AI Hold Assistants: The End of Waiting on Live Reps

Waiting on hold is one of those customer service experiences everyone recognizes instantly: the chipper voice that says “your call is important,” the looping music, the feeling that your time is being quietly siphoned away. It’s also wildly expensive. When a contact center has to pay people to “be available” while customers sit in a queue, everyone loses—customers waste time, agents start calls already behind, and operations teams keep adding headcount to solve a problem that’s mostly traffic management.

That’s why Google’s reported test of a feature called “Talk to a Live Rep” matters for anyone who runs a support organization. The concept is simple: Google places a call to a business on your behalf, waits on hold, and then calls you back once a live representative is available. Users get their time back. Businesses get a preview of what the next decade of customer contact might feel like: not “call volumes” from humans, but call volumes from AI assistants acting on humans’ behalf.

This post is part of our AI in Customer Service & Contact Centers series, and I’m going to take a stance: AI hold assistants aren’t a gimmick. They’re a forcing function. If your queues are long, AI will route around them—either through call-back automation, AI intermediaries, or self-service that actually works.

What Google’s “Talk to a Live Rep” signals for contact centers

Answer first: It signals that “waiting” is becoming optional—and that customers will increasingly expect AI-powered call handling to buffer them from hold times.

From the RSS summary we have: Google is testing a feature that will call a business, sit on hold, and then call the user back when an agent is available. Even without the full product details, the direction is clear. Google is treating hold time as a solvable interface problem.

That’s a big shift because hold time has traditionally been framed as a staffing problem (“hire more agents”) or a forecasting problem (“predict demand better”). Those matter, but they’re not the only lever.

An AI intermediary flips the experience:

• The customer’s time stops being the “buffer” in the system.
• The network (or assistant) becomes the buffer.
• The contact center queue still exists—but it becomes less painful for the person.

This is exactly the kind of consumer-facing change that ends up reshaping B2B operations. When Apple normalized visual voicemail, customer expectations changed. When ride-share apps normalized ETAs, “where’s my driver?” calls dropped. If Google normalizes “I don’t wait on hold,” your customers will bring that expectation to your contact center.

Why this hits harder in late 2025

Answer first: Seasonality and peak periods make “hold avoidance” even more valuable—AI assistants will feel most useful when your queues are worst.

It’s December 2025. For many industries, this is prime time for demand spikes:

• Retail and delivery: order issues, returns, lost packages
• Travel: delays, rebooking, cancellations
• Financial services: year-end account changes, fraud alerts, tax documentation
• Healthcare: benefit changes, appointment scheduling

Peak volume is when your queue pain is most visible—and when customers become least tolerant of it. A “hold for me” button is the exact kind of feature people will adopt fast during seasonal surges.

The real problem isn’t hold music—it’s queue architecture

Answer first: Long waits usually come from mismatched demand and routing, not just “too few agents,” and AI can reduce the mismatch.

Most companies get this wrong: they treat hold time as one metric to shrink. The bigger issue is why customers are landing in queues in the first place.

In a typical contact center, queues swell because of a few predictable failures:

1. Low containment in self-service (customers can’t complete tasks in IVR/chat)
2. Poor intent detection (calls routed to the wrong team)
3. Authentication bottlenecks (verification takes too long)
4. Batch events (outages, policy changes, shipping delays) that spike demand
5. One-size-fits-all SLAs (everyone waits equally, regardless of urgency)

An AI assistant that holds for the customer doesn’t fix those root causes. But it does something operationally important: it makes the queue invisible to the customer.

That’s good for experience, but it can be dangerous if you stop there. If customers can “queue without feeling it,” you may actually see more attempts to reach a live rep. Which means contact centers need to pair the convenience layer with AI deflection, smarter routing, and better workforce planning.

A practical way to think about it: time-shifting vs. problem-solving

Answer first: “Hold for me” is time-shifting; contact center AI should also do problem-solving.

There are two different value plays:

• Time-shifting tools: callback, virtual hold, AI that waits on hold
• Problem-solving tools: agent assist, knowledge automation, workflow automation, conversational self-service

Time-shifting protects customers from queue pain. Problem-solving reduces the queue.

The organizations that win in 2026 won’t choose one. They’ll run both.

If AI assistants call your business, how should your contact center respond?

Answer first: Treat AI callers as a new “customer channel” with policies, detection, and optimized flows—otherwise your phone system becomes the weak link.

If Google (and others) start placing calls on users’ behalf, your IVR and agent workflows will interact with non-human callers more often. That isn’t science fiction; we already see automated dialers, robocalls, and basic voice bots. The difference here is intent: these assistants are trying to help real customers complete legitimate tasks.

Here’s what I’d put on an operations roadmap.

1) Design a “virtual hold” path you control

Answer first: If you don’t offer a high-quality callback experience, customers will adopt third-party hold assistants that you don’t control.

Callback isn’t new, but many implementations are clunky: missed calls, lost queue position, no context when the call connects. Upgrade it.

Minimum viable callback experience:

• Offer callback early (not after 20 minutes)
• Preserve queue position
• Confirm preferred number and allow scheduling windows
• Provide SMS confirmation and status updates (where compliant)
• When connected, pass a reason-for-call summary to the agent

If you do this well, an external “hold for me” feature becomes less necessary.

2) Assume the assistant has zero patience for your IVR

Answer first: AI can handle menus, but complex trees increase errors and drop-offs—simplify to improve both human and AI success rates.

A messy IVR hurts everyone. AI assistants may be better than humans at pressing “2-1-4,” but they still face:

• ambiguous prompts
• long legal disclaimers
• “say or press” confusion
• nested menus that reset on silence

Simplify routing using intent-first prompts (“Tell me what you’re calling about”) backed by strong speech recognition and NLU—and provide a fast escape hatch to an agent for edge cases.

3) Add verification that doesn’t punish legitimate automation

Answer first: You need anti-fraud controls, but overly aggressive bot-blocking can block real customers using assistive tools.

If an AI assistant is calling on behalf of a user, authentication gets tricky. You can’t just hand over account details because a bot asked nicely.

A sane approach looks like:

• Allow the assistant to hold a place in line and gather non-sensitive context (issue type, order number, callback number)
• Require the actual customer (or verified identity flow) for account-specific actions
• Use step-up verification: OTP, secure links, or in-app verification where possible

This protects customers without turning your phone experience into an obstacle course.

4) Instrument what’s happening (or you’ll misread your volumes)

Answer first: If AI intermediaries grow, your contact center analytics must separate “attempts,” “holds,” and “connected conversations.”

If a third-party assistant places calls that sit in queue, your systems may interpret that as increased demand, even though the customer isn’t actually consuming agent time until the handoff.

Track:

• Abandon rate vs. assistant-held rate
• Queue time to agent vs. time-to-resolution
• Repeat contact rate (did the problem get solved?)
• Containment rate in self-service

If you don’t separate these, you’ll chase the wrong staffing targets.

Where AI reduces wait times for real (not just cosmetically)

Answer first: The biggest wait-time reductions come from better routing, higher containment, and faster handle times—AI can improve all three.

“Talk to a Live Rep” is a consumer-friendly wrapper. Inside the contact center, the most reliable ways to reduce wait time are operational.

AI routing: stop sending callers to the wrong place

Answer first: Intent-based routing reduces transfers, and fewer transfers means fewer queues.

If 10–20% of calls are misrouted (common in complex organizations), you’re creating extra queue load. AI-based intent detection can route to the right skill group earlier, reducing:

• transfers
• re-explaining the issue
• average handle time (AHT)

AI self-service: raise containment without annoying people

Answer first: Good conversational self-service handles the “simple but frequent” work that clogs queues.

The goal isn’t to block customers from agents. It’s to handle things like:

• order status and delivery updates
• appointment scheduling
• password resets
• refunds/returns policy explanations
• billing balance and payment status

The trick: connect self-service to real systems of record, so it can complete tasks, not just answer FAQs.

Agent assist: speed up calls once they connect

Answer first: Shorter calls reduce queue time for everyone behind them.

When agents get real-time help—suggested replies, next-best actions, knowledge surfacing, form auto-fill—AHT drops and first-contact resolution improves. Even a modest reduction in AHT during peak season can mean the difference between “manageable queue” and “meltdown.”

Snippet-worthy truth: The fastest way to shrink a queue is to reduce the minutes spent per solved problem.

People also ask: what happens when bots start calling businesses?

Will businesses need to “support the bots”?

Answer first: You don’t need a bot-only lane, but you do need bot-tolerant design—simple prompts, clear confirmations, and predictable flows.

Treat it like accessibility. Make your phone experience robust enough that different callers (humans, assistants, screen readers, translation tools) can succeed.

Does this increase spam and robocalls?

Answer first: It could raise baseline automated traffic, which makes call authentication and fraud detection more important.

But don’t conflate helpful user-authorized assistants with spam. Overblocking automation often harms real customers first.

Could AI assistants replace human agents?

Answer first: They replace waiting and repetition more than they replace empathy and judgment.

The near-term value is removing low-value minutes: holds, transfers, identity checks, and copy-paste work. Humans still handle exceptions, emotional situations, negotiation, and complex troubleshooting.

What to do next if you run a contact center

Answer first: Build your own “no-hold experience,” then use AI to reduce the queue itself.

If Google’s experiment becomes a mainstream behavior, customers will reward companies that respect their time. Here’s a practical 30-day plan I’ve seen work:

1. Audit your top 10 call drivers (by volume and by cost)
2. Add/upgrade callback with queue position preservation
3. Fix the top 3 routing failures causing transfers
4. Automate one high-volume workflow end-to-end (not just an FAQ)
5. Roll out agent assist for knowledge + after-call work reduction

Then measure what actually changed: wait time, AHT, first-contact resolution, and repeat contact rate.

If you’re building your 2026 roadmap for AI in customer service, “Talk to a Live Rep” is a useful reminder: customers don’t care how sophisticated your contact center stack is. They care that their problem gets solved without losing an hour of their day to hold music.

So here’s the forward-looking question I’d ask your team: If a customer can delegate waiting to an AI assistant, what else will they expect to delegate next—and are you ready for that handoff?