Tag: customer support ai strategy

  • Inside the AI Customer Service Shift: What 166 Leaders Told Me About Teams, Roles, and ROI

    Inside the AI Customer Service Shift: What 166 Leaders Told Me About Teams, Roles, and ROI

    I wanted to cut through the hype and see what’s actually changing inside customer service teams as AI agents like Fin move from pilots to production. So I analyzed 166 interviews with support leaders, managers, and frontline specialists to understand how roles, workflows, and team structures evolve once AI becomes part of everyday work.

    The anecdotes were already loud: AI tools are transforming customer support. But the scale, shape, and consistency of that transformation? Less clear. I went to the source—the practitioners living it—to quantify what’s real and what’s next for customer support AI strategy.

    Here’s what I gleaned from the data.

    TL;DR — What’s changing

    AI is reorganizing core CS operations: Nearly every team (≈95%) reported meaningful workflow changes. Triage, routing, translation, and categorization are increasingly automated. Hybrid human+AI systems are taking their place.

    Frontline work is changing to AI oversight: Humans now QA, monitor, and test AI outputs. When it comes to handling queries, they step in for nuance, rather than repetition.

    Structural change is widespread but uneven across companies: 83% reported new responsibilities or roles. Some built AI pods, while others retained traditional setups.

    Tier 1 headcount demand is falling: 28% saw hiring freezes, slowdowns, or natural attrition at Tier 1 level as AI Agents manage more requests and improve operational efficiency.

    Skill gaps are widening inside teams: Data literacy, QA, and cross-functional communication are all rising in value. For many companies, long-term role strategy is lagging behind.

    Research methodology

    The goal of this research is to understand how many customer service teams have changed their roles, responsibilities and ways of working due to adopting AI agents, as well as understanding how these changes manifest within their organizations.

    For this study, the data chosen consists of interviews conducted by the research team, either with Intercom customers or prospects. This data was chosen because the focus of the interviews revolved around the individual experience of the participant, which gives a higher chance of information related to role changes to be present.

    The data was collected using Snowflake by pulling all interviews stored in gong conducted by a member of the research team from 01-01-2025 to 14-10-2025.

    After the data was pulled, a python script was used to clean the conversation corpus for each conversation retrieved. Common English stopwords (e.g. “and”, “very”, “with”, etc.) were removed, as well as all the text associated with a speaker in the conversation that was not the interview participant(s). This was done to reduce the computational power required for the conversation coding, avoid API timeouts and reduce costs.

    After the corpus was cleaned, the OpenAI API was employed, alongside a prompt, to code each conversation using closed codes defined in a closed codebook.

    The codes used were:

    No role change mentioned: No explicit changes to roles, teams, or reporting lines are attributed to AI/Fin.

    Role responsibilities changed due to AI/Fin: Duties/ownership moved between humans and AI/Fin, or scope of a role changed because AI/Fin handles tasks.

    Team structure/reporting changed due to AI/Fin: Org/team boundaries, team charters, or reporting lines changed due to adopting AI/Fin.

    Headcount/hiring impacted due to AI/Fin: Hiring plans, headcount, staffing coverage, or shifts/rotations changed due to AI/Fin.

    Workflow/process changed due to AI/Fin: Steps, triage/escalations, routing, or playbooks changed because AI/Fin alters the process.

    Other organizational changes due to AI/Fin: Other changes inside the organization due to AI/Fin that don’t involve a change in responsibilities, team structure/reporting lines, headcount or workflow/processes changes.

    Data analysis

    166 conversations were retrieved. More than 90% of all conversations report some sort of change either in their role, team, or processes due to implementing Fin, or a similar AI product, with only 13 participants reporting no changes.

    Across these conversations, each one could have multiple types of change associated with it (M = 2.35, Med = 2, Min = 1, Max = 4, N = 166).

    More specifically, after implementing Fin or a similar AI product:

    94.58% participants reported having their processes and workflows disrupted

    82.53% participants reported seeing their role and responsibilities change

    27.71% participants reported changes in company headcount or hiring

    6.02% participants reported their team structure or reporting lines changing as a result

    Additionally, 16.27% participants reported a change for a different reason from the ones highlighted above (“Other organizational changes due to AI/Fin”).

    Sample representativeness

    The sample is representative with a confidence level of 90% and a margin of error of ±6.4% (accounting for an overall unknown population size). The individual confidence intervals for each type of change are as follows.

    Workflow/process changed due to AI/Fin: 157 (94.6%), 90% CI: 91.7% – 97.5%

    Role responsibilities changed due to AI/Fin: 137 (82.5%), 90% CI: 77.7% – 87.4%

    Headcount/hiring impacted due to AI/Fin: 46 (27.7%), 90% CI: 22.0% – 33.4%

    Other organizational changes due to AI/Fin: 27 (16.3%), 90% CI: 11.6% – 21.0%

    No role change mentioned: 13 (7.8%), 90% CI: 4.4% – 11.3%

    Team structure/reporting changed due to AI/Fin: 10 (6.0%), 90% CI: 3.0% – 9.1%

    Thematic analysis

    1) Automation and AI integration replacing manual steps (94.58%). I see AI workflows embedding into every stage of support. Manual triage, routing, translations, and repetitive responses shift to Fin or similar systems, while agents focus on human-in-the-loop oversight.

    Agents’ day-to-day work now revolves around monitoring or fine-tuning AI outputs, not replying to the same questions. In many teams, conversations enter Fin first; humans only step in when nuance or exception handling is required. Testing, QA, and rollout practices have matured too—teams track Fin’s accuracy and iterate intentionally.

    2) Humans shift to oversight, AI handles execution (82.53%). The role resets are unmistakable. Support agents and managers move from high-volume execution to optimization, configuration, and measurement. New roles emerge—AI specialists, automation managers, Fin owners—while responsibilities migrate toward strategic analysis and quality assurance.

    Duties are redistributed: Fin takes on refunds, triage, simple messaging, even parts of the sales process. I’ve watched some careers pivot toward product/ops or AI systems strategy as managers coordinate testing and monitor adoption metrics.

    3) Reductions or slower growth due to efficiency gains (27.71%). Efficiency is real. Many teams reduce Tier 1 headcount needs or slow hiring because AI absorbs simpler requests. Others reallocate people to complex work or AI management. A few still expand—adding automation engineers, implementation specialists, or technical AI leads—but not at past growth rates.

    The upshot: organizations handle more volume while stabilizing or reducing staffing, especially at the frontline tier.

    4) New AI teams, flatter orgs, fewer escalation layers (6.02%). I’m seeing organizational design catch up to the tech. Some companies form dedicated LLM or automation teams. Others flatten hierarchies, design around workflow complexity instead of region, or merge roles. Dedicated escalation layers shrink as Fin routes or resolves more autonomously.

    Team design is getting more modular and data-driven, with clearer ownership for configuration, governance, and Agent Analytics.

    5) Broader digital transformation and operational modernization (16.27%). Beyond support, companies are modernizing their operating model: automation-first, digital self-service, better data foundations, and new vendor ecosystems. Collaboration patterns between data, ops, CX, and product/engineering are tightening, with a culture of experimentation and continuous improvement taking hold.

    How have customer service roles and responsibilities changed due to Fin/AI agent implementation?

    Implementing Fin or a similar AI agent profoundly changes how an organization operates, with around 95% of participants reporting some level of change in their processes after implementation. These systems have significantly reshaped the workflows that customer service teams are used to. Tasks once performed manually, such as ticket triage, routing, repetitive responses, and translations are now handled by AI agents.

    “This marks a clear transformation in how customer service agents work: moving away from directly resolving customer queries to focusing on more analytical and procedural work”

    As a result, customer service agents’ responsibilities have shifted from performing manual tasks to monitoring and fine-tuning the AI agent whenever its output is inaccurate or incomplete. This marks a clear transformation in how customer service agents work: moving away from directly resolving customer queries to focusing on more analytical and procedural work, such as testing, QA, and performance analysis of AI outputs.

    Human agents who still handle conversations tend to do so either because the AI agent cannot yet respond adequately, or because of an organizational choice to retain human involvement for sensitive or high-value interactions. Nevertheless, the need for such roles is diminishing. Around 28% of participants reported a reduction in Tier 1 staff or a hiring slowdown or a full hiring freeze, as AI agents increasingly manage simple requests and organizational attention shifts towards improving automation efficiency.

    “In some cases, this has led to the creation of specialized AI teams, reorganizations around workflow complexity, or the merging and redefinition of existing roles”

    However, this transformation is not uniform across companies. While some roles have disappeared (particularly escalation layers), others have emerged. Many organizations are reallocating existing staff to AI management or hiring new technical profiles such as automation engineers, implementation specialists, and AI leads. In some cases, this has led to the creation of specialized AI teams, reorganizations around workflow complexity, or the merging and redefinition of existing roles.

    Around 83% of participants reported changes to their roles or responsibilities following the introduction of Fin or similar AI agents. Specifically, customer service agents who no longer handle basic queries now focus on managing AI performance, reviewing Fin tasks and improving automation outputs. Managers oversee AI evaluation and implementation, coordinate testing, and monitor AI metrics such as resolution and involvement rates. In some organizations, new dedicated roles have emerged—AI specialists, automation managers, or Fin owners—reflecting a strategic shift toward automation-first, digital self-service models.

    These structural shifts are also cultural. I’m seeing teams embrace experimentation, versioning, and eval-driven development while deepening collaboration with data, operations, and product/engineering. The move from outcomes vs output OKRs is palpable: leaders are measuring containment, deflection, CSAT, and time-to-resolution with new rigor.

    Overall, a widespread transformation is underway. Roles are broadening, responsibilities are diversifying, and cross-functional collaboration is becoming the norm. Given the pace of gen ai improvement and the rise of agentic AI patterns, I expect these shifts to intensify.

    This evolution raises two important questions

    Firstly, do customer service agents possess the skills required to succeed in these new roles? While they are experts in customer interaction and company policy, their work now demands new competencies in data analysis (e.g. reporting AI agent performance and how it changes over time), quality assurance/debugging (e.g. Fin output testing and versioning), and cross-functional communication (e.g. if help from another team is required, drafting a business case to justify the resources required could be needed).

    Secondly, what long-term strategies are companies adopting to support these evolving roles? Some are reorganizing entirely around automation, while others retain traditional structures. For those undergoing transformation, it remains unclear whether these changes are part of a deliberate strategic plan aimed at achieving specific performance outcomes, or the result of experimentation without defined goals.

    Ultimately, Fin’s success— and of AI in customer service more broadly— depends not only on the technology itself but on the people and strategies that shape its use. In my experience, the winners invest early in data literacy, robust QA, clear ownership, and governance; they align product, ops, and CX around a shared AI roadmap; and they measure what matters with disciplined Agent Analytics. That’s how you turn AI workflows into durable customer and business outcomes.

    Inspired by this post on The Intercom Blog.

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  • Implementing Agentforce the Right Way: A Practical Playbook with Pendo and Salesforce

    Implementing Agentforce the Right Way: A Practical Playbook with Pendo and Salesforce

    I think about Agentforce implementation the same way I think about any high-stakes product launch: start with outcomes, instrument relentlessly, and iterate in tight loops. When agentic AI touches core workflows in Salesforce, the winners are the teams that combine rigorous product strategy with thoughtful CRM integration and product-led growth tactics.

    Learn the ways in which Pendo helps companies design and iterate on their agentic strategy for Salesforce.

    My working playbook begins with clarity. Before a single agent is deployed, I align with stakeholders on the highest-value “jobs” inside Salesforce—reducing case handle time in Service Cloud, accelerating lead qualification in Sales Cloud, or improving data hygiene for revenue operations. That alignment shapes our agentic AI approach and prevents us from shipping clever agents that don’t move the metric that matters.

    From there, I treat telemetry as a first-class requirement. I instrument the end-to-end journey with Pendo so we can observe when an agent triggers, when it falls back, when it hands off to a human, and how those moments affect conversion, CSAT, and cycle time. I refer to this observability layer as Agent Analytics, and it’s the backbone of evidence-based iteration.

    Guidance is equally critical. I use Pendo’s in-app guides to onboard admins and frontline users directly inside Salesforce, deliver contextual tooltips that explain what the agent will do next, and collect feedback within the flow of work. That combination shortens time-to-value and builds trust, which is essential for customer support ai strategy and change management.

    Iteration is where the compounding returns show up. I run A/B testing on prompts, decision policies, and handoff rules; evaluate performance on real user cohorts; and promote what works. This is classic product-led growth applied to agentic AI—ship small, measure precisely, and scale winners. Prompt engineering is not a one-time task; it’s a continuous discovery loop.

    I also weave in governance from day one. Privacy-by-design, data governance, and AI risk management aren’t add-ons—they are design constraints that shape what the agent is allowed to see and do. The guardrails live alongside the experience: clear disclosures, reversible actions, and easy ways for users to override or escalate.

    Finally, I operationalize the learning loop. Weekly reviews with a product trio (PM, design, engineering) examine Pendo dashboards, qualitative feedback, and Salesforce outcomes. If an agent is underperforming, we adjust prompts, refine retrieval, or simplify the decision tree. If it’s exceeding targets, we expand the use case and systematize the pattern.

    When teams ask me for the “right way” to implement Agentforce, my answer is simple: treat your agent like a product. Measure with Pendo, guide inside Salesforce, and iterate until the business outcome moves. That’s how we turn promising agents into durable advantages.

    Inspired by this post on Pendo – Perspectives.

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  • Beyond the Support Iceberg: Gradient Labs’ Multi‑Agent Breakthrough That Actually Gets Work Done

    Beyond the Support Iceberg: Gradient Labs’ Multi‑Agent Breakthrough That Actually Gets Work Done

    When a customer reports a stolen credit card, the frontline play seems straightforward—freeze it. But that’s just the visible tip of a much larger customer support iceberg. Underneath sits the real work: dispute filings, fraud investigations, merchant communications, proactive outreach, and follow-ups that unfold over days across multiple systems. Most AI support tools only touch the surface; they don’t coordinate or close the loop. That gap is exactly where my product instincts kick in—and why this story matters.

    I recently listened to a conversation with Jack Taylor (Product Engineer) and Ibrahim Faruqi (AI Engineer) from Gradient Labs, an AI-native startup building agents that automate the full scope of customer support in fintech. Their approach resonated with the challenges I see every day in customer support automation: fragmented workflows, regulatory complexity, and the need for human-in-the-loop moments. Gradient Labs has architected a platform with three coordinating agents—"inbound, back office, and outbound"—all built on a shared foundation of "natural language procedures, modular skills, and configurable guardrails."

    What impressed me most was how they "Let non-technical subject matter experts define agent behavior through natural language procedures—no coding required." That’s a powerful way to remove engineering bottlenecks, accelerate iteration, and keep the domain experts—those closest to fraud, disputes, and compliance—directly in control. In my experience, this design choice alone can compress lead times from weeks to hours and aligns perfectly with continuous discovery and eval-driven development.

    At the heart of their platform is orchestration. They "Architected a state machine orchestrator that manages turns, triggers, and skill selection across long-running conversations." That "turn" architecture is built for the messy reality of async, multi-day support. They treat "Skills as modular agent capabilities—and how they're scoped deterministically per turn," ensuring the system stays predictable and auditable. They also confront a nuanced challenge most teams dodge: "Defining "done" for outbound agents when the customer isn't the one ending the conversation." That’s where deterministic criteria, timers, and clearly scoped outcomes matter as much as the model beneath.

    Compliance is not an afterthought—it’s baked into the core. Gradient Labs "Built guardrails as binary classifiers with eval pipelines, tuning for high recall on critical regulatory checks." In regulated domains, optimizing for recall on high-stakes checks is the right call; you can tolerate a few extra reviews, but you can’t miss a potential fraud signal. More broadly, they frame "Guardrails as classification problems: balancing recall and precision for regulatory compliance." That mindset is exactly how I like to merge AI risk management with product velocity.

    Crucially, they avoid the trap of fully autonomous optimism. "Ask a Human: a tool call that brings humans into the loop for approvals or missing APIs" gives the system a safety valve for novel or high-risk cases. I also appreciated the explicit "Ask A Human Tool" pattern, which cleanly integrates approvals, policy exceptions, or data gaps without derailing the workflow.

    Quality doesn’t happen by accident. They "Designed an auto-eval system that samples conversations for human review to catch edge cases and build labeled datasets" and built "Auto-eval pipelines that flag conversations for manual review and feed labeled datasets." That closed-loop evaluation flow is the backbone of sustainable performance in agentic AI. Combine this with targeted instrumentation—think CSAT, first contact resolution, deflection rate, time to resolution, and escalation rate—and you get a real Agent Analytics discipline, not just logs and dashboards.

    The "iceberg" metaphor is more than a catchy visual. It’s a blueprint for scoping multi-agent platforms that work across the entire customer journey. With "inbound, back office, and outbound" agents coordinating on complex tasks like fraud disputes, the system can transition cleanly from intake to investigation to resolution—without dropping context or asking customers to repeat themselves. This is what genuine customer support automation looks like when it’s grounded in real operations.

    Under the hood, the team leans into robust design choices that matter at scale: the "Complexities of Natural Language Input" are managed with explicit state and skill scoping, "Deterministic Skill Execution" reduces flakiness, and "Customer-Specific Guardrails" ensure compliance remains aligned to each client’s policies. Add their focus on "APIs and Customer Tools Integration" and the result is a platform that can actually take action—not just answer questions.

    If you’re building in this space, here’s how I’d apply these lessons. Start by mapping the iceberg: enumerate back-office steps, approvals, and SLAs that follow the initial customer touchpoint. Capture those steps as "natural language procedures" owned by SMEs. Implement a "state machine orchestrator" to manage "turns, triggers, and skill selection" across multi-day workflows. Treat "guardrails as classification problems" and tune for high recall on high-stakes checks. Introduce "Ask a Human" early to handle missing APIs or policy exceptions. Finally, operationalize learning with "auto-eval pipelines" and tight, eval-driven development loops. That’s how multi-agent platforms deliver measurable outcomes in fintech support.

    If you want to hear the full conversation, you can listen on Spotify or Apple Podcasts. You’ll also hear a nod to the "Incident.io episode – Referenced in the conversation," and a thoughtful take on the "Future of Multi-Agent Systems."

    In short: this is a shift from simple Q&A bots to agents that can coordinate, comply, and complete. It’s the kind of multi-agent platform work that moves the needle for customer support in fintech—and a compelling template for any product leader scaling agentic AI and AI workflows beyond the tip of the iceberg.

    Inspired by this post on Product Talk.

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  • 2026 Support Capacity Playbook: Bold AI Automation, Smarter Staffing, Zero‑Surprise SLAs

    Capacity planning has always been a high-stakes exercise in customer service, and when you miss, the signal shows up fast in backlogs and SLAs. I’ve lived that pressure across multiple cycles, and 2026 will reward teams that plan differently. AI fundamentally changes capacity planning because it changes the work. It resolves the bulk of your volume, speeds up execution, and elevates the complexity and value of what humans handle. The consequence is simple: planning models must evolve. This is the final installment in my 2026 customer service planning series, and I’m focusing on the tension every leader feels right now—be ambitious about automation, but avoid the trap of understaffing if your assumptions don’t hold. My goal is to share how AI changes the logic of capacity planning, what I’ve learned implementing these practices with my team and with customers, and the common traps to avoid. Traditional planning rests on relatively stable assumptions: volume grows predictably, work types stay consistent, handle times don’t swing dramatically, and productivity improves slowly with better tools and training. In an AI-first model, none of that is guaranteed, and the fundamentals flip. The mix of work changes as AI absorbs a growing share of simpler conversations, leaving humans with deeper, more time-consuming issues that demand human-to-human connection. Demand can actually increase when you remove friction, so AI can both resolve more and attract more volume. Human time splits differently as teammates solve customer problems and also review AI behavior, give feedback, improve content, and support system-level work. Performance becomes dynamic, not fixed—automation rate isn’t a one-time number; it can rise with care and fall with neglect. If you plan for 2026 using a pre-AI model—assuming similar productivity, similar work mix, and a linear relationship between volume and headcount—you will underestimate what it now takes to run a high-performing support organization. There are many metrics you can track, but the one to put at the center is automation rate (AI Agent involvement rate × AI Agent resolution rate). This single construct tells me what share of total volume AI actually resolves, how much work remains for humans, how much additional demand humans can absorb, and how ambitious I can be with headcount. Early in the journey, I prioritize raising involvement—getting the AI involved in more conversations. Once involvement is high, I shift to resolution on the hardest remaining work, where each additional 1% of automation can represent several people’s worth of capacity. In my 2026 plans, automation rate sits alongside projected inbound volume, average “output” per person for the more complex work that remains, and occupancy—how much time is allocated to customer-facing interactions versus operational and strategic work. Together, those inputs give a realistic picture of how many people you need and where they should spend their time. First, plan boldly on automation, but match it with investment. I do not cap automation assumptions at 40–50% “because AI is new.” Many teams are already modeling 60%, 70%, even 80%+ for 2026—when they invest in AI ownership and content. The investment is non-negotiable: named ownership for AI performance (AI ops, knowledge management, conversation design), clear automation targets by work type (e.g., informational vs. personalized vs. actions vs. deep troubleshooting), realistic expectations for what’s easy to automate and what’s not, and a concrete plan to raise automation over time in monthly or quarterly steps rather than a single jump. To decide where to invest first, I dig into the data. I start with the biggest volume drivers, separate content-led issues from those dependent on data or complex procedures, assume higher resolution potential for content-led topics once the knowledge base is in shape, and set more modest initial resolution expectations for system-dependent flows. Then I stair-step improvements as the systems, data contracts, and workflows mature. In short, bold automation goals only work when paired with the team structure, content, and systems required to reach them—and the discipline to iterate. Second, expect human “output” per person to go down. That’s a mindset shift. Historically, we assumed individual productivity would stay flat or tick up as tools improved. In an AI-first model, humans handle fewer conversations but more complex, cross-functional issues—and create more value despite lower case counts. I model a lower “cases closed per person” than prior-year baselines, explicitly assume the remaining work is more complex and time-consuming, and redefine productivity to include system-level work like AI Agent improvements, content updates, and policy or workflow change management. I also report “capacity created” from automation alongside human outputs, so leadership sees the full picture. Third, rethink occupancy: more time off the queues, on higher-value work. Traditional occupancy splits time between inbox and training, meetings, and breaks. Now there’s an expanding “out-of-inbox” portfolio that directly affects AI performance and overall capacity: reviewing AI-handled conversations, improving AI Agent triaging and handovers, contributing to content and procedures, feeding insights to product and engineering, and supporting system changes that reduce future volume. I set lower inbox occupancy targets than before and make the rationale explicit. People aren’t working less—they’re working differently. In planning, I assume more time spent on improvement and system work, make it visible (for example, X% in inbox and Y% on AI and system improvement), and treat this as critical, not a “nice to have.” If you don’t proactively allocate it, it won’t happen—and your automation and performance targets will suffer. Fourth, work with the finance team early, and treat your plan as a set of assumptions. Capacity planning with AI is a set of bets across automation rate, human output, demand growth, occupancy, and where surplus capacity (if any) goes. I bring finance in early, show that the plan is dynamic and directly tied to AI performance, and label every lever as an assumption with ranges. I commit to a quarterly review cadence with finance to compare assumptions versus reality and adjust headcount, targets, and investment as needed. The risks are real: if automation grows slower than expected and you stop backfilling too early, you’ll be understaffed for months. Hiring and onboarding take time, so course-correcting late creates strain. If you do produce surplus capacity, have a clear strategy to reallocate those teammates to higher-value work—improving systems, feeding insights back to product, supporting new channels, and driving proactive CX—rather than defaulting to reductions. I also set explicit guardrails—if automation rate misses by five points for two consecutive months, we pause planned reductions and revisit hiring gates. If it over-performs, we shift people into backlog eradication, content upgrades, or proactive outreach, so we bank compounding value. To set your team up for success in 2026, anchor your plan on automation rate, be honest that humans will handle fewer but harder conversations, and protect time for system improvements. Partner early and often with finance, avoid shrinking too fast, and design a plan for surplus capacity so you’re never caught flat-footed. If AI is going to handle the majority of your customer conversations, your plan has to be designed to help it do that well and to keep your team set up for meaningful, sustainable work. A 2026 plan built on adaptable assumptions—not fixed predictions—will hold up as your work, your systems, and your customers’ expectations continue to change. If you’d like future editions like this, subscribe and stay close—I’ll keep sharing what’s working, what isn’t, and how to tune your customer support AI strategy in real time.

    Inspired by this post on The Intercom Blog.

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  • 6 AI Strategies to Accelerate Business Growth: Unlock Revenue, Cut Costs, Scale Faster

    6 AI Strategies to Accelerate Business Growth: Unlock Revenue, Cut Costs, Scale Faster

    I’ve spent the last few years weaving AI into core product workflows, and the pattern is clear: when we pair disciplined product thinking with pragmatic AI Strategy, growth compounds. The question I hear most isn’t if AI can help, but where to begin and how to de-risk the journey while moving fast.

    AI for business growth starts with one of these six strategies. See how companies use AI to unlock revenue, cut costs, and scale smarter and faster.

    1) Revenue acceleration with unified customer intelligence. I start by connecting behavioral analytics and CRM integration to a unified analytics platform, then layer a retrieval-first pipeline so large language models can surface high-intent accounts, churn signals, and next-best actions. With Amplitude analytics and A/B testing, we validate AI-driven playbooks for upsell, cross-sell, and win-back—turning insights into measurable lift rather than novelty.

    2) Cost reduction through targeted automation. Not all automation yields the same outcome. I look for repetitive, high-volume processes where quality is easy to verify—customer support ai strategy with AI-assisted deflection, accounts payable automation, and security workflows like threat detection and response. Combining agentic AI with clear guardrails reduces handle time, frees teams for higher-value work, and keeps error rates within acceptable thresholds.

    3) Faster time-to-market via eval-driven development. Speed without signal is noise. I lean on eval-driven development to instrument models, measure drift, and tighten CI/CD loops. We track DORA metrics like deployment frequency while using gen ai for product prototyping to compress discovery and delivery. Frameworks and tools such as Claude Code help engineers iterate safely behind feature flags so we can ship learning, not just code.

    4) Personalization that drives activation and retention. Growth sticks when onboarding is contextual. I use in-app guides, product tours, and thoughtful tooltip design powered by LLMs for product managers to tailor the first-run experience. With retention analysis and outcomes vs output OKRs, we align personalization with the moments that matter—activation, habit formation, and expansion.

    5) Trust-by-design to scale responsibly. AI risk management, privacy-by-design, and data governance are not afterthoughts; they are growth enablers. By defining policy, red-teaming prompts, and practicing context window management, we reduce rework, limit incident management, and maintain compliance across markets. Clear review gates make it easier to say yes to more AI use cases without compromising customer trust.

    6) Voice and agent experiences that feel like product, not add-ons. When prompt engineering for voice and voice AI agent patterns are integrated into the core journey—guided onboarding, smart handoffs, proactive notifications—engagement rises. Agent Analytics turns conversations into product signals we can act on in roadmapping and sprint planning, closing the loop between user intent and product improvement.

    My playbook for getting started is simple: pick one revenue and one efficiency use case, define success upfront, and ship a narrowly scoped MVP with robust analytics. Use continuous discovery with product trios to refine prompts, data sources, and experience design. Then scale what works, retire what doesn’t, and let evidence—not hype—set the roadmap.

    If you’re evaluating where to apply gen ai next, these six lanes offer fast paths to impact without sacrificing governance or customer trust. The companies I’ve seen win treat AI as a capability within the product, not a separate project—and they measure it with the same rigor they use for any critical feature.

    Inspired by this post on Product School.

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  • Make Every Answer the Last: Building a Self-Improving AI Support Engine for 2026

    Make Every Answer the Last: Building a Self-Improving AI Support Engine for 2026

    Once I’ve defined the right roles on my team, the next move is to design an operating model that makes progress a habit. My goal is simple: every interaction should strengthen the system so the AI Agent keeps improving over time.

    I anchor the team on a mantra that has never failed me: “The first time you answer a question should be the last.” That single statement reframes support as a compounding system rather than a one-off activity.

    The ambition is to ensure every resolution makes the next one faster and more accurate, so fewer issues repeat, quality compounds, and support scales naturally. That doesn’t happen by accident—it requires intentional design.

    In practice, this comes down to four essentials: clear ownership of performance, guardrails that make iteration fast and safe, feedback loops that turn learning into routine upgrades, and a culture that celebrates the work of improvement—not just the outcomes. Here’s how I put that into play.

    First, I start with clear ownership. Ambiguity is one of the most common reasons AI performance plateaus. When no one truly owns how the AI Agent performs, feedback gets lost, issues linger, and improvements stall.

    On high-performing teams, I assign a single owner—often an AI ops lead—responsible for making the AI Agent better. They review resolution trends to spot underperformance, make targeted updates to content, configuration, and behavior, coordinate with product and engineering on systemic blockers, and set improvement priorities, targets, and timelines. The title matters less than the mandate; what matters is clear authority to drive change across teams.

    Real-world example: At Dotdigital, AI performance plateaued after a strong start—resolving around 2,800 conversations per month for three consecutive months. To drive resolution rates up, the team created a dedicated support operations specialist role, filled by an experienced agent with deep product knowledge. This person will focus on refining snippets, improving content, and enhancing the AI’s resolution capabilities.

    Second, I make iteration fast and safe. As the AI Agent takes on more volume and complexity, change can start to feel risky—so teams hesitate, and performance stalls. Lightweight governance fixes that by making the path from insight to action predictable.

    I keep the rules simple and explicit: which changes need review (and which don’t), who the decision-makers are, how we test updates before they go live, where feedback flows so it’s seen and acted on, and when progress gets reviewed on a steady cadence. Governance isn’t bureaucracy—it’s what keeps improvement routine and safe.

    Real-world example: Anthropic ran a focused “Fin hackathon” sprint to improve their AI Agent’s resolution rate. The team audited unresolved queries, identified underperforming topics, and created or updated content to close gaps. They converted frequently used macros into AI-usable snippets, monitored Fin’s performance during live support, and continuously refined content based on real interactions. This structured approach enabled rapid improvement while maintaining quality standards.

    Third, I build a system that learns by default. AI performance isn’t static, but many organizations treat it like a one-time implementation. The most successful teams operationalize learning: they analyze where the AI Agent struggles and feed those insights directly into structured improvements.

    The signals are straightforward: review common handoffs to humans, track unresolved queries by topic or intent, measure resolution rate trends over time, and use those inputs to prioritize fixes and content upgrades. Whether you follow a formal loop like the Fin Flywheel framework or something lighter, the goal is the same—make improvement inevitable.

    Fourth, I treat content as competitive infrastructure. Your AI Agent is only as good as what it knows. As George Dilthey, Head of Support at Clay, put it: “That’s when we realized: AI doesn’t just come up with information out of nowhere, you have to feed it. We were spending all our time evaluating tools when we should’ve been focused on content.”

    I operationalize knowledge like infrastructure: every topic has a clear owner, content is structured, versioned, and ingestion-ready, new products ship with source-of-truth content by default, and changes ship on a schedule—not when someone finds time. This is the backbone that differentiates teams who scale confidently from those who stall out.

    In my organization, we’ve evolved our New Product Introduction (NPI) process by aligning early with R&D on a single, canonical source of truth that becomes the foundation for all downstream content—including what the AI Agent uses to resolve queries. By embedding content creation into launch readiness, not as an afterthought, we’ve consistently hit 50%+ resolution rates on new features from day one.

    Finally, I make belief visible. Even the best system will stagnate if people stop believing in it. Belief can fade quietly unless you reinforce it on purpose. I keep it strong by sharing specific wins regularly, highlighting improvements with metrics, and recognizing the people behind the gains—then giving them space to lead. This isn’t just about morale; it keeps everyone aligned on the bigger play.

    When you put it all together—clear ownership, safe iteration, a learning system by default, and content as infrastructure—AI performance compounds. As the AI Agent gets better, the entire support model becomes faster, more reliable, and truly scalable. That’s the foundation of a modern, AI-first support organization.

    Next, I’ll take this a level deeper and share how capacity planning changes when AI handles the majority of inbound volume and your team shifts into higher-value roles. If scaling with confidence is the goal, this is where the operating model pays off.

    Inspired by this post on The Intercom Blog.

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  • Spain’s Tough New Customer Service Law: What It Signals—and How AI Keeps You Compliant, Fast, and Human

    Spain’s Tough New Customer Service Law: What It Signals—and How AI Keeps You Compliant, Fast, and Human

    Support teams in Spain just got the clearest signal yet that the old way of doing things won’t cut it anymore. As I look at the details, I see more than a regulatory hurdle—I see a blueprint for the modernization many of us have been pushing toward for years.

    The signal arrives in the form of one of the most ambitious customer service regulations in Europe—a law designed to strengthen consumer protections and set clear expectations for fair, transparent, and personalized customer service. Among its measures: new protections against spam calls, stronger transparency requirements, safeguards around personalized interactions, and measurable standards for speed, accessibility, and complaint handling within customer support.

    It’s a significant shift, especially for large enterprises and essential-service providers. While the initial reaction might be anxiety about audits and penalties, the larger opportunity is hard to ignore: this law compels us to build modern, resilient support operations that scale, perform, and earn trust.

    Spain is often an early mover in consumer-protection regulation, and this shift could signal what future standards across the EU might look like. For EMEA leaders, this is a moment to reevaluate operating models, invest in automation thoughtfully, and ensure customer experience improvements directly support regulatory compliance.

    Below, I break down what the law requires, what it means in practice, and how AI Agents like Fin can help teams meet regulatory expectations while delivering faster, more personal support at scale.

    The law applies in full to providers of regulated services, including water, energy, passenger transport, postal services, pay-audiovisual media, and electronic communications, and also to any company (or group) that meets certain size and turnover thresholds, even if their core business falls outside those sectors.

    Large companies (those with more than 250 employees and over €50 million in turnover) also hold additional obligations, particularly around multilingual support in Spain’s co-official language regions.

    While the law is still moving through its final approval stages, the direction is clear: a broad set of obligations will apply to reinforce consumer rights, ensuring they can: Reach support quickly. Speak to a human when needed. Get clear information during outages or service disruptions. Have complaints handled promptly and on time.

    1. 95% of support calls must be answered within three minutes

    This raises the bar significantly for responsiveness, especially during spikes, outages, billing cycles, or seasonal surges. Most support systems are not built for this level of agility. In my experience, you can’t hire your way to this metric sustainably—you have to design for it.

    2. Customers must be able to speak to a human on request

    Automation is allowed, but it cannot be the only option. At any point during a call, a customer must be able to transfer to a human if they ask for one. Companies cannot trap customers in automated loops. The practical implication: every workflow needs a reliable, audited escape hatch to a person.

    3. Support lines must be free of charge

    Premium-rate numbers are prohibited. Customer service cannot generate revenue for the business, nor may it be used to upsell products. This cleanly separates service from sales and reduces consumer friction.

    4. Essential services must offer 24/7 support for continuity issues

    Electricity, water, gas, telecoms, and transport providers must always be reachable at all hours when customers need to report service interruptions. That means coverage, triage, and routing must be always-on.

    5. Complaints must be resolved within 15 days – or within five days for undue charges

    This halves the previous general complaint window of 30 days and adds a much faster path for billing-error complaints. Companies must maintain records, assign tracking numbers, and ensure timely follow-up. Your case management discipline will make or break this requirement.

    6. No spam calls or unwanted commercial pressure

    Companies must identify business calls with a designated prefix, and customer -service calls with a different one. Telecom operators will be required to block calls that do not use these codes. Additionally, contracts obtained via unsolicited calls will be legally null and void, protecting consumers from being pressured into commitments they never intended to make.

    7. Companies must maintain a unified complaint-tracking system

    All complaints, claims, and incidents must be recorded in a centralized system to ensure traceability. If your data is fragmented across tools, this is a call to centralize and standardize intake.

    8. Companies must pass annual external audits

    These audits assess whether customer service processes are meeting the required standards. In practice, that means consistent processes, measurable outcomes, and reliable evidence.

    9. Better linguistic and accessibility rights

    Large companies operating in regions with co-official languages must be able to provide support in those languages. They must also ensure their customer service is accessible for vulnerable consumers, such as those with disabilities or older adults. Multilingual and accessible by design is the new default.

    10. Fairer contract renewals

    Companies must provide customers with 15 days’ notice prior to automatic renewal of online subscriptions and make cancellation simple. This is both a compliance and customer trust win.

    Most support systems weren’t built for this level of speed or operational rigor. But the steps required to comply are the same ones that make service better for customers—and better for the teams delivering it. That’s why I view AI as an essential capability, not a bolt-on.

    With the regulatory expectations clear, the question becomes: what does a modern, compliant support operation look like? For me, it blends human empathy with intelligent automation, proving auditability without sacrificing experience.

    This is where AI plays a meaningful role. Not as a replacement for humans, but as a reliable front line that can handle a wide range of queries, including the most complex ones that require real depth, while keeping queues under control.

    Adopting an AI Agent like Fin helps teams build a support model that meets regulatory expectations and improves customer experience across all your channels. Here’s how.

    Many organizations will struggle to meet the three-minute standard during normal times, let alone during spikes or busy seasons, without unsustainably scaling their teams. Fin can help by reducing the number of calls that reach your phone lines and Fin Voice will ensure the ones that do are handled quickly.

    Reducing avoidable call volume before it reaches the queue

    Many of the queries teams receive are predictable: outage updates, billing questions, account changes, and other repeatable issues. Fin can resolve these instantly across several channels, including live chat, SMS, email, and WhatsApp, using the content and processes your team already maintains. I’ve seen this alone cut peak-time pressure dramatically.

    Answering the phone immediately

    For customers who do call, Fin Voice can pick up straight away. It provides natural, conversational responses based on your existing knowledge and helps your team stay responsive during busy periods.

    Making it easy to reach a human easier during spikes

    When queues build up, Fin can capture the reason for the call, gather details, and prioritize the most urgent issues. If you offer callback options, Fin can help schedule them quickly so customers avoid long wait times, which is key for staying compliant during peak periods.

    The law requires customers to reach a real person whenever they request one. Fin supports this by keeping the path to a human clear and dependable: every interaction includes an option to speak to a person, and that option is accessible until the issue is resolved; when chosen, Fin hands over full context so human teams don’t start from scratch; if you show team availability or wait times, Fin can surface that information for customers; escalations can be prioritized to ensure faster pickup; alerts can notify on-call staff when urgent issues arise. On the phone, Fin Voice follows the same principle. Callers can request a transfer at any moment, and Fin routes the call to the right team with context intact.

    Essential-service providers must be reachable at any hour when customers need to report service interruptions. Fin can help you meet this requirement without building a full overnight staffing model.

    Always-on answers and triage

    Fin provides first-line support at any hour of the day or night. Fin Voice brings this capability to the phone, giving callers immediate help even when your human team is offline. Fin can also direct customers to the latest updates you’ve published, such as outage information or status pages.

    Routing urgent issues to the right people

    When an issue requires human judgment, Fin gathers the necessary details and routes it to the appropriate on-call team using your existing after-hours processes. Teams can set up notifications so urgent issues are seen quickly.

    Proactively surface what matters most

    With AI Insights, Fin can also monitor for emerging patterns in customer conversations through Trending Topics. This means that if there’s a sudden spike in reports about a specific outage or a recurring question about a new process, Fin can flag these trends in real time. Your team is alerted to what’s top-of-mind for customers, so you can prioritize updates, publish targeted FAQs, or escalate critical issues, ensuring your support stays relevant and responsive, even overnight.

    Complaints and outages often create the biggest spikes in volume, and the new law increases pressure to respond quickly, keep customers informed, and maintain complete records. This is exactly where structured AI intake adds value.

    A more structured complaint intake

    Fin can recognize when a customer is lodging a complaint, gather required information, and initiate a record in your existing system with a clear ID assigned from the outset.

    Clear ownership and deadline alignment

    Your team can then use your case-management tools to apply the 15-day resolution timeline (or five says for undue charges). Fin’s structured intake helps ensure that ownership and next steps are visible, rather than buried in unstructured notes.

    Faster, more consistent outage communications

    During service interruptions, Fin can share the latest published information, provide estimated fix times when available, and direct customers to live updates. On the phone, Fin Voice can triage incident-related calls quickly so callers aren’t waiting for a human agent just to receive basic information.

    While multilingual support is only mandatory for large companies operating in co-official language regions, it remains essential for meeting consumer expectations. Fin helps by supporting multilingual, natural language interactions across voice and other channels; operating within channels that support accessibility features, like channels compatible with screen readers or commonly used messaging apps; and offering “request a call” paths and collecting the necessary information up front so teams can follow up quickly for customers who prefer phone support.

    The law prohibits customer service interactions from generating additional revenue or being used to offer new products. With Guidance, you can set Fin up to stay firmly within these boundaries by shaping how it responds, which topics it should avoid, and what it should prioritize when a customer is seeking help or lodging a complaint.

    The law raises expectations around documentation and audit readiness. Fin helps by making customer interactions more structured and consistent: when a conversation involves a complaint, Fin can ensure the required information is captured and a clear ID assigned; that ID can follow the interaction so it remains easy to trace; consistent intake gives you better visibility into key metrics regulators care about, like response times, time to first human contact, escalation volume, and whether complaints are resolved within required timelines; transcripts, summaries, and metadata can be retained until cases are resolved, supporting audit requirements; many organizations maintain internal compliance playbooks outlining processes and owners. Fin’s structured intake helps keep these practices reliable; leverage Insights to identify trending topics, optimize processes and measure service quality.

    Spain’s new customer service law raises the bar on speed, access, and accountability. It’s natural to worry about how your team will cope, especially if your support operation has grown organically across tools and regions. I’ve seen how quickly burnout and chaos can set in when expectations rise faster than capacity.

    The reality is that meeting these expectations through people alone would put unsustainable pressure on already stretched support teams. The risk of burnout and operational chaos is real, which is why an AI Agent like Fin can bring welcome relief.

    By handling everything from high-volume, repetitive questions to many of the deeper, more involved issues customers raise, Fin keeps queues manageable and prevents the strain from falling entirely on your human team, helping everyone stay above water as expectations rise.

    For companies operating across the EU, adapting early to Spain’s stricter expectations can build resilience for whatever comes next—whether that ends up being driven by regulation or customer demand. Now is the time to align compliance, AI strategy, and customer experience into a single, measurable operating model.

    Inspired by this post on The Intercom Blog.

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  • Unlock Instant Product Answers: How AI-Powered Resource Centers Elevate In‑App Help

    Unlock Instant Product Answers: How AI-Powered Resource Centers Elevate In‑App Help

    I’ve spent years watching users bounce between product screens, docs, and support tickets when they hit a roadblock. The fastest path to value is always the same: deliver relevant, contextual help exactly when and where the user needs it. That’s why I’m excited about the next wave of in-app guidance that blends behavioral data with AI to anticipate intent and remove friction in real time.

    Announcing Resource Centers, Amplitude’s newest in-product help feature that uses behavioral data and AI to serve help content users actually need.

    Here’s why that matters. In a product-led growth model, in-app guides, product tours, and just-in-time tips are essential to onboarding and user activation. When help content is informed by real behavioral signals—events, cohorts, milestones—it stops being a static knowledge base and becomes a living system that adapts to a user’s journey. That means fewer context switches, faster time-to-value, and more confident users who can self-serve their way to outcomes.

    In practice, the most effective resource centers are opinionated and contextual: they surface content by role, plan, and lifecycle stage; trigger nudges based on key events; and offer multiple modalities (microcopy, short clips, interactive guides) so users can choose how they learn. They also respect pacing, avoiding notification fatigue with rate limits and prioritization rules. Think of this as high-quality UX writing paired with data-driven orchestration—useful, discoverable, and never in the way.

    Execution matters. Start with a clear content taxonomy, map help assets to journey stages, and establish a content ops cadence so guides stay fresh. Partner closely with data governance to ensure privacy-by-design and transparent consent for behavioral data usage. Then wire in feedback loops—thumbs up/down, quick polls, and session replays—so you can continuously discover gaps and iterate quickly.

    Measure impact with the same rigor you apply to product features. Track activation rates, time-to-first-value, self-serve resolution rates, reduction in ticket volume on targeted topics, and downstream retention. Use A/B testing to validate which interventions move the needle, and segment results to learn what works for new users versus power users. When results differ, treat that as a design signal—not a failure—and refine the targeting.

    Rollout thoughtfully. Pilot with a high-friction workflow, localize the help content to the user’s context, and set clear exit criteria before scaling. Align with customer support and success so your resource center becomes the canonical source for in-app help, not yet another content silo. Over time, unify insights across Amplitude analytics and your support stack to close the loop between product behavior and help outcomes.

    As product leaders, our goal is simple: reduce effort and increase confidence for every user. AI-assisted, behaviorally triggered resource centers are a pragmatic step toward that future—meeting users where they are, with exactly what they need, at the moment they need it.

    Inspired by this post on Amplitude – Best Practices.

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  • Own Your AI: 4 Essential Roles to Supercharge Support and Prevent Performance Drift by 2026

    Own Your AI: 4 Essential Roles to Supercharge Support and Prevent Performance Drift by 2026

    AI doesn’t fail because the model is bad, it fails because ownership is missing.

    When someone truly owns your AI, everything changes. Resolution and automation rates climb, the system self-improves, and the customer experience transforms in ways a dashboard alone will never show you.

    This is part three of our five-part series on customer service planning for 2026. We’ll be sharing all five editions on our blog and on LinkedIn.

    If you’d rather have them emailed to you directly as they’re published, drop your details here.

    Last week, we introduced the four roles that make AI actually work in a support organization. These roles are already showing up inside the teams who are scaling AI the fastest, and this week, we get closer to the ground.

    Here’s what these roles look like in practice — what they do, how they work, and why your AI performance will inevitably drift without them.

    AI operations lead — owns AI performance, every day. I think of this person as the air-traffic controller for our AI Agent. I treat the AI as a living system that needs ongoing supervision, evaluation, and tuning. This role is accountable for what leaders care about most: quality, reliability, and continuous improvement.

    The AI ops lead sees the whole picture: conversation quality, missing knowledge, flawed assumptions, unexpected failures, new opportunities for automation, and the subtle signals that the system is beginning to drift. In practice, that vigilance is the difference between steady gains and slow decline.

    Day-to-day, here’s what I expect from this role.

    1. Reviews AI conversations and surfaces performance patterns. The AI ops lead monitors the AI Agent’s behavior — the tone shift after a product launch, a sudden dip in resolution for a specific intent, or conversation clusters revealing new customer behavior. They scan for anomalies, trends, and early warnings, with an emphasis on what’s happening right now, not last week. Without this intentional ownership, I’ve watched a 2% dip turn into a 10% drop in days.

    2. Prioritizes fixes and improvements. Once patterns emerge, they triage fixes like a product team handles bugs. Missing or incorrect content? They route it to the knowledge manager. Behavioral issues? They adjust guidance and guardrails. Action or system issues? They partner with the automation specialist. This connective tissue turns individual fixes into compounding improvements.

    3. Defines and maintains AI guardrails. Leaders everywhere worry about AI doing things it shouldn’t. This role answers that fear by establishing clarification logic, escalation rules, “never answer” policies, and safety boundaries. The goal is predictable behavior that protects customer trust — an essential pillar of any AI Strategy and AI risk management practice.

    4. Aligns reporting with leadership. The AI ops lead reports on resolution rate, CX Score, CSAT, automation coverage, and hours saved — making the economic impact visible. That visibility is a foundational step in any credible customer support ai strategy.

    Why this role exists now. AI systems are dynamic and require constant tuning. A small dip in quality quickly becomes an operational issue, and no existing role naturally owns that. When someone does, teams feel the benefit almost immediately.

    Knowledge manager — builds and maintains the structured knowledge AI depends on. I hear the same thing from leaders again and again: AI is only as good as the content you give it. This role is rapidly evolving from classic knowledge management into knowledge strategy — part content designer, part systems thinker, part information architect. Their job is to build the knowledge scaffolding that lets AI answer accurately, consistently, and safely.

    Here’s how the knowledge manager creates leverage.

    1. Writes, maintains, and improves support knowledge — continuously. After every product change, they update articles, remove duplication, resolve contradictions, and pay down “knowledge debt” that quietly erodes accuracy. The upkeep is shaped by AI performance; when patterns expose gaps, they fix the source.

    2. Structures knowledge for AI, not for browsing. Traditional help centers are for humans skimming pages. AI needs clean intent signals, crisp formatting, and clearly structured language. The knowledge manager designs that structure as intentionally as the content itself.

    3. Works hand-in-hand with AI ops. Many performance issues stem from missing or unclear knowledge. When the AI ops lead surfaces recurring misunderstandings or low-resolution categories, the knowledge manager resolves the root cause at the source.

    4. Ensures accuracy and compliance at scale. As AI handles more sensitive situations, the knowledge manager safeguards correctness, currency, and compliance — critical for data governance and regulatory alignment.

    5. Develops a cross-functional knowledge strategy. The role creates a canonical, cross-functional source of truth that product, engineering, product marketing, go-to-market, and support (AI and human) can all rely on.

    Why this role exists now. This is one of the highest-leverage positions in an AI-first support org. Teams like Rocket Money and Anthropic are hiring knowledge managers because AI accuracy depends on the quality of knowledge feeding it. Without this role, resolution rate caps out early and never climbs.

    Conversation designer — designs how the AI speaks, clarifies, and interacts. AI isn’t just a tool customers use; it’s a representative they interact with. Tone, clarity, pacing, and conversational structure matter, especially in voice. Every word affects perceived expertise, trustworthiness, and brand. The conversation designer ensures the AI feels human-friendly without pretending to be human — the sweet spot that builds trust without misleading customers.

    In my experience, staffing conversation design early accelerates results. It changes not only how we tune AI, but how we understand the end-to-end customer experience.

    Here’s what great conversation design looks like.

    1. Shapes the AI’s tone, voice, and communication style. This role refines phrasing, tunes politeness, adjusts how confusion is handled, and shapes micro-interactions that determine whether customers feel cared for or dismissed. On voice channels, natural cadence is make-or-break.

    2. Designs flows for high-value conversations. They design how the AI clarifies intent, branches, communicates uncertainty, verifies details, escalates, hands off, and returns to the main thread without feeling mechanical — treating customer experience as a product with language as the interface.

    3. Translates procedures and complex workflows into natural language and logic. As AI runs structured procedures and actions, this role becomes a conversational system architect, translating SOPs into conditional logic with exceptions and fallbacks. For example, in Intercom, our conversation designer uses Simulations to run simulated conversations to see where the AI Agent gets confused, over-confident, or awkward, and refine flows until the interaction feels effortless end-to-end.

    4. Ensures transitions to humans feel smooth and respectful. Handoffs should provide clear context to the human agent and maintain continuity so customers never feel dropped.

    Why this role exists now. As AI becomes the primary interface, conversation design directly influences trust, brand perception, and operational outcomes. It’s a core competency for any Generative AI and LLMs for product managers program.

    Support automation specialist — builds the backend actions that allow AI to do real work. If the conversation designer shapes expression, this role shapes capability. They transform AI from an answering machine into an outcome engine by bridging AI and the systems it must safely and deterministically act on.

    Support teams increasingly expect AI to do what a human would do: refund a charge, adjust a subscription, verify an identity, update an account setting, or pull relevant data. That expectation creates a new technical role at the edge of support, ops, and engineering.

    What I rely on this specialist to deliver.

    1. Creates and maintains backend workflows the AI executes. This includes building and maintaining: Fin Tasks. Fin Procedures with embedded steps. Action flows that call internal and external APIs. Automations that span billing systems, user identity layers, CRM objects, subscription entitlements, refund tools, and more. They ensure the AI can act compliantly and predictably — the playbooks that turn intent into action.

    2. Owns the integrations required for advanced automation. Many problems require data elsewhere — billing platforms, internal databases, systems of record. The specialist ensures the AI can retrieve, validate, and use that information safely, often partnering closely on CRM integration and internal services.

    3. Partners closely with product and engineering. Some workflows require new endpoints, permission layers, safety gates, or deterministic fallbacks. This role drives those changes across the stack.

    4. Ensures reliability and safety at every step. Guardrails, validation logic, exception handling, safe execution paths — all are essential. They confirm that the AI has access to the correct data, the action matches policy, edge cases are accounted for, risky flows have deterministic constraints, and every action is auditable and reversible.

    Why this role exists now. Customers don’t want answers, they want outcomes. AI can now deliver those outcomes, but only with the right backend scaffolding. This role modernizes operational architecture and unlocks end-to-end automation.

    How these roles work together — the new operating loop. These roles aren’t silos; they’re interdependent parts of one system. The AI ops lead identifies patterns and performance gaps. The knowledge manager resolves inaccuracies or missing content. The conversation designer improves clarity, tone, and flow. The automation specialist expands the system’s ability to take action. Each improvement compounds the next, moving you from early automation to transformational resolution rates through continuous refinement.

    This loop is what separates teams that plateau early from teams that scale AI into a reliable, high-performing system — the essence of a durable AI Strategy.

    How to get started (even if you can’t hire all four roles today). Most teams phase into this model: assign partial ownership, formalize responsibilities, then specialize as AI volume grows. Here’s the progression I recommend.

    Phase 1: Assign ownership. Give each role’s core responsibilities to someone who can devote five to 10 hours weekly. Early on, support ops, enablement, senior ICs, and technically inclined teammates can anchor the work.

    Phase 2: Formalize the responsibilities. As AI resolves more queries, optimization becomes core operational work. Formalizing ownership prevents performance drift and knowledge debt.

    Phase 3: Specialize and hire. Once AI handles 50–70% of incoming volume, these responsibilities become full-time roles. Investing in specialization becomes essential infrastructure for the next scale stage.

    The bottom line. AI changes the shape of your support team. These four roles — AI operations lead, knowledge manager, conversation designer, and support automation specialist — form the backbone of the AI-first support organization. They bring order to a constantly changing environment and enable AI to deliver the outcomes leaders and customers expect heading into 2026.

    Next week, we’ll continue the 2026 planning series with a deep dive into org design models for AI-first support teams — how to structure people, workflows, and accountability in a world where AI resolves most conversations before a human ever sees them.

    To follow along with the series and have each new edition emailed to you directly, drop your details here.

    Inspired by this post on The Intercom Blog.

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  • Unlock Clarity and Confidence: How the New CX Score Transforms Every Customer Conversation

    Unlock Clarity and Confidence: How the New CX Score Transforms Every Customer Conversation

    Customer expectations have never been higher. People expect fast, accurate, and effortless support, every time—and across industries, from ecommerce to financial services to healthcare, customer experience has become one of the most strategic levers for achieving durable competitive advantage.

    Here’s the challenge I’ve seen again and again: you can’t improve what you can’t see. For years, most support organizations have been making decisions based on only a tiny slice of their customer interactions, captured through surveys that reach only the most motivated (or frustrated) voices. In my own program reviews, the most revealing insights often hid in the conversations that never made it into CSAT or NPS.

    We created CX Score to change that. CX Score gives teams a complete view of the customer experience across every meaningful conversation—no CSAT or NPS surveys required. I wanted a signal that reflected reality, not just a vocal minority.

    After launching CX Score, we saw many teams immediately use it to understand performance trends, highlight experience issues, and surface gaps across support operations. That early momentum validated the approach and showed us where to go deeper.

    As adoption grew, new opportunities emerged. CX leaders found value from CX Score—but they also wanted the model to capture more nuance and identify the specific drivers leading to negative or positive scores, giving them clearer direction on where to focus. I heard the same ask from my own leadership peers: make it explainable and actionable.

    That’s what we’ve built into the latest iteration of CX Score. If you’ve been using CX Score for a while and have noticed it shift recently, that’s an expected evolution. A recent shift in scores does not mean your support quality has dipped or that Fin or your team is performing worse than before—this one-time shift reflects a more advanced, more complete model that understands customer experience more deeply with even greater coverage.

    Why CX Score needed to evolve

    In the initial release, CX Score evaluated each conversation using a combination of sentiment, resolution, and support quality signals. It provided strong early insight and surfaced experience trends that were previously invisible. But as we analyzed real-world conversations across thousands of companies, it became clear that even these combined signals didn’t fully capture the nuance of how customers actually experience support—especially in moments where the outcome was technically correct, but the path to get there involved unnecessary friction, repeated explanations, or unresolved product limitations.

    This evolution of CX Score builds on that foundation. It incorporates deeper contextual understanding of the entire interaction, creating a more complete and accurate reflection of the customer experience. As a product leader, that depth matters because it turns a lagging metric into a coaching and prioritization system.

    How CX Score has evolved: deeper, more actionable insights

    We expanded the CX Score evaluation criteria. CX Score now looks beyond just how your team replied, and into the broader context of the customer’s experience—including reasons that may be outside your support team’s direct control but still influence how your customers feel.

    Alongside core support quality signals, we’ve introduced several new dimensions that capture what customers are actually reacting to:

    Answer quality (Fin): How well Fin answered the customer’s queries—were responses clear, accurate, and able to resolve the issue without contradiction or repeated clarification?

    Answer quality (Teammate): How well a human teammate answered the customer’s queries, using the same criteria: clarity, accuracy, and resolution without contradiction or repeated clarification.

    Customer effort: How much effort the customer had to put in to get help (e.g. repeating themselves, multiple handovers, chasing follow-ups).

    Strong emotion: Whether the customer expressed strong positive or negative emotions (e.g. joy, gratitude, frustration, anger).

    CX analytics dashboard with a CX Score of 3 and a donut chart of drivers: policy feedback, answer quality, customer effort, product or service feedback, and strong emotion beside an AI agent chat transcript.
    The new CX Score adds context to every conversation: a donut chart surfaces drivers like policy feedback and effort, while a side panel explains why this interaction earned a 3 based on signals from an AI agent chat.

    Product/Service feedback: Whether the customer praised or criticized the product (e.g. features, bugs, design gaps, etc.) or the service (e.g. delivery, reliability, onboarding, performance, etc.).

    Policy feedback: Whether the customer praised or criticized a company policy (e.g. refunds, returns, account rules, limits, eligibility, etc.).

    Broader coverage: more of your support volume now contributes to CX Score

    Previously, some conversations couldn’t be scored reliably, especially short, simple, or low-context exchanges—which meant your CX Score was based on only a subset of your total support volume. With this update, CX Score now uses a wider set of criteria to evaluate each interaction. The result: more conversations qualify for scoring, fewer gaps in coverage, and a CX Score that reflects your true support mix—not just the longest or most detailed threads.

    Greater transparency with richer, more informative summaries

    We’ve made it much clearer why each conversation received the score it did. Right inside the product, every scored conversation now surfaces the specific reasons that influenced its rating—things like high customer effort, strong negative emotion, or product feedback. This added visibility makes it much easier to understand what’s driving your CX Scores, build trust in how they’re calculated, and confidently use them in reporting, coaching, and decision-making.

    On top of that, conversation summaries now weave these reasons together with context from the customer’s original query. Instead of scanning the full thread, you can quickly see what happened (the core issue and how it was handled) and why it was scored that way (the key signals that impacted the rating). In my workflow, this shift lets me move from reading transcripts to taking action much more quickly.

    From visibility to taking action

    As customer experience becomes one of the clearest ways businesses can differentiate, teams need more than visibility—they need clarity on where to invest their time and how to improve. With deeper context and clearer reasoning behind every score, CX leaders can quickly identify what’s working, what needs fixing, and what to prioritize. CX Score moves from being a measurement tool to a system for continuous improvement.

    What this unlocks for CX teams: Automatically flag conversations for review. Route threads with high customer effort, strong negative emotion, or low answer quality to QA, team leads, or specialists. Auto-forward product feedback to the right teams. Send conversations with product or policy criticism directly to Product, Engineering, or Ops channels, with no manual triage required. Spot operational issues such as handoff loops, unclear answers, or inconsistent workflows. Share transparent, explainable insights directly with leadership.

    The future of CX measurement

    CX Score isn’t just another metric. It’s becoming a new standard. Some customers have already chosen to replace CSAT entirely, using CX Score as their primary measure of experience quality because of the broader coverage, deeper context, and clearer paths to action it offers. This reflects a broader shift across the industry: as new competitors emerge and product differentiation narrows, customer experience is becoming one of the most strategic ways to stand out; measuring it accurately and understanding it deeply is now essential.

    Our focus going forward is to help teams diagnose issues faster, prioritize with confidence, and improve at scale. This is the foundation we’ll continue to build on: turning every conversation into insight, and every insight into action.

    The new CX Score is rolling out gradually to all customers and will be in your workspace by December 3rd.

    Want to see CX Score in your workspace? Get started →

    Inspired by this post on The Intercom Blog.

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  • The Customer Service Roles AI Needs to Thrive: A Practical Playbook for High-Impact Support

    The Customer Service Roles AI Needs to Thrive: A Practical Playbook for High-Impact Support

    When AI Agents resolve the majority of customer conversations, the shape of your support team has to change. I’ve experienced this shift firsthand: the moment AI begins to carry the volume, your people must pivot from answering individual questions to engineering the system that consistently delivers quality outcomes.

    The old tiered model built around queue management, handoffs, and volume-based productivity no longer fits. AI now handles the bulk of customer interactions, and that changes the role of your human team entirely. Responsibilities evolve, and success is measured differently. It goes beyond just adding automation to existing ways of working. You’re building an operating model that’s entirely new.

    Most teams don’t hire a dedicated AI function from day one. They start by distributing a few critical responsibilities across existing team members, and formalize those responsibilities as AI becomes central to how support works. That’s exactly how I recommend getting momentum without over-hiring too early: prove value fast, name clear owners, and then scale.

    Once you have executive support and a clear strategy in place, these are the four foundational roles we believe are key to getting AI off the ground in a meaningful way:

    1. AI operations lead

    Responsibilities: Owns day-to-day AI performance. Tracks quality. Tunes behavior. Prioritizes fixes. Drives iteration.

    Skillset/background: Often promoted from support ops. Deep understanding of workflows, systems, and tooling. Strong analytical and cross-functional coordination skills.

    Why you need this: Without clear ownership, performance drifts. This role ensures the AI Agent constantly improves.

    Blue corporate graphic with grayscale headshot and a quote about GenAI creating new customer success roles, such as digital support engineer and an automation success team, highlighting career paths.
    AI isn’t replacing support—it’s opening doors. This visual highlights how GenAI is spawning roles in customer success, from digital support engineers to automation success teams, and unlocking clearer, upward career paths.

    In my teams, this role becomes the heartbeat of AI performance—instrumenting quality feedback loops, triaging failure modes, and aligning fixes across product, data, and support ops.

    2. Knowledge manager

    Responsibilities: Owns macros, snippets, and help content. Maintains structured, accurate inputs the AI Agent depends on.

    Skillset/background: Often promoted from support ops. Deep understanding of workflows, systems, and tooling. Strong analytical and cross-functional coordination skills.

    Why you need this: Without clear ownership, performance drifts. This role ensures the AI Agent constantly improves.

    Every generative AI system is only as good as its knowledge. I’ve learned the hard way that inconsistent or stale content erodes trust—both for customers and internal stakeholders. A rigorous knowledge manager prevents that.

    3. Conversation designer

    Table summarizing customer service AI roles: AI operations lead, knowledge manager, conversation designer, and support automation specialist, with columns for responsibilities, required skills, and why each role matters.
    Build a winning AI support team with four core roles: an ops lead to drive quality, a knowledge manager to keep content accurate, a conversation designer for tone and flow, and an automation specialist to power customer actions.

    Responsibilities: Designs how the AI Agent communicates by focusing on tone of voice, structure, handoff logic, and interaction flow. Tunes how responses feel.

    Skillset/background: Background in content design, UX writing, or support enablement. Deep grasp of policy, CX standards, and conversational nuance.

    Why you need this: This role ensures the AI Agent speaks like your brand – clearly, helpfully, and in line with customer expectations.

    This is your brand’s voice in motion. A strong conversation designer sets the guardrails that keep interactions on-brand, compliant, and empathetic while still efficient.

    4. Support automation specialist

    Responsibilities: Builds workflows and backend actions the AI Agent can execute.

    Skillset/background: Background in support engineering, systems, or tooling. Works closely with product and engineering teams.

    Blue corporate graphic with a grayscale portrait beside a bold quote advocating 'player‑coaches' over a traditional management layer, Gamma branding, theme: building AI‑ready customer service teams.
    AI in customer service thrives with player‑coaches—hands‑on leaders who build, mentor, and iterate with the team. This quote-driven graphic signals a move away from heavy management toward agile, coaching‑first support operations.

    Why you need this: Enables the AI Agent to take action – not just respond. This role translates customer intents into business systems.

    In practice, this role unlocks the jump from “answering” to “resolving.” They wire up secure actions, map intents to outcomes, and partner with engineering to keep latency low and reliability high.

    Introducing new AI-first roles doesn’t mean your existing functions disappear. But they do need to evolve. For AI to scale effectively, every function in your support organization must shift its focus from managing queue-level activity to improving the system’s performance:

    Enablement trains human agents to work with the AI Agent: managing handoffs, tuning responses, and understanding how to give feedback that improves the system.

    QA evolves from reviewing conversations to reviewing the quality of the customer experience and behavior of the AI Agent: where the AI succeeds, where it falls short, and how the system as a whole performs.

    Workforce management plans capacity based on automation coverage, not just inbound volume.

    You’ll also need a new kind of leadership to make this model work. The traditional support leader doesn’t map cleanly to an AI-first organization. You need a new layer: leaders who are part strategist, part operator. They roll up their sleeves to analyze the AI Agent’s performance, refine content, and debug handoffs, but they also coach the team through a new way of working.

    Org chart of customer service with a VP of Support over three pillars: Human Support, Support Operations and Optimization, and AI Support, detailing roles like agents, insights/WFM, CS enablement, conversation design, and knowledge management.
    Customer service is reorganized for the AI era: a VP of Support leads human support, ops and optimization, and a new AI support function—adding conversation design, knowledge management, and systems analysis alongside agents, insights, and WFM.

    This is the “player-coach model” – leaders who actively shape both the system and the people within it.

    These leaders see the AI Agent as a teammate to manage, not just a tool to monitor. They can’t be purely people leaders or purely systems thinkers. They need to be both, and they’re emerging as a critical hire in support right now.

    Some teams are restructuring their organizations around the AI Agent as a core product, not just a support tool. Here are some real-world examples:

    At Dotdigital, a dedicated “Fin Ops” specialist role was created to refine content and improve AI performance.

    At Clay, a dedicated GTM engineer role has been established as part of the ops team with a focus on making support more efficient at scale using Fin. Additionally, a support engineering function has been embedded directly in the CX organization to help reduce volume by fixing bugs and building internal tools.

    Lightspeed created a dedicated Digital Engagement team to manage Fin’s optimization, and formalized a triangular model that brings together technical teams, frontline experts, and content specialists.

    In my experience, the most resilient org designs align around three pillars: Human Support, AI Support, and Support Operations and Optimization. Each pillar carries distinct ownership yet shares accountability for AI performance. That structure keeps the team focused on outcomes over output and makes continuous improvement everyone’s job.

    Blue Rocket Money graphic featuring a grayscale portrait beside text about a modern support team, emphasizing redesigning work so humans focus on high-value tasks alongside AI.
    AI shouldn’t replace your agents—it should elevate them. This Rocket Money quote highlights a modern support model where automation handles the busywork and people concentrate on high‑value, human moments.

    Once AI Agents handle most conversations, your team’s work moves from “answering questions” to “designing and improving the system that answers questions.” They become the force that steers quality, rather than the one that carries the volume.

    This is why new roles are important. It’s not because they’re trendy, but because the performance of your support organization now depends on the performance of AI, and no AI Agent succeeds without clear ownership of content, behavior, workflows, and improvement cycles.

    That’s the pattern we’ve seen from working with so many teams:

    They name owners early.

    They distribute responsibilities before they formalize them.

    They anchor teams around AI outcomes, not ticket outcomes.

    And they hire leaders who can manage both the system and the people.

    If you take one thing away from this week’s article, let it be this: if AI is going to handle the majority of your customer conversations, your team needs to be designed to help it do that well.

    Your roles, responsibilities, and leadership approach are now part of the architecture of AI performance.

    Next week, we’ll go deeper into how these roles actually operate day-to-day – the workflows, responsibilities, rhythms, and collaboration patterns that make an AI-first support organization run.

    Inspired by this post on The Intercom Blog.

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  • Intercom is now a Shopify Plus Technology Partner: AI-powered support to scale ecommerce

    Intercom is now a Shopify Plus Technology Partner: AI-powered support to scale ecommerce

    I’m thrilled to share that Intercom is now a certified Shopify Plus Partner on the Technology Track. As someone who obsesses over product quality, speed, and measurable outcomes, this milestone reflects the rigorous standards we hold ourselves to and the trust Shopify Plus merchants can place in our solution.

    The Shopify Partner Program Technology Track supports the largest Shopify merchants by helping them find the apps and solutions they need to build and scale their business. The program is available specifically for Shopify Partners who provide a level of product quality, service, performance, privacy, and support that meets the advanced requirements of Shopify Plus merchants.

    As a Technology Partner, Shopify has recognized Intercom as a provider trusted to help high-growth ecommerce brands scale.

    “The Shopify Partner Program Technology Track is designed to meet the advanced requirements of the world’s fastest growing brands. We’re happy to welcome Intercom to the program, bringing their insight and experience in Customer Support to the Plus merchant community.”

    — Jeff Kennedy, Head of Product Partnerships, Shopify

    For Shopify Plus merchants, this certification means that our integration is vetted and optimized, and that our roadmap aligns with Shopify’s priorities. In practice, that translates into faster resolutions, less context switching, and more personalized conversations—without compromising privacy or performance.

    Over the past year, we’ve launched a series of enhancements to our Shopify integration to give merchants more control and speed in support, including:

    Data Connector templates so our AI Agent Fin can fully resolve requests from customers who want to get information about their Shopify order.

    Multi-store support for merchants to manage conversations from multiple storefronts in one inbox.

    Inbox order actions for merchants to take actions like editing shipping addresses, cancelling and refunding whole orders, deduplicating or creating duplicate orders based on existing ones, all without leaving the conversation.

    EU workspace support to ensure merchants stay aligned with EU data residency requirements.

    Space-themed gradient banner with large serif headline 'Get started with the #1 AI Agent today' and a prominent white button reading 'Start a free trial'; minimal, cinematic website hero.
    Launch your AI customer service faster—this hero graphic invites users to try the #1 AI agent with a bold headline and clear CTA, emphasizing practical, real‑world demos over polished Hollywood sizzle.

    Updated data mapping and custom fields to keep Shopify order data and customer profiles fully in sync.

    These updates make it faster and easier for merchants to resolve queries, personalize conversations, and drive loyalty, all from one platform. I’ve seen these capabilities reduce average handle time and minimize escalations—especially for complex order changes and post-purchase workflows.

    We’re already seeing how our Shopify integration is helping merchants scale their support and deliver better customer experiences: teams are deflecting routine inquiries with AI while empowering agents to focus on high-value, relationship-building conversations.

    Our team is continuing to invest in Shopify-specific capabilities. Here’s what we’re working on:

    Expanded Fin Tasks for complex order actions with new pre-built workflows.

    Enabling Model Context Protocol (MCP) support.

    Smarter product search powered by Shopify data.

    These additions will help merchants resolve faster, personalize at scale, and stay ahead of rising customer expectations – particularly as we approach peak season. We’ll continue to ship in tight feedback loops with Plus merchants to ensure each improvement moves the needle.

    If you’re a Shopify Plus merchant, learn more about how we can help you scale your support with Fin, the best performing AI Agent for ecommerce. Ready to move fast? Get started with Fin now.

    Inspired by this post on The Intercom Blog.

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