Delivering a perfect customer experience has always been the goal for support teams, and now with AI, this is actually possible.
In The 2026 Customer Service Transformation Report, we found that all teams are experiencing early wins from AI, like improvements to their speed, efficiency, and ability to serve customers across languages and time zones. These wins are helping support teams gain capacity and the further they push the technology, the more they are able to deliver quality improvements.
This signals a shift in priorities. As AI proves it can handle more, teams can focus on the customer experience in ways they previously couldn’t.
This week, we’ll explore how deep AI implementation unlocks new ways to bring value to customers.
This is part three of our five-part deep dive into our new research: “The 2026 Customer Service Transformation Report.” We’ll be sharing all five editions on our blog and on LinkedIn.
If you’d like to get straight to the report, download it here.
From reactive to proactive: driving better customer experiences
Traditionally, support teams were stuck in a reactionary state. With a growing backlog, volume outpacing headcount, and complex queries tying up the most experienced team members, there was little time to think about ways to improve.
But this year, things are changing. According to our latest research, 58% of support teams view improving customer experience and satisfaction as their top priority for 2026. That number has more than doubled since the previous year, when just over a quarter (28%) of respondents cited it as a top priority.
As AI takes on more of the manual work, it opens up humans to focus on actively improving the support experience. In other words, when the AI is working, the measure of success moves to how well it’s working.
At Intercom, we’ve been watching this transformation first-hand. As our Senior Director of Human Support, Bobby Stapleton, said on a recent episode of The Ticket podcast, mature deployment of an AI Agent provides “breathing room” for support teams. While we used to be focused on deflection, we can now look for opportunities to deliver consistently excellent experiences. This might mean increasing access to support, minimizing friction on the way to a resolution, or anticipating needs before they arise.
In our own support team, we opened up support to trial customers, achieved faster first response rates, and provided consultative sessions to onboarding customers. All while absorbing a 300% increase in total demand, without expanding headcount. This was possible due to our deep integration of Fin.
Across the industry, a similar story is being told.
When teams initially deploy AI, only 9% say they can always meet customer expectations. That number triples as teams reach a mature level of deployment.
Even as customer expectations remain high, they are more likely to be met by teams that deeply integrate AI into their support operation, creating robust systems and ownership models around it.
Bridging great support across channels
Heading into 2026, our data shows that teams are planning to invest in omnichannel support experiences. This makes sense; to meet and exceed customers’ expectations, you need to be wherever they are. Teams hoping to use support as a differentiator must prioritize consistency, regardless of location.
Planned investment for 2026 is distributed nearly equally across chat, email, and social messaging (36% each), closely followed by phone/voice (31%).
This shift signals that teams have moved beyond asking “Which channel should we optimize?” to “How do we deliver consistent AI-powered experiences everywhere our customers are?”
The opportunity here is huge. Teams that crack omnichannel consistency have a chance to continue bridging the divide between what customers expect and what they can deliver. Every interaction can be an opportunity to exceed their expectations and build long-term trust.
Omnichannel in action
Our customer Clay is a great example of a team that’s cracked consistency across channels.
Support is one of their main growth drivers so as their customer base expanded and ticket volume increased, they needed to scale without compromising on quality.
What makes Clay’s support organization particularly interesting is their strong community focus. Much of their early support effort was concentrated in Slack, where they built close, transparent relationships with customers. However, this focus on a single channel became a point of friction as they grew. Customers wanted the flexibility to reach them via email and in-app chat and Clay needed a way to deliver the same high standard of support everywhere.
To bring a unified support experience across these channels, Clay deployed Fin.
Today, Fin is involved in 90% of all queries and handles half of Clay’s total volume, upwards of 7,000 queries a month. All the while, first response rates have significantly improved and Clay’s support team is able to provide proactive, high-impact work.
That includes identifying content gaps for the education and content marketing teams, helping customers before they even ask a question, and surfacing feature requests and recurring challenges to the product team.
Clay proves that when support is truly great it can become a competitive edge. With AI, teams can stop working around limitations, and focus on delivering a superior customer experience that makes a difference. That’s the kind of progress that’s exciting to see.
Building superior customer experience with an AI Agent
As you plan to scale your own AI Agent and build towards mature deployment, here are five principles to keep in mind to provide consistently excellent customer experience.
1. Treat customer experience like a product
Treating support as a product means designing, building, and managing your support experience with the same rigor and accountability you would apply to your core product.
Just like product teams:
- You define goals (faster onboarding, higher CSAT or CX Score, lower churn).
- You map flows (AI starts the conversation, human handovers, proactive nudges).
- You instrument the journey (track handoffs, drop-offs, success states).
- You run tests and ship improvements (tone tweaks, fallback paths, training updates).
- You own the outcomes (gather feedback, measure performance, use insights to continuously improve the system).
2. Lead with AI, back with humans
AI isn’t replacing the human touch. It’s redefining when, where, and how it’s most valuable.
In a scaled model, AI becomes the first responder: the default entry point for every conversation (and the end point for most of those conversations too). But it doesn’t work in isolation. The experience should be hybrid by design.
Humans step in where they add value, like during high-stakes issues, and these handoffs should feel seamless. Beyond this, support teams focus on ways to improve AI performance or the customer’s overall journey.
3. Be proactive
Use AI to anticipate customer needs and offer help, guidance, or nudges before they become problems. This will help you maintain momentum and deepen your customers’ trust in both your product and your team.
This has been a key initiative for Intercom’s own support team.
As our Senior Director of AI Support, Ruth O’Brien, says: “While it’s amazing to have Fin resolve so many inbound queries from our customers, imagine if they never had to ask those questions in the first place? Or if you could reach out to customers who you’ve identified are at risk? We’re starting to use Fin more proactively, pointing out moments to customers where they may need to take action, or sharing tips with them for longer-term success.”
4. Build for trust
Some customers still assume AI won’t help them. You’re dealing with the legacy of bad chatbots that gave vague answers, clunky menus, and left people in endless loops.
You build trust in AI by showing that it works. At scale, every interaction becomes a test. And every successful resolution is proof.
It’s important you don’t hide your AI Agent behind layers of “choose an option.” Get the customer to the AI Agent as soon as possible so they see it’s a different type of interaction. And ensure when a human is needed, they join the conversation with all the context they require to resolve the most complex queries efficiently. Over time, a well built system will show customers what’s possible.
5. Make it feel personal
Your AI Agent represents your brand. The way it speaks, follows policies, and responds matters. With AI Agents, you can use capabilities like tone control, fallback logic, and language preferences to align the experience to your brand’s standards.
Consistency builds trust, but personality builds connection and loyalty.
Perfect is possible
For many companies, transforming the customer experience is AI’s biggest promise. By driving gains in efficiency and allowing for consistency across channels, teams can prioritize what customers really want: comprehensive, fast, and personal support. Not just when they reach out, but throughout their journey.
To be transformative, great customer experience must scale. With a deep implementation of AI, that is possible.
Next week, we’ll look at how AI reshapes the support organization. With AI Agents taking on more work, human roles are evolving beyond queue-level activity to focus on improving system performance.
You can follow the weekly series here on our blog, or subscribe on LinkedIn to see it on your feed.
