实践中的转型：如何在提升客户体验的同时实现 81% 客服自动化

Leading the Support function for a company that builds a leading Agent and AI-forward customer service platform is a unique, exciting, and daunting experience all at the same time.

Unique, because we get to use the same technology as our customers. We have the exact same experience as them. Not many support teams can say that, and it places us in a special position to be the voice of the customer to the rest of the organization.

Exciting, because we get to try all of the new features and capabilities of Fin and the Intercom helpdesk. Given the company’s focus on AI innovation, that means access to some remarkable tools to help us deliver an incredible customer experience.

And daunting, because the expectations of Intercom’s own Customer Support (CS) team are very high. If we can’t deliver incredible support using our own technology, we undermine its value proposition.

This is final part of our deep dive into our new research: “The 2026 Customer Service Transformation Report.” We’ve shared all the editions of this series on our blog and on LinkedIn.

If you’d like to get straight to the report, download it here.

When Intercom changed its focus in late 2022 to prioritize the customer service use case, we undertook a critical review of the support experience we were delivering and committed to driving meaningful change under an AI-first framework.

Three years on, Fin now resolves over 81% of all our customer support volume, delivering immediate and high-quality resolutions. We have absorbed a 300%+ increase in customer demand since 2022 without proportional headcount growth. Without Fin, we would have needed at least 100 additional CS team members to meet that demand and our improved service levels – a net saving to Intercom of between $7.5M–$9M annually.

Throughout this series, we have shared research from our 2026 Customer Service Transformation Report and explored how forward-looking teams are transforming their customer service with AI.

This final article is our story of transformation and how we have achieved a mature deployment of Fin.

The problems we set out to solve

In 2022, our challenges looked familiar to any modern support organization:

• We faced increased support demand from new and existing customers: Intercom was launching major features and changes at speed, driving up overall customer conversation volume and requiring additional headcount for the CS team.
• Our support policy (as defined by our service level objectives) was not based on a high bar: In most cases, we were only committed to “business hours” coverage for the majority of our customers, impacting first response times. Even with SLOs that were not considered best in class, we were struggling to meet our commitments.
• We wanted to do more: As we pivoted our strategy, we wanted to open new routes to our support team, such as providing support to website visitors with technical questions and to trial customers.

What we did

We made a very conscious decision to become our own best reference customer. As Intercom embraced the opportunity that generative AI presented to transform customer service, we intentionally moved to an AI-first strategy for our Customer Support team.

We started with the highest-volume, informational queries and saw our resolution rates climb quickly. With that foundation in place, we pushed Fin further, training it on deeper documentation and internal procedures, and eventually giving it the ability to take actions on behalf of customers. As Fin took on more complex work, our results started to compound.

The key focus areas were:

1. Early adoption and building trust

When “AI Assist” features came to the Intercom Inbox, the CS team got early exposure to AI and were empowered to provide feedback directly to our product teams. This built awareness and trust across the team about what we were trying to achieve with AI, and helped shape the product roadmap.

Our CS team was also the first beta customer for Fin. We started by rolling it out to a subset of customers to assess the impact on the customer experience carefully. With no adverse reaction and an initial resolution rate of over 25%, we made the decision to deploy Fin to most of our customer segments within a matter of weeks.

2. Knowledge management

It was recognized quickly that time and effort spent tuning our help center and other knowledge assets for Fin would pay dividends. We transitioned our Help Center Manager into a new role of “Knowledge Manager,” with a dedicated remit to optimize content for Fin.

We also embedded knowledge creation into our “New Product Introduction” (NPI) process, setting a target that Fin would be able to resolve at least 50% of customer issues at every new product and feature launch. Over time, we introduced new knowledge sources to Fin, including our “Developer Documents,” to help it handle increasingly complex issues.

We also built a culture of continuous improvement across the team, encouraging support teammates to identify content gaps and improvements that would further enhance Fin’s ability to answer questions. We continue to allocate dedicated “out of the inbox” time for this work.

3. Conversation design

To ensure a consistent, high-quality customer experience as we introduced Fin into the customer journey, we established a brand new role of “Conversation Designer.” This role considers the end-to-end customer journey – not just with Fin, but also what customers experience when conversations are handed over to a human. It’s focused on removing friction and making the experience seamless across channels.

The conversation design role has been central to optimizing the customer experience as we have driven Fin’s resolution rate higher. We used Intercom’s Workflows to introduce “skills-based routing,” ensuring that when a customer asks to speak to a human, the conversation is quickly passed to a team member with the relevant skills to resolve it. This is now handled by Fin directly using a feature called “Attributes.”

4. Organization changes

As we scaled our use of Fin, we needed to rethink the structure of the team itself. We established a dedicated AI Support team under a senior CS leader, focused on continuously optimizing Fin and defining our AI adoption strategy for other parts of the customer journey.

We restructured our human support roles into two new job families (“Technical Support Specialist” and “Technical Support Engineer”) to better reflect the increasingly complex work coming through to the team. And we expanded our Support Operations team to include a focus on optimization, with a goal of using AI to better support Enablement, Workforce Management, QA, Process Management, and Data Insights.

Alongside these structural changes, we reset expectations about the balance of time spent directly supporting customers versus improving AI. That shift in mindset was as important as any of the structural changes we made.

5. Pushing Fin further

As new capabilities came on stream, we were early adopters:

• Fin Guidance: Multiple Guidance rules provide additional controls and a more personalized, targeted experience for customers.
• Fin Tasks and Procedures: Enables Fin to carry out activities such as updating customers on incident status and deep troubleshooting for technical issues.
• Insights: AI-driven dashboards provide deep insight into Fin’s performance and surface recommendations for further optimization. Insights also provides a Customer Experience (CX) Score for every customer interaction, enabling more targeted improvement efforts and opening up new ways to close the loop with customers who have had a poor experience.

What we achieved

What started as a concentrated effort to improve our customer support experience has turned into the strongest case study for what Fin can achieve when it is fully embraced by an organization.

Fin now resolves over 81% of all our customer support volume, and has allowed us to absorb a 300%+ increase in demand without proportional headcount growth. Over 90% of our customers now benefit from improved first response performance, 24/7 coverage, and outbound phone support.

What the numbers don’t fully capture is what Fin’s involvement has made possible for the team. With volume absorbed by Fin, our CS team has pivoted to consultative support activities – working with customers on their next best actions, deepening their use of Intercom, and contributing directly to retention and expansion. Customers that receive these consultative engagements adopt Fin at a much deeper level and achieve greater support success.

What was once a reactive, volume-driven team is now a function that generates significant revenue.

What’s next

Customer expectations are constantly increasing, and it is important that we do not stand still. We are building on the progress we have made by embracing the Fin Flywheel – an actionable framework for Fin’s ongoing improvement and optimization.

The Fin Flywheel has four stages:

• Train: Teach Fin to resolve even the most complex queries with Procedures, knowledge, and policies.
• Test: Run fully simulated customer conversations from start to finish to see exactly how Fin will behave before going live.
• Deploy: Set Fin live across every channel – voice, email, chat, and social – for consistent support wherever customers reach out.
• Analyze: Use AI-powered Insights to analyze and improve Fin’s performance and deliver better customer experiences.

We are also investing in our support teammates so they can adjust to the new world of AI – taking on more complex work and being valued for the subject matter expertise, consultative engagement, and empathy they bring to the role.

We will continue to develop and share best practices for deploying an Agent, based on our own experience with Fin and the lessons learned from our most forward-looking customers. These are captured and continually evolving in The Agent Blueprint.

Transformation takes commitment

What we’ve described here is our own experience, but across this series we’ve seen the same patterns emerge in teams that are genuinely transforming with AI.

The ones achieving the most aren’t treating AI as a tool bolted onto existing processes, they’re rebuilding how support works around it, investing in knowledge and people alongside technology, and treating it as a continuous discipline rather than a one-time deployment.

That’s the real change that’s required. And for support teams willing to make it, there’s a very real opportunity to redefine what customer service is capable of delivering.

You can find the full series here on our blog, or subscribe on LinkedIn to see it on your feed.