Response Time: Vol. 39

You satisfy your customers, but can you satisfy our curiosity?

With Sam Barrett, Head of Customer Experience at Runna.

Please tell us a little bit about your company and what you do there.
I’m Sam, Head of Customer Experience at Runna. We’re a running training platform that aims to make running easy, enjoyable, and effective for all.

What word or phrase in customer service jargon should be retired?
“Circle back.”

Which celebrity would be really great at your job?
Mikel Arteta.

What’s the most valuable thing that working in customer service has taught you?
Be kind to EVERYONE! It doesn’t matter their role, title, or experience. Be kind and lead with care.

Describe the essence of great customer service using only three words.
Tell your friends.

Which movie robot would you choose as your AI sidekick, and why?
WALL-E. He’s kind, friendly, and supportive and whilst he doesn’t steal the show, he deserves a lot of credit for getting the job done. Everyone needs a WALL-E!

What can you do that a bot will never be able to replicate?
Provide hyper-personalized and individually specific coaching guidance to first-time runners.

How do you go the extra mile for your customers?
I frequently share my personal running routes with our customers. I’ve spent hours finding the best routes where I live, and when I see a customer based in my hometown I go above and beyond to share all the hidden gems.

Do you identify more with the title “customer support,” “customer service,” “customer success,” or “customer experience,” and why?
We identify most with “customer experience.” We wanted to encapsulate the end-to-end experience of our customers and show CX as a value-add, rather than a cost to the business. “Customer service” sounded too transactional for us and I’m glad we made the change.

What’s the one piece of advice you would give to your peers in the customer service industry?
Bring your team along with you on the journey! Especially when you are making big decisions and changes. It’s important to keep your team informed at all stages and get them bought into the process. That way, when you roll out a change, everyone in your department knows what to expect.

What’s the worst customer service you’ve ever experienced?
I once flew from London to Santiago and got stuck in Toronto with an American airline. They were rude, impatient, and didn’t want anything to do with us! They compensated us with a $35 voucher for food for 24 hours and put us up in a terrible hotel nearby. As if it couldn’t get any worse, they left us stranded on the way back and made us sleep in the airport.

What’s the best thing a customer has ever said to you?
We’re super fortunate that our customers tell us every day how much our app is changing their lives. It’s a really beautiful thing, and to play a small part in helping them is such an honor and why I love doing what I do.

Where do you get your support leadership news?
I’m part of a network called “Customer Support Stories,” which is great for connecting with other like-minded leaders in the industry.

What do you wish people knew about working in customer service?
It’s the bedrock of any successful business and normally a very good reflection of the company’s culture and values.

Conversation closed… for now 😏

If you’re interested in being featured in our Response Time series, you can share your insights on customer service – and what your greatest productivity hack is – with us here.

Intercom vs Zendesk: Two AI agents put to the test

We all know that generative AI is transforming the customer service industry.

AI agents are already handling customer queries with impressive accuracy, and the teams using the right AI solutions are seeing remarkable results. They’re resolving more issues faster and delivering better customer experiences, which is allowing human support agents the freedom to focus on more complex, high-value interactions.

However, identifying the right AI solution is not easy amid all the noise. Our research shows there are significant performance gaps when it comes to resolution rates, accuracy, and quality between different AI agents on the market.

We’ve repeatedly tested our Fin AI Agent against competitors’ offerings to ensure it performs optimally in every way. Here, we’ll show you how Fin compares to Zendesk’s AI agent and walk you through the research process to give you an in-depth understanding of why Fin is the superior choice.

Fin is the best AI agent on the market – with stats to prove it

Let’s start with some numbers.

When we put Fin head-to-head against Zendesk’s AI agent, the difference isn’t just noticeable – it’s remarkable. Three things in particular stood out:

In 80% of cases, Fin provided better answers across the board, demonstrating superior performance in accuracy, completeness, and overall quality. This isn’t just about getting the answers right; it’s about delivering the kind of experience that builds customer trust and loyalty.

“Fin can handle twice the number of complex questions Zendesk’s AI agent can, transforming what’s possible with automated support”

Fin is also much more capable at handling complexity. Unlike Zendesk’s AI agent, which defaults to basic responses when faced with challenging queries, Fin maintains natural conversations by asking clarifying questions. And now, Fin can answer more types of questions with the recent addition of actions. This sophisticated capability means Fin can handle twice the number of complex questions Zendesk’s AI agent can, transforming what’s possible with automated support.

Perhaps most impressively, when dealing with questions that require pulling information from multiple sources – the kind of query that typically needs human intervention – Fin achieves a 96% answer rate, significantly outperforming Zendesk’s 78%. For support teams, this means more queries resolved automatically, faster response times, and happier customers.

These numbers are compelling – but how did we get to them? Here’s an overview of the research process we followed.

How we compared Fin to Zendesk’s AI agent: A look at our evaluation process

Step 1: Setting the stage

To evaluate the AI agents in an unbiased manner, we needed a completely neutral dataset of help articles and relevant questions that we knew were grounded in the articles.

Using ChatGPT 4, we created a fictional bed and breakfast website with 48 comprehensive articles, all of which we loaded into Fin and Zendesk’s AI agent, ensuring a fair playing field.

We also generated 200 customer questions based on the 48 articles. Some were straightforward, while others required piecing together information from multiple articles.

We asked all 200 questions to both Fin and Zendesk’s AI agent.

Step 2: Checking the outputs for hallucinations

Before we started judging the outputs, we checked for any made-up information – hallucinations – in the responses. We found that there was no statistical difference in the hallucination levels between the two AI agents.

Step 3: Judging the answers

We used four advanced AI models (Anthropic Claude 3 Opus, GPT-4, GPT-4 Turbo, and GPT-4 Omni) to act as impartial judges. These “judges” had access to the articles and question bank, and were instructed to vote on the answers provided by both Fin and Zendesk’s AI agent for given questions while considering the articles as the source of truth.

To determine a winner, we applied the Elo rating system, which calculates a score based on which AI agent delivered the better answer, according to the AI judges. Over hundreds of such “competitions,” a clear winner emerged.

The results were clear: when pitted side-by-side, Fin’s answers are almost always better than Zendesk AI agent’s.

Step 4: Digging into the details

We wanted to dig deeper into what specifically made Fin’s answers better. So, we looked more closely at how Fin outperformed Zendesk AI agent in the following areas:

• Providing a direct response.
• Giving the most “readable” answers for humans.
• Delivering a complete resolution of the query.

A direct response

Fin outperformed Zendesk’s AI agent by providing more direct responses to every question type. The most notable difference was in its ability to answer “hard” questions, where Fin answered more than double the questions Zendesk AI agent did, and questions that required piecing together information from multiple sources, where Fin provided answers to 96% of the questions, and Zendesk AI agent only managed 78%.

The most “readable” answers for humans

Accurate answers are one thing, but how they’re structured matter a lot for the end user experience. Fin provided more comprehensive answers than Zendesk’s AI agent, with the average response coming in at 120 words compared to 50 words. Fin’s responses were also formatted to be more scannable, including elements like newlines and bulleted lists.

A complete resolution of the query

Looking at the direct answer results we got, we estimated the probability of a complete resolution provided by Fin by applying the following formula:

In relative terms, Fin was 66% more likely to provide a resolution for a query when both Fin and Zendesk AI agent provided an answer. Similar to the results we saw with the direct response investigation, Fin was also the winner across every answer category.

A few notes on research limitations

While our test was thorough, it had some limitations:

• We used a simulated help center, not real-world data.
• AI judges are great, but they might not perfectly match human judgment.
• The two products we tested have different features, and this could impact the results to an extent.

Overall, these findings clearly demonstrate Fin’s superior performance in direct testing. But beyond the numbers, there are several crucial advantages that make Intercom the clear choice for forward-thinking support teams. Here’s what this means for your business in practical terms.

What sets Intercom’s Fin apart

Flexibility to use Fin as part of Intercom’s seamless AI-first platform – or whatever CS platform you’re currently using

First, we understand that every support team has unique needs. That’s why we’ve made Fin incredibly flexible – you can either use it as part of our comprehensive AI-first system or integrate it with your existing platform, including Zendesk and Salesforce, and access all of its benefits. There’s no need to overhaul your entire support stack or disrupt your team’s workflow – Fin can help you get results in whatever way suits you best.

Pricing that makes sense: 99¢ per resolution

We want AI to be accessible for every team, so we’ve also taken a radically different approach to pricing. While other vendors lock you into complex contracts with hidden costs, we keep it simple: 99¢ per resolution. This transparent, outcome-based model means you only pay for actual value delivered. You don’t have to worry about spending a large chunk on something that doesn’t actually help move your business ahead.

Innovation that keeps you ahead

The thing that has always set Intercom apart is how fast we move. When it comes to AI, staying ahead matters because the sooner you get the latest capabilities, the better your automated customer experience will be.

We’re rolling out new features and capabilities at an unprecedented rate, continuously improving Fin’s performance based on millions of real customer interactions. When you choose Intercom, you’re not just getting today’s best-performing AI – you’re partnering with the most innovative company in the space, ensuring you’ll stay ahead of the curve as AI technology continues to advance.

The future of customer service is here – and it’s already delivering results

Many companies are making noise about their AI capabilities. But what they’re not doing is backing up this noise with evidence. From our research, it’s clear that Intercom’s Fin AI Agent outperforms a significant competitor – Zendesk AI agent – in providing the best, most accurate, high-quality answers. This means you can bring it onboard as part of your team and fully trust in its abilities to resolve a huge share of your customer queries, freeing up your human teammates to focus on more meaningful work.

“In a market full of noise and ambitious claims, we let our results do the talking”

Since this research was conducted, we’re proud to share that we’ve raised the bar even higher by launching Fin 2, our next-generation AI Agent. Delivering human-quality support, it’s capable of achieving a 51% resolution rate straight out of the box, with some of our customers achieving up to 86% after spending some time refining its use. To date, Zendesk is still marketing their first-generation AI agent.

What’s particularly exciting is that this is just the beginning. In a market full of noise and ambitious claims, we let our results do the talking. The data is clear, the performance gaps are real, and the future of customer service is already here. Are you ready to see what Fin can do for your team?

Announcing our latest guide: ‘The New Economics of Customer Service’

Today, we’re excited to share Intercom’s latest guide – The New Economics of Customer Service. In this guide, we unpack how AI enables support teams to offer high-quality support at scale, in an efficient and cost-effective way.

⚡️ Ready to dive straight in and learn how AI broke the linear customer service growth model? Grab a copy →

Growth is a crucial component of any business, and the truth is that sustainable growth is impossible without customer service.

More customers inevitably means higher support volume. And without the resources to handle rising demand for support efficiently, those customers will be left with a poor experience, a bad taste – and a strong desire to take their business elsewhere.

“You can have speed, provide a great customer experience, or keep costs low – choose two”

But scaling customer service alongside business growth has traditionally been a tricky balance to strike. To meet rising demand, the only real option was to add more and more headcount to your support team, which was costly, time-consuming, and unsustainable. This has always been a catch-22 situation for businesses, leading to the all-too-common refrain: you can have speed, provide a great customer experience, or keep costs low – choose two.

AI has changed that; now, there’s another way.

The linear growth model has been broken, which means support leaders no longer have to grow their teams at pace to meet demand. Instead, they can use AI tools to manage increasing support volume quickly and without driving up costs, all while providing a great customer experience.

In other words, it’s now possible to unlock the trifecta of better, faster, and cheaper customer service.

What’s inside

Our new guide reveals how the changing economics of customer service are unlocking new ways for support teams to drive impact and bottom-line results, and offers practical strategies to get set up for success with AI-first customer service.

You’ll learn:

• How to quantify the ROI of AI-first customer service: Understand all the factors you need to consider in order to demonstrate AI’s real value and impact on your bottom line.
• The opportunity cost of not adopting AI: Discover the hidden costs of postponing AI-first customer service – from limited scalability to reduced competitiveness.
• The value-creating opportunities being unlocked for support teams: Explore the changing roles of support agents, and how AI is freeing up time for them to focus on more proactive, revenue-generating, and value-creating work.
• The impact AI is having on support teams, right now: Get real-world advice and strategies from other support leaders for getting started with AI, winning exec buy-in, and driving results.

The economics of customer service have been changed forever

By breaking the linear growth model, AI has created untapped potential for support teams to deliver value to both their customers and their business. At Intercom, we’re saving in the region of $1.75–2 million a year with AI, all while delivering a faster, more personalized support experience for our customers.

These are the new economics of customer service: turning a former cost center into a value driver, and using AI to fuel powerful customer experiences that lead to compounding long-term ROI.

Ready to learn how? Grab a copy of The New Economics of Customer Service here

Braving busy holidays: Reduce customer service stress with automation and AI

The holidays are coming, and for consumer-facing businesses, that often results in a huge tide of shoppers turning to your support team for help. How can you get on top of high conversation volumes, and still provide speedy, personal support during the busiest times of the year?

It’s an easy answer: bring in AI. In the space of a year, we’ve come a long way with this remarkable technology. Providing exceptional (read: personal, efficient) support no longer means needing to add extra headcount or face burnout. Working hand-in-hand with an AI agent, like Fin 2, our latest next-generation release, your human team can now resolve support queries proactively and automatically.

Here are our top tips for making the most of an AI agent like Fin and providing world-class support in a considerably easier way.

Tip 1: Avoid burnout and still provide 24/7 support with an AI agent

No matter how dedicated your support team is, they can’t (and shouldn’t have to) work around the clock during busy holiday periods, such as Black Friday and Cyber Monday. The good news? AI agents like Fin can provide instant responses and accurately resolve queries 24/7.

“AI bots will provide speedy, spot-on answers even when your team is out of office or otherwise engaged”

If you have a knowledge base, you can set up your AI agent and point it at your help content in minutes. Then when a customer asks a question, your bot will provide speedy, spot-on answers to queries even when your team is out of office or otherwise engaged.

The best part is you have 100% control over how you use this automated support. Being able to properly switch off is crucial for avoiding burnout during this time of year, so make use of an AI agent in whatever way best suits your team. Some folks might like to have AI completely take over answering certain questions, while others might like to toggle between automated and human support – with the AI agent being turned on during certain hours and human team members taking over when they’re online. It’s completely up to you.

Tip 2: Automatically answer holiday FAQs in a way that feels personal

Answering the same questions over and over again can feel like running on an endless treadmill for your team. This is especially true during the holiday season when customers have more questions about shipping, delivery times, and holiday discounts and deals.

Luckily, that’s a thing of the past with support from AI agents that can instantly resolve FAQs and reduce your conversation volume. Fin is excellent at this – straight out of the box, it can resolve up to 51% of all queries with 99.9% accuracy. Just in time for the holidays, we’ve put a bow on this level of efficiency and launched a few new sparkly features to ensure Fin not only clears your queue in record time, but makes it a merry experience:

• Fin’s advanced Knowledge Hub helps it maintain up-to-date information about your products and services, ensuring accurate responses throughout the holiday season without requiring constant updates from your team.
• New behavior features allow you to customize Fin’s tone of voice to match your brand’s holiday communication style, choosing from five preset tones or adjusting between concise and conversational responses to maintain consistency across all customer interactions.
• The ability to communicate fluently in 45 languages, ensuring all your customers across the world receive the same high-quality support.

Sounds great, but wondering which questions are best to automate? We recommend digging into data from previous years and identifying patterns in what simple, frequent queries are eating into your team’s bandwidth most.

If you’re looking for inspiration, here’s a list of common questions our customers leave to Fin:

• What are your shipping options, prices, and timelines?
• What is the shipping process for international orders?
• What are your holiday return and exchange policies?
• How do I apply the promo code?
• What’s the deadline for placing an order to guarantee delivery before Christmas?

With Intercom, you can also create Custom Answers to ensure Fin serves the right customers with the right tailored solutions and suggested actions at the right time. For example, if a customer asks about modifying a holiday order, Fin can connect to your order management system, pull in the exact order details, and help them make changes – all within the Messenger.

These kinds of personal touches speed up resolutions, enhance satisfaction, and build strong customer loyalty for your brand.

Tip 3: Set customer expectations around response times

What’s one of the fastest ways to frustrate a customer? Failing to set expectations or deliver on your promises. Again, this is something teams no longer have to worry about, with an AI agent on hand to instantly solve simpler queries while the humans deal with more complex conversations.

“Customers can then plan their next move based on real-time information, instead of waiting around for a response”

If there is any reason that customers might have to wait longer during the holiday rush, build trust and goodwill by proactively communicating response times. Customers can then plan their next move based on real-time information, instead of waiting around for a response that might take a few hours.

With Intercom’s Messenger, you can set clear expectations for holiday shoppers. First, tailor the Messenger intro to share your team’s availability or other important customer service issues. Second, if you’re providing extended holiday hours, you can adjust your team’s office hours and expected response times.

To bring even more visibility to your team’s availability, you can set Fin up to let your customers know what your office hours are and when your team will be back online if they’re looking to speak with a human.

Tip 4: Route complex issues to the right team ASAP

People getting stressed out during the holiday season is inevitable. With all sorts of things happening behind the scenes, it’s important to go into this period with understanding and patience – for your customers and your support team. With that in mind, answers to some questions, such as emotionally charged and complex queries, are probably best handled by a human.

We’re working on a “Category Detection” feature for Fin to help ease the pressure around resolving stressful scenarios quickly. This new feature will analyze language used and automatically identify customer frustration or urgent issues in real-time.

Whether someone is experiencing a technical issue or is understandably angry about a misplaced holiday delivery, Fin can route their conversation to the right team immediately. This intelligent routing works seamlessly across all channels – chat, email, and WhatsApp – ensuring consistent support no matter how customers reach out.

Another helpful thing we did ourselves was set up both Slack and PagerDuty integrations with our Messenger to trigger emergency out of hours notifications for certain scenarios. This ensured we never missed anything critical and could use the most effective option to help our customers, regardless of when they experienced an issue.

Tip 5: Beat your customers to it by being proactive

What’s even better than automatically resolving frequent questions? Proactively solving issues and resolving queries before they ever become problems!

Use banners, automated pop-up messages, or whatever jumps out to your customers on your website or within your product, to provide answers to your top 10 holiday questions. Whether it’s delivery cut-off dates or return policies, make the most important information visible so customers can self-serve.

“By transparently informing your customers of issues, you can help them to make more informed choices and manage their expectations”

It’s also a good idea to use your AI agent or other attention-grabbing features, like a banner or targeted message, to proactively communicate any known issues to customers. By transparently informing your customers of processing times, website bugs, or shipping delays before they make their order, you can help them make more informed choices and avoid unnecessary confusion or frustration.

Tip 6: Empower customers to resolve their own issues

Today’s customers overwhelmingly prefer self-service. So much so, that by 2030, Gartner estimates that a billion service tickets will be raised automatically by customer-led automation. In turn, providing a seamless self-service experience lets you reduce time spent on simple issues and improve your holiday bottom line.

Fin 2 takes self-service to the next level by accessing customer data to provide personalized responses. For example, when a customer asks about their holiday order status, Fin can access their specific order information and provide real-time updates. Using action templates, Fin can even help customers make simple changes to their orders, such as updating shipping addresses or modifying gift options, without requiring human intervention.

As we mentioned before, Fin is fluent in 45 languages. It can automatically detect a customer’s language and serve them relevant answers in that language, providing a great global customer service experience. What’s more, when you integrate third-party apps, like Stripe and Shopify, with Fin, your customers can go beyond just chatting to complete actual transactions.

Tip 7: Proactively help customers break past friction

The average cart abandonment rate in 2024 is a significant 70.19% according to a recent study. Cart abandonment rates typically peak during busy shopping periods, like Black Friday and Cyber Monday. One of the best ways to help customers complete their order is by triggering a targeted, automated proactive message on the checkout page to preemptively answer their questions.

For example, if customers are on the shipping page for a few minutes, they might have questions about your shipping times, returns policy, or something else. You can trigger an outbound message that links to your top FAQs related to purchasing so they get the relevant information upfront without needing to reach out for support.

Stay on top of your holiday customer service

The holidays are a huge opportunity to attract more customers and drive revenue, and in the past that came with extra demands on your support team. Now, AI has truly revolutionized customer service – so much so that you can genuinely handle the holiday season just like any other time of year.

With a helping hand from cutting-edge support tools like Fin, your human team can spend more time focusing on the more meaningful, emotional parts of the job that they enjoy. The dual ability to meet demands quickly and efficiently, and also connect deeply with people when it counts means you’ll win the hearts and minds of customers and they’ll keep choosing your business again and again.

Response Time: Vol. 38

You satisfy your customers, but can you satisfy our curiosity?

With Victor Salinas, Head of Customer Success at VMetrix.

Please tell us a little bit about your company and what you do there.
VMetrix is a software as a service (SaaS) platform designed for financial institutions that allows them to manage investments, risks, and treasury operations in a comprehensive way. It includes everything from financial asset trading to portfolio accounting. I’m Head of Customer Success, and responsible for managing the relationship between our users and the company, for which Intercom has become an essential tool.

What word or phrase in customer service jargon should be retired?
“Unfortunately, this option is not available, but we will strongly consider your suggestion for future versions.”

Which celebrity would be really great at your job, and why?
Ryan Reynolds, because he takes everything with humor.

What’s the most valuable thing that working in customer service has taught you?
You don’t truly understand the customer’s needs until you speak with them every day.

Describe the essence of great customer service using only three words.
Care for them.

Which movie robot would you choose as your AI sidekick?
Baymax from Big Hero 6.

What can you do that a bot will never be able to replicate?
Find alternative solutions within our system so that the user doesn’t have to stop their work.

What’s the most embarrassing thing you’ve ever said/done to a customer?
Responding to a customer with information intended for someone else.

Do you identify more with the title “customer support,” “customer service,” “customer success,” or “customer experience,” and why?
“Customer success,” because I consider myself a partner in their daily growth.

What’s the one piece of advice you would give to your peers in the customer service industry?
Build solutions based on their (the customer’s) needs.

What’s the worst customer service you’ve ever experienced?
When people are more concerned with charging me than with solving my problems.

What’s your greatest productivity hack?
Make lists and prioritize tasks, then set follow-up reminders.

What book are you reading at the moment?
Fall in Love with the Problem, Not the Solution by Uri Levine.

If customer service was an Olympic sport, what would be the main event?
A marathon, because relationships are built over time and in the long run.

What’s the best thing a customer has ever said to you?
“I knew I needed something, but I didn’t know what it was until you helped me understand it.”

What gif best describes your mental state right now?

Where do you get your support leadership news?
From LinkedIn, Intercom’s blog, and through discussions with my team and leaders.

What do you wish people knew about working in customer service?
Two things: collaborating with customer service is an excellent way to improve and build software, and the client doesn’t always know exactly what they need.

If you wrote a book about your experiences in customer service, what would the title be?
“It Takes Two to Dance.”

Conversation closed… for now 😏

If you’re interested in being featured in our Response Time series, you can share your insights on customer service – and what your greatest productivity hack is – with us here.

Response Time: Vol. 37

You satisfy your customers, but can you satisfy our curiosity?

With Kelly Burnette, Classroom Success Manager at Writable from HMH.

Please tell us a little bit about your company and what you do there.
Writable builds lifelong writing and reading skills for students in grades 3-12. We are now a part of HMH, and our team helps drive innovation in education technology through thoughtful and intentional AI tools. As a Classroom Success Manager, it is my job to make sure that educators using our program are supported when they need it most – usually in front of a class full of students!

What’s the most valuable thing that working in customer service has taught you?
The power of listening. Oftentimes, a customer will think their problem is one thing when it is actually something entirely different. By truly listening and knowing my product, I can help resolve the problem at its core and improve their experience.

Describe the essence of great customer service using only three words.
Timely. Clear. Kind.

Which movie robot would you choose as your AI sidekick, and why?
EVE from Wall-E. She knows her mission and isn’t going to back down from it, even when she’s told to. My mission is to help the customer, and sometimes I have to break some rules to do so!

What can you do that a bot will never be able to replicate?
I have a shared experience with our customers, knowing what it’s like to use technology in the classroom as a teacher. No bot can combine my product expertise and personal experience to understand our users’ unique realities.

What’s the most embarrassing thing you’ve ever said/done to a customer?
I’ve mistyped a lot of things because we respond very quickly, but I don’t really get embarrassed! I find my customers usually appreciate the humanity of the interaction.

Do you identify more with the title “customer support,” “customer service,” “customer success,” or “customer experience,” and why?
All of the above! We have a very specific role – “Classroom Success.” That’s because in the world of education, the most important place our app needs to work is in the classroom. Our teachers don’t have time to wait for us to escalate issues or ask other teams, so we are uniquely trained to be tech support, curriculum experts, implementation gurus, and customer advocates all at once.

What’s the one piece of advice you would give to your peers in the customer service industry?
Tag those kind comments for a rainy day. Most customer experiences are positive, but those negative ones can really mess up your attitude. I like to tag my bright spots and appreciative chats so when I’m feeling run down I can go back and remember that I do make a positive difference in my customers’ days.

What’s the worst customer service you’ve ever experienced?
I refuse to call our internet provider – I make my partner do it. The hoops I have to jump through to get to the correct person, and the way they try to manipulate customers into buying higher speed internet when they can’t deliver the quality we already pay for is insane.

What’s your greatest productivity hack?
I block the first 15 minutes of the day to lay out my priorities for this day, this week. I block the last 15 minutes to check on whether I accomplished those things, which gives me a chance to celebrate my wins and feel prepared for what comes next.

What book are you reading at the moment?
I am a fan of fantasy and fiction. I just finished Two Twisted Crowns by Rachel Gillig – it’s a really great duology!

If customer service was an Olympic sport, what would be the main event?
Juggling multiple high priority conversations at once! If a customer asks, “Are you still there?” you are disqualified.

What’s the best thing a customer has ever said to you?
“I am going to go teach all my teammates what you showed me.” Turning customers into champions. 💪

What gif best describes your mental state right now?

Where do you get your support leadership news?
I always attend Intercom’s webinars and check out the resources. I also stay active on LinkedIn and the communities there.

What do you wish people knew about working in customer service?
It’s not as awful as it’s made out to be. Yes, we have our bad days, but you get the chance to connect with a lot of different people. More often than not, our customers are grateful and kind to us, and we get to make a difference in their day. I love getting to turn a negative experience into a positive one for them.

If you wrote a book about your experiences in customer service, what would the title be?
“Is There Anything Else I Can Help You With?”

What’s the strangest thing a customer has asked you?
I once had a customer ask if I could come over to help them figure out how to check their work email at home. They were in Oregon, I’m in Virginia.

What’s your most used emoji in customer chats?
😅

Conversation closed… for now 😏

If you’re interested in being featured in our Response Time series, you can share your insights on customer service – and what Olympic sport customer service would be – with us here.

Fin over email: How we built a multichannel AI agent

Email is an essential channel for support, but email conversations lead to slower resolutions for customers when compared with synchronous channels like live chat.

With the advent of AI-first customer service, a lot of frontline customer queries are now being dealt with by LLM-powered AI agents. Our own Fin AI Agent resolves more than 50% of customer queries immediately.

However, there’s a perception that AI agents can only function over chat. Our research has shown that many customer service leaders continue to equate AI to chat experiences, rather than thinking about how it can deliver support across multiple channels, just as human agents can.

Well, we’re changing that perception with the latest updates to Fin AI Agent – customers can now get instant responses to their emails.

Customers can now get AI answers to their emailed support questions

Getting Fin AI Agent to work over email presented some interesting technical and UX challenges – here, we dive into the process and share some of our learnings.

How Fin for email works

When a user contacts a business’ customer support team via email, Fin AI Agent will automatically jump into the conversation to resolve the issue. Fin’s answers use generative AI technology to create the answer based on a range of support content using the Retrieval-Augmented Generation (RAG) framework.

Fin not only provides direct answers to queries, it’s also more conversational, with the ability to ask clarifying questions if the user’s initial message isn’t clear enough to find the best response. For the most complex cases that Fin isn’t able to answer, Fin will seamlessly hand over to a support agent.

Our development journey

When Intercom launched Fin AI Agent in March 2023, it was the first generative AI-powered customer service agent on the market. We tapped into learnings from our previous machine learning-based product, Resolution Bot, to inform what a generative AI Agent could look like. Since then, we’ve continued to improve and expand our offering by introducing completely new features or rolling out improvements to the underlying model, thereby increasing resolutions.

Starting from first principles

When it came to defining how we would build Fin over email, we didn’t have a blueprint for what the solution should look like. Email as a channel is very different from chat, so we were unsure whether Fin over email should work in the same way. This is where our “Think big, start small, learn fast” principle became relevant, and pushed us to apply first principles thinking.

We started with research to better understand why email automation was important for customers, what kind of requirements they had, and what impact we could anticipate if we built Fin over email. The insights were summarized into a doc called an “Intermission”, which we create at the start of all product initiatives, in keeping with our “Start with the problem” principle.

Iterative development

We decided to start small with an alpha version as there were many assumptions to validate. The team proceeded to build the technical foundations and a very simple teammate experience – just enough to be able to set Fin live on email, but with no bells and whistles. Since we already had a lot of the building blocks in place – a solid email solution and a very flexible automation system (Workflows) – we were up and running quickly.

“This close partnership is at the heart of how we work in R&D – it allows us to move fast as we have tight feedback loops with the customers who will use and benefit from our product”

We reached out to a handful of Fin AI Agent customers, who have a high number of monthly email conversations, to provide us with feedback on what we had built so far. This provided us with enough insight to define scope for our open beta release.

At Intercom, we are very fortunate to be able to partner with our customers as we make progress on our thinking. We work closely together to understand their needs and gather feedback on our initial solution. This close partnership is at the heart of how we work in R&D. It allows us to move fast as we have tight feedback loops with the customers who will use and benefit from our product.

The early feedback helped us shape our open beta. At this stage, we kicked off a more in-depth design phase, resulting in an artifact called an “Interconcept”. This phase of development is driven by the product designer and outlines a set of different approaches, each with a list of pros and cons.

When we were ready to start building, the lead product engineer created a project plan to outline what we needed to build and in what sequence, making it very easy to bring the rest of the team together. Once we launched Fin over email to open beta, we focused on monitoring usage and gathering as much feedback as possible, aiming to uncover any necessary improvements or new functionality required for general availability.

Challenges and considerations

Despite the team working on Fin AI Agent for over a year, amassing deep usage insights and seeing a great deal of success, making Fin work over email came with its own challenges.

Technical challenges

In 2022, prior to the generative AI explosion, Intercom launched the ability to run automations and chatbots over different channels, such as WhatsApp, SMS, and email. At the time, email already proved to be a more complex channel to automate.

From a technical perspective, some examples of challenges we faced when working with email automation were:

• Email deliverability was out of our control – mail clients (such as Gmail and Outlook) can block addresses and throttle usage.
• Multiple queries in the same message happen more often over email, meaning that we needed to ensure we process them separately so no context is lost.
• Converting automated content for chat (which tends to be shorter, separate messages) into a single email with correct formatting (i.e a heading, the body, and an email signature) was not a trivial task.

User experience considerations

Besides the technical challenges, we also had to solve problems that impacted the end user experience.

For most end users, talking to an AI agent over email is a much less established habit than talking to one over live chat, which meant that we had to design an experience that took all the standard expectations around email into account, rather than trying to replicate a chat experience over email.

It was also important for us that the experience felt natural and intuitive so that end users felt comfortable with interacting with an AI agent over email.

We had to consider many differences between live chat and email when designing the new experience, such as:

• As email is an asynchronous channel, conversations don’t have an instant back-and-forth like they do over live chat and customers often have to wait longer to receive a response to their question.
• The email content is usually longer and contains more information, whether that’s text or images.
• Interactive steps that you can add to chat conversations, such as buttons, don’t quite translate over to email.
• Setting expectations that a user is talking to an AI agent requires different visual cues in email than over live chat.
• Emails render very differently across a number of email clients (e.g. Gmail and Outlook), resulting in a long list of design requirements.

Adapting our underlying AI architecture

Lastly, with the learnings gathered from both the technical and end user experience challenges, we partnered with Machine Learning scientists and engineers to create a new component in the AI agent’s underlying architecture specifically for email. Different to our original AI agent over chat, this new agent was developed with the specificities of email in mind, such as:

• Ability to process multiple questions from a single message separately; for example, it can directly answer some queries and clarify others in the same email response.
• Not processing email signatures containing images that are not relevant to the query.
• A built-in mechanism to ignore spam and automated emails.

As the expectation for a response over email isn’t as instantaneous as chat, we were also able to perform some more complex LLM querying for better and more robust answers without significantly impacting the response times.

Fin over email in action

The impact for our customers has been immediate. For instance, Robb Clarke, Head of Technical Operations at RB2B, reported these astonishing results:

“RB2B 2x’d its user base in the last 58 days but my support team is fielding 45% LESS inquiries thanks to one major change, Fin AI Agent started handling email replies. This simple yet powerful change saved us from handling an additional 493 tickets. At 15 minutes per ticket, that’s about 123 hours saved. If you’re not using it yet, you’re missing out. The efficiency and time savings are game-changers – 12 months from now, our team of 2 is going to be acting like a team of 20.”

Within the first month of release, Fin processed over 1 million end user emails. Fin has provided an AI-generated answer to over 81% of the email conversations it has been involved in, automatically resolving more than 56% of them on average.

Fin over email is available now. Learn more about how it can transform your customer support experience, or check out this instructional video, which shows you how to set it up to support your customers.

Your customer service experience has to deliver great support everywhere your customers expect to communicate with you, and that means AI agents have to be able to deliver support in those channels too. With Fin AI Agent, that omnichannel AI-powered support experience is a reality.

Evolving Intercom’s database infrastructure

Intercom is rolling out a major evolution of our database architecture moving to Vitess – a scalable, open-source MySQL clustering system – managed by PlanetScale.

For many years, Intercom has used Amazon Aurora MySQL as our default database. With the addition of our custom sharding solution for high scale data, Aurora MySQL has allowed us to scale our databases with relative ease. It has supported hundreds of terabytes (TB) of data, 1.5 million reads per second, and tens of thousands of writes per second. Aurora MySQL has served us well as the source of truth for the majority of Intercom’s most critical production data.

“We deeply understand the importance of reliability because we experience it firsthand”

For our customers, when Intercom is down, critical parts of their business are affected. They expect flawless uptime, and so do we, even accounting for unforeseen disruptions or planned database maintenance. Our own teams – including Customer Support, Sales, Product, Engineering, IT, and more – rely heavily on our platform every day. An outage doesn’t just impact our customers; it impacts us directly. We deeply understand the importance of reliability because we experience it firsthand.

In late 2023, as we reviewed our database architecture, several factors led us to seek improvements: enhancing the customer experience, addressing operational friction, and keeping pace with a shifting database landscape.

Our review surfaced these goals:

• Eliminate downtime due to database maintenance and writer failovers.
• Reduce the complexity and cognitive load of working with databases across engineering teams.
• Streamline the migration process and improve the latency of running large-scale database table migrations.
• Achieve straightforward, low-effort scaling of MySQL for the next decade.

We aim to build “boring” software and are committed to running less software, choosing to build on standard technologies, and outsource the undifferentiated heavy lifting. With this in mind, we decided earlier this year to move our database layer to Vitess managed by PlanetScale within our AWS production accounts.

Why Vitess?

Vitess is a MySQL-protocol compatible proxy and control plane for implementing horizontal sharding and cluster management on top of MySQL. Originally developed by YouTube and now used by companies such as Etsy, Shopify, Slack, and Square, Vitess combines MySQL features with the scalability of NoSQL databases. It offers built-in sharding capabilities that enable database growth without necessitating custom sharding logic in the application.

Vitess automates tasks that impact database performance, such as query rewriting and caching, and efficiently handles functions like failovers and backups using a topology server (a system that keeps track of all the nodes in the cluster) for server management. It addresses the lack of native sharding support in MySQL, facilitating live resharding with minimal downtime and maintaining up-to-date, consistent metadata about cluster configurations. Importantly, it also acts as a connection proxy layer which would eliminate the majority of database related incidents we’ve had in recent years. These features effectively provide unlimited MySQL scaling.

Why PlanetScale?

PlanetScale builds upon Vitess by offering a managed platform that provides an exceptional developer experience and handles the undifferentiated heavy lifting of managing the underlying infrastructure. Their expertise, which includes core Vitess team members, allows us to benefit from advanced features like advanced schema management, database branching, and automated performance optimization.

The details around scale and challenges below largely relate to our US hosted region – the infrastructure in our European and Australian regions is similar but at a smaller scale. PlanetScale will be rolled out to all regions.

Supporting high scale: 2011 to 2024

As Intercom scaled, we adapted our database strategies in three main ways:

• Get a bigger box: In the very early days of Intercom, scaling our databases was straightforward – we simply upgraded to larger and more powerful database instances. This vertical scaling approach allowed us to handle increased load by leveraging AWS’s flexible and ever improving instance types. With a maintenance window, we could move to instances with more CPU, memory, and I/O capacity as our data and traffic grew. However, this strategy has its limits. There’s only so much capacity you can add before hitting the ceiling of what a single machine can handle, both in terms of hardware limitations and ability to perform certain operations such as database migrations.
• Functional sharding: To move beyond the constraints of vertical scaling, from 2014 we started implementing functional sharding within our architecture. This involved splitting our monolithic database into multiple databases, each dedicated to specific functional areas of our application. For example, we separated our conversations table out into its own database. By distributing the load across dedicated databases, we reduced contention and improved performance for specific workloads. This has its drawbacks, cross-database queries became more complicated, and maintaining data consistency across different shards required additional coordination through multi-database transactions. As AWS introduced larger and more powerful database instances, this scaling strategy has remained relevant.
• Move to RDS Aurora: Soon after AWS released RDS Aurora in 2015, we eagerly migrated to RDS Aurora from the original RDS MySQL offering. Aurora’s architecture decoupled storage from compute, and allowed us to easily scale-out using read-replicas, avoiding replication lag and other problems that existed in traditional MySQL implementations at the time.

Sharding per customer

As our customer base and data continued to expand significantly, we faced database scalability challenges that could no longer be addressed by vertical scaling or functional sharding. To overcome this, we implemented customer sharding by horizontally partitioning our data based on customer identifiers. This approach allowed us to distribute the load more evenly across multiple database clusters and scale horizontally further by adding new database clusters as needed. Effectively, each customer would have their own database for high scale data (e.g. conversations, comments, etc.).

“Our sharding solution enabled us to handle billions of data rows and millions of reads and writes per second without compromising performance”

Building our own sharding solution was a substantial undertaking which we completed in 2020. We dedicated a team to develop a tailored solution using technologies we were already familiar with. This enabled us to handle billions of data rows and millions of reads and writes per second without compromising performance. Thanks to this setup, we were now able to migrate large-scale tables that we hadn’t been able to touch for years, unlocking easier and faster feature development.

Managing this sharded environment introduced new complexities. For example, our application had to incorporate logic to route queries to the correct shard and simple migrations, for example adding a new table, would take days to complete. This was better than not being able to change these tables at all, but still not optimal.

What problems did we see in our current setup?

Connection management

Intercom operates a Ruby on Rails application with its primary datastore being MySQL. In the USA hosting region, where the vast majority of Intercom workspaces are hosted, we run 13 distinct AWS RDS Aurora MySQL clusters.

A problem of this architecture is connection management to MySQL databases. There are limits on the maximum number of connections that can be opened to any individual MySQL host, and on Amazon Aurora MySQL the limit is 16,000 connections. Intercom runs a monolithic Ruby on Rails application, with hundreds of distinct workloads running in the same application across thousands of instances, connecting to the same databases.

“The use of ProxySQL allows us to scale our application without running into connection limits of the RDS Aurora MySQL databases”

As each running Ruby on Rails process generally needs to connect to each database cluster, the connection limit is something we had to engineer a solution for. On most of the MySQL clusters, the read traffic is sent by the Ruby on Rails application to read-replicas, which spreads the connections out over a number of hosts, in addition to horizontally scaling the query load balancing across the read-replicas.

However, for write requests, we need to use a different approach, and in 2017 we rolled out ProxySQL to put in front of the primary writer nodes in each MySQL cluster. ProxySQL maintains a connection pool to each writer in the MySQL clusters and efficiently re-uses connections to serve write requests made by our Ruby on Rails application. The use of ProxySQL allows us to scale our application without running into connection limits of the RDS Aurora MySQL databases.

In the last year, we’ve experienced a number of outages related to our use of ProxySQL. These issues arose particularly when we attempted to upgrade to ProxySQL 2.x and utilize new features like its integration with RDS Aurora read replicas, which led to instability and outages.

Database maintenance

Maintenance windows are a necessary evil of most database architectures, and nobody loves them. For many of our customers, when Intercom is down, large parts of their business is down too. This is increasingly relevant as Intercom builds out features such as Fin AI bot, which can resolve large amounts of conversations for our customers.

Maintenance windows are something we’ve avoided unless absolutely necessary and when needed, run the majority of them at the weekend in order to reduce the impact for our customers. With AWS Aurora, any upgrades or planned instance failovers (for example, for increasing the size of a database instance) required maintenance windows with customer impact ranging from five to seventy minutes.

For instance, during our upgrade from Aurora 1 to Aurora 2, we conducted ten maintenance windows across our regions, each causing actual disruptions between twenty and seventy minutes.

We knew we needed to do better here, and remove the need for maintenance windows entirely.

Intercom’s database architecture 2024 and beyond – enter PlanetScale

While these methods have allowed us to scale with relative ease, the database landscape has changed dramatically. Compared to 2019, when we decided on our custom application sharding approach, there are now more options for building practically infinitely scalable databases appropriate for Intercom.

Embracing Vitess and PlanetScale

To address the limitations and complexities of our existing database architecture, we have embarked on a journey to adopt Vitess managed by PlanetScale. This transition represents a significant evolution in our approach to database management, aiming to enhance scalability, reduce operational overhead, and improve overall availability for our customers. We have already migrated several databases and have many more to transition in the coming months. The benefits we’re already seeing include:

Simplifying connection management

One of the immediate benefits of Vitess is its ability to act as a single connection proxy layer through its VTGate component. VTGate is a stateless proxy server that handles all incoming database queries from the application layer. It intelligently manages connection pooling and query routing, effectively multiplexing a large number of client connections over a smaller number of backend connections to the MySQL servers.

“VTGate allows us to scale our application seamlessly without worrying about connection constraints”

By centralizing connection management, VTGate eliminates the 16,000 connection limit per MySQL host that we previously faced with Aurora. This removes the need for ProxySQL in our architecture, reducing a massive source of complexity, and potential points of failure. VTGate also provides advanced query parsing and can route queries based on the sharding key or even handle scatter-gather queries across multiple shards when necessary. This allows us to scale our application seamlessly without worrying about connection constraints or overloading individual database instances.

Zero-downtime maintenance and failovers

Vitess offers advanced features like seamless failovers, which are critical for eliminating customer downtime during maintenance operations such as software upgrades and changing instance sizes. Its built-in failover mechanisms ensure that if a primary node goes down, a replica can take over almost instantaneously without impacting ongoing transactions. This aligns perfectly with our goal of providing flawless uptime and eliminates the need for extended maintenance windows that disrupt our customers’ operations. With the clusters we’ve already migrated, we can refresh the database instances without any noticeable impact on our customer-serving metrics.

Native Sharding Support

Perhaps the most significant advantage of Vitess is its native support for horizontal sharding. Unlike our previous custom sharding solution, Vitess abstracts the complexity of sharding away from the application layer. Our engineers no longer need to write custom logic to route queries to the correct shard; Vitess handles it automatically based on the sharding scheme we define.

“This reduction in cognitive load allows our teams to focus more on delivering new features and less on managing database intricacies”

In time, we will also be able to combine our functionally sharded databases into a single logical database thereby reducing the complexity we introduced to maintain data consistency across the databases. For example, currently, if a new comment is created, three individual databases must be kept in sync. This reduction in cognitive load allows our teams to focus more on delivering new features and less on managing database intricacies.

Streamlined migrations and scalability

Running large-scale database migrations has been a pain point due to the time and complexity involved. Migrations on our largest non-sharded tables can take months to complete. Vitess addresses this with its online schema change tools operating on sharded data, enabling us to perform migrations with minimal impact on performance. Additionally, scaling horizontally becomes a straightforward process. Need more capacity? Simply add new shards, and Vitess will manage the data distribution without requiring significant changes to the application.

Partnering with PlanetScale

By choosing PlanetScale to manage our Vitess deployment within our AWS production accounts, we leverage their expertise and the contributions of the Vitess core team members they employ. PlanetScale provides a developer-friendly experience and takes on the undifferentiated heavy lifting of managing the underlying infrastructure. This partnership ensures that we benefit from best-in-class database management practices while allowing us to remain focused on what we do best: building our AI-first customer service platform for our customers.

One of the standout features PlanetScale offers is its advanced schema management capabilities. PlanetScale enables non-blocking schema changes through a workflow that allows developers to create, test, and deploy schema modifications without impacting the production environment. This is facilitated by their concept of database branching, akin to version control systems like Git. Developers can spin up isolated database branches to experiment with changes, run tests, and then merge those changes back into the main branch seamlessly. This drastically reduces the risk associated with schema migrations and empowers our engineers to iterate faster, ultimately accelerating our product development cycles. Just like with Git, if a database schema change is pushed to production and an issue is discovered, it can be reverted easily.

“This new mechanism improved the latency of the previously expensive query by 90%”

PlanetScale also allows for net new mechanisms we can use to serve requests. For instance, we recently used materialized views to optimize the counting of open, closed, and snoozed conversations for teammates. This new mechanism improved the latency of the previously expensive query by 90%, leading to a faster teammate experience and reduced database load.

Additionally, PlanetScale provides automated index and query optimization tools. The platform can analyze query performance and suggest or automatically implement index improvements to enhance database efficiency. This proactive approach to optimization reduces the operational overhead typically associated with manual database tuning – everyone on the team can now operate like a world class database expert. These improvements ensure that our queries run efficiently and allow us to maintain high application performance, which translates to a smoother and more responsive experience for our customers.

Challenges faced during migration

Moving the databases that are responsible for Intercom’s most critical data is a major undertaking and it has not been without its challenges. Despite thorough planning and testing, we encountered several issues that provided valuable learning opportunities and ultimately strengthened our migration strategy as we move across more databases.

Latency spikes due to cold buffer pools

One of the initial hurdles was unexpected latency during the cutover of one of our core databases to PlanetScale. When we redirected traffic to the new Vitess cluster, we anticipated some initial latency as the database caches warmed up. However, the latency spikes were more significant and lasted longer than expected – particularly in one availability zone.

This was primarily due to cold buffer pools on the MySQL instances within Vitess. Since these instances had not served production traffic before, their caches were empty. As a result, queries that would typically be served from memory had to fetch data from disk, increasing response times. While we anticipated this problem, we expected only a few seconds of latency, however in reality it continued for twenty minutes and made the Inbox slow to respond to customer requests.

To mitigate this for subsequent migrations we’ve implemented read traffic mirroring to pre-warm the buffer pools before redirecting live traffic. By simulating traffic to load frequently accessed data into memory, we can reduce the initial latency spikes during future migrations.

Disk I/O saturation and resource limits

During periods of high load after the initial cutover and at traffic peak, we observed that some replica servers were experiencing disk I/O saturation. The replicas reached the maximum IOPS allowed by their attached storage volumes. This led to increased CPU utilization in the “iowait” state, further degrading performance.

“Scaling down by removing excess capacity is significantly faster and less disruptive than scaling up under pressure”

The root cause was that the replicas’ IOPS were under-provisioned for the workload they needed to handle. To resolve this, we initiated the scaling out of additional replicas. However, adding new replicas was time-consuming due to the size of our data – restoring backups to new instances and allowing them to catch up with replication took several hours. During this period, standard operations in the Inbox were 1.5 to 3x slower than usual, with Workload Management most affected – slowing to between 5x and 10x normal latencies.

Our takeaway from this is that we will significantly overscale all clusters as we move across load. Scaling down by removing excess capacity is significantly faster and less disruptive than scaling up under pressure.

Configuration changes and unexpected interactions

We also faced challenges when certain configuration changes interacted poorly with application behavior. For instance, increasing the transaction pool size and the maximum transaction duration seemed beneficial in isolation. However, combined with a surge of scheduled operations, for example bulk unsnoozing of conversations on the hour, these changes led to resource saturation. The database was flooded with long-running transactions, causing latency and errors impacting the Inbox.

The road ahead

Our migration to Vitess is more than just a technological upgrade; it’s a strategic move to future-proof our database architecture for the next decade and beyond. By embracing Vitess and partnering with PlanetScale, we’ve positioned ourselves to provide even greater reliability, scalability, and performance for our customers.

“The lessons we’ve learned and the mitigations we’ve implemented have set us up for success as we continue migrating our remaining infrastructure”

So far, we’ve successfully migrated our databases related to our AI infrastructure and one of our most critical databases powering the Inbox. These early migrations have validated our decision and provided invaluable insights. The lessons we’ve learned and the mitigations we’ve implemented have set us up for success as we continue migrating our remaining infrastructure.

Looking ahead, we’re excited about the possibilities that Vitess and PlanetScale open up for us. The native sharding capabilities will allow us to simplify our database architecture, reducing complexity and operational overhead. Our teams can focus more on delivering innovative features and less on managing database intricacies, ultimately enhancing the experience for our customers.

Pioneer 2024: Intercom’s first ever AI customer service summit, in summary

We’ve just hosted our inaugural AI customer service summit, Pioneer, and there are so many incredible insights and stories to share.

Thousands of customer service and tech enthusiasts joined us, both in person in London and online via livestream, to explore how AI is transforming the support space. The energy and excitement was evident through all the talks, customer sessions, and enthusiastic conversations.

The event was a true celebration of the pioneers leading the way at a time of great change. We announced Fin 2, the most advanced AI Agent in the industry, put the spotlight on our wonderful customers who spoke about how they transformed their support in an AI-first world, and heard from our Co-founder and Chief Strategy Officer Des Traynor on how AI is getting real.

Renowned tech writer Benedict Evans spoke about cutting through the AI hype of the moment to realize actual real-world value, and in particular how to think about the changes ahead.

Some of our amazing customers shared their lessons and experiences adopting AI-first customer service, sparking lots of conversations among the audience.

And we ended the day with a special live recording of our Off Script series with two incredible guests: English actor, author, and comedian Stephen Fry and musical pioneer, visual artist, and activist Brian Eno. They discussed what technology has meant for them, and what AI means for society at large.

You can watch the on-demand recordings here and catch some of the key takeaways below. Enjoy.

Next-generation, now

Our Chief Executive Eoghan McCabe kicked the day off with a big vision of how AI is transforming customer service. He also articulated the big scope of Intercom’s ambition. As he put it, “Our commitment to you is that you’ll never find a better-performing or feature-rich customer service AI agent anywhere on the market.”

To reinforce that commitment, our Chief Product Officer Paul Adams launched Fin 2, our next-generation AI agent that deliver our highest resolution rates with powerful new capabilities to handle your frontline support. In Paul’s words, “Fin 2 is the first AI agent that delivers human quality service. That has been our mission and it can do it. We’ve built it in partnership with all of you.”

This new generation AI agent is the culmination of so many years building customer service tools and incredible effort on the part of our product teams. And this technology is only getting better from here. An inspiring keynote at an inspiring time for the industry.

Lighting the way forward

Intercom Co-founder and Chief Strategy Officer Des Traynor spoke about how this AI thing is getting real. That doesn’t mean it will be smooth sailing, as he admits – we’re somewhere in the peak of expectations in the AI adoption hype curve. Expect to see a lot of people questioning the value – people will ask hard, skeptical but sometimes important questions.

But as Des explained, eventually every aspect of a product will change – who uses it, who buys it, the pricing, performance, and more. The whole landscape will get rewritten across all industries. But it won’t happen all at once or as quickly as we think. Instead, the evolution of AI will be like the adoption of electricity, as Des put it. Electricity didn’t happen overnight, but when it did it fundamentally transformed everything.

“Electricity didn’t happen overnight – it was 1879 when Edison filed a patent for the light bulb,” Des says. “It was the 1900s before things like London lit up. And then the second order effects, well, the 9-5 that we all know and love came from the fact that electricity existed. Then late night shopping and shift work. All of these things were only possible because of the rollout of electricity. That’s the sort of second and perhaps third order effects from one dude filing a patent for a light bulb in 1879.”

The future, as Des sees it, is bright and getting brighter.

Paradigm shifts

“World-renowned influencer and AI expert” Benedict Evans, as he jokingly described himself, delivered a typically perceptive presentation on how to think about the ways in which AI will transform industries such as customer service.

Evans cited examples of how technological transformations has previously disrupted industries to guide how to think about what happens next – basically, there are no clear answers, but boundless possibilities ahead. As Evans pointed out, our assumptions around the trajectory of these technologies are often misplaced – spreadsheets didn’t spell the end for jobs in accountancy and finance, for example.

Evans followed it up with a fascinating Q&A session with Des, where they explored the implications for customer service, and the key foundational shifts that will determine how this generative-AI era plays out.

“Every 15 years or so we’ve gone through one of these platform shifts,” Evans pointed out. “It changes how we do our work, changes what the tools are, changes what tools can be built, changes what kinds of companies and what kinds of products we all use.”

A personal history of technology with Fry and Eno

The day concluded with an extraordinary meeting of minds, as Stephen Fry joined Brian Eno for a live recording of our Off Script series.

And Off Script it most certainly was – a winding, illuminating conversation about their respective relationships with technology. They swapped stories about how tools such as the Mac and the synthesizer shaped their careers and lives, and how their initial enthusiasm for technology has given way to a good deal of skepticism as the impact of social media has become a greater part of our lives.

The pair have enjoyed a front-row seat in the world of technological transformation over the past half century – as became clear with first-person anecdotes featuring the likes of Apple designer Jony Ive, pioneering AI researcher Marvin Minsky, and Amazon founder Jeff Bezos, to name a few.

They also shared their thoughts on how AI will affect culture and society – a cautious perspective, it’s fair to say, informed by their own creative efforts and concerns about the impact of AI on the entertainment and arts industries.

Ultimately, though, Fry and Eno reflected on the potential of AI technology to transform lives for the better, when used in the right way, for the right reasons.

“Let the machine do what the machine does,” Fry said, “and the better machines do things the more attention you can give to what it is that humans do and what humans are. As AI takes over various clerical and bureaucratic jobs, logistical jobs, and so on, the the more your work every day will be about people, it will be about imagination, it will be about creativity, it will be about fresh thinking.”

Innovator sessions

Throughout the day, we had wonderful conversations on the Innovator Stage with three of our customers: Angelo Livanos, Senior Director of Global Customer Support at Lightspeed Commerce; Natalie Hurst, Director of Customer Success at Nuuly; and Constantina Samara, Head of Support at Synthesia.

These customers are seeing real results using AI, right now. And they shared just how much Fin is transforming their support operations, team dynamics, and customer experience. If you missed the live conversations, we’d highly recommend you check out the on-demand recordings. But in the meantime, here’s a quick recap:

Lightspeed Commerce

As an AI-forward company, Lightspeed was excited to use AI tools like Fin AI Agent and AI Copilot to allow them to do their jobs better, further enjoy their work, and delivers better experiences for their customers. But with hundreds of agents supporting customers in multiple regions and languages, they couldn’t just flip a switch and roll AI out overnight.

To make sure their teams were set up for success to make the most of Fin, they placed a great deal of focus on training, ongoing support and enablement, clear and frequent communication, and cementing alignment on the ultimate vision and goals.

Angelo spoke in depth about how Lightspeed navigated this period of change. By bringing the whole company on the AI journey with them, Lightspeed’s support team were able to generate a ton of excitement across the company. As Angelo put it, “It’s building a bit of a cult following of people that are saying, ‘This has done some pretty great stuff. How do we tap into this now?’”

The great stuff in question? Resolution rates of up to 65% and 31% more conversations closed daily by agents. So it’s easy to see why there’s such company-wide excitement.

You can catch up on our full conversation with Angelo here.

Synthesia

A 690% increase in customer contact in just four months is almost hard to imagine, but that’s exactly what Constantina and her team at Synthesia faced this year. Instead of 40,000 customers seeking support each month, they were suddenly seeing 316,000.

When that happened, Constantina’s priority was to leverage AI and automation to help her team manage that spike – which (spoiler alert) they did, with great success.

As Constantina said, “Without the level of automation we have with Fin and Intercom, I’d have needed a team of 150 people to manage that.” But by leaning on these tools, they empowered a whopping 98.3% of the 316,000 customers seeking support to resolve their query themselves. In other words, only 1.7% of those customers needed to speak with the team.

So not only were they able to scale support to tackle an enormous spike, they actually freed up time for support agents to deal with meaningful conversations on a daily basis, and even explore other exciting opportunities to create impact – like what “premium support” could look like, and how they could offer support as a service.

You can dig into the details of our chat with Constantina here.

Nuuly

With support volume on the rise, Natalie expected to have to significantly grow her team to keep up with demand. Natalie explained that for her, around 50 associates is a sweet spot for a support team; anything larger makes her feel disconnected from each individual employee.

So therein lay the challenge for Nuuly: how could the team meet increasing demand without dramatically adding headcount?

The answer? (One more spoiler alert) Fin.

Since adding Fin to the team and embracing a human-AI approach to support, Natalie has been able to free up her support associates to handle queries that require human empathy and judgment, and spend more time building strong relationships with their customers and teammates. The combination of Fin and other Intercom automation features have also enabled Natalie to slow projected staff growth by 40%, which lets Nuuly’s tight-knit support team maintain their team size and culture.

You can check out our chat with Natalie here.

Pioneer innovator spotlight: How Synthesia managed a 690% spike in customer contact without increasing headcount using AI and automation

We spoke with Constantina Samara, Head of Support at Synthesia, about the impact of AI and automation on scaling support in a cost-effective way, changing customer attitudes towards these technologies in customer service, and lessons learned from rolling out AI. 

In just four months, the number of Synthesia’s customers seeking support on a monthly basis rose from 40,000 to 316,000 – a 690% increase. To meet this level of demand without AI and automation, the team would have needed to grow to 150 people, but with Intercom, they were able to swiftly tackle the spike without increasing headcount, all while reducing resolution time by 96% and maintaining high customer satisfaction.

Let’s take a closer look at how they did it. 

Can you tell us a bit about Synthesia, your role, and how you came to be a customer service leader?  

Synthesia is an AI video generation platform that enables our customers to create studio-quality videos with AI avatars and voiceovers in over 120 languages. My role is Head of Support, and my path to becoming a leader in this space actually happened sort of unexpectedly. I have a background in psychology and an interest in human behavior, so when I found myself in a customer service role, I wanted to apply my background to analyzing interactions with customers and teammates and understanding what makes people respond in certain ways to different situations.

“I’m really passionate about service being the best service”

I’m really passionate about service being the best service, and by understanding the behaviors of your customers and your team, you can use those insights to create the best possible experiences for them.

What motivated you to implement AI with Intercom? 

We’re a fast-growing business, and naturally, as our customer base scales, our support volume increases alongside it. We were seeing our customer contact rate rise by anywhere from 20-30% month over month, which was becoming challenging to manage with the number of people we had on the team. 

We knew we needed to leverage AI and automation to help us, and that Intercom had the tools to help us do that. 

Did you face any challenges during the implementation? How did you go about solving them?  

We encountered two main obstacles that we needed to overcome: 

1. Preparing our knowledge base

We started testing Fin AI Agent as soon as it was in beta, and that’s when we had the hard realization that our knowledge base was really not fit for purpose. The responses we were getting back were kind of all over the place – Fin was contradicting itself because our knowledge base was clearly contradicting itself. So I’d say the biggest challenge for us was having to almost redo our knowledge base. Of course, we had a bit of a starting point, but it was a big piece of work. 

In the early stages of optimizing our help content, we joined Fin in every second customer conversation to make sure we were getting it right. That was challenging at the time, but really beneficial and has helped hugely in the long run. We can now be sure that Fin has what it needs, and that our knowledge base is up to date and accurate. So even for customers who navigate to the help center and don’t open a conversation with Fin, they now have much better information available to them too. 

“The return on investment you see when you’re successful with [Fin] far outweighs the cost of having to introduce a role or two to make it a success in the first place”

To help us revamp our knowledge base and get it ready for Fin, we needed to hire people to manage that work. That’s obviously an extra cost, and something I’d imagine many businesses are having to weigh up right now, but the return on investment you see when you’re successful with it far outweighs the cost of having to introduce a role or two to make it a success in the first place. 

2. Getting buy-in from the support team

The second challenge we faced was implementing it in a way that the support team was on board with. And that ultimately came down to how we positioned the rollout. 

Something I hear a lot in the customer service space – and that we encountered ourselves – is that there’s a fear around AI taking jobs and meaning support teams won’t be required anymore. So we wanted to offer our team reassurance that we were bringing in AI to alleviate pressure and enable them to be more satisfied and fulfilled in their roles, rather than answering refund questions over and over, every single day. 

It’s a really fine balance between introducing automation and maintaining team engagement. Because automation can be great, but if you’ve lost engagement with your team and they don’t have the same passion and energy to provide the same level of service that they did before automation, you’ve lost human support. 

Once the team actually started using AI, they were able to truly realize its impact and the opportunities it created for their roles. They suddenly had more time to do investigative work and actually learn and grow, whereas before they were just doing those repetitive tasks. Without us even going out to the team and trying to gather that feedback, they were coming to us and saying, “We haven’t seen questions about X for a very long time,” and we were like “Yeah, because Fin resolved 1,000 of them.” That was a big milestone moment where the benefits came to the forefront for the team – that they were able to deal with meaningful conversations on a daily basis. 

Fast forward to now and we’ve never seen so much engagement in support. If anything, Fin has now increased appetite on the team to introduce as much AI and automation as possible. 

What level of impact have AI and automation had on your support operations? Any highlights or metrics you could share? 

The ability to manage our rising customer contact rate was definitely a highlight for us. Like I mentioned, we were seeing a 20-30% increase in customers seeking support month over month, but between April and August of 2024 alone, we saw an increase of 690%. Instead of 40,000 customers seeking support each month, we were suddenly seeing 316,000. 

“Even if we continue to see a big increase each month, I don’t anticipate us having to increase our team headcount for a significant amount of time”

Without the level of automation we have with Fin and Intercom, I’d have needed a team of 150 people to manage that. But with their tools, we were able to handle the spike without having to grow our team to meet the demand. In fact, of the 316,000 customers seeking support in August, 98.3% were able to resolve their query through self-serve support, which meant only 1.7% needed help from our agents. And even if we continue to see a big increase each month, I don’t anticipate us having to increase our team headcount for a significant amount of time. I think that showcases the gravity of the benefits that we’re getting from Fin AI Agent and Intercom’s other automation features.

Outside of that, we’re also seeing results in other areas, like: 

• CSAT: Our human CSAT is consistently high, currently sitting at 93%. And since implementing Fin, our Fin CSAT has actually doubled. One thing that I keep finding across different industries and people I speak to is that there’s a fear of frustrating customers and decreasing customer satisfaction by introducing AI and automation. I personally think it’s really important to bust that myth and let people know that’s actually not the case. We’ve got some really good stats that can evidence that. You just need to invest the time in setting it properly.
• Fin AI Agent answer rate: Our Fin answer rate is anywhere up to 98%, which in my opinion is really good. That means that in nearly all of the conversations it’s involved in, it’s able to understand and provide an answer to a customer’s question. 
• Fin AI Agent resolution rate: Right now our resolution rate with Fin is 55%, which frees up a lot of time for our team. Our goal is to get that number up to 80% in a controlled manner, so that’s a big area of focus for us in the coming months.  
• Resolution time: Since launching Fin, our resolution time has gone from five days and five hours to four hours and 37 minutes – a 96% decrease. 

Do you think AI has changed customer behavior at all? If so, how? 

I’ve noticed a remarkable change in customers when it comes to Fin, specifically. Automation is not new to support, but more often than not, you’d find that customers would greet any level of automation with dissatisfaction straight away and seek human support. And I think that was down to lack of intelligence behind those automations in the past, where it was always a tick box activity of sending something out to the customer that wasn’t really relevant or didn’t cover what they were trying to achieve. Whereas with Fin, the change in customer behavior I’m seeing is that they’re a lot more receptive. 

“That level of intelligence that sits behind [Fin] has really changed the dynamics with customers and automation:

I’ve got so many examples of conversations where customers are thanking Fin for giving them the right response. So that level of intelligence that sits behind it has really changed the dynamics with customers and automation. 

What does the next chapter of AI-first customer service look like at Synthesia? 

AI isn’t a “turn it on and let it work its magic” kind of technology. It requires maintenance and optimization to make sure it’s successful. So we’ll continue to enhance our knowledge base and train Fin to give the best possible answers to our customers, and part of that will be identifying our outlier questions to further expand Fin’s coverage rate across our support volume.

And now that we have more time freed up on the team, we’re exploring what “premium support” could look like and how we can offer support as a service. There’s no way we would be able to do that if we didn’t have the level of automation we have with Intercom and Fin and if it wasn’t successful.

I’m also really excited for the next wave of Intercom’s AI features. We’ve gotten a preview of what’s in the works and I think those new features will help us achieve our goal of reaching 80% resolution rate and continue to scale our support in a way that protects our team and is cost-effective. For example, Fin being able to take actions and read data in the background, or being able to customize its tone of voice. These will completely change how we interact and support customers, and being able to customize the tone of voice 

What advice would you give to other customer service leaders embarking on this journey based on your own experience? Any lessons learned? 

One lesson I’ve learned is to involve your teams in the process from day one. Let them know what it is that you’re trying to achieve and make them part of the objective. Tell them what the problem is and have them be part of how you scope this. Because nine times out of 10 that enables them to get bought into what you’re trying to do. And not only that, but it also gives them the chance to highlight challenges and issues your customers and support functions are facing that you’re not necessarily aware of. 

So even though we’re seeing record levels of engagement in support now, had I brought the team in prior to going live and made them part of the implementation process, I think we would have seen more engagement from the outset. That was a big lesson for me.