Today we’re launching an /ideas podcast.
/ideas is all about what it really takes to become an AI-first company.
The show brings together leaders from forward-thinking companies to share how they’re reworking the way they build, operate, and think in the age of AI.
These are honest, fast-moving conversations – the kind that usually happen off the record. From overhauling product and engineering processes to reshaping marketing teams, /ideas captures how companies are navigating the biggest shifts in modern work.
No playbooks. No hype. Just real-time learning from those at the forefront.
In the first episode I talk with Co-founder and Chief Strategy Officer Des Traynor about how AI is reshaping everything: how we build products, structure teams, and go to market.
As Des says in the episode, be careful you don’t build the MiniDisc!
“And what I mean by that is, the MiniDisc for like a 10-year period was the hottest product and the best way to listen to music. Untouchable. And it’s just a fleeting moment in time. Some of these roles that people have hard-coded or tattooed onto their sense of self and identity – are similarly just fleeting moments in time.”
You can follow on YouTube, Apple Podcasts, Spotify or wherever you get your podcasts.
At Intercom, we’re experimenting with new products and features every day, especially with AI. We’re exploring what the latest AI models can/can’t do.
We are now sharing some of the work in progress to give people a sense of what we’re up to, and maybe start a discussion with us. We’d love that!
AI-powered Suggestions
For the first time ever, you can now improve AI agent performance – with a single click.
AI-powered Suggestions is a groundbreaking new feature that helps you fix gaps in content, data, or actions to improve Fin’s performance.
No detective work. No guessing. No weeks of analysis. Just clear improvements you can implement instantly.
Check out this demo from Ahmad on our Product Team to see AI-powered Suggestions in action!
We think this is a really big deal, and huge step forward.
We worked hard to build this breakthrough, AI-powered metric, because the way we measure customer service is broken.
CSAT has really poor coverage and inaccurate answers, only giving us a view of less than 10% of our customers’ experience. It’s the metric we love to hate. So why do we still use it?
CX Score will become the new standard because it is much better: it gives you a complete view of your support quality across every customer conversation – no surveys required.
How it works:
CX Score uses AI to score every customer conversation from 1 to 5, based on resolution, sentiment, and service quality – all in real time.
It lets you see exactly how Fin will respond to any of your actual customers, so you can test with confidence before going live. It’s in closed beta for the moment, but Ben has recorded this demo you can watch.
Fin AI Agent as MCP Client
Earlier today we shared how Intercom is using MCP to give AI systems like Claude access to real-time customer data from Intercom. Great for teams without access to our helpdesk to benefit from a wealth of customer data.
Product Engineer Luis Alvarez Aguilar has been working on the next step in that journey: making Fin act as an MCP client so it can connect with third-party and internal business tools, access data, and take action for customers.
In this demo, Luis shows how Fin connects to a Stripe MCP server to:
✅ Answer customer pricing questions
✅ Pull product plan details
✅ Generate a live payment link—all inside a conversation
We’re still in early stages, but it’s exciting to see Fin connecting with external tools through open standards. More to come soon.
Intercom MCP Server
This week, Senior Product Engineer Davy Malone built an Intercom MCP (Model Context Protocol) server using Cloudflare Workers—and it’s been a game changer.
It allows AI systems like Claude to access real-time data from Intercom: user profiles, conversation history, ticket metadata, and more.
For our engineers, this means less time manually pulling context across systems—and faster, smarter troubleshooting when issues arise.
And we’re just getting started. Soon, our AI agent Fin will also be able to connect to MCP servers to access data and perform actions in external systems.
If you’re curious about where this is going, we’ve shared more on our product vision for MCP here.
April 2025
Setting Fin Voice Live
Last week, we shared an early look at Fin Voice, our AI agent for the phone channel.
In this demo, Artem Ankudovich show’s how to take the next step by moving from a testing environment to going live once you’re confident in Fin’s tone, accuracy, and logic.
It’s exciting to see Fin step in as a frontline agent, handling real queries and handing off to another team when needed.
Fin Voice
Phone support is powerful – but also one of the hardest channels to get right. It’s often slow, tough to scale, and frustrating for customers stuck on hold. That’s why we built Fin Voice – our new Voice AI agent that helps businesses deliver instant, 24/7 phone support, without the wait.
In the demo, you’ll see how Fin responds to common support questions in the Fin Voice testing environment. No matter how large your help center is, Fin can instantly give the right answer and guide your customers – just like a human agent. It’s a glimpse of what support could look like without phone trees, hold music, or long wait times.
We’re still in the early stages, but the potential is big.
AI-Generated Content from Video
We want to share with you an experiment we built at a recent Intercom hackathon around converting videos into great content.
Inside Intercom, we share a lot of product videos internally as we develop and test new features. An idea Senior Group Product Manager Chris Dalley and Staff Product Manager Peter Bar tested was whether we could use AI to convert these product videos into great Help Center articles complete with auto-generated product screenshots – turns out yes we can!
This was a small experiment, but it points to a future where support content could is much easier to create, update, and scale – all from recording a quick video.
It’s still early thinking, and we’re not yet sure where it fits – maybe we should add it into the product? Also curious what approaches you have tested for creating great AI content?
Multilingual Workflows
Scaling support used to mean more tools, more workflows, and more manual effort.
AI is shifting that. And our Multilingual Workflows beta is part of that shift. It lets teams build once and serve every customer, in any language, without duplicating effort!
Check out this working demo, and share what’s helping your team scale.
AI Category Detection for Fin AI Agent
AI is transforming how customers get support. In particular, it’s reshaping the invisible parts, like how conversations are assigned to the appropriate teams.
When a customer reaches out, they expect to be understood and quickly directed to the right place. Intercom’s AI Category Detection feature helps Fin do exactly that. It analyzes each conversation to identify things like the request category or sentiment, and then saves this information as conversation data that can be used for routing, reporting, and more.
In this demo, we show how it can be used to automatically assign conversations to the right support team. No long menus for customers, no manual triage for teammates.
The feature is in closed beta, and we’re still learning more. Stay tuned!
OTP for Fin Tasks
As AI Agents take on more complex and sensitive workflows, security can’t be an afterthought. It has to be built in from day one.
At Intercom, we’ve designed Fin with security as a first principle, enabling customers to configure multiple layers of protection, real-time verification flows, and safeguards that help maintain trust between agents and their customers.
In this demo, Fin supports a customer through a policy amendment, verifying their identity and then guiding them through the process step by step.
Take a look and let us know what you think.
AI Generated Fin Task Instructions
Last week, we shared a demo of Fin showing how we’re blending rules based (deterministic) and natural language based (generative) logic in Fin so that our customers can configure the exact customer journeys they need.
This is a very important thing to learn about and understand if you are using or building AI Agents.
But this whole area is new, it has never existed before the AI era, and so people need to learn how to do it. To help them, we’ve built a feature that uses AI to suggest instructions. Today we’ve a nice follow up demo showing how you can write structured Task instructions using natural language in Fin.
March 2025
Fin Tasks
The future of AI Agents is generative and deterministic workflows blended together, and Agents that complete full Tasks for customers.
Most businesses have complexity, and in exploring different ways Fin can work for our customers, it’s really clear that they need both generative and deterministic steps in single workflows. Here is a demo of our work in progress.
Fin in the Help Center
Knowledge bases are a critical line of defence for support teams but they can also become a bottleneck, leaving customers to sift through hundreds of articles to find what they need.
So we ran an experiment: using Fin to deliver faster, more direct answers inside the Help Center.
We’re still early in the test but already learning a lot about how AI can reshape self-serve support.
Fin Messenger experiments
Over the past month we have been very busy running a series of experiments on the design of Fin in our Messenger. We decided to challenge numerous design decisions that were meant to address problems from a different era.
Every new experiment lead to theories, every theory lead to new ideas and ideas with the best rationales made it to their respective A/B tests. The results were as interesting as the process itself. This demo shows the results, and how we think about our AI products and using data to refine our design decisions.
Fin Testing
As more customers use Fin, we’re learning how important it is to test how Fin answers different types of questions.
So we’ve been building new powerful testing tools. Here is a working beta of one of them.
What makes it so powerful is the ability to bulk test how Fin answers a set of questions. Plus, right below the answers, you’ll find details on the inputs used to generate each response, making it easy to troubleshoot and refine.
We’re already seeing how this is helping customers feel more confident in how Fin responds – and giving them the tools to make those responses even better.
Fin Guidance Assistant
We mortal humans are still trying to learn how to use AI systems. The more we talk to them, the more weird and wonderful things we learn about how they ‘think’. LLMs are a generative technology which makes them unpredictable at times.
But using AI for business, we need to be able to control parts of what they do. We built Fin Guidance to do this, so you can guide Fin. But guidance is like advice, you can give good or bad advice, and so you can give good or bad guidance!
And we’ve seen customers doing this, trying to give good guidance but learning through trial and error that it doesn’t quite work as intended. So we’re now building tools to help our customers write good guidance. This is cutting edge stuff, we’re using Anthropic’s Claude 3.7 Sonnet with Extended Thinking.
February 2025
Real time Inbox Translations
Customer Service teams need to support their customers in many languages, and so end up hiring multilingual speakers, using different translation tools, and ultimately adding a lot of complexity to their support operations.
But AI is excellent at real time translations. So we’ve been building that into the Intercom Inbox. Now any customer and any agent can seamlessly converse, no matter their language.
Fin over API
So far, Chat has been the dominant interface for us to communicate with AI. Makes sense, it is familiar. But that is going to change, and soon. We’re seeing customers who want to build their own interface to Fin (our AI Agent) so we’re building Fin over API. Now, any interface is possible.
Giving Fin Guidance
When Support teams hire new people, they train them, they give them guidance on what to say and how to react in different scenarios.
They need to do the same for AI Agents, so we’ve been building that into Fin.
I was going to title this ‘Why can’t we let self-driving cars kill anyone?’ but I thought that might be a bit too much.
Nonetheless, the facts don’t lie. Human drivers kill 1.3 million people every year. Think about that number for a moment.
Meanwhile, whilst it is early, numerous studies show that self-driving cars are often safer, but there is uproar if a self-driving car is anywhere near a fatal accident. One death, and we’ve pushed the acceptance of the technology back years.
In fact, this pattern of humans holding new technology to a much higher standard than they hold humans to, even when there is overwhelming evidence that the new automation technology is better, is very common throughout history. Automation is judged harshly for minor imperfections, whilst major human errors go unnoticed. There are important implications here for anyone designing AI products. When we study this, we learn that there are multiple human biases at play, and they show up consistently across many examples:
- Automated elevators: People wouldn’t use them without a human operator, despite significant reductions in accidents. People complained about doors occasionally opening slightly off-level with the floor (yet human operators were worse), or had momentary hesitation or slight jolting before moving.
- Airline autopilot: Massively improved flight safety, far exceeding human pilot reliability. Yet mistrusted by pilots for minor imperfections such as slight deviations in assigned altitude, slightly jerky movements during course corrections, and (can you imagine!) overly cautious approaches and landings.
- ATMs: Criticised for slight delays dispensing cash (despite the huge queue inside the building), and slow user interfaces. Customers preferred human tellers, despite them making many more mistakes.
The list of new, better technology, being held to previously unseen high standards, and judged for minor imperfections goes on:
- 1900s Automatic telephone switchboards
- 1930s Automated traffic signals
- 1970s Industrial robotics in car manufacturing
- 1980s Automated stock trading systems
- 1980s Automated subway trains
- 1990s Digital medical diagnostic tools
- 2000s Automated voting machines
I’ve been thinking about this a lot because we see a similar, fascinating pattern when new customers try Fin. In evaluating what Fin can do, and Fin’s performance, many customers hold Fin to a much higher standard than they hold their human team to. Even when Fin is faster than humans, and more accurate more often, the feedback is ‘Fin is too slow’, ‘Fin made too many mistakes’.
For example, Fin can issue refunds to customers. To do so, Fin needs to:
- Check the product purchase history
- Check the refund policies
- Check the customer record
- Approve
- Talk to the payment system to issue the refund
- Get back to the customer to tell them the refund has been approved and issued
- Update all customer records.
For any AI Agent to do that accurately, consistently, is impressive. But it might take 90 seconds.
Few humans can do this in 90 seconds. They take way longer. Sometimes up to a day.
Fin will do this in 90 seconds, and often less, every time. When we should be thinking ‘This technology is like magic’, we judge it harshly instead. It’s like everyone’s experience with airplane wifi. Instead of thinking ‘it’s amazing I have the internet up here in this flying box of metal’, we think ‘this internet speed is shit’.
Sometimes Fin can make a mistake, or a minor misunderstanding. And people think ‘Fin made a mistake with that answer, this technology isn’t good enough yet’. Yet their human team makes way more mistakes all the time.
Why is this? And how do we design for it?
We can’t try and persuade people to abandon psychological biases that have existed for hundreds of years. But we can study them, and design around them. In this case, there are three main ones:
Automation bias leads us to over-scrutinize automated systems. Our brains naturally offload cognitive tasks, creating discomfort when we lose perceived control, and this discomfort magnifies minor technological imperfections.
Possible solutions:
- Products need to clearly show users how and why automated decisions were made. If you’ve wondered why AI tools expose so many events, even when they move past at incomprehensible speeds, now you know. We’re exploring different ways of making Fin’s reasoning visible and understandable:
- Remind people of the facts, showing real comparisons between AI and Human performance. We do a lot of this in Fin Reporting.
Status quo bias makes us instinctively resistant to change because we fear losses more intensely than we desire gains, leading to irrational preferences for familiar human processes, even when automation is demonstrably better.
Possible solutions:
- Introduce automation incrementally, mixing familiar interactions with new automated experiences to ease users into acceptance.
- Leveraging familiarity by designing automated interfaces and interactions that closely mirror familiar human-based workflows, reducing cognitive friction.
We designed Fin to blend deterministic and generative workflows. This makes it easy for many to incorporate Fin into their existing workflows as a first step. We encourage customers to rethink their whole setup, but sometimes this is the best way to get started.
The availability heuristic causes us to judge the likelihood of events based primarily on how readily specific examples come to mind, rather than relying on accurate statistical assessments. This cognitive shortcut arises because the human brain prioritises information that’s vivid, emotional, or easily recalled. As a result, rare but dramatic incidents, such as a single highly publicised automated mistake, are disproportionately memorable and thus perceived as more frequent or dangerous. Conversely, everyday human errors, though statistically far more common, lack the emotional intensity and vividness necessary for easy recall, making them significantly underestimated in our risk perception.
Possible solutions:
- Prioritise extra effort in designing initial user interactions to be smooth and error free
- Amplify positive experiences to create memorable narratives
- When errors occur, transparently communicate how rare they are compared to overall success.
Remember that AI products are new. Many will suffer from these biases and more, and if we want fast adoption timelines, we will need to design around them if the products are to be successful.
In building and selling software, there are two main ways to do it: Sales-led, or Self-serve. There is no ‘one right way’, but over the last 15 years of Saas, the pros and cons of each became very clear. The question we face now is: which one is better for building AI products? Or is there a new way that is better than both?
We’ve been deep in this now for 2 years, let’s step through it.
Self-serve means that customers do everything themselves: learn about the product, sign up, explore the product, and succeed or fail based on whether they could get set up well, and see enough value to continue. Successful self-serve requires an excellent product experience, excellent new user experience, and excellent in-product onboarding. Self-serve is product first. Everything you experience is real running code. The big risk here is that great potential customers never get set up properly, and conclude that the product is poor (even when it isn’t!). Worst case, the customer puts something live that doesn’t work properly and they damage something: their customer engagement, their data systems, their brand.
Sales-led means that customers get help from a Sales team who are trained to help them understand the product, sign-up, get activated, see success, and continue to expand their usage. Products with a sales-led motion tend to be complex, and even confusing, but the sales team will help explain everything. Sales-led is partnership first. But because you’re not directly interacting with running code, sometimes the product is a promise more than it is real. Worst case it is jazz hands and vapourware.
Startups and naive product people (yes this was once me) have a tendency to shit on sales-led motions because they aren’t product first. But that’s myopic. Some of the best customer experiences are sales-led and partnership first. In its best form, this partnership is a deep collaboration on understanding a customer’s problems, and configuring the product to best meet their needs. Hmm, understand problem > configure solution, that sounds like product development…?
Many businesses start with a simple product, but as they add customers, those customers have feature requests, and as they add the features, the product becomes more powerful and more complex. It becomes harder to maintain a great user experience, harder for people to learn it all by themselves. This battle to scale a great user experience has tortured me for years. So many businesses start self-serve, but by necessity add Sales later.
So, how do you approach AI products?
The difference between Saas products and AI products is that Saas is predictable and AI is not. Saas products are direct manipulation, CRUD apps, where users click UI and by doing so, they create/read/update/delete data in a database. Designed well, they are easy and predictable to use because we’re all using the same components: text fields, dropdowns, buttons, etc. You don’t write an email or Slack message, hit the send button and wonder what will happen next. Even when complex, we can teach people what to do.
In contrast, AI products are unpredictable. We do wonder what will happen next when we ask Claude a hard question, or give Cursor a task. When the feedback comes, we do wonder how it worked.
In Saas, the unpredictability is with the humans involved. Are people following our policies? Are they applying their training? Can I trust my team to do a good job?
In AI products, the unpredictability has been moved to the AI layer. Is the AI following our policies? Is the AI applying its training? Can I trust AI to do a good job?
Because the unpredictability is in the AI layer, all companies building AI Agents for Customer Service have a heavy Sales-led motion. And not just Sales, but a team of people working in deep partnership with the customer and building bespoke new software together.
This is what we’re doing with Fin (our AI Agent). As well as Sales and Success, we have PMs, Designers, Data Scientists, Engineers, all building directly with customers to understand their customer data, their customer conversations, their knowledge and systems, and building a solution that works for that customer. Often we’re really changing their perceptions, they start by having a go themselves, concluding it doesn’t work very well, but then we get involved to help and advise and every metric improves.
We’re doing this with many individual customers and then using what we learn to design a product that is valuable much more generally to lots of other businesses. We’ll expand on this another time, but this deep builder partnership model is a new way to build software. Because of the unpredictable nature of AI products, and the complexity in any one business’ customer data and systems, deep partnership is required to get customers to resolution rates that match the technology’s potential: 80%+ customer queries excellently resolved by AI.
And yet. Intercom’s history was self-serve. We deeply believe in only ever marketing and selling real product. Real running code. No bullshit, no vapourware. Like many others, we added Sales-led later. In fact, during the boom years we went too hard on Sales-led, and had to rediscover our roots as a passionate product company building a product that anyone can try, anytime, without having to talk to anyone.
So Intercom has self-serve flows for everything. And we have 1000s of customers who have been using Intercom for human support successfully adding Fin without any human help from us. Despite the unpredictability, many are working it out themselves. They are getting 50%+ resolution rates through their own perseverance.
So this week we went a step further. We shipped the ability to sign-up for Fin on our competitor platforms all by yourself.
This is risky. What if many new customers try it, don’t get activated, don’t see the value, and think the product doesn’t work? What if they tell their peers? What if it damages Fin’s reputation? None of our competitors let people do this.
But we believe in the power of open software that anyone can try. We believe that people want to play with new things. And there is great energy, excitement, and value in that.
So please go have fun 🙂
Every Customer Experience and Service leader knows that AI is the future, and their future. Act now, or be left behind.
We already have AI Agents that can instantly, accurately, and reliably resolve 50%+ of customer queries for many businesses. For some, we are at 70-80%+. That alone will change an industry, but this is just the beginning. AI right now is the worst it will ever be. Technology only goes in one direction.
Every Customer Service leader is busy building the next generation of their customer experience. But it’s hard. In particular, it’s hard to keep up with the pace of technology advancement, and the implications. Get your head around a latest LLMs capabilities, and there is a new one. Understand one technical concept in AI (“I know what reinforcement learning is”), and there is another one to learn (“what is distillation?”).
What we can say with confidence, is that on some timeline, AI is going to be able to answer close to all customer queries. Today, humans still do the majority, but the volume will shift to AI.
The challenge is that no one knows the exact timeline to that world. And depending on the timeline, how we redesign our customer experience will change a lot. Time it wrong, and we will have wasted huge resources designing for a world that doesn’t exist. We need to make an educated guess around two key variables:
- Technology progress. What types of queries can AI accurately answer today? Soon?
- Adoption progress. How fast/slow are people adopting, what barriers exist?
To get this right, we need to look at where we are, and what might happen next.
Where we are
History is full of examples of the technology being good enough, but the adoption being slower with some groups due to legitimate and perceived barriers. We’ve done a lot of research at Intercom to understand this, analysing millions of real customer questions, to categorise them and understand how they might be answered by AI. Broadly, there are four types of query, and the timelines are different for each one. How much of each query exists depends on the business type, but aggregated, you can think of them as 4 equal buckets of 25% of volume:
- Informational.
The answer is the same for all customers. “Do you ship to Ireland?”, “Do you have a free plan?”.
This just requires the AI to access up to date knowledge to answer. - Personalised.
The answer is different for each customer. “Where is my order?” “Why is this feature not working as expected for me?”
This requires the AI to read individual customer records to answer. - Actions.
The answer requires an Action to be taken. “We have refunded you”, “We have upgraded your subscription”.
This requires the AI to be able to read and write to customer databases, and communicate with other internal/external people/teams/businesses. - Troubleshooting.
This requires the AI to be able to understand complex customer situations, complex business logic, and complex business systems, and to be able to read, write and sometimes redesign those systems.
It requires the AI to be able to deeply communicate with other people (and at some point other AIs), to learn, and sometimes negotiate.
So where are we on our technology and adoption timelines?
Today, the best AI Agents (check out Fin), set up and configured well, can answer 100% of Informational queries. There is no technology barrier left here. The only barrier is human configuration, which requires CS teams to put in the effort to have accurate content, and a system to keep it up to date. AI is helping here too, with suggested content to close knowledge gaps, etc. Any CS team not trying to implement this is behind on the adoption timeline.
The best AI Agents can also answer close to 100% of Personalised queries. There is no technology barrier here, the barrier is putting in the effort to ensure high quality data, and high quality data integrations. Configuring this (and Actions below) is where many of the leading companies are right now. Fin is doing this for different types of businesses, from showing a customers invoice due date for Tibber, and using customers plan and device details for Firsty. It is possible to get very high numbers resolved here if you put in the effort.
The best Agents are starting to do different types of Actions. This is the current frontier on the technology timeline and we’ve Actions live with over 250 customers. The technology is there, and works for well understood and documented Actions. But it requires more human setup and configuration work, and often it requires business logic to be properly documented for the first time. This has been a key insight from our research and development with customers. Many businesses don’t actually have their processes properly documented. Often it is local knowledge passed on from person to person. And what is documented is often wrong or out of date.
Today, these business processes need to be manually documented so we can teach the AI what to do. But it won’t stay like that forever.
What might happen next
All Customer Service leaders should be planning and executing now for a world where all Informational, Personalised, and Actions queries are 100% resolved by AI. On the technology timeline, this world is already here, we are now working out how to configure things. If you are not here, you are behind on the adoption timeline.
Next, we get into an educated guessing game, and it is part of all CS leaders’ jobs to have an opinion here.
AI is likely coming that will be able to do two very powerful things:
- Work out the undocumented business logic. It will be able to analyse deep, complex systems, synthesise what it learns, and create, asking for clarifcations from people along the way.
- Work out how to connect and code integrations itself. As well as figuring out and documenting the business logic, it will take action based on it. It will write and commit code by itself, improving the underlying systems without human intervention.
This AI is going to feel like magic. This AI is going to really change your job (and many others).
- When do you think it is coming?
- And what timeline are you planning for it to arrive?
There are two timelines, you need to bet on one of them:
The Acceleration timeline: Magic AI Agents within 12-18 months
The biggest tech companies, along with the biggest AI Labs, are making huge capital investments to produce bigger and more powerful models. They wouldn’t be making these investments if they didn’t believe that the scaling laws will continue, and greater scale will give us greater power. If we take the advancements made in 2024, and extrapolate out, we might have Agents that can understand complex business logic by the end of 2025, or early 2026. The founders and leaders of the biggest AI Labs have given us these timelines.
- What does human support look like when we have AI this powerful?
- Do we still have humans talking directly to customers?
- Or do humans only talk to other internal humans (like finance, or engineering, or legal) and then instruct the AI Agent on how to proceed?
- Or does the AI Agent do all of it: talk to the customer, then ask questions in internal Slack channels, then get back to the customer.
If you think the capital investments will pay off, then you bet on this timeline, and design your customer experience appropriately, starting now.
The Slowdown timeline: No magic AI Agents for 2-3 years
AI advancement slows down. The scaling laws stop. The big capital investments don’t pay off early. We get better versions of what we have today (better reasoning models), but no breakthroughs. We still have a lot of heavy manual configuration to get high % AI resolutions. Humans with deep domain expertise and experience are the only ones who can answer complex customer queries.
Customer Service still changes a lot compared to pre-AI, but we still have very large volumes of human support.
So what should you do?
AI is advancing fast, and it’s hard to keep up. But the best Customer Service leaders are all-in, and are rebranding themselves as AI CS Leaders.
By thinking in terms of query types to automate, they are making things practical and seeing huge success. You can too:
- First do Informational (and do it now or really be left behind).
- Then do Personalised and Actions (and start now because they require deep integrations, and documenting business logic).
Find an AI Agent company you can deeply partner with. Like Intercom. We have a large, sophisticated AI team, and have been first to market with many new models and innovations. We can teach you, and we can learn together.
Whatever you do, do it now. If you don’t have an AI Agent in beta, or live, it’s getting late. There are so many success stories, so it is almost certainly the case that your competitors are getting an edge on you.
If you are on the Slowdown timeline, and we hit the Acceleration timeline, your entire role and org might be at risk of completely disappearing before you have time to react. However, by assuming the Acceleration timeline, you can not only future proof your career, you can set a new career path, by being part of the group of people who figure this out earliest, enabling incredible experiences and levels of service for your customers.
That at least, is what I am doing.
Customer Service leaders care most about one thing: the quality of the customer experience they deliver.
That’s wonderful. That’s the good news.
The bad news is that most don’t measure it properly, or well, or sometimes at all. If that’s you and before you rage close this, think about it, we both know it’s true. We have some inconvenient truths to face up to.
The better news is that AI is going to save us, I’ll get to that at the end.
Inconvenient truth 1: Everyone uses different metrics
If this was an excellent disciplined area, we would all agree on the most important metric, the second most important, and so on. But we don’t. We all care about completely different metrics, and we bend them to suit ourselves.
Some obsess about First Response Time. Some don’t even look at it.
Some obsess about Average Handling Time. Some don’t even look at it.
What about First Contact Resolution? Same story.
And don’t forget CSAT, we use it, but it seems we all agree in private that it is a terrible metric.
Inconvenient truth 2: We don’t measure the actual experience of our customers
What’s a good customer experience? A satisfied customer. So let’s talk about CSAT.
CSAT has always been a metric people love to hate. It is a crude measure that captures no nuance, nor whether the customer query was actually resolved. Customers famously only answer 1 or 5 on the scale. 1 being extremely dissatisfied, and the experience was terrible. 5 being satisfied. OK, but sometimes that satisfaction isn’t real, it is a customer not wanting to get someone in trouble, hitting the 5 despite being entirely dissatisfied.
The worst thing about CSAT though is that we’re only getting answers from the 10% of customers who bother to fill in the survey. So for 90% of customers, the vast majority of them, we’re completely blind as to whether we delivered a great experience.
Inconvenient truth 3: Everyone adds on a lot of intuition and vibes
Once we analyse our numbers, FRT, AHT, or whatever you care about, we then add a new lens: subjectivity and vibes. We use our gut, our instincts. Makes sense: everything quantitative we use is a proxy, so we need to layer on how we feel things are going.
The numbers say x, but it doesn’t feel like that. So we report on how we feel, as much as on what we see. We bring some art into the science. But measurement should be science.
OK, we are where we are, here is the good news.
The good great news is that all these metrics were designed for the world of 100% human support, and that world doesn’t exist anymore. We are already deep into a world of AI and Humans delivering customer service together.
So we need, and are getting, new metrics. Not only that, we’re getting some superpowers to go with them.
Resolution Rate
A hard metric that measures whether the customer query was resolved. Customers are explicitly asked if their query was resolved. And we can use AI to double check.
AI CSAT
Now this metric is like magic. It solves both of the problems with CSAT (low coverage and unreliable) and is really a ‘Customer Experience Score’.
AI can analyse every single customer conversation (100% coverage!), and can reliably and accurately do two things:
- It can determine the customer sentiment. Was the customer happy? Sad? Did they start with one emotion and end up with another?
- It can determine whether the query was actually resolved. Because AI knows from conversation history what an excellent, accurate resolution looks like for any given query, we can have AI check if the query was properly resolved. No fake dissatisfaction here.
Also, people don’t care if they get the AI in trouble, so they are honest in their appraisal.
‘AI CSAT’ though? We might need a new name.