Tag: customer support ai strategy

From Resolutions to Outcomes: How We Price AI Agents Fairly and Amplify Customer Value

I’ve long believed a simple truth about AI in customer support: if AI is going to earn trust, pricing has to be aligned with value. That principle has guided my product decisions and the way I hold our teams accountable for measurable outcomes, not activity.

When we shared our perspective on pricing AI Agents in 2023, we made a simple argument: if AI is going to earn trust, pricing has to be aligned with value. At the time for Fin, that value was clear. You pay when the AI resolves a customer’s problem. If it doesn’t, you don’t. That’s fair, easy to understand, and grounded in results, not activity. We were the first to introduce this pricing model because we believed that pricing and value should be inherently linked.

That belief hasn’t changed, it’s grown stronger over time. What’s changed is what Fin can do. As we expanded capabilities and pushed deeper into complex workflows, it became clear that measuring value solely by end-to-end resolutions no longer captured the full picture of impact.

Resolutions were the right place to start. Historically, we measured value based on whether Fin fully resolved a conversation on its own. These are known as resolutions and they gave support teams a clear way to measure ROI, easily comparing the cost of AI versus human support. They also aligned our incentives with our customers, as our revenue was directly tied to Fin’s performance.

That clarity worked. Today, more than 7,000 teams use Fin. Our average resolution rate across customers has increased every month and now stands at 67%, even as Fin increasingly handles more complex queries. That progress came from building an Agent that could take on harder problems and still deliver.

But as Fin got more powerful, “success” stopped being binary. I saw this first-hand in customer design sessions where policy, risk, and compliance needs rightly demanded human-in-the-loop confirmation. We weren’t failing to deliver value; we were delivering it differently.

Over the last couple of years, we invested heavily to ensure Fin could handle the most complex parts of support. As Fin’s capabilities expanded, customers began pushing what Fin can do for them by deploying Fin deeper into their workflows to handle the toughest queries.

In some cases, this required Fin to work in tandem with a human agent because that’s what customer policies and oversight needs dictated. Subscription changes, transaction disputes, billing issues, and other multi-step support scenarios can often require Fin to gather context, read and write to external systems, and execute actions before handing off to a human agent for confirmation.

Fin is still doing what it was configured for – intentionally handing off after doing more of the heavy lifting, saving valuable time for support teams and overall time to serve for their customers. But our pricing metric only recognized value when the conversation ended in a full “AI resolution” (i.e. a human was never involved).

That’s why we’re evolving Fin’s pricing metric from resolutions to outcomes. This shift reflects how customers now define value: not just in full automation, but in safe, efficient progress toward the right result across complex, multi-step, and policy-constrained workflows.

An outcome represents when Fin successfully completes the action it was configured to perform, as part of a conversation. Resolutions are still one type of outcome Fin can deliver, where it handles the issue end-to-end. Another type of outcome can be a Procedure where Fin gathers context, takes action, and hands the conversation off when that’s what customers configured it to do.

Kick off your journey with the #1 Agent—an AI partner designed to turn resolutions into real outcomes. Tap “Start a free trial” to explore faster, smarter customer service and see how Fin delivers value from day one.

Increasing end-to-end AI resolutions is still a core component of scaling Agents, but they are no longer the only measure of Fin's success and utility. Especially as Fin takes on more complex work. Moving to outcomes recognizes that solving a customer problem with full automation isn’t always appropriate. It’s about getting to the right result, safely, and efficiently.

As Fin’s capabilities expand, teams should feel empowered to use it in more nuanced, collaborative work. Outcomes support that by allowing customers to design workflows that meet compliance requirements and include a human agent when necessary. From a product management standpoint, this is how we align incentives, keep risk controls intact, and still accelerate time-to-value.

Fin is becoming even more powerful at handling complex, multi-step support queries. With outcomes, we can support that growth without constantly reinventing how value is measured. And this change gives us a strong pricing foundation that can scale as Fin continues to grow and take on more roles beyond service. This aligns with our vision of Fin becoming a “Customer Agent,” capable of handling the entire customer experience.

What this means for pricing is intentionally straightforward. An outcome will be counted when Fin successfully completes an action it was configured to perform, as part of a conversation. That keeps the model predictable for finance leaders while staying transparent for operators and product teams managing AI workflows.

The pricing model stays simple and the definition of value becomes more accurate. In other words, we’re doubling down on fairness, predictability, and competitiveness—core tenets for any consumption SaaS pricing strategy tied to real business impact.

When we first wrote about outcome-based pricing, we said that trust is the currency of AI. That’s still true. Trust is earned when customers see pricing move in lockstep with utility and risk posture, especially as gen AI and agentic AI take on higher-stakes tasks.

Pricing has to feel fair, it has to be predictable, and it has to stay competitive. Evolving from resolutions to outcomes isn’t a departure from that belief. It’s the natural maturation of how we measure value as AI moves from simple Q&A into complex procedures and human-in-the-loop collaboration.

Fin has grown more powerful because customers asked more of it. Outcomes are how we reflect that progress honestly, while staying true to the same principles that guided us from the start. This is product strategy in action: align incentives, measure what matters, and scale what works.

And as Fin continues to get stronger, we’ll keep holding ourselves to the same standard: price based on the value delivered. That’s how we build durable trust, sustainable ROI, and a better customer experience at scale.

Inspired by this post on The Intercom Blog.

March 12, 2026
February Fin Breakthroughs: Master complex workflows, natural voice, 2-minute Shopify, smarter ops

Every update we shipped this month removed a specific constraint on what teams can do with Fin. In my world, the demo-to-production gap shows up as complexity, control, and confidence. Can the agent handle the query that actually matters? Will it sound right on a call? Can the team deploy it without filing an engineering ticket? Can managers understand what it’s doing? That’s the bar I hold us to.

This month, we delivered answers to all four. Here’s how.

Procedures and Simulations (0:51). The hardest problem in AI-powered customer service isn’t answering FAQs—it’s executing complex queries with real business logic and real consequences if anything goes wrong. Think billing refunds, multi-step flows, and actions that must be right the first time.

We made it dramatically easier to build and manage Fin for those complex queries—without pulling in an engineer. You can author in natural language, test every step in simulation, and deploy with confidence.

The workflow starts with AI drafting the procedure from your existing source material. You edit in natural language, with structured hooks to pull in live data, apply business logic, and add code for deterministic control where you need it. That’s how you handle multi-step flows with the precision that matters when things go wrong.

Simulations are the test environment. Define a test case, pass in the data Fin would receive in a real conversation, and watch it work through each step. You see what Fin is doing, why, and whether it’s meeting the criteria you set. Full transparency at every point. I’ve run these end-to-end myself, and there’s a particular confidence that comes from watching it work before it goes anywhere near a customer.

A conversational moment from the February Fin Product Updates recap: two teammates trade insights with laptops open, while a bold pull-quote drives home the promise—Fin removes complexity to start selling and supporting in under two minutes.

For a deeper look at Procedures and Simulations, head to fin.ai/procedures.

Fin Voice: three major updates. When something’s off in chat, it can take a few exchanges to notice; on a call, it’s immediate. Pronunciation, noise handling, and tone all matter because they’re the customer’s first impression.

Pronunciation rules (4:18). Fin has high out-of-the-box pronunciation accuracy, but it doesn’t know your brand—your product names, your industry terminology, the way your company uses certain words. Alihan Zinna, Staff ML Scientist, showed this with an IKEA example: without pronunciation rules, Fin mispronounced both “IKEA” and a product name; after adding rules, both were corrected and sounded natural.

New natural voices (5:48). We’ve added 11 new voices tuned to a range of brand tones so you can choose one that sounds like it truly belongs to your company—not a generic AI assistant.

Background noise reduction (6:28). People call from airports, shops, and busy offices. Fin now monitors background noise continuously and increases noise reduction when the environment demands it. No configuration needed. As Alihan put it, “This is one of those things customers really notice when it’s not working. The goal was to make it invisible. That’s what we built.”

Catch up on February’s Fin Product Updates with a walkthrough of the Call Metrics dashboard—saved filters, hold‑time tiles, missed and declined call counts, and a monthly breakdown that helps support teams act faster.

Shopify setup experience (8:21). Fin began as a Service Agent and is quickly becoming a Customer Agent—working across the whole lifecycle to support, sell, and guide, even before a customer has an issue. The revamped Shopify setup is a clear step forward.

Shopify catalogs are complex—thousands of products, variants, and dynamic inventory—and connecting all of that to an agent has historically been painful. We removed the friction.

Setup now takes three steps: first, connect your store. Second, install the Messenger directly in Shopify—no code, just a few clicks. Third, deploy Fin. Total time: under two minutes. We timed it live.

What that unlocks is real. In the demo, a first-time snowboarder asked for recommendations. Fin searched the catalog, reasoned about attributes that matter to a beginner (there’s no “beginner” tag in the catalog), personalized suggestions by height and weight, and added a board to the cart.

Even better, one customer updated their website copy to promote a sale. Fin immediately picked up the new context and began recommending sale items, nudging shoppers to add more to the cart to access a discount—no extra configuration required. It read the situation and acted.

See how the latest Fin update streamlines support scheduling. A product expert walks through Holiday Office Hours, showing how to set default hours, track response metrics, and add closures so teams stay consistent.

Three steps, and you have a real-time shopping assistant that knows your store and sells on your behalf.

Helpdesk improvements (12:31). Fin works with any helpdesk, but many teams consolidate to take advantage of our native Intercom helpdesk integration. We’ve shipped 19 helpdesk improvements in 2026 so far; two from this month stand out.

11 new call metrics. Hold time, outbound dial time, missed and declined calls, call terminating party, and more. These give leaders the visibility to analyze workload distribution and call handling quality in detail.

Holiday office hours. Teams no longer need to manually update office hours for every public holiday. This was the most upvoted request in our community, and we shipped it.

Across the board, we removed the constraints that hold teams back: the complexity ceiling in automation, the quality ceiling in voice, the setup barrier in Shopify, and the operational overhead in the helpdesk.

We closed out the month with a Star Wars–style crawl of 22 additional updates. All features mentioned here are live and available now. Explore more at fin.ai/updates. More to come—see you next month.

Inspired by this post on The Intercom Blog.

March 10, 2026
Turn Support Wins into a Company-Wide AI Blueprint for Consistent, End-to-End CX

Building a great end-to-end customer experience with AI means going beyond support, and I’ve seen firsthand how transformative that shift can be when we treat every interaction as part of one cohesive journey.

Every customer touchpoint, from the first sales conversation through to post-sales support and success, is an opportunity to get it right. Other teams are now turning to AI to transform how they show up for customers, and support, which led the way, has already written the blueprint. In my role, I focus on making that blueprint actionable across the entire lifecycle.

In The 2026 Customer Service Transformation Report, it’s clear most businesses are thinking about what’s next, with more than half planning to scale AI to other departments. Interestingly, they often cite their early success with AI in support as motivation for the move. This makes support teams uniquely positioned to help lead the transition, a strategic role unimaginable just two years ago.

In this piece, I share how teams are introducing AI to other parts of the business, how to think about this expansion effort, and the new opportunities it creates for support leaders who want to drive a unified customer experience.

Support was the first proving ground for AI, and our research suggests that businesses are now planning to expand its use to other areas based on the results it’s yielded so far. Fifty-two percent of respondents said that their organizations are actively planning to scale AI to other departments in 2026.

What will this look like? Leading companies are already finding out.

Wins in support are setting the pace for company-wide AI. Survey results rank the drivers: proven success in support (57%), the push for a unified customer experience (49%), scaling other functions without more headcount (33%), and cross-department demand (31%).

My favorite example is WHOOP, the fitness wearables company. They offer a premium product which makes their sales conversations more consultative than transactional. Customers want to know “Which membership is right for me?” or “How often do I need to charge my WHOOP?” According to Emily Shirley, Business Manager for Growth Product at WHOOP, if someone chatted with the inside sales team, they were twice as likely to convert, but they didn’t have enough reps to respond to incoming queries fast enough. Customers could wait more than 10 hours for a reply.

With a big product launch on the line and an anticipated spike in prospective customer conversations, their three-person team needed help. So they deployed Fin to the "Join" page, the final step before purchase.

With Fin resolving 84% of inbound questions, the sales team was able to focus on high-value leads. Together, they drove a 130% increase in attributable sales. The team is now exploring ways to expand Fin beyond FAQs, focusing on personalised conversation flows, multi-product recommendations, and richer data capture. As Emily says: “There are so many parts of the buyer journey where this applies. We’ve only scratched the surface.”

It’s clear there’s a desire to push AI to other parts of the customer lifecycle, but there is a risk hidden in this expansion. If sales, customer success, and other departments all launch their own Agent, each operating in isolation, you can end up fragmenting the very thing our research says teams want to create. The second-most cited reason for pushing AI beyond support: desire for a unified customer experience.

Without shared context, each handoff becomes a source of friction where customers could receive inconsistent answers or be asked to repeat information. I’ve watched even well-intentioned AI rollouts struggle here—great local wins, but an overall journey that feels disjointed.

A translucent UI visual maps a support-led AI blueprint that scales across the business—from SDR and sales to custom assistants—anchored by layers for goals, memory and user context, business knowledge, and interoperability.

The opportunity (and the challenge) is to keep the customer at the center. Instead of department-specific Agents that operate independently, we must strive for cohesion. That means shared memory, consistent governance, and connected AI workflows that respect the customer’s history and intent across channels.

This is the future I’m building toward: solutions like Fin becoming a “Customer Agent,” capable of handling the entire customer experience. This will mean Fin can function in many roles, supported by a memory that grows with the customer over time and deep knowledge of the business, creating a seamless experience for every interaction. In practice, that’s agentic AI designed to collaborate across teams, systems, and journeys—without losing context.

Pushing AI into new parts of the business requires someone to own the process. And for many organizations, that’s the support team. Nearly a third of respondents (32%) confirmed their customer service teams are leading their business' AI transformation strategy.

This presents a real opportunity for support teams to shape the future of customer experience. Instead of each function reinventing the wheel, support can act as a center of excellence, defining shared standards, guardrails, and operating practices that drive performance.

“You already manage the most complex, high-volume customer interactions; you have rich data on customer needs and behavior; and you know how Agents perform in the real world. Those insights will be invaluable as AI scales across your business.”

Leaders are racing ahead with real AI in support. Explore the 2026 Customer Service Transformation Report to see where deployment is stalling, benchmark your team, and get practical steps to scale automation that delights.

In my organization, when we extended AI from support into sales, we deliberately brought our conversation design expertise, Agent Analytics, and governance models along with it. One team owns the orchestration, memory strategy, and CRM integration so a customer can start with a sales question and end up with a support one—without ever feeling a seam. That continuity is where journey mapping meets product strategy and turns into measurable outcomes.

As Agents like Fin expand their capabilities and move into new areas, I expect many customer service leaders will see their roles expand to include AI implementation across the customer journey. It’s a natural progression for product management leadership in support: owning the experience, the data, and the operating model.

Achieving perfect customer experience is AI’s biggest promise. But in order to get there, teams need to be smart about the solutions they deploy. A unified Customer Agent capable of handling the entire journey end-to-end will have a significant advantage, delivering consistent, context-aware experiences across every interaction.

The Customer Agent future is being built right now, and it’s starting with the team pioneering AI transformation from the very beginning: support. For leaders in these organizations, this is a rare opportunity to shape how customer relationships will be built and maintained in the AI era.

If you’d like to dig deeper into the data and benchmarks guiding these decisions, download The 2026 Customer Service Transformation Report.

Inspired by this post on The Intercom Blog.

March 5, 2026
From Tickets to Strategy: How AI Is Rewriting Support Careers—and Why Now Is the Moment

To truly transform with AI, I’ve learned it’s never just about the technology—it’s about redesigning how we work. The teams that win don’t bolt AI on; they re-architect around it. That means rethinking roles, workflows, and governance to build a system that sustains and improves AI performance over time.

In The 2026 Customer Service Transformation Report, teams at every stage of maturity describe human agents taking on more proactive work—training AI systems, handling the hardest queries, and owning tasks that demand judgment. Job descriptions are shifting, too, with many organizations explicitly adding AI-related responsibilities.

I’m also seeing a clear rise in dedicated AI specialists. Conversation analysts, knowledge managers, and AI operations leads are fast becoming standard. For support professionals, this opens new, higher-leverage career paths—and creates a talent pipeline that blends service excellence, data fluency, and product thinking.

Support once centered on queue-level activity—ticket triage, routing, translations, and answering FAQs. Now, as AI handles more frontline interactions, our human roles are moving up the stack toward optimization, oversight, and continuous improvement.

According to the latest research, 45% of teams report updating job descriptions to include AI-related responsibilities, with 40% saying their human agents are now more focused on training AI systems. Another 27% report that human agents primarily handle the most complex escalations and edge cases, while a quarter say agents are doing more consultative and strategic work.

Even at the initial deployment stage, 16% of teams report spending less time handling support volume since implementing AI – and among teams who’ve reached maturity, that figure rises to 28%.

When Intercom’s Research, Analytics & Data Science (RAD) team interviewed 166 of our customers, similar themes emerged. Nearly all participants (≈95%) reported meaningful workflow changes, with manual processes being handled by AI, and humans focusing more on monitoring or fine-tuning AI outputs. Eighty-three percent of participants also reported seeing their team’s roles and responsibilities change to become more strategic and supervisory in nature.

AI is reshaping support teams: organizations are adding conversation analysts (32%), knowledge managers (30%), AI operations leads (28%), and support automation specialists (24%). Just 8% report no new AI roles.

It’s not just the work that’s evolving; organizational structures are, too. Some teams are reallocating existing talent into AI-focused roles; others are hiring entirely new skill sets. Many of the most common job titles in this space didn’t exist two years ago.

Consider a Senior AI Knowledge Manager, Beth-Ann Sher, who transitioned from a help center manager role. Like many careers transformed by AI, her work evolved from administrative to strategic. Instead of focusing solely on customer-facing, self-serve content, her mandate expanded to designing and optimizing knowledge inputs that directly improve AI Agent Fin’s performance—work that materially lifts resolution rates.

Or look at a Senior Conversation Designer, Fred Walton, hired specifically for an AI-first function. He focuses on frictionless customer journeys with Fin, smoothing handoffs between automation and human support while keeping customer satisfaction front and center—hallmarks of mature AI workflows and conversation design.

In high-performing organizations, roles like these typically sit within a dedicated AI support team under senior CS leadership. Clear ownership and accountability for AI performance is critical; without it, optimization stalls and trust erodes.

These shifts aren’t isolated. Take Robb Clarke from RB2B. He went from Head of Technical Operations to Head of AI. With Fin, his focus moved from repetitive support questions to managing knowledge and improving the system behind it—freeing him to be proactive about product improvements and fix issues before they hit customers.

Or consider Eric Broulette from Bloomerang, a support leader who leaned into AI and became the VP of Support and Education. By deploying Fin, his team found breathing room to invest in what’s next. Agents stepped into new roles, contributed to meaningful projects, and built skills that had previously felt out of reach. As Eric puts it: “Do not wait to embrace AI. It will unlock more career growth for your teams than you can imagine.”

Leaders are racing ahead with real AI in support. Explore the 2026 Customer Service Transformation Report to see where deployment is stalling, benchmark your team, and get practical steps to scale automation that delights.

Bringing AI into support will eventually change every agent’s day-to-day work. For leaders at the start of the journey, that can feel daunting. My perspective: the most successful teams treat this as an operating model shift, not a tooling rollout—anchored in AI Strategy, governance, and continuous improvement.

Be transparent about what’s changing, why it matters, and how success will be measured. Define how AI performance will be evaluated (resolution rate, containment, CSAT impact), empower agents to train and improve the system, and communicate how responsibilities will evolve. When teams help build the AI, they’re invested in making it great.

Here’s the playbook I rely on with support leaders: First, reset expectations about time allocation—less time in the queue, more time improving the AI system that serves the queue. Second, elevate knowledge management as a core capability. Prioritize content quality and coverage for your AI Agent, and carve out dedicated “out of the inbox” time so every agent contributes. Third, keep outcome metrics—especially resolution rate—front and center. It gives the team a north star for experimentation and iteration.

Scaling AI is as much a people challenge as it is a technology challenge. As automation takes on more work, support roles become more proactive, strategic, and cross-functional—even early in the journey. Responsibilities expand, new roles emerge, and team structures adapt to concentrate on and amplify AI performance. In the process, support careers are transformed.

If you’re leading this shift, now’s the moment to reimagine your operating model: clarify ownership, invest in knowledge and conversation design, adopt eval-driven development, and build the muscle for continuous improvement. That’s how you move from tickets to strategy—and unlock compounding value for your customers, your business, and your teams.

Inspired by this post on The Intercom Blog.

February 27, 2026
12 Game-Changing Updates to Fin Procedures & Simulations for Complex Queries

Today, I’m excited to share 12 major updates to Fin’s Procedures and Simulations—the foundation that lets Fin handle complex work while keeping teams fully in control of the customer experience.

In my work building AI workflows with product and support leaders, I’ve seen how the right blend of natural language instructions, deterministic controls, and fully agentic behavior turns Fin into a reliable problem solver. Procedures make this blend possible by enabling Fin to act like a human—yet with the repeatability and governance of software. Simulations then let us test those complex Procedures at scale before they reach customers, so we can deploy with confidence.

Together, these capabilities make Fin self-manageable, transparent, and ready for genuinely complex work.

Here’s what’s new at a glance: we’ve made Procedures easier to build and maintain; enhanced deterministic controls for precision and policy compliance; expanded agentic behavior so Fin can adapt in real time; and delivered more powerful Simulations to validate end-to-end workflows before go-live.

Why did we build this? Many teams see early AI gains in speed, coverage, and cost to serve—but then hit a ceiling. They keep AI confined to simple automation and information retrieval, rather than setting it up to handle the nuanced, multi-step workflows they still trust to humans. We designed Procedures and Simulations to remove that ceiling, so teams can confidently set up, govern, and iterate on complex AI workflows without bottlenecks.

Follow the AI lifecycle as it cycles from Analyze to Train to Test to Deploy. This streamlined loop spotlights the TRAIN phase, underscoring faster iteration and feedback that power more capable procedures and realistic simulations.

We also heard that teams needed an easy way to connect data so Fin could reliably check customer status or eligibility and then take action. And they didn’t want to route through engineering every time they needed to create or amend logic for mid-conversation decisions. Procedures combines natural language instructions and intuitive data connector setups. You tell Fin in your own words how you want it to behave, and you’ll be guided through creating conditional steps so Fin will react consistently, with the option to add in any code snippets for circumstances where absolute precision is required. Once you build one Procedure, we believe you’ll want to build several, so Fin will constantly read the conversation it’s in to ensure it’s following the most relevant Procedure, and jump to a more relevant one if the user intent changes.

I know that taking something like this live the first time can feel like a leap of faith. That’s exactly why we built Simulations—to test Procedures comprehensively, uncover edge cases, and launch with confidence.

Reaching mature deployment takes a deliberate, ongoing commitment to training workflows, validating them before deployment, measuring performance in production, and refining them over time. At Intercom, we call this the Fin Flywheel: train, test, deploy, analyze. Procedures form the foundation of the train stage, and Simulations make the test stage reliable at scale. Together, they enable Fin to handle complex work, and teams to stay in control of it.

Procedures: Define exactly how Fin handles complex work. With Procedures, I can set Fin up to resolve complex, time-consuming queries that require multiple steps or business logic. Fin follows standard operating procedures and applies sound judgment—just like a seasoned teammate—so even complicated queries are resolved in controllable, predictable ways.

A snapshot of the Procedures builder in action, mapping a clear path for handling damaged food orders while letting teams train Fin on examples, target channels, quickly test updates, and publish with Set live.

Procedures combine three powerful elements. First, natural language instructions. You write a Procedure in plain language, just like documenting a process for a new teammate. You can paste in your existing SOPs, write from scratch, or let AI draft them for you, then iterate yourself.

What’s new: Draft Procedures with AI. Share an outline of your process and Fin drafts a complete Procedure using your conversation history, knowledge hub content, and relevant data. If additional context is needed, it prompts you with clarifying questions to make sure the Procedure is thorough and tailored to your use case, significantly reducing setup time. For example: if you’re creating a refund workflow, the system can draft conditional paths for eligibility, approval thresholds, and verification steps based on your historical cases and policies.

What’s new: Break complex workflows into Sub-procedures. Write a process once and reference it across multiple Procedures by breaking it down into reusable steps, called Sub-procedures. This makes workflows easier to read, faster to build, and simpler to maintain as things change.

Second, deterministic controls. Natural language is flexible, but some steps need to be exact. You can layer in deterministic controls where precision matters, starting with a fully natural language Procedure and introducing structure gradually where it adds value: conditional steps (branching logic) to handle decision points so Fin’s behavior is consistent and predictable; data connectors so Fin can pull information from your tools or take actions automatically; code snippets for when absolute accuracy is essential; and checkpoints to pause for approval or hand off to a teammate.

Fin demonstrates structured troubleshooting: a transaction dispute flow with eligibility checks, clear IF/ELSE steps, and quick Data Connector actions like freezing a card or pulling invoices, streamlining complex support tasks.

What’s new: Instruct Fin to read specific content from your knowledge hub. You can set clear rules for Fin to reference a specific policy or article from your knowledge hub in defined situations so Fin always surfaces the right context in a conversation.

What’s new: Explicit Procedure switching under defined conditions. You can set rules that deterministically trigger a switch to a different Procedure, for example, escalating to a complaints Procedure if specific risk signals are detected mid-conversation.

What’s new: Internal notes for human handoffs. When Fin hands off to a teammate, it can now include internal notes with relevant context so the person picking up the conversation knows exactly what happened and what needs to happen next.

Third, fully agentic behavior. Because real conversations rarely follow the happy path, Procedures let Fin reason through what’s happening and adapt—jumping to the right step or switching Procedures entirely if a customer changes their mind or the issue shifts.

Procedures and Simulations in action: Fin rehearses a food order damage scenario, confirming details and progressing through each trigger. Teams validate complex flows end to end as steps turn green and outcomes are tracked.

What’s new: Automatic Procedure switching. If a customer starts in a billing workflow but then asks about cancelling their subscription, Fin transitions to the relevant Procedure without forcing the customer to restart.

What’s new: Structured data extraction from uploaded files. Fin can now extract structured data directly from PDFs and images uploaded by customers—like invoices, forms, or receipts—and use that data within the conversation. Customers don’t have to copy and paste or repeat themselves.

As MONY Group put it:

“ If a customer starts down one path but their issue turns out to be something else entirely, Fin adapts seamlessly – no more getting stuck in loops or forcing customers into the wrong workflow. ”

Simulations help teams rehearse procedures and verify outcomes before going live. Run all tests or launch a new one to ensure Fin handles tricky customer scenarios—from damage confirmation to refunds and missing subscriptions.

The result is a conversation that feels fluid, but always follows your intended rules.

Making complexity easier to manage is just as important as unlocking new capabilities. Beyond the core updates, we’ve focused on creation, governance, and scale—while keeping ownership with your team.

What’s new: Improved instruction authoring. We’ve made it easier to write, edit, and structure Procedures, so building and updating them takes less time and requires less effort.

What’s new: Reporting on when Procedures trigger, resolve, or hand off. You can now track how Procedures are performing directly within the Procedures UI, seeing exactly when they trigger, when they resolve, and when they hand off to a teammate. This visibility helps you spot issues early and improve over time.

Customer stories from Raylo and Mony Group show how Fin now resolves payment issues and complex claims in-chat, checks account data via APIs, and lifts CSAT to about 94%, highlighting the impact of Procedures and Simulations.

Simulations: Test complex workflows at scale before they reach customers. Simulations let you validate how Procedures will perform before anything goes live, and continuously revalidate as things change. Deploying complex AI can feel uncertain; Simulations remove that uncertainty so you can launch with confidence and iterate safely.

You can simulate full conversations. For any Procedure, choose a user or customer segment and run a complete, multi-turn simulated conversation. You see every step Fin takes, how it applies your rules, reasons through decisions, and where it passes or fails—giving you the observability to debug and fix issues before they ever reach customers.

What’s new: Upload images for richer testing. Simulations now support image uploads, so you can test workflows that involve receipts, invoices, or forms—the same inputs your customers actually send.

What’s new: Clearer visibility into Fin’s reasoning. You can now see exactly how Fin is thinking through each step of a Simulation, making it easier to understand behavior, catch unexpected decisions, and refine Procedures with confidence.

You can also use AI to create, store, and rerun tests. Writing test coverage manually doesn’t scale. Fin’s AI Assistant generates Simulations directly from your Procedures, suggesting realistic edge cases like partial refund disputes, missing invoice uploads, or no subscription found, so you can expand coverage without expanding overhead. All the Simulations you create are stored in a central library. When a product changes, a policy updates, or a Procedure is edited, hit “run all” to instantly check whether anything has regressed. This applies the same rigor to AI automation that engineering teams bring to software testing.

What’s new: AI-suggested Simulations. You can now use AI to generate a full set of Simulations from any Procedure. The AI Assistant suggests realistic variations based on your workflow, so you can build comprehensive test coverage fast.

Customers are already seeing this in production. “Fin can now handle payment-related queries that were never possible before… The impact on CSAT and overall CX has been pretty shocking – the Payment Information procedure CSAT is sitting at ~94%, and CX score is significantly higher than our average.” – Raylo

“Procedures have fundamentally changed what we can achieve with Fin. Previously, complex processes like cashback claim investigations could only be handled through a static form on our website… Now, Fin can handle these sophisticated scenarios in real-time within the conversation itself. It checks account information via API calls, makes complex decisions, and guides customers through the entire claims process dynamically.” – MONY Group

Procedures and Simulations are available now. I’m eager to see how teams use these updates to scale agentic AI, deliver faster resolutions, and raise the bar for customer experience—without sacrificing control, compliance, or quality.

Inspired by this post on The Intercom Blog.

February 25, 2026
How Deep AI Transforms Support Into Proactive, Omnichannel CX—No Extra Headcount Needed

For years, I chased the elusive goal of delivering a perfect customer experience. Today, with AI embedded in our support operations, that standard is finally within reach—and it’s reshaping how we prioritize, design, and scale service.

In “The 2026 Customer Service Transformation Report,” teams report early, tangible wins from AI: faster responses, higher efficiency, and consistent coverage across languages and time zones. Those gains create the capacity we’ve always needed. The more we push the technology, the more quality improvements we unlock.

This marks a fundamental shift. As AI takes on more, our focus can finally move from firefighting to crafting the customer experience. When the AI is working, the measure of success becomes how well it’s working—across accuracy, tone, resolution, and end-to-end journey quality.

I’ve seen this transformation firsthand. Mature AI deployment gives my team “breathing room,” so we can design for consistently excellent outcomes rather than obsess over deflection. That means widening access to support, removing friction on the path to resolution, and anticipating customer needs before they escalate.

In our own support organization, we opened support to trial customers, accelerated first response times, and added consultative sessions during onboarding. We absorbed a 300% increase in total demand without adding headcount—made possible by deep integration of an AI Agent and a disciplined AI strategy.

Teams with mature customer service deployments are nearly three times likelier to say they always meet increasing expectations—27% vs 9% at initial rollout—highlighted by bold orange and gray comparison bubbles.

Across the industry, the pattern is similar. 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 expectations rise, the organizations that deeply integrate AI—complete with clear ownership, robust instrumentation, and continuous improvement loops—are the ones most likely to meet (and exceed) the bar.

Looking ahead to 2026, I expect omnichannel consistency to become a key differentiator. The data shows planned investment is distributed nearly equally across chat, email, and social messaging (36% each), closely followed by phone/voice (31%). The question is no longer “Which channel should we optimize?” but “How do we deliver a consistent, AI-powered experience everywhere our customers are?”

Teams that solve for omnichannel consistency will bridge the long-standing gap between what customers expect and what support can deliver. Every touchpoint becomes an opportunity to exceed expectations and build durable trust.

Consider Clay, a team that scaled support without sacrificing quality. Support is one of their main growth drivers, and as their customer base expanded, ticket volume surged. Early on, they concentrated much of their effort in Slack, cultivating close, transparent community relationships. But relying on a single channel created friction as they grew; customers wanted the flexibility of email and in-app chat, and Clay needed to deliver the same high standard everywhere.

Where AI investment is headed for customer service in 2026: chat, social, and email lead at 36%, with phone/voice close behind at 31%. A bold visual snapshot of shifting channel priorities in CX.

By unifying their support experience with an AI Agent, Clay brought consistency across channels. Today, AI is involved in 90% of all queries and handles half of Clay’s total volume, upwards of 7,000 queries a month. First response rates improved significantly, freeing the team to focus on proactive, high-impact work.

That work includes identifying content gaps for education and content marketing, reaching customers before they need to ask for help, and surfacing feature requests and recurring challenges to product teams. Clay proves that when support is truly great, it becomes a competitive edge.

So how do you build a superior customer experience with an AI Agent? Here are five principles I use when scaling toward mature deployment.

1) Treat customer experience like a product. Treating support as a product means designing, building, and managing the support experience with the same rigor as your core product. 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).

Leaders are racing ahead with real AI in support. Explore the 2026 Customer Service Transformation Report to see where deployment is stalling, benchmark your team, and get practical steps to scale automation that delights.

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 is the first responder and the end point for most conversations. Humans step in where they add the most value—particularly during high-stakes issues—and those handoffs should feel seamless. Meanwhile, your team focuses on improving AI performance and optimizing the end-to-end journey.

3) Be proactive. Use AI to anticipate needs, guide customers before problems arise, and nudge them toward successful outcomes. This is where customer support AI strategy shines—moving from reactive triage to journey orchestration that protects momentum and builds trust.

4) Build for trust. Many customers still carry the legacy of clunky chatbots that delivered vague answers and dead ends. You earn trust by showing that your system works. Don’t hide your AI Agent behind layers of “choose an option.” Get customers to the AI quickly, demonstrate real problem-solving, and ensure that when a human is needed, they join with full context to resolve complex issues efficiently.

5) Make it feel personal. Your AI Agent represents your brand. The way it speaks, follows policies, and responds matters. Use tone control, fallback logic, and language preferences to align the experience to your standards. Consistency builds trust; personality builds connection and loyalty.

Perfect really is possible. With deep AI implementation, you can scale comprehensive, fast, and personal support across channels—so customers feel supported not just when they reach out, but throughout their journey. That’s the promise of modern AI workflows in support, and it’s what will separate leaders from laggards in the years ahead.

Inspired by this post on The Intercom Blog.

February 20, 2026
Deeper AI Integration, Clearer ROI: How Mature Deployments Redefine Support Economics

Over the last year, I’ve had the same conversation with a lot of support leaders.

They’ve deployed AI and are seeing initial efficiency gains, but want to push beyond these early results and achieve meaningful transformation.

When AI is first introduced, the gains show up quickly. Teams resolve higher volumes of queries, free up capacity, and deliver faster responses. But the real opportunity for impact extends well beyond those initial wins. As AI becomes more deeply integrated into support operations, taking on harder, more complex work, those results compound, new ways to create and measure value open up, and the economics of support change entirely. That shift is where I spend most of my time with leaders—turning early efficiency into durable business value.

This sits at the heart of “The 2026 Customer Service Transformation Report.” In this reflection, I explore how deeper integration compounds impact and why that makes business value easier to articulate across the organization—especially to finance and product peers who need to see outcomes, not just output.

The teams going deeper are seeing higher returns. The research shows that 62% of support teams have seen their customer service metrics improve since implementing AI, with early wins showing up most clearly in speed and efficiency. But for teams that have reached mature deployment (where AI is fully integrated into operations) that number jumps to 87%.

As AI programs advance, measurement confidence surges. This chart shows how ROI tracking rises from 35% in exploring to 70% in mature deployments—evidence of a widening execution gap in customer service.

The same pattern holds for the ability to measure ROI. Among teams in early exploration, just 35% say they can measure their return on AI investment, but for teams at the mature deployment stage, that rises to 70%. In my experience, this is the moment the conversation shifts from “is AI working?” to “how much leverage are we creating?”

As AI becomes more embedded in support workflows, what teams choose to measure starts to change. In the early stages of deployment, ROI is typically understood through improved customer response times, lower cost to serve, and freeing up capacity. Teams focus on how much time AI creates and whether it’s relieving pressure on the support organization. These signals help validate that the system is working, but they say little about how that capacity is ultimately used.

As deployments mature, measurement starts to reflect a different intent. Instead of stopping at time saved, teams look at where that capacity is reinvested—into higher value customer work and revenue-generating activities. ROI becomes less about relief and more about leverage. I encourage teams to set targets for capacity redeployment and tie them directly to activation, retention, and expansion outcomes.

The report data shows this clearly. Across all maturity stages, the most commonly cited measure of ROI is "time freed up that the support team can use to focus on value-adding activities for customers." But at mature deployment, that signal intensifies, with 73% of teams citing it, compared to 56% at early exploration.

Mature AI deployments reveal clearer ROI: teams report more time freed for value-adding customer work (73% vs 59%) and more hours redirected to revenue-generating tasks (56% vs 34%) than initial rollouts.

What’s also interesting is that 56% of mature teams say freed capacity is being directed toward revenue-generating activities, up from 34% at initial deployment. That’s a powerful indicator that AI is shifting from a cost narrative to a growth narrative.

The result is a shift in economic intent: from measuring what AI saves to demonstrating how the capacity it creates is reinvested to drive growth. As a product leader, I anchor this conversation in outcome-based metrics and clear counterfactuals: what would it have cost to deliver the same experience without AI?

As AI takes on more work, the question moves from “does it save money?” to “how does it change the economics of support?” Legacy support economics were built for linear growth: more customer tickets meant more headcount, more outsourcing, and more software costs. Success was measured through containment—the number of queries that didn’t reach human agents. These models worked when volume and effort were tightly linked, but AI doesn’t scale linearly, and it needs to be evaluated differently.

To sustain AI investment and expand its impact, teams need to move beyond cost-cutting narratives and build a clearer case for business value. When done right, AI goes far beyond improving support efficiency. It rewires the financial model, breaking the link between support costs and revenue growth, and turning support into a contributor to customer activation, retention, and lifetime value. This means treating your AI Agent as a new workforce capability that changes how your support function creates and captures value. Here’s what value looks like in an AI-first model:

Deeper AI integration decouples growth from headcount. This split chart shows support volume surging while team size plateaus, revealing how automation unlocks scale, reduces costs, and makes ROI easier to prove.

Human productivity: Your team focuses on more strategic areas, not the queue.

System improvement: Every resolved query makes the system smarter.

Revenue influence: Support becomes a lever for activation, retention, and growth.

Organizational agility: You scale service without scaling headcount.

Leaders are racing ahead with real AI in support. Explore the 2026 Customer Service Transformation Report to see where deployment is stalling, benchmark your team, and get practical steps to scale automation that delights.

How does this look in practice? Intercom offers a compelling example with Fin. What started as a focused effort to improve their customer support experience has become one of the clearest illustrations of what happens when AI is fully embraced across an organization.

Since 2022, Fin has helped Intercom absorb more than a 300% increase in customer demand while improving the consistency of delivery—including supporting new routes into support for trial customers and website visitors. Today, Fin is involved in 97% of their customers' conversations. Of those, it resolves 83.5% end-to-end, putting their overall automation rate at 81%.

That depth of deployment allowed Intercom to scale service without scaling headcount. Without Fin, they would have needed at least 100 additional support teammates to meet rising demand and service standards.

As Fin took on the majority of day-to-day volume, the human support team shifted toward consultative work—helping customers adopt Fin more deeply, succeed faster, and unlock more value from the platform. Intercom now tracks metrics like “direct revenue generated” and “expansion revenue influenced” to understand the impact of these consultative support activities. This repositioned support from a cost center to an active contributor to long-term growth.

The throughline from The 2026 Customer Service Transformation Report is that deployment depth makes a significant difference. Teams that are investing in deeply integrating AI are reshaping how support scales and contributes to growth. Value becomes clearer as AI takes on more work, and support leaders can articulate that value to the rest of the business.

The gap between these teams and those still in the early stages is widening. A select group of pioneers are setting a new bar for what AI-powered customer service can deliver, and understanding what they’re doing differently is the first step toward closing that gap. If you want to dive deeper into the data and frameworks, you can download the report here: https://www.intercom.com/customer-transformation-report?utm_source=blog&utm_medium=internal&utm_campaign=20260128-report-owned-2026cstransformationreport&utm_content=chapterseries_2

Inspired by this post on The Intercom Blog.

February 13, 2026
Build CX Scores You Can Defend: My 5-step playbook for transparent, trustworthy AI metrics

“You don’t have to trust the algorithm; you can see exactly why a conversation earned the score it did.”

We recently shared how we redesigned CX Score to deliver deeper, more actionable insights across every conversation. The most common follow-up from support leaders was simpler and incredibly important: “Can I trust it?” It’s the right question—and it’s the one I use as my own bar for whether a metric is ready for the C‑suite.

CS teams are the subject matter experts on customer experience. They understand the nuance of what customers feel, the context behind every interaction, and the difference between a technically resolved issue and a genuinely satisfied customer. I’ve learned, conversation by conversation, that any metric we ship has to capture that nuance at scale—or it doesn’t deserve to be used.

We built CX Score to give support teams a complete view of how their customers feel across every conversation. It surfaces what’s working, what’s not, and why—so leaders can communicate impact clearly and drive change across support, product, and the wider business.

A CX Score in action: repeated CSV export failures trigger a low score and customer frustration, while the AI agent clarifies next steps and gathers details—turning raw signals into actionable support insights.

Here’s exactly how I approached building a trustworthy metric that support leaders can inspect, explain, and defend.

1) It’s grounded in how support teams define quality. I started with how experienced support professionals actually evaluate conversations—collecting real examples of strong, mixed, and poor interactions across industries, identifying the specific factors that shape overall experience, and writing plain-English rules for each. The result: CX Score applies the same criteria a trained support professional would use, not generic LLM assumptions.

2) It’s aligned with human judgment. We created a dataset of thousands of real customer conversations spanning multiple industries, languages, channels, and agent types. Each was manually reviewed by experienced support professionals—with two reviewers per conversation where possible and disagreement resolution to create stable consensus labels. The result: CX Score is trained and tested to behave like an expert reviewer, not a language model making broad guesses.

A modern CX analytics view shows how conversations flow from chat, email, and mobile into AI assistance, then to resolutions and sentiment outcomes—turning messy support data into a single, defensible CX Score.

3) It’s engineered by AI specialists. CX Score isn’t a prompt attached to an LLM. It’s a production system built by Intercom’s AI Group: 37 ML scientists and 350 engineers whose full-time focus is AI for customer service. The system includes specialized handling for long transcripts, model configuration tailored for support language and subtle sentiment, prompt engineering designed to default to neutral when evidence is weak, and a multi-stage evaluation pipeline that checks for precision, consistency, and reliability. The result: A metric built by a team that understands LLM behavior in production support environments, where accuracy and consistency matter most.

4) It’s validated statistically, not qualitatively. Trust requires measurement, not vibes. We tested CX Score across standard ML metrics: Precision (when the model flags a negative experience, how often do humans agree?), recall (how many human-identified issues does it catch?), and F1 score (the balance between both). We set an explicit bar: F1 above 0.8, representing high agreement with human judgment. We reran these evaluations through every revision, checking for regressions or biases, and I focused especially on negative experiences, because a false negative hides a real problem. The result: CX Score meets a measurable standard before it ships—not a gut check, a statistical requirement.

5) It was battle-tested with real customers. Lab accuracy isn’t enough. Customer environments are messy: Varied ticket types, mixed languages, unpredictable edge cases. Before release, we ran a multi-phase field test—shadow-scoring conversations with both old and new models, validating sensible behavior across agent type and conversation length, then rolling out to a controlled customer group who confirmed the scores felt right, reasons were clear, and insights were actionable. The result: CX Score shipped because real teams told us it made sense in practice, not because it passed internal tests.

From conversation to clarity: this visual maps the drivers behind a CX Score. Explore how policy feedback, answer quality, and effort combine to produce defendable insights support leaders can act on.

The importance of explainability. One of the most critical choices I made was ensuring CX Score isn’t a black box. Every score comes with clear reasons, concrete excerpts, and a short explanation of what influenced the rating. This turns the metric into something you can inspect, audit, and explain to executives. You don’t have to trust the algorithm. You can see exactly why a conversation earned the score it did.

A metric that evolves with your business. Customer expectations shift. Products change. AI improves. A trustworthy metric can’t be static. CX Score evolves with the same commitments that shaped its redesign: Evaluate the real signals that shape customer experience, keep the logic simple and interpretable, and ensure leaders can make clear decisions from it. It’s built to be a durable source of truth across every conversation.

The takeaway. In a world where products look the same and AI can generate any interaction, customer experience is one of the few differentiators that actually matters. Support leaders have built that expertise conversation by conversation. What they’ve lacked is a measurement system that could validate it at scale—one that’s reliable enough to report to the C-suite, explainable enough to defend in strategy meetings, and rigorous enough to drive real decisions. That’s what CX Score is designed to be: A metric that reflects the reality support leaders see every day, backed by the technical rigor to make it credible everywhere else.

Want to see CX Score in your workspace? Ask your admin to enable it for your team, and start using explainable AI insights to improve customer experience and coach with confidence.

Inspired by this post on The Intercom Blog.

February 9, 2026
Go Deep or Get Left Behind: How AI Deployment Depth Transforms Customer Service

AI adoption is everywhere. I see more teams every quarter moving from pilots to production—and increasing their budgets accordingly. But the gap between “using AI” and truly transforming with it is widening fast. Launching an AI Agent is easy; building a mature, AI-powered support operation is where the real work—and the real value—lives.

In the new research, the "2026 Customer Service Transformation Report," the difference comes down to depth of deployment. It’s not enough to dabble. Teams that design their operations around AI are pulling away from those who treat AI like a bolt-on feature.

This article kicks off part one of my five-part deep dive into the research. I’ll unpack the data, share what I’ve learned leading product and AI strategy, and translate it into practical steps you can apply now. If you’d like to go straight to the source, you can download the report here.

First, the macro picture: 2,470 global support professionals across industries were surveyed to understand current AI usage, challenges, and the 2026 opportunities. The headline is clear—AI investment is now table stakes. 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. Those investments are already paying off: Over three-quarters of CS teams (77%) say AI is meeting or exceeding expectations, delivering faster response and resolution times, always-on coverage, cost savings, increased capacity, and multilingual support that scales globally.

And yet, 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’s the tell: most teams are skimming the surface and leaving meaningful performance gains on the table.

Most service teams are still early in AI adoption. Only 10% report mature deployment, while 26% are scaling, 35% are in initial rollout, and 26% remain in exploration, with 3% unsure.

When I map the data to what I’ve seen in the field, the maturity difference shows up immediately in outcomes. Teams at mature deployment don’t just automate repetitive tasks; they build AI into critical workflows, give it real responsibility, and iterate continuously. Beyond automating the bulk of their manual work, they’re using AI to proactively engage customers and perform tasks on their behalf.

The results follow. 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. That quality shift is how support evolves from a cost center to a value driver. Great experiences don’t just prevent churn; they create advocacy and become a reason customers choose you. The more you trust your AI Agent with meaningful work, the more it creates the conditions for higher-quality, more consistent support.

One example I point to often: Lightspeed. They operate a complex product across regions and languages, with tens of thousands of monthly requests. When they adopted Fin in early 2023, they needed a solution that could scale with that complexity—and they treated the transition like a first-class change program.

They leveraged foundational training and built custom, in-house modules aligned to their processes. They supported their team post-launch and worked closely with leadership to align on the goals and benefits of AI. In a large, distributed org, that executive alignment created ownership and momentum. Their VP of Information Systems, Yamine Gluchow, put it perfectly: "It’s not magic. If you invest in understanding, adoption, and great content, AI performance takes off."

Mature AI Agent rollouts deliver bigger gains in customer service—outperforming initial deployments in automation, proactive engagement, and task completion (63% vs 52%, 51% vs 41%, 45% vs 28%)—showing how depth drives measurable impact.

Their outcomes reflect that depth: An 88% involvement rate. 72% of Fin conversations resolved without human intervention. 43,000+ customer requests resolved monthly. Service in 12+ languages across 100+ countries. Stable CSAT—with improvement in some markets.

What impressed me most was the complexity Fin now resolves. A merchant in France asked about tax invoices—normally a long phone call to check back-end data and explain rules step by step. Instead, Fin handled the conversation in French, provided an accurate end-to-end explanation, and earned positive CSAT. That’s what mature deployment looks like: a system that absorbs complexity and delivers correct, efficient results at scale.

So how do we build toward that level of maturity? In my experience, this journey requires a mindset shift and operational rigor—not just a bigger AI budget.

Rethink how you approach support. If you were building from scratch today, you’d design around AI from day one. 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." Treat AI as infrastructure, not a feature. That shift impacts your org design, workflows, and what “good” looks like.

Leaders are racing ahead with real AI in support. Explore the 2026 Customer Service Transformation Report to see where deployment is stalling, benchmark your team, and get practical steps to scale automation that delights.

Secure executive sponsorship early. You won’t scale without C-suite backing. AI reshapes how support works, how teams are structured, how performance is measured, and how cost and value flow. Align your CFO on ROI, your CCO on journey design, and your CEO on customer experience as a strategic advantage. Early wins are great—but the compounding gains only come when leadership backs AI as infrastructure, not a one-off cost save.

Assign clear ownership for AI performance. One common failure mode: no one owns the AI. Stand up an AI operations lead or support ops specialist to review resolution trends and handoffs, tune content and configuration, coordinate on systemic issues, and drive a prioritized improvement roadmap. Without this role, feedback loops break and performance plateaus.

Treat content as critical infrastructure. Your AI Agent is only as good as the knowledge it can access. Ensure coverage for the topics it must handle, keep information accurate and current, and structure content so it’s easy for AI to consume. Make maintenance part of BAU, not a quarterly fire drill. A clean, governed, retrieval-first pipeline dramatically increases autonomous resolution.

Build a continuous improvement system. AI performance isn’t static. Train your AI Agent by expanding its knowledge, refining behavior, and connecting new data sources to handle more scenarios autonomously. Validate changes against real scenarios before they ship. Roll out updates in a controlled way across channels and segments. Use performance data to find patterns—frequent handoffs, low-resolution topics—and decide what to improve next. I often point to the Fin Flywheel (Train → Test → Deploy → Analyze) as a practical example of turning performance data into action.

The big takeaway from the "2026 Customer Service Transformation Report" is encouraging: investment is widespread, and early returns are real. The bigger opportunity is to turn those early wins into durable transformation. Teams leaning into AI as infrastructure—supported by executive alignment, clear ownership, strong content, and a continuous improvement loop—are already separating from the pack.

Next up in this series, I’ll dig into how leading teams measure success. Beyond simple cost savings, mature deployments tie AI to clear ROI and strategic impact—shifting more work into value-adding, revenue-generating territory. Follow along here, or subscribe on LinkedIn to get the next installment in your feed.

Inspired by this post on The Intercom Blog.

February 5, 2026
The AI Deployment Gap Is Widening—Accelerate to Mature ROI and World-Class CX in 2026

I’ve watched AI adoption accelerate dramatically over the last year, and the momentum is undeniable. Teams everywhere are experimenting, piloting, and operationalizing AI—but the ways they’re doing it, and the outcomes they’re seeing, vary widely.

Our latest research shows that 82% of senior leaders invested in AI for customer service in 2025, and 87% plan to in 2026. That’s the new baseline. The differentiator now is depth—how far AI is embedded into core workflows, accountability, and measurement.

Teams with mature AI are almost twice as likely to achieve higher, more consistent support quality. Our survey shows 43% of advanced adopters citing this benefit compared with 24% of early deployments.

But while most teams are using AI, our 2026 “Customer Service Transformation Report” shows that this usage is not equal. A gap is opening up between teams that have deployed AI at a surface level and those that have integrated it deeply. I see this firsthand: shallow deployments answer FAQs; deep deployments redesign processes, policies, and teams.

Survey results highlight the AI deployment gap: nearly nine in ten organizations with mature AI see improved customer service metrics (87%), compared with 62% across all respondents, visualized with bold circles.

For this year’s report, we surveyed over 2,400 global customer service professionals across a range of industries to see how they’re using AI today, where it’s paying off, and what they’re betting on as they plan for 2026. The findings mirror my experience leading AI Strategy and AI workflows at scale.

As AI programs advance, measurement confidence surges. This chart shows how ROI tracking rises from 35% in exploring to 70% in mature deployments—evidence of a widening execution gap in customer service.

We found that for many teams, AI is still doing narrow work like answering simple questions or handling small parts of workflows. These teams are seeing benefits, but only a fraction of what’s possible. Meanwhile, a smaller group is pulling away. They’ve put AI at the core of their service operation, integrating it into critical workflows, giving it more responsibility, and continuously improving it over time. That’s the hallmark of mature deployment.

Customer service priorities are shifting fast. By 2026, improving CX tops the list at 58%, cost and efficiency climb, and quality moves to third as teams prepare to scale operations and evolve skills.

The difference in results and overall support experience – for both teams and customers – is significant. Here’s how I interpret the data and what I recommend to close the gap.

Survey insights from the 2026 customer service transformation report reveal how AI reshapes support roles: 45% of teams updated job descriptions and 40% ramped up AI training, while human agents focus more on complex escalations.

AI adoption is the norm, depth makes the difference. According to senior leaders, 82% of organizations invested in AI in 2025, with 87% planning to invest in the year ahead. Despite this widespread investment, only 10% of teams report having reached a mature level of deployment, where AI is fully integrated into operations and working at scale. In my playbook, maturity means end-to-end ownership of well-defined workflows, robust guardrails, and clear success criteria.

Early AI wins are fueling expansion beyond support. Survey results show 57% cite proven success, 49% aim for a unified customer experience, 33% need to scale without adding headcount, and 31% see demand from other teams.

Reaching this level of maturity is where AI’s real value lies. We found that 43% of teams with mature deployment report higher quality and consistency across support – nearly double the rate of those still in the exploration or initial deployment stages. That aligns with what I see when we move from point solutions to platform thinking and agentic AI patterns.

Leaders are racing ahead with real AI in support. Explore the 2026 Customer Service Transformation Report to see where deployment is stalling, benchmark your team, and get practical steps to scale automation that delights.

ROI becomes clearer with deeper integration. The economic benefits of AI tend to show up first in speed and throughput, and they show up fast. Across all respondents, 62% say their customer service metrics have improved since implementing AI. Most often, teams report their initial gains in efficiency and scale—faster responses, shorter handling times, and the ability to resolve more conversations with the same team—all driving lower cost per interaction.

But the deeper teams go with deployment, the more the results start to show in the metrics. We found that among teams that describe their AI deployment as mature, the cohort of respondents reporting improved metrics as a result of AI rises from 62% to 87%. What’s more, teams with more mature deployments are significantly more likely to say they can measure the return on their AI investment. My advice: instrument everything upfront, baseline rigorously, and use eval-driven development to iterate with confidence.

The bar has moved from ‘does it work?’ to ‘is it actually good?’ More than ever, teams are focused on improving customer experience and satisfaction, with 58% saying it’s the top priority for 2026. That number has more than doubled since last year, when just over a quarter (28%) of respondents cited it as a top priority. As AI assumes repetitive work, your people can shift from reactive triage to proactive journey design. Now is the time to invest in quality frameworks, prompt engineering standards, and LLMs for product managers to close the loop between product, ops, and CX.

Important support work now extends beyond the inbox. AI is reorganizing core customer service operations as it starts to take on a higher volume of work and more complex tasks. Even at the initial deployment stage, 16% of teams report spending less time handling support volume since implementing AI – and among teams who’ve reached maturity, that figure rises to 28%. I’ve seen new roles emerge—AI operations managers, conversation designers, and model evaluators—alongside upskilling for agents into higher-order troubleshooting and relationship building.

Support is creating the blueprint for AI deployment across the business. Support was the proving ground for AI, and our research suggests that businesses are now planning to expand its use to other areas based on the results it’s yielded so far. Fifty-two percent of respondents said that their organizations are actively planning to scale AI to departments like customer success, marketing, and sales in 2026. The two most cited driving forces behind this decision are the success support has seen with AI to date and a desire to create a unified customer experience. Treat your support stack as a reusable platform: shared services, governance, and reusable components accelerate adoption in adjacent functions.

Seize the opportunity to close the gap. Having or not having AI isn’t a question anymore. What you should be asking now is how close you are to mature deployment, where AI is capable of tackling nuanced, high-stakes work. Those who have reached this stage show that going deep is what unlocks real value. That’s the opportunity. Push AI to do more, bring it to more channels, use it to resolve the most complex queries, and close the gap before it becomes too wide to close.

This might seem daunting. But trying new things always is. What we’re experiencing now is a defining moment for customer service, and the teams that are leaning in are actively building the future. As this report shows, what works in customer service now will become the blueprint for how organizations transform the full customer journey with AI. If you want the benchmarks and the playbook to accelerate from pilots to production-grade outcomes, I recommend reviewing the full “2026 Customer Service Transformation Report.”

Inspired by this post on The Intercom Blog.

January 28, 2026
Stop Drowning in Dashboards: Real-Time Digital Analytics for Finserv Contact Centers

I’ve sat in enough finserv contact center reviews to know the pattern: wall-to-wall dashboards, weekly exports, and colorful charts that still leave teams asking, “So what should we do next?” The truth is, more dashboards rarely create better decisions. What we need is digital analytics that translates signals into action—fast, precise, and privacy-safe.

When I say digital analytics, I mean a unified analytics platform that captures real-time behavioral data across voice, chat, IVR, email, and in-app journeys, then operationalizes it for agents, supervisors, and automated workflows. See how real-time behavioral analytics helps finserv contact centers lower costs, improve resolution speed, and deliver better member experiences.

Dashboards tend to be lagging, siloed, and optimized for reporting, not resolving. They spotlight vanity metrics, bury journey-level friction, and rarely surface the “next best action” that actually moves a member request toward resolution. By the time a trend shows up in a weekly readout, the expensive part—handle time, repeat contacts, churn risk—has already accumulated.

Real-time digital analytics flips that script. Instead of passively describing performance, it continuously detects intent, risk, and friction as interactions unfold—then powers targeted responses. For example, it can route high-risk transactions to specialized agents, prompt dynamic guidance during an escalated call, or trigger a proactive message that deflects a repeat contact. In practice, that means fewer transfers, faster resolution speed, and measurable reductions in operating costs.

For finserv specifically, the payoff is immediate. Agent Analytics surfaces coaching opportunities (e.g., where scripts stall or compliance steps get missed). Retention analysis identifies members at churn risk after a negative experience. Journey analytics exposes where authentication fails or balance inquiries overwhelm queues, so you can intelligently deflect to self-service. And when a potential fraud signal appears mid-session, real-time insights can prioritize routing and alerting without sacrificing compliance.

Implementation should be iterative and outcomes-driven. Start by instrumenting the top five journeys that drive the most cost or dissatisfaction (lost card, fraud dispute, loan status, password reset, payment issue). Tie each to clear outcomes vs output OKRs—think first-contact resolution, repeat-contact reduction, containment rate, and average time-to-resolution—so every analytic signal earns its keep. Then activate insights inside the workflow: agent assist prompts, smart routing, and targeted follow-ups that close the loop.

Governance matters just as much as speed. In a regulated environment, privacy-by-design and data governance are non-negotiable. Build data access controls, audit trails, and consent management into your operating model from day one. Align analytics with regulatory compliance requirements to ensure that what you measure and automate is defensible, explainable, and safe for members and the business.

To accelerate learning, pair digital analytics with controlled experiments. Use A/B testing on IVR flows, authentication steps, and post-call follow-ups to quantify what truly reduces transfers and repeat contacts. Define a minimum detectable effect (MDE) upfront so tests are fast and conclusive. Run continuous discovery with cross-functional product trios (operations, data, compliance) to turn insights into shippable improvements every sprint.

On the stack side, focus on connecting systems you already trust. CRM integration ensures that context follows the member, while tools like Amplitude analytics, Pendo, or Intercom can instrument key digital touchpoints. Whether you choose build vs buy, the principle is the same: consolidate signals into a unified analytics platform, then push decisions and guidance back into the tools agents and members already use.

The cultural shift is from reporting to decisioning. Instead of celebrating more charts, celebrate faster resolutions and fewer escalations. Replace static executive reports with alerting and action playbooks. Make it trivial for supervisors to see what changed, why it mattered, and which play to run next. That’s how you convert data into durable operating advantage.

The mandate is clear: stop drowning in dashboards. Move to digital analytics that captures behavior in real time, respects compliance, and powers operational decisions where they matter most—in the member journey. When you do, cost curves flatten, resolution speed climbs, and member trust compounds.

Inspired by this post on Amplitude – Perspectives.

January 23, 2026
Turn Every Support Ticket into Product Truth: My Playbook for Data-Driven CX Wins

Support tickets are the rawest signal of product truth. Leading product teams at HighLevel, I’ve learned that the fastest way to build what customers value is to transform frontline conversations into a repeatable, data-driven system for discovery, prioritization, and execution.

What if your support and product teams could unlock CX insights to turn every ticket into strategic product intelligence? Explore how.

Here’s the operating system I rely on. First, I connect our support stack (think Intercom and our CRM integration) into a unified analytics platform so every conversation, tag, and resolution is queryable. I don’t just count tickets—I segment them by product area, customer segment, lifecycle stage, and revenue impact to reveal patterns that roadmaps can act on.

Next, we standardize a shared taxonomy. Agents apply concise, high-signal labels (problem type, severity, intent), and we augment that with AI-driven auto-tagging to reduce noise and improve recall. The result is trustworthy “voice of the customer” data that product managers and support leaders can both stand behind.

Prioritization then becomes rigorous and fair. I weight themes by severity, frequency, ARR exposure, and time-to-value, and tie them directly to outcomes vs output OKRs. Amplitude analytics helps me quantify impact—what’s breaking activation, what’s dragging conversion, what drives retention analysis—so the backlog reflects business outcomes, not opinions.

Discovery is continuous by design. Product trios (PM, design, engineering) run weekly reviews of the highest-signal themes, recruit users straight from recent tickets, and prototype solutions quickly. We validate ideas with A/B testing when appropriate and ship targeted in-app guides to reduce confusion before it becomes a ticket.

Crucially, we close the loop. When we release a fix or improvement, we notify affected customers and the agents who flagged the issue. We track downstream effects—ticket deflection, CSAT, feature adoption, and time-to-resolution—so everyone sees how customer support ai strategy accelerates product-led growth.

This approach also builds culture. Empowered product teams treat support as a strategic partner, not a cost center. Agents become co-creators of the roadmap, and PMs gain a steady stream of product discovery opportunities grounded in real user outcomes.

If you’re getting started, a simple 30-60-90 can help: in 30 days, unify the data and agree on taxonomy; in 60, instrument dashboards and adopt a weekly insights ritual; in 90, align priorities to OKRs, launch targeted fixes, and measure business impact. That’s how tickets turn into product truth—and how CX insights drive compounding wins.

Inspired by this post on Amplitude – Perspectives.

January 8, 2026