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Why and how an AI agent won a customer service award

The CX Pod · 2026-06-30 · 17 min

0:00--:--

Key moments - from our scoring

Substance score

53 / 100

Five dimensions, 20 points each

Insight Density11 / 20
Originality9 / 20
Guest Caliber13 / 20
Specificity & Evidence12 / 20
Conversational Craft8 / 20

Nathan Christensen of Henry Schein One shares how the company successfully integrated a voice AI agent into its customer service operation serving 70,000+ dental practices. Facing a customer service crisis when a major healthcare outage flooded their lines, the company needed a scalable solution beyond traditional chatbots. Rather than replacing human agents, they deployed Claire - an emotionally intelligent voice AI agent with access to 15,000 knowledge base articles - to handle initial troubleshooting and information gathering. The strategy emphasizes meeting customers where they are: experienced dentists accustomed to phone support, not chat interfaces. Claire now processes 1,500 calls daily, reducing resolution time by 80% while maintaining what Nathan describes as the highest CSAT in company history. The implementation reveals that success requires treating AI onboarding like employee onboarding, focusing on psychological adoption alongside technology, and building strong ROI business cases. The approach extends beyond support into training and sales processes.

Key takeaways

  • →Voice AI agents in healthcare require emotional intelligence and trust-building through personification - Claire was tested by agents yelling and cursing to ensure she handles abuse gracefully.
  • →Henry Schein One's AI agent reduces call handling time by 80% while improving CSAT to record levels by eliminating hold times and enabling immediate issue resolution or warm handoffs to live agents.
  • →Successful AI implementation requires treating it as employee onboarding with tight documentation, daily iteration based on data, and clear business case modeling to justify token costs and ROI.
  • →The handoff strategy from AI to live agent preserves customer experience by passing detailed transcripts of the AI conversation to human agents, eliminating customer repetition and accelerating resolution.
  • →Adoption barriers are primarily psychological - gaining customer trust and assuring internal teams that AI augments rather than threatens their roles - not technological.

Guests

Nathan Christensen

Topics in this episode

Henry Schein ONEVoice AI agentsCustomer satisfaction (CSAT)Claire (AI agent)Dental softwareEmotional intelligence in AIKnowledge base articlesHealthcare customer serviceCall handling automationAI trust and adoption

Questions this episode answers

How can AI agents handle angry or abusive customers in healthcare settings?

Henry Schein One specifically trained their voice AI agent Claire by having support staff yell, curse, and swear at her to test her emotional intelligence; she remains calm and composed without escalating frustration, which was a key differentiator when evaluating vendors.

What information does the AI agent pass to human agents when handing off a call?

Claire records all conversation details - the customer's issue, initial troubleshooting steps, and discussed scenarios - directly into the ticketing system so live agents receive full context and can skip redundant questioning, significantly reducing total handle time.

Why did Henry Schein One choose a voice AI agent over a chatbot?

Their customers are experienced dentists trained over 15 years to call for support; they needed to meet customers where they were accustomed to phone interaction rather than forcing adoption of a chat interface.

How many calls can Claire handle and how much faster is she than live agents?

Claire handles approximately 1,500 calls per day and resolves or gathers information for them in about one-fifth the time a live agent requires.

Did implementing Claire improve or hurt customer satisfaction scores?

Customer satisfaction reached the highest level in Henry Schein One's history after Claire's deployment, primarily because customers no longer experience hold times and get immediate answers or rapid handoffs to live agents.

What our scoring noted

Our reviewer’s read on each dimension, with quotes from the episode.

Insight Density

11 / 20

The episode offers a handful of genuine operational insights - stress-testing the agent via abuse scenarios, the 1,500-calls-a-day scale, and treating AI onboarding like employee onboarding - but these are diluted by generic framing, analogies to hotels and airlines, and a meandering intro. The insight-per-minute rate is moderate at best.

we actually had our support agents go and yell and curse and swear at claire and like put her through a whole ringer
The difficulty isn't getting her to have the knowledge of our product. It's getting her to gain the trust of our customers.

Originality

9 / 20

The award ceremony anecdote is genuinely memorable and distinctive, and the observation that customers initially sounded more robotic than the AI is a sharp reversal of expectations. Beyond that, the frameworks - meet customers where they are, iterate daily, build a business case - are largely recycled AI-adoption talking points.

a lot of times our customers sounded more robotic than the AI agent did, which was interesting
we gave Claire a customer service award in front of our whole company at our town hall

Guest Caliber

13 / 20

Nathan Christensen is a credible practitioner who has deployed voice AI at meaningful scale - 70,000 customers, 60 products, 1,500 calls per day - at the world's largest dental software company. He is mid-level operational rather than C-suite, and speaks from direct implementation experience rather than theory.

we're taking about 1,500 calls a day by an AI agent
we could give this you know 15,000 knowledge base articles and overnight it would know everything about our product

Specificity & Evidence

12 / 20

The episode includes useful concrete figures - 1,500 calls/day, one-fifth the handle time, 15,000 KB articles, 70,000 customers, 60 products - but stops short on financials, exact CSAT scores, specific vendor names, and outcome deltas. 'Highest CSAT ever' and 'several million' remain unquantified.

we're taking about 1,500 calls a day by an AI agent. It's actually handling those calls in about a fifth the time as a live agent
it takes months for us to train and onboard new live agents that can handle our 60 products that handle about 70,000 customers

Conversational Craft

8 / 20

The host asks some genuinely useful questions - on agent personification, the handoff mechanics, and lessons learned - but never pushes back on vague claims like 'highest CSAT ever' or probes the cost model mentioned briefly. The interview stays in testimonial mode rather than producing productive friction.

Was there some decisions made in how you might personify that agent?
what does the handoff look like because i know voice ai agents are great but when you have to hand off to the actual live agent

Conversation analysis

Computed from the transcript - who did the talking, and the words that came up most.

Most-used words

customer26agent20customers15live13claire13agents11voice11help9experience9call7issue7information7support6dental5today5issues5

Episode notes

Claire handles 1,500 calls a day and never gets tired. The AI customer service agent for resolves issues in a fifth of the time of a live agent and recently accepted a customer service award at an all-company town hall. But she's not replacing anyone - she's making every human agent more effective. Nate Christiansen of Henry Schein One breaks down how to deploy AI that elevates your team, not threatens it, and why the biggest barrier to AI adoption in CX is change management.

Full transcript

17 min

Transcribed and scored by The B2B Podcast Index.

Hi, and welcome to the CXPod. I'm your host, Liz Golgowski of the Customer Strategist Journal. Let's start this episode with a question. When I say dentist, what comes to mind?

Maybe it's that you need to schedule your next cleaning, or you're thinking of a memorable root canal, or maybe having braces as a kid like I did. But whatever you're thinking of, chances are it's not AI. Healthcare, medical, dental, is very personal, and the use of AI within the space is especially sensitive, which is why I'm excited today to talk to Nathan Christensen of Henry Shine, a leading provider of dental supplies and technology, to practices around the world. He's got some unique perspectives on how to embed AI, specifically customer-facing AI agents, in an industry where people and physical touch really matter.

So thanks for joining us today, Nate. Welcome to the program. Thanks, Liz. Excited to be here.

So before we dive in, can you tell us a little bit about Henry Schein, Henry Schein One, and then your role? Yeah. So Henry Schein One, where I work, is we're the largest dental software company in the world. So a lot of times people go to dental school, but they don't necessarily go there to run a business and have to run all the operations of it and so we're the one-stop shop or the microsoft equivalent for a dental office that allows the dentists to worry about their patients while we help take care of filing their claims helping with their insurance verifications helping with their ledger helping schedule their appointments and giving them kind of the full suite to solve their issues.

Great. So just to set a baseline, I do this with all my guests. How would you define what a good customer experience might look like for your clients and customers? For sure.

So our customers, a lot of them are older or more seasoned, experienced dentists, and they've been trained over the last 10, 15 years to call us just about for any kind of need or issue. And we've given them that white glove, hands-on kind of experience. And really our goal is to keep the human interaction in place. So we'll talk a little bit about AI and how we still do that with AI.

But we really haven't lost that vision of providing customers the issues they need resolved as fast as possible. So when my wife has me call the hotel or the airline, she hates to have her call that. So she gives me to call that. And really, at the end of the day, I want to get my issue fixed with the airline or with the hotel as fast and as quickly as possible.

That's the first thing that matters for me, as well as making sure it's accurate and done properly. And so the biggest thing is experience that we're giving our customers with high quality, like trust that's accurate, that's quick, that's efficient and resolves their issues. So how does then that play out as you start to incorporate things like AI within your customer service organization? I hear you've got the AI agents alongside human agents.

You haven't completely replaced them. So tell me about how you've approached using AI in that customer facing role. And, you know, this definitely could be kind of a prickly situation. Like you said, some of your customers calling in may not be used to it or potentially averse to it.

I'm just curious how you've kind of gone about using AI. Yeah, for sure. So I think first we can talk about what a not ideal customer experience looks like. And this was a couple of years ago.

We had an outage of one of the companies that we work with or worked with. It was a major health care outage that affected a lot of health care companies And as a result our phone lines got completely piled up in full Customers were waiting on the phone for 20 minutes And that not an issue you can resolve immediately because it takes months for us to train and onboard new live agents that can handle our 60 products that handle about 70,000 customers. So we had to come up with a scalable solution.

Now, a lot of people say, well, now implement a chatbot. And we do have chatbots that can help and answer calls and questions. But in the healthcare industry specifically, and where a lot of our dentists are experienced with answering and wanting to talk on the phone, we had to meet the customers where they were. And so we were able to explore multiple different AI, voice AI tools and different vendors.

the technology for some was not quite there and for others it was fantastic and we were able to choose one of the best ones that we could find and essentially we found that we could give this you know 15,000 knowledge base articles and overnight it would know everything about our product which helped answer a lot of the questions and alleviate a lot of that initial training burden and kind of the Q&A type of side of these calls. So today we're taking about 1,500 calls a day by an AI agent.

It's actually handling those calls in about a fifth the time as a live agent is. And then we still have those live agents on the other side that are redirected to by the AI agent, but now the customer is not having to restart. So this scenario would be is, hey, I have this problem with this product. Can you help me?

It will gather all the initial information. And if it can't resolve it for the customer, it now passes that information straight to the live agent. So is that a voice AI agent? This is a voice AI agent, yes.

So this is just a topic I'm curious about as I hear people talking about using some of these voice AI agents. Was there some decisions made in how you might personify that agent? Like, do they have a specific voice? Do they have a specific personality or using of humor or anything like that?

Did your discussion not only how to use it and use knowledge base, but also make it feel like an interaction a customer would want to have? Did that go into it? And if so, what kind of decisions did you make? Yeah, absolutely.

It was huge. And I would say as we first started, a lot of times our customers sounded more robotic than the AI agent did, which was interesting. And one of the big problems we had to handle and face is we are used to calling and yelling at Alexa or yelling at Siri to play our right song on Spotify. And we all have that experience where it plays the wrong song or we get on a phone tree and we're like, this thing is stupid.

Like, I'm just so frustrated. Get me to a live agent. And so initially our customers are accustomed to anything that's a fake voice is bad. Get me to a live agent.

And so we really had to work on the marketing, the personification and that intro message to gain their trust. So our agent, her name is Claire, and we use that because she provides clarity for our products. and then we've marketed to our customer hey we can now answer your questions 24 7 call us at midnight if you need claire will be able to answer anything and help you she has all of our products on her fingertips and knows things well and then we've really trained her voice as well she she's emotionally intelligent so one of the things that's interesting and this was a big thing as we were looking through a lot of vendors because there's a lot of fakers as well that claim to have a good voice ai solution and they're just building it on the side right now um and so one of the things we had to do is we actually had our support agents go and yell and curse and swear at claire and like put her through a whole ringer and if it was you or me and someone was doing that to me I probably would crumble or get mad or like how could you talk to me like this?

And Claire handles it like a champ. And so we really focus primarily on that emotional intelligence. The difficulty isn't getting her to have the knowledge of our product. It's getting her to gain the trust of our customers.

And you refer to the AI agent as her, right? You try to make it feel like another person in the mix. Another person in the mix, yes. Right.

Which then, I want to hear about, I had heard that you actually had Claire win a customer service award alongside some of your other live agents. Can you talk to me a little bit about that? Yeah, that's right. Why and what she did that was so great?

So our company, we do have like quarterly awards that we give out to exceptional employees. And look, we have fantastic employees here. And Claire by no means is a replacement of the actual people that we have. But it was kind of an exciting moment to share.

So we did give Claire a customer service award in front of our whole company at our town hall. And she did give like a little acceptance speech and was very willing and excited to receive that. And so it was interesting. We actually even had to train her to give the acceptance speech because she is so, I'd say, humble and focused on solving the customer issue.

And so we're like, okay, you need to, in training her, you need to actually, you know, say thank you and talk a little bit more about what this means for the company and for you. and such. So it was a fun, fun little side activity we did. Great.

Now, how are employees and customers reacting to Claire? And do you have any other results? You said you're able to do things a lot faster, but any other good KPIs or results that you've seen from using? Yeah, for sure.

So I think the biggest thing is our CSAT, which is interesting. You'd expect your CSAT to be lower, potentially as a result of having these tools. But this last year, our customer satisfaction score was the highest we've ever had in the history of our company. Now, that is a result of having tools like Claire, and it's also a result of some of our fantastic people in our support organization and their hard work.

So it's a collective effort, but we did have the highest CSAT. And I think a big reason for that is customers don't have to wait on hold ever to get answers to their questions. So, you know, if you call any major hotel or airline or any kind of support group, you're going to wait on hold, even if it's great support. And with Claire, she answers immediately and most cases resolves your issues.

And if not, she's gathering that information and getting it ready to transfer to that live agent. So it's a positive experience that way. Other things that we're doing is we're actually working on embedding Claire into some of onboarding some of our training some of our even sales types of processes and so it really expands further than even just customer support as well it's kind of becoming a part of our product so to say and the whole customer journey and then what does the handoff look like because i know voice ai agents are great but when you have to hand off to the actual live agent is there time built in Or is there information that gets sent to the live agent so they are aware?

Just again, for that customer experience that the customer doesn't have to repeat themselves, even though they've had a whole conversation. Can you talk to me a little bit about the strategy around the handoff? The ideal handoff, and this is still a little in the works, but the ideal handoff is going to be if a customer says, hey, can I talk to a live agent? I have this issue.

Or if it's a scenario that they do, Claire will say, absolutely. now the wait time is three minutes let's gather this information I'll put you in that queue and then we hand you over as soon as that person available and so then she able the customers like not frustrated to talk to her now and actually give her a chance and see maybe she can answer their questions maybe not But the customer's in the queue still getting to that live person. Now today what it is, is she would gather that information and then it records it all on our ticketing system for that customer.

And the agent, as soon as that agent picks up the phone on the customer, they've got the initial information around that customer, what was discussed with Claire, and a lot of the initial troubleshooting kind of scenarios that does take a lot of time in your normal support call. And so we're finding that it is saving time for our live agents, and they're able to be a little bit more effective on getting straight to resolving their issue rather than trying to have to troubleshoot what the issue is.

so what have you learned so far about integrating voice ai in the customer service organization what have been some challenges some best practices just other lessons learned yeah you know i think there's two things so one i think we assume ai is this magical tool that's going to solve all our issues as soon as i turn it on people will adopt but it's more of a psychology experiment than it is even on the tech side. So our biggest limitation isn't implementing the tech. It's how do we help train our customers to be accustomed to using this tool and to trust it and to gain confidence in it, as well as how do we help our internal team members recognize that this tool will help them and not threaten or harm them.

And to this point, it has been a help and an aid for our company. And then I think another aspect is there is just there's certain stigmas around different AI tools that within higher levels of the company, you have to build a strong business case. You have to understand what is this actually going to cost and create a strong model. and it's hard today in this world we have you know it cost us many tokens to do this but we had to really be tight on our pilot to translate what it would actually cost based on and what the impact of that cost would be so that we could build a strong business case to actually prove out the viability of this as well because it's not worth it if it's going to cost us several million and just increase expenses.

And, you know, it does help the customer experience, but like at some point we can't, you know, do it if it's going to hurt us. Of course. Yeah. No, that's a good, it's a balance that companies are trying to figure out because it's not easy proven until you do it.

Right. So it's, it could be a tough balance. So do you have any advice for our audience who are starting to explore voice AI agents or anything a nugget of wisdom maybe to share? Yeah, I think one of the biggest things I would say is when you're training an AI agent, you have to look at it as if you're onboarding a new employee.

And a lot of times companies are poor at onboarding their new employees. They just assume that the employee will figure it out over time. And then they blame the AI agent when it doesn't do something right or it hallucinates but the reality is is they don't have documented processes and so i would say the key to success here is to be really tight on the job that you need it to be done and to just iterate every day make a change see the data see how it's it's adding value or hurting yourself and you're just making tweaks it's just kind of a fun little science experiment every day we're seeing how we can make it better for our customer experience that's great well nathan thank you so much it's been a pleasure and uh i really appreciate you sharing your story it's it's a it's a challenge that a lot of companies are facing so it's nice to hear from a company that's that's kind of seeing it work so thank you again for sharing absolutely thank you lids Appreciate it.

Related episodes across the Index

Other episodes covering the same guests and topics, from across The B2B Podcast Index.

  • Navigating the AI Landscape Without Titles with Ankit Jain, CEO of Infinitus SystemsSaaS Scaling Secrets · on Voice AI agents87 / 100
  • How To Make Your Dental Practice Exit Stress-Free with Maja Thompson [CPD Available]Dentists Who Invest Podcast · on Henry Schein ONE85 / 100
  • Rana el Kaliouby: Culture, not tech, softens AI's impactWorkLab · on Emotional intelligence in AI81 / 100
  • Ep. 197 - The SaaS Retention Problem Starts Before the Customer SignsSaaS Backwards · on Customer satisfaction (CSAT)75 / 100
  • The Graveyard of Brilliant Health Tech Products: Lessons from 22 Years Inside Henry Schein ONEThe TechDental Podcast · on Henry Schein ONE72 / 100
  • #19 - Darren Fay: Change Management and RevOps for Organizational GrowthA Slice of SaaS · on Henry Schein ONE62 / 100

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