matter (and finish them end-to-end)

Don’t launch with 50 intents. Launch with a handful where you can truly resolve the issue.

High-value intents in many contact centers include:

• Order status + delivery exception handling
• Appointment scheduling/rescheduling/cancellation
• Password reset and account access
• Billing: invoice copy, payment status, plan changes
• Returns/exchanges/refunds (with policy-aware eligibility)
• Address updates and profile changes

Pick intents where:

• The steps are predictable
• The required data exists in your systems
• The business rules can be expressed clearly

Then implement end-to-end actions, not just answers.

Snippet-worthy rule: If your chatbot can’t complete the last step (the action), customers won’t count it as help.

Make “talk to a human” easy—then reduce how often it’s needed

Hiding escalation is the fastest way to create rage clicks and churn risk.

A better pattern is:

• Offer escalation early for high-friction signals (“agent”, “representative”, repeated failure)
• Use the bot to gather only essential context
• Hand off with a structured summary (intent, entities, steps attempted, sentiment)

That last part is where many teams drop the ball. If the agent has to ask the same questions again, the chatbot didn’t save time—it just added a step.

Use the bot to do triage, not interrogation

Customers hate questionnaires. They don’t mind two smart questions that get them to the right resolution.

What works:

• Ask for an identifier only when needed (order number, email, phone)
• Use quick replies for common paths
• Confirm understanding (“You want to reschedule tomorrow’s appointment, right?”)
• Keep messages short and scannable

What fails:

• Long multi-part prompts
• Too many open-ended questions
• “Choose from these 12 categories” menus

Contact center implementation: where “good bots” are won or lost

The difference between a decent chatbot and a profitable one is contact center operations. That means integration, governance, and QA—just like voice.

Build a real bot QA program (yes, like agent QA)

If you already score calls and chats, score the bot too.

A pragmatic bot QA scorecard includes:

• Correct intent classification
• Correct data retrieval (no stale status, wrong account)
• Policy compliance (refund windows, eligibility rules)
• Tone and clarity (no rambling)
• Escalation accuracy (don’t escalate too early or too late)
• Handoff completeness (agent receives context)

Review the worst conversations weekly. Ship fixes weekly. A monthly cadence is too slow for customer-facing AI.

Connect chatbot metrics to retention risk

Remember that 53% switching stat. If you can’t connect bot performance to churn risk, you’ll optimize the wrong thing.

Track bot sessions that include:

• Multiple failed attempts
• Customer sentiment drop (explicit or inferred)
• Abandonment after “we can’t do that”
• Repeat contact within 7 days for the same issue

Then treat those sessions like red alerts. They’re not “bot misses.” They’re customer moments that could cost revenue.

Prepare your agents for AI-assisted service (or they’ll resist it)

Agents don’t hate AI. They hate extra work.

If the bot hands off cleanly, agents usually love it because:

• They get structured context instead of a blank slate
• They spend less time on rote verification
• They handle more interesting work

If the bot hands off poorly, agents become your loudest critics—and they’ll be right.

“People also ask” about AI chatbots in customer service

Here are practical answers your team can use when evaluating or improving a contact center chatbot.

Are customer service chatbots worth it in 2025?

Yes—when designed for resolution and integrated with back-end systems. A bot that can’t take actions will usually increase repeat contacts and harm CSAT.

What’s the best way to reduce customer skepticism about chatbots?

Be transparent, keep the path to a human obvious, and make the bot consistently helpful on a small set of high-frequency tasks. Consistency beats cleverness.

Should a chatbot try to sound human?

Not as a priority. Customers care more about speed and accuracy than personality. Clear, concise language with a predictable flow tends to outperform “chattier” bots.

What KPIs matter most for customer service automation?

Start with:

• Containment with CSAT protection
• First-contact resolution for selected intents
• Handoff rate and handoff quality
• Repeat contact rate
• Time-to-resolution (end-to-end)

A practical checklist: “better than most” in 30 days

If you want quick progress, focus on fundamentals that customers feel immediately.

Here’s a 30-day plan I’ve seen work in real contact center environments:

1. Week 1: Pick 5 intents and define “resolved” for each (with the final action included).
2. Week 2: Fix handoff so agents receive the customer’s goal, identifiers, and steps attempted.
3. Week 3: Add failure routing (when confidence is low, escalate early with context).
4. Week 4: Launch bot QA with weekly reviews and a backlog of fixes.

If you do nothing else: stop measuring success by deflection alone. Measure it by outcomes and repeat contact.

What Regal’s story should push you to do next

Consumer skepticism about AI chatbots isn’t going away by itself. The fastest way to change sentiment is to ship a chatbot experience that feels like help—not like a wall.

Regal’s claim that its customer service chatbots are “better than most” should land as a challenge to the market: most implementations are still leaving money on the table by treating automation as a channel, not a capability.

If you’re responsible for AI in customer service and contact centers, ask yourself one forward-looking question: If your chatbot vanished tomorrow, would customers feel relief—or would your service operation fall behind? That answer tells you whether your automation is actually working.