实践中的转型：为何 AI 部署深度决定成效

It’s clear that AI adoption is widespread. More and more businesses are using and investing in the technology.

However, in our latest Customer Service Transformation Report, it emerged that this usage is not equal. There’s a deployment gap opening up between teams that are just scratching the surface with AI and those that are going deep with it. In other words, it’s becoming clear that launching AI is easy, but transforming with it is not.

So, how do you transform with AI? Over the next five weeks, we’ll dig into the key findings from the report to identify what leading teams are doing differently. We’ll show you how to make the most of the opportunity that’s on the table and close the gap before it gets too wide.

This is part one 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.

AI adoption is the norm, depth makes the difference

We surveyed 2,470 global support professionals across a range of industries to understand how teams are currently using AI for customer service, their challenges and pain points, and the opportunities they’re going after in 2026.

Overall, we found that organizations have recognized the value of investing in AI for customer service. Eighty-two percent of senior leaders say their teams invested in AI in the past year and 87% say they plan to invest in 2026.

These initial investments are paying off. Over three-quarters of CS teams (77%) say AI is meeting or exceeding their expectations, delivering faster response and resolution times, always-on coverage, cost savings, increased capacity, and the ability to serve more customers around the globe with multilingual support.

However, only 10% of organizations say they have reached a “mature” level of deployment, where AI is fully integrated into operations and working at scale.

That tells us that most teams are only scratching the surface of what’s possible and are leaving significant improvement opportunities on the table.

The maturity difference shows up in the results

Teams that have reached mature deployment report a more sophisticated use of the technology. They’ve put AI at the core of their service operation, integrating it into critical workflows, giving it more responsibility, and continuously improving it.

Beyond automating the bulk of their manual work, they’re using AI to proactively engage with their customers and perform tasks on their behalf.

This gap between basic adoption and deep integration helps explain the variance in outcomes. Of the teams that have reached mature deployment, 43% report higher quality and consistency across support – nearly double the rate of those still in the initial deployment stage.

High-quality support is essential for transforming support from a cost center to a value driver. Great experiences don’t just get customers to stay, they encourage them to advocate. Great support becomes a reason to choose you and that’s what teams with mature deployment are building towards.

The more you trust your AI Agent with meaningful work, the more it creates the conditions for higher-quality, more consistent support.

What mature deployment looks like in practice

One of my favorite examples of a team that’s seen results from committing to going deep with AI is Lightspeed.

They’re a big organization with a complex product, operating across multiple regions and languages. When they adopted Fin in early 2023, they were handling tens of thousands of monthly support requests and they needed a solution that could scale with that complexity.

What stands out to me about their approach is how deliberately they handled the change management piece. They leveraged the Intercom Academy for foundational training, but also developed in-house training modules specifically tailored to their own processes and provided ongoing support for team members following launch.

Critically, they also worked closely with their leadership team to ensure everyone was aligned on the goals and benefits of implementing AI. In a large, geographically dispersed team like theirs, that coordinated effort fostered a sense of ownership across the organization.

Their VP of Information Systems, Yamine Gluchow, summed it up well: “It’s not magic. If you invest in understanding, adoption, and great content, AI performance takes off.”

With this foundation in place, Lightspeed has gone on to achieve:

• An 88% involvement rate.
• 72% of Fin conversations resolved without human intervention.
• 43,000+ customer requests resolved by Fin monthly.
• Service in 12+ languages, across 100+ countries.
• Stable CSAT – and even improvement in some markets.

And Fin is not just handling simple queries, it’s increasingly resolving complex, multi-step requests. Yamine shared one striking example: a merchant in France reached out with questions about their tax invoices. Normally, this would have required a lengthy phone call with an agent, checking the back-end data and explaining the rules step by step. Instead, Fin handled the entire interaction in French, providing an accurate explanation end-to-end, and earned a positive CSAT response.

By reaching mature deployment, Lightspeed could create a system to handle this intricacy and deliver a correct and efficient result for its customers. By going deep, it unlocked real value.

Building towards mature deployment

Reaching maturity isn’t something that just happens. It requires deliberate investment – not just in the technology, but in a completely new way of working.

Here’s where to focus your efforts:

Rethink how you approach support

If you were building support from scratch today, you’d design around AI from day one. That’s the mindset shift required here. As Grant Lee, CEO of Gamma, puts it: “If you want to unlock the real value of AI, you have to design for it, not retrofit around it.”

The teams that have scaled AI successfully treat it as infrastructure, not as a feature they’ve added on. It fundamentally changes the nature of support work, and you need to evolve your systems and ways of working around it.

Secure executive sponsorship early

You can’t scale AI deployment without C-suite backing. AI reshapes how support works, how teams are structured, how performance is measured, and how cost and value flow through the organization.

That means aligning your CFO around ROI, your CCO around journey design, and your CEO around customer experience as a strategic advantage. Even if you’ve seen early wins, the bigger opportunity won’t materialize without leadership who understand that AI is infrastructure, not just a cost-saving tool.

Assign clear ownership for AI performance

One of the most common reasons AI performance plateaus is that no one owns it. You need someone responsible for monitoring how your AI Agent performs, identifying where it’s struggling, and driving continuous improvement.

This often looks like an AI operations lead or a support ops specialist who:

• Reviews resolution trends and handoff patterns.
• Makes targeted updates to content and configuration.
• Coordinates with other teams on systemic issues.
• Sets improvement priorities and timelines.

Without this role, feedback gets lost and performance stalls.

Treat your content as critical infrastructure

Your AI Agent is only as good as the knowledge it can access. High-performing teams recognize that content is what determines whether AI can resolve queries or not.

This means:

• Ensuring coverage for the topics your AI needs to handle.
• Keeping information accurate and up to date.
• Structuring content so it’s easy for AI to consume.
• Making content maintenance part of your regular workflow, not an afterthought.

Build a continuous improvement system

AI performance isn’t static. The most successful organizations create feedback loops that make improvement routine:

• Train your AI Agent by expanding its knowledge, refining its behavior, and connecting new data sources to handle more scenarios autonomously.
• Validate changes against real scenarios before they reach customers to catch issues early.
• Roll out updates in a controlled way across specific channels and customer segments.
• Use performance data to spot patterns, like frequent handoffs or low resolution topics, and identify what to improve next.

At Intercom, we use a framework called the Fin Flywheel (Train → Test → Deploy → Analyze) to systematize this, but the key principle is universal: create a repeatable process that turns performance data into action.

Lead the charge

The 2026 Customer Service Transformation Report clearly demonstrates the widespread investment in AI for customer service. This adoption is great to see, and it opens the door to something bigger. The opportunity ahead is for teams to move from initial wins to complete transformation – and the teams investing in that shift are already seeing the difference.

We’re in a defining moment for customer service, teams leaning in are leading the way and reaping the benefits. Next week, we’ll look at how these teams are measuring their success. Beyond simple cost savings, customer service teams in mature deployment are focused on clear return on investment and strategic impact, driving towards value-adding and revenue-generating work.

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