I’m stepping into 2026 with a practical playbook for marketing analytics—one forged at the intersection of product management, go-to-market strategy, and AI Strategy. My lens is simple: connect data to decisions, decisions to outcomes, and outcomes to revenue. If you’re serious about product-led growth, this is the year to turn your unified analytics platform into a true competitive advantage.
Start 2026 off with a bang with exclusive insights and predictions from some of marketing analytics’ most influential voices. See what they have to say.
The biggest shift I expect is from channel-centric dashboards to journey-centric systems that stitch together product usage, CRM integration, and campaign performance. When Amplitude analytics or Pendo data sits alongside HubSpot pipeline metrics, we stop arguing about attribution models and start instrumenting the full revenue motion. That’s how marketing, product, and sales align around one truth: activation, engagement, and expansion drive sustainable growth.
I’m betting on deeper adoption of A/B testing with a rigorous minimum detectable effect (MDE) discipline and cohort-led retention analysis. Vanity metrics won’t cut it. Teams that operationalize outcomes vs output OKRs and tie experiments to LTV, CAC, and payback will outperform. The win is not more tests—it’s better tests that translate into compounding user activation and retention.
Gen AI will supercharge analysis, but not replace analytical thinking. I see LLMs for product managers accelerating root-cause analysis, surfacing anomalies, and explaining drivers behind conversion shifts. The craft moves from “pulling reports” to “asking higher-quality questions,” then validating with sound statistical methods. The highest-leverage teams will pair gen ai with strong taxonomies, clean event schemas, and clear definitions of North Star metrics.
Data governance becomes a growth enabler, not a compliance cost. With privacy-by-design, consented data, and well-documented schemas, your models become more accurate and your campaigns more resilient. When governance is strong, personalization sharpens, lookalike models improve, and executive confidence in the numbers rises—unlocking faster, bolder bets.
Product-led growth analytics will mature from “feature usage” to “value moments.” I’m focusing my teams on measuring time-to-value, depth-of-use, and expansion signals embedded in in-app guides, product tours, and contextual tooltips. The companies that make value visible earlier—and measure it precisely—will see outsized improvements in trial-to-paid and expansion.
Operationally, I expect tighter cadences between discovery and delivery. Product trios will partner with marketing to run continuous discovery on messaging, onboarding friction, and pricing signals. When insights flow directly into campaign creative and in-product experiments, learning cycles compress and the cost of delay drops.
If you’re building your 2026 roadmap, here’s my short list: consolidate tools into a unified analytics platform, standardize event taxonomies across web, product, and CRM, formalize MDE for every A/B test, and align OKRs to activation and retention milestones. Do this, and you’ll turn fragmented data into a durable growth engine—one that compounds every quarter.
Inspired by this post on Amplitude – Perspectives.
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).
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 →
When I think about how I stay sharp as a product leader, one principle anchors my approach: design your learning system—don’t leave it to chance. Communities of practice are that system. They turn curiosity into a habit, accelerate product discovery, and strengthen product management leadership across empowered product teams.
I recently dug into a powerful conversation on the All Things Product podcast that explores how product people can intentionally design their own communities of practice—and why that matters for long-term learning and growth. The insights apply whether you operate as an independent coach or you’re scaling continuous discovery inside a product org.
I appreciated the contrast in learning styles. Teresa shares an introvert-friendly approach to continuous learning: curating a personal learning network (PLN) filled with people she wants to learn from. Petra contrasts that with a more collaborative style—learning with others through small peer groups, hackathons, and local meetups. Together, they unpack how each approach supports curiosity-driven development, how to find your “definition of good” when starting something new, and the habits that make learning a deliberate practice.
In my own practice leading product trios and shaping outcomes over output, I rotate between these modes. When I need speed or depth on topics like product discovery or stakeholder management, I learn from people: I curate a tight set of voices, reverse-engineer their decisions, and study how they frame trade-offs. When I need new patterns or accountability, I learn with people: I form small peer circles to review experiments, pressure-test roadmaps, and critique discovery plans. Both paths create momentum—one by focus, the other by feedback.
Key takeaways I’m acting on right now:
– What a “community of practice” really means in modern product work: the infrastructure that makes continuous discovery sustainable—and keeps empowered product teams aligned on craft.
– The difference between learning from people vs learning with people—and when to use each depending on whether you need depth, breadth, or accountability.
– How to find like-minded peers for collaborative learning: start with one person you respect, ask who they regularly spar with, attend one local meetup with a clear learning goal, and follow up with a structured exchange.
– Building your Personal Learning Network (PLN): set a theme (e.g., pricing, product roadmapping and sprint planning), prune it quarterly, and track “who I’m learning from” with the same rigor you track stakeholders.
– Personal knowledge management as a product skill: treat notes, highlights, and artifacts as a system, not a junk drawer—so insights compound and are easy to retrieve when you need them.
– Why curiosity-driven learning builds stronger product intuition: schedule time for curiosity and socialize it with peers so it scales beyond individual motivation.
– How committing to talks, books, or courses drives deeper learning: public commitments create productive pressure and force you to clarify your thinking.
Here’s the simple playbook I use with my team: define a quarterly learning theme; curate a small PLN aligned to that theme; assemble a peer circle (PM, Design, Eng) for monthly critiques; commit to shipping one artifact publicly (a talk, guide, or internal workshop); and close the loop with a short write-up on what changed in our decisions, discovery cadence, or bets. It’s lightweight, measurable, and fits neatly alongside product-led growth priorities.
Two quotes from the discussion capture the spirit perfectly:
“Nobody on that list knows they’re in my personal community of practice.” — Teresa Torres
“Sometimes you don’t know your new definition of good until you start learning.” — Petra Wille
If you’d like to go deeper, you can listen to the episode on your favorite platform:
Listen to this episode on: Spotify | Apple Podcasts
Curation – All Things Product with Teresa & Petra episode
Hamel’s Blog
AI Evals for Engineers and PMs course by Hamel Husain (get 35% off through Teresa’s link) on Maven
Harold Jarche’s Personal Knowledge Management workshop
Petra’s book, Strong Product Communities – The Essential Guide to Product Communities of Practice
I’d love to hear how you’re designing your own community of practice. What’s your learning theme this quarter? Which peers are you building with, and what commitments are helping you go deeper? Drop your thoughts—I’ll share my own PLN stack and peer-circle cadence in a future post.
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:
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.
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
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.
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.
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.
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.
I’ve learned the hard way that the fastest, lowest-risk growth lever is hiding in plain sight: reactivating the users we already earned. When our team prioritized win-back programs over new acquisition, we unlocked higher net revenue retention, shorter payback periods, and stronger product-market signal—with a fraction of the spend.
"Discover why reactivating dormant users delivers better ROI than new acquisition. Learn how to identify and bring back at-risk users via targeted campaigns." That insight matches what I see daily: win-back campaigns compound value because they capitalize on existing familiarity, prior data, and stored intent.
Here’s the ROI logic I use. New acquisition burns budget on education and trust-building before value is realized. Reactivation, by contrast, taps into latent demand and prior setup, which means lower effective CAC, faster time-to-value, and higher LTV recapture. In retention analysis, these programs often outperform prospecting by a wide margin because the user already knows how to get value—they just need a relevant nudge.
To find the right users to re-engage, I start with leading indicators of risk: declines in weekly active use, feature decay (e.g., key workflows not triggered), shrinking session depth, and unresolved outcomes. Amplitude analytics or a unified analytics platform help me segment cohorts by recency, frequency, and monetary signals, then rank accounts by churn propensity. I also track intent proxies like billing pauses, reduced seat utilization, and cooling support contact.
I group users into three practical tiers: “at-risk” (recent value decay), “dormant” (no critical events in the past 30–60 days), and “churned-eligible” (post-cancel window with a viable path back). Each tier gets a distinct message strategy, incentive structure, and time horizon. The goal is to match the intervention to the activation friction each group faces.
For creative strategy, I anchor on the outcome they originally hired us to deliver. I lead with the value proposition they care about, not the features. A strong win-back narrative reminds users of the job-to-be-done, showcases what’s improved since they last engaged (new capabilities, performance, integrations), and offers an effortless next step—often a guided “return-to-value” flow or a one-click way to pick up where they left off.
Channel orchestration matters. I use Intercom and Pendo to deliver contextual nudges, in-app guides, and lightweight product tours that meet users at the precise moment and screen of friction. With CRM integration, we coordinate email and SMS for timely follow-ups, then reinforce success in-product with progressive tooltips and checklists. The best-performing sequences pair a personalized message, a sharp call-to-outcome, and a low-friction path back to activation.
Experimentation is non-negotiable. I run A/B testing on subject lines, offers, and in-product prompts, and size tests with a minimum detectable effect (MDE) that’s realistic for each segment. We personalize content by prior feature use, industry, and plan tier to avoid generic blasts that underperform. Over time, the library of proven treatments compounds, and the system becomes predictively better at catching risk earlier.
Measurement should be unambiguous. I define “reactivation” as the return to a qualifying level of usage that mirrors healthy customers (e.g., core event completion in a set window), not just a login. I track reactivation rate, time-to-reactivation, reactivated revenue, payback, and LTV uplift versus holdout cohorts. Cohort views in Amplitude analytics reveal whether improvements are persistent, and whether we’re driving true behavior change or short-term spikes.
Trust is part of the strategy. We build privacy-by-design into all outreach and respect user preferences. Clear value exchange (why this message, why now, how to opt out) consistently improves response rates and strengthens long-term relationships—win-backs should feel helpful, not harassing.
Operationally, I pair product-led growth with lifecycle marketing: product teams ship the “return-to-value” experiences; growth teams run the orchestration; customer success brings context from the field; and analytics sets guardrails and success criteria. When executed as a system, win-backs turn from occasional campaigns into a durable, compounding growth engine.
If you’re chasing growth in a tight market, start here. Your next quarter’s ARR may be sitting in dormant cohorts that are one relevant nudge—one fast path to value—away from coming back.
Inspired by this post on Amplitude – Best Practices.
Every week, I’m in conversations with product leaders, engineers, and security teams who are trying to ship AI features faster without compromising trust. The tension is real: stakeholders want velocity, customers want transparency, and regulators want accountability. That’s exactly where modern data governance earns its keep.
New AI pressures are redefining what good governance takes. Learn how to build better frameworks, move fast with confidence, and keep your data from being a black box.
In my role leading product management, I’ve learned that robust data governance isn’t a compliance checkbox—it’s a strategic capability. When we treat governance as a product, we architect for clarity, safety, and speed. That means aligning AI Strategy with day-to-day delivery so teams know what they can ship, when, and why.
Here’s the practical blueprint I rely on. First, establish ownership and a shared language. Create a living data catalog, lineage maps, and clear data classifications so teams know which assets are sensitive, regulated, or eligible for training LLMs. Second, harden privacy-by-design and least-privilege access. Bake PII detection, secrets management, and role-based policies directly into your workflows. Third, bring quality and observability to the forefront: instrument data contracts, monitor drift, and track model performance across environments. Finally, implement model governance end to end—dataset cards, model cards, bias testing, human-in-the-loop review, and a repeatable evaluation harness.
To move fast with confidence, make governance invisible and automated. Treat policies as code in CI/CD, gate deployments with pre-merge checks, and fail builds that violate data contracts. Log prompts and outputs responsibly, route unsafe patterns to red-teaming, and use a retrieval-first pipeline to anchor models on verified sources rather than fragile context stuffing. This is how we scale AI product development while keeping audit trails complete and costs in check.
Avoiding the black-box problem starts with transparency. Document assumptions, training data sources, and known limitations—then expose explanations where it matters in the product experience. Pair this with a unified analytics platform to tie telemetry, feature flags, and user feedback to model changes. When something goes sideways, your observability, incident management playbooks, and threat detection and response processes should make root-cause analysis fast and defensible.
If you’re building your program from scratch, use a 30-60-90 approach. In the first 30 days, inventory systems, classify data, and map high-risk use cases. By day 60, formalize RACI for governance, deploy access controls, and set up your evaluation pipeline with golden datasets and measurable acceptance thresholds. By day 90, operationalize incident response, conduct tabletop exercises, and wire governance outcomes into OKRs—think time-to-approval for high-risk changes, reduction in production incidents, and model evaluation pass rates.
This playbook pays off in board conversations and with customers. You can articulate your AI risk management posture, show measurable progress on regulatory compliance, and demonstrate how governance accelerates—not hinders—delivery. Most importantly, your teams gain the confidence to experiment, knowing there’s a safety net that protects users, the brand, and the business.
If your organization is wrestling with how to balance innovation and control, start small, codify what works, and scale with intent. With the right foundations in data governance, AI becomes an engine for durable advantage—not a source of sleepless nights.
Inspired by this post on Amplitude – Perspectives.
I treat ChatGPT as a force multiplier across the entire product lifecycle—from discovery and strategy to delivery and growth. Unlock workflows, prompts, and real PM tips showing how ChatGPT quietly reshapes product management behind the scenes.
My goal is pragmatic: turn generative AI into repeatable, measurable leverage for product discovery, product roadmapping and sprint planning, stakeholder management, and product-led growth without sacrificing quality, privacy-by-design, or judgment. This is how I apply LLMs for product managers in a way that strengthens customer empathy and speeds up decision cycles.
In discovery, I use ChatGPT to synthesize interviews, categorize sentiment, and surface emergent themes faster than a manual pass. I’ll feed it anonymized notes and ask for Jobs-to-be-Done statements, contradictory signals to validate, and the top three risks to our hypotheses. When the corpus gets large, I pair it with a retrieval-first pipeline and apply context window management so outputs stay grounded in real customer data.
On strategy and positioning, I draft and refine a crisp value proposition, clarify points of parity, and identify competitive differentiation. I ask ChatGPT to convert inputs into outcomes vs output OKRs, pressure-test assumptions, and produce a one-page narrative that even non-technical stakeholders can engage with. The result is faster alignment and fewer meetings to get to the same level of clarity.
For planning and delivery, I use ChatGPT to accelerate PRD outlines, user stories, and acceptance criteria, while explicitly requesting edge cases, failure states, and non-functional requirements. I’ll have it map risks to mitigations and suggest simple instrumentation aligned to DORA metrics and incident management readiness—useful when we’re iterating within a CI/CD cadence.
In experimentation, ChatGPT helps me frame strong A/B testing plans, calculate a minimum detectable effect (MDE), and sanity-check sample sizes. I also use it to translate metrics into plain language updates for the team, connect learnings to the next experiment, and propose follow-up analyses for retention analysis or activation bottlenecks.
For growth and onboarding, I prompt ChatGPT to generate hypotheses for user activation, in-app guides, and tooltip design that match personas and JTBDs. It drafts variations I can quickly test through Pendo or similar tools, supports product-led growth motions, and helps craft contextual copy that aligns with our value proposition without adding cognitive load.
Stakeholder communications get sharper and faster. I’ll ask for concise executive summaries, a version tailored for engineering leaders, and another for customer-facing teams. It’s especially effective for QBRs vs OKRs updates, where I need crisp narratives tied to outcomes, plus a plain-English articulation of risks and trade-offs for empowered product teams.
The guardrails matter. I set clear AI risk management boundaries, prevent any sensitive data from entering prompts, and align usage with data governance and regulatory compliance requirements. I also version and review prompts just like product artifacts, so the best ones evolve into a durable AI product toolbox the whole team can use.
If you’re getting started, pick one high-friction workflow—say, interview synthesis or PRD drafting—and timebox a week to build a repeatable prompt set and review rubric. Measure cycle-time savings and quality deltas, then expand to a second workflow. Within a month, you’ll have a lightweight operating model for AI Strategy that compounds across your roadmap.
Every breakthrough we ship in AI reinforces a simple truth I live by: "Companies that prioritize data quality, governance, and structure will accelerate their AI initiatives the fastest." That statement captures the difference between flashy demos and durable, scalable products. In my experience, the strongest AI Strategy starts with the discipline to treat data as a product, not an afterthought.
When teams rush to production with generative AI or LLMs, the first issues rarely come from the model itself—they come from the data. Poor lineage leads to hallucinations, inconsistent schemas inflate costs, and weak access controls erode trust. For LLMs for product managers, this is the gap between a compelling prototype and a reliable system customers depend on every day.
Let me clarify what I mean by data quality, governance, and structure. Quality is completeness, accuracy, freshness, and consistency across sources. Governance is policy, ownership, and accountability—privacy-by-design, regulatory compliance, and AI risk management built in from day one. Structure is the architecture: clear data contracts, standardized schemas, metadata and lineage, and role-based access that keeps sensitive signals protected while enabling speed.
Here’s the product playbook I use to operationalize this. First, map critical sources and define data contracts at the edges so producers and consumers can move independently. Second, standardize schemas and entity resolution to eliminate ambiguous joins. Third, enforce privacy-by-design with policy-as-code and automated redaction. Fourth, converge analytics into a unified analytics platform so definitions, freshness, and observability are shared. Fifth, instrument end-to-end lineage and quality SLAs with alerting. Finally, close the loop with human feedback and labeling to continuously improve model performance.
For generative AI workloads, a retrieval-first pipeline is essential. Unify trusted sources (product analytics, CRM, support, docs), embed and index them with guardrails, and focus on context window management to keep prompts lean, relevant, and cost-effective. This approach improves response quality, reduces token spend, and makes updates near-real-time—without retraining the base model every week.
Measure what matters. Tie model outcomes to product metrics through rigorous A/B testing, and size experiments with minimum detectable effect (MDE) so you can ship confidently. Use product analytics to verify that better data actually improves activation, retention, and support deflection. When teams can trace an AI improvement back to a specific data-quality fix, they invest in governance with conviction.
Culture closes the gap. Empowered product teams and product trios (PM, design, engineering) make crisper decisions when data stewards are embedded and accountable. Clear ownership, shared definitions, and transparent dashboards reduce friction with security and compliance while speeding up delivery. This is how product management leadership sustains velocity without trading away trust.
The bottom line: if we want faster, safer, and more scalable AI, we start with the data. Build strong foundations, treat governance as enablement, and structure every step so improvements compound. With that in place, Generative AI stops being a science experiment and becomes a durable competitive advantage.
Inspired by this post on Amplitude – Perspectives.
I’ve long believed the most resilient software companies master two hard things at once: they move decisively from mid-market to enterprise, and they ship multiple “best-of-breed” products without losing focus. The operating model that makes this possible — running 16 “startups within a startup” — resonates with how I build product organizations. In this piece, I’m unpacking the frameworks I use to make that model work at scale, from “product-market-sales fit” to capacity-driven go-to-market.
Why do companies get stuck in the mid-market? In my experience, it’s rarely just sales execution. It’s usually a product readiness gap hiding inside a distribution story. Enterprise customers expect battle-tested architecture, deep security and compliance, robust RBAC, data governance, audit trails, and predictable SLAs. They also expect a clear value proposition, strong references, and a crisp “who do we beat and why” articulation. If any one of those is fuzzy, your deals elongate or disappear. The fix starts by designing intentionally for enterprise and mid-market from day one: plan for scale, extensibility, change management, and procurement complexity — then validate with lighthouse customers, not just friendly pilots.
Sometimes the hardest enterprise move is saying no. I’ve advised teams to walk away from a marquee logo like Netflix when the requirements force unnatural acts that derail your roadmap. It feels counterintuitive — especially when the logo is irresistible — but your ideal customer profile must govern priorities. Your long-term velocity compounds when you align deeply with the customers who value your native strengths.
I differentiate between “product-market-fit” and “product-market-sales fit.” The former tells me a product delivers undeniable value; the latter tells me my distribution system can reproduce that value at scale. I watch for signals beyond anecdotes: win rates by segment, cycle time, ramp time to first deal, multi-threading depth, net revenue retention, and the percentage of customers who expand within two quarters. When these lag, I diagnose whether I have a product problem (insufficient value or clear “must-have” outcomes) or a distribution problem (positioning, enablement, or segmentation). The diagnosis determines whether I ship features, sharpen messaging, or rewire the motion.
On go-to-market, I build a capacity-driven machine instead of chasing deals. That means matching pipeline health to quota capacity, calibrating territories to intent density, and instrumenting enablement so new reps reach productivity with consistent talk tracks and crisp objection handling. I prefer simple, repeatable plays that compound: a precise ICP, strong proof packages, and a pricing model that meets customers where they are. When those are humming, founder-led GTM transitions smoothly to a scalable sales engine without losing the product’s original edge.
Hiring your first head of sales is a leverage point. I look for four things: pattern recognition in my specific segment, a builder’s mindset (process and playbooks without bureaucracy), rigorous pipeline hygiene, and the ability to partner with product on “where we win and why.” In the interview, I run scenario loops: how they’d disqualify non-ICP deals, how they’d recover a late-stage stall, how they’d deliver the first 90 days plan, and how they’d coach to a consistent message. Early founders absolutely need to learn sales — not to become the forever closer, but to encode customer truth into the product and the motion.
Strategic timing matters, too. There’s a well-known case of selling three days pre-IPO; whether or not you’d make the same call, the lesson stands: market timing, certainty of outcome, and board alignment are strategic variables, not afterthoughts. A healthy board brings independent thinking, timely guidance on capital and risk, and a unified narrative — especially when the market is volatile.
On competition, I pressure-test our narrative around points of parity and a “binary differentiator.” In crowded markets, incremental advantages don’t move the needle. You need one thing customers can’t ignore — faster time-to-value, a step-function in accuracy, or a cost curve that resets the category. I ask every team to prove a binary outcome: if we’re in the eval, there’s a clear, testable reason we win.
Launching multiple products simultaneously demands ruthless clarity. I structure the org as “startups within a startup,” each with its own GM, product roadmap, and GTM targets, but anchored to a shared platform for identity, data, and extensibility. Product managers operate as mini-entrepreneurs — owning P&L-like metrics, customer outcomes, and crisp product positioning — while a central platform team ensures consistency and speed. The rallying cry across these teams is simple: “We need to be best of breed.” If a product can’t credibly win on its merits, we either sharpen it until it does or we stop investing.
Execution lives in the details. I emphasize outcomes vs output OKRs, product trios for tight alignment, and continuous improvement powered by CI/CD so we can learn faster. We track DORA metrics like deployment frequency to ensure our cadence supports enterprise reliability. Weekly operating reviews focus on value delivered: have we solved the customer’s core job, and can our sales and success teams prove it with repeatable stories? When the answer is yes, expansion follows naturally.
Bringing it all together: moving upmarket, building “product-market-sales fit,” and running 16 product lines under one roof is achievable with the right structure and discipline. Design for enterprise from the start, let your ICP guide every trade-off, anchor GTM in capacity and repeatability, hire sales leaders who build with you, enforce a “binary differentiator,” and empower product managers as owners. Do that, and the “startups within a startup” model becomes a force multiplier — not just a slogan.
I’ve spent years leading product organizations where the best outcomes emerged from a tight handshake between design rigor and product strategy. The role that consistently sits at that high-impact intersection is the UX product manager. Done well, it’s the engine of product-led growth: deeply empathetic with users, relentlessly focused on outcomes, and fluent in both discovery and delivery.
Curious about the UX product manager role? Discover how it overlaps with design, PM, and why it might be the next step in your career.
At its core, a UX product manager owns the customer experience end-to-end while steering the business toward measurable outcomes. I translate user insights into prioritized problems, shape the solution space with designers and engineers, and validate decisions with data. Unlike a traditional PM who may skew toward market sizing and business cases, or a designer who may emphasize interaction patterns and visual systems, I integrate both frames to ensure we ship experiences that users adopt, retain, and recommend.
On the design side, I work hand-in-hand with product designers and UX writing to define the problem, craft flows, and stress-test usability. I obsess over clarity, affordances, and friction—especially during onboarding. Strong UX writing often makes or breaks first-run experiences, and I treat microcopy as part of the product, not an afterthought.
On the product management side, I anchor teams on outcomes vs output OKRs, facilitate product discovery, and drive prioritization against clear value propositions. I operate within empowered product teams and build tight product trios with design and engineering so we can validate assumptions fast, reduce waste, and increase the surface area for innovation.
Day-to-day, my craft blends qualitative research and quantitative analysis. I lean on tools like Amplitude analytics, Pendo, and Intercom to instrument funnels, run A/B testing, and perform retention analysis. When I experiment, I’m explicit about the minimum detectable effect (MDE) to avoid inconclusive reads. I measure the impact of changes on activation, time-to-value, and core feature adoption—and I make sure we can trace improvements to specific user segments.
User activation is my early warning system. If activation is lagging, I revisit the first-mile experience: guidance, progressive disclosure, in-app guides, product tours, and contextual tooltip design. I also ensure our onboarding is sequenced around the critical path to value rather than a feature parade. When activation improves, downstream KPIs like retention and expansion usually follow.
If you’re looking to become a UX product manager, start by strengthening three pillars: customer insight, product strategy, and experience design. Build a habit of continuous product discovery—co-creating with users, running lightweight experiments, and synthesizing findings into actionable decisions. Learn to translate insights into a product roadmapping and sprint planning cadence that energizes the team and keeps stakeholders aligned.
Your portfolio should read like a decision journal, not a gallery of screens. For each case study, frame the problem, outline constraints, describe alternatives considered, and show the experiments you ran. Include the metrics that mattered (activation, adoption, retention), the instrumentation you used, and the decisions you made when data was ambiguous. Hiring managers want to see your thinking under uncertainty and how you rallied cross-functional partners.
Communication and stakeholder management are differentiators. I tailor narratives for executives (trade-offs and business impact), for engineers (clarity on constraints and sequencing), and for design (user jobs, heuristics, and the narrative arc of the experience). Clear, frequent updates keep momentum high and reduce thrash, especially when priorities shift.
On the execution side, I make sure delivery never drifts from discovery. Every sprint is tied to a learning goal or outcome. We pair quick prototypes with production experiments, and we celebrate killing ideas that don’t move the needle. That discipline keeps us focused on outcomes and accelerates iteration speed without sacrificing quality.
Finally, a few career accelerators: get comfortable with analytics, learn the language of UX writing, practice story-based demos, and go deep on onboarding patterns. If you can move activation, you can change the trajectory of the business. Pair that with a strong perspective on product-led growth and you’ll be ready to lead product work that compounds.
The UX product manager role is a force multiplier. It’s where rigor meets empathy, and where design and PM converge to create experiences customers love—and businesses rely on. If that intersection energizes you, you’re already on the right path.
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.
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.
Like many support leaders right now, I’m deep in 2026 planning. The more I map scenarios and stress-test assumptions, the clearer one thing becomes: the way work gets done has fundamentally changed, and that change must reshape our customer service organization.
In 2026, you won’t get the full value of AI by keeping your org chart, systems, and operating model the same. You need to think differently about how support is structured, how performance is owned, and how your systems evolve around an AI-first model. That’s the lens I’m using across my team and our cross-functional partners.
To help you do the same, I’m launching a 2026 customer service planning series. Over the next five weeks, I’ll share how I’m approaching roles, skills, organizational design, and an operating model that makes AI the backbone of support—not a bolt-on feature.
We’ll publish each edition here and on LinkedIn. If you’d rather get them by email as soon as they go live, drop your details and I’ll send each edition straight to your inbox.
But before you can make any of those decisions, you need the right mindset and the right internal conditions for change. That’s where I’m starting this week.
Week 1: Start with a mindset shift
If you were building support from scratch today, you’d design around AI from day one. That’s the mindset to carry into 2026—and it’s the mindset I’m using to guide investment and accountability.
Too many teams still treat AI like a feature instead of infrastructure. They tack it onto existing processes, limit scope to tier-one issues, and never evolve the organization or systems around it. I’ve seen that approach stall progress and fragment the customer experience.
Those teams are thinking too small. They chase incremental efficiency, underinvest in the system change required to make AI successful, and get stuck. The result: a reactive team, a choppy customer experience, and value left on the table.
AI Agents are fully capable, end-to-end resolution engines. They fundamentally change the architecture of support.
To plan effectively and get the most value out of the technology, you need to adjust your mental model. Here are the mindset changes I’m prioritizing.
1) Move from ‘AI as a tool’ to ‘AI as infrastructure’
For the past decade, support systems have been the intermediary between customers and human support agents. AI isn’t an intermediary, it’s the first touchpoint (and often the last), the primary resolver, it manages workflows, orchestrates handoffs, and takes real actions.
Planning with the “AI is a tool” mindset leads to small optimizations that don’t move the needle. Planning with the “AI is infrastructure” mindset lets you redesign around the real sources of value creation.
Here’s what I’m designing around in 2026:
• Clear ownership of Agent performance
• A feedback loop that never shuts off
• A shared understanding of when humans step in
• Systems that evolve as AI capabilities expand
This framing sets up every decision that comes later in your planning process.
2) Look at how the work is changing
You need to plan your 2026 support organization around what the distribution of work will be—not what it is today. AI has shifted where volume goes, what humans spend time on, where judgment is needed, how performance is measured, and how the customer experience is designed.
If your planning assumes the current distribution is stable, you’ll design the wrong structure. I’m modeling for the work that’s coming, not just the work on our queue today.
3) Think like a product leader
When customers primarily interact with your AI Agent, support becomes responsible for designing the customer experience—not just managing it.
“Support is becoming a product function, and you are becoming a product leader”
Design your 2026 support org for AI from day one. This Gamma testimonial shows how an AI agent (Fin) resolves 80%+ of inbound requests, letting a small team scale customer service efficiently without increasing headcount.
Support is now a product surface, and support teams act like AI product teams. They:
• Design the customer experience
• Create and curate the knowledge layer that drives AI quality
• Maintain continuous improvement loops and tune system behavior over time
This is a big shift. Your planning—hiring, skills, rituals, and metrics—needs to reflect that evolution.
4) Redefine performance
This is a big mental leap for support leaders. Traditional performance was measured on speed and satisfaction, but AI performance is measured on resolution, impact, and system reliability.
Planning for 2026 means assuming that:
• Humans will handle a smaller % of volume.
• Customer experience will be shaped by AI’s performance, not throughput
When AI handles the bulk of your support volume, you need new metrics for how your team creates value. In practice, that means instrumenting AI and human-in-the-loop workflows with the same rigor you’d apply to a customer-facing product.
5) Understand that your value increases as AI takes on more work
You need to re-orient your team around AI’s performance to get the most value out of it. The more complex work you give it, the higher impact it will have.
Instead of routing complex, messy questions straight to your human team, shift their focus to improving the AI system so it can take on more over time.
Automating low-effort questions reduces noise, but automating complex workflows changes the economics of your entire team. It creates asymmetric returns that compound as AI absorbs the work that once demanded the most time and skill.
6) Plan for adaptability
A big difference between traditional planning and 2026 planning is simple: change will be constant.
“Change is hard, but the teams that adapt will be the ones who get the most out of this opportunity”
AI learns, evolves, and improves continuously. I’m asking, “How do we build an organization designed to adapt fast as the system evolves?” That question is informing everything from team topology to knowledge governance and experimentation cadence.
Food for thought
Heading into 2026, your org chart will look different—and that’s a good thing. Your people will play new, more meaningful roles as designers, curators, and stewards of an AI-first customer experience.
Once you accept that 2026 demands a different way of thinking, working, and planning, you can move to the next stage: designing the support organization that fits this future. I’ll share exactly what that looks like next week, including roles, skills, and ownership models that have worked well in my experience.
Want the full series delivered by email? Drop your details and I’ll send each edition to your inbox as soon as it’s published.
