Tag: product-led growth

AI Evals for Product Managers: How I Measure Agent Quality—A Beginner’s Playbook

I’ve led multiple AI agent launches, and the single most reliable way I’ve found to ship with confidence is to treat evaluations as a product capability, not a side project. When we make AI quality measurable, predictable, and comparable over time, we move faster, reduce risk, and build trust with customers and stakeholders.

Learn how product managers use AI evaluations to measure agent quality. Covers traces, LLM judges, offline evals, online evals, and how to connect evals to product outcomes.

Why does this matter so much in product management? Because agent quality is only meaningful when it drives adoption, satisfaction, and revenue. I use eval-driven development to align the day-to-day iteration of prompts, policies, and workflows with business outcomes like activation, retention, and Net Recurring Revenue (NRR). That alignment turns AI quality from an abstract notion into a roadmap lever.

First, traces. Traces are the spine of evaluation for agentic AI: they capture inputs, intermediate steps, tools invoked, and final responses. I instrument traces to make reasoning visible—what the agent tried, where it hesitated, and why it chose a path. With that visibility, I can compare prompts, policies, and tools, and I can teach the team to fix the root cause instead of patching symptoms. This is also where Agent Analytics becomes real: we move from anecdotes to observable behavior trends across cohorts and use cases.

Next, LLM judges. I use model-as-judge to score qualities like helpfulness, coherence, or adherence to brand and policy. The trick is calibration. I pair LLM judges with a small, high-quality human-labeled set to ground the scale, then monitor drift as models, prompts, or data shift. LLM judges help me evaluate at speed, but I still spot-check edge cases and highly regulated flows to balance efficiency with risk controls.

Offline evals come first. Before I expose users to changes, I run fixed test suites representing core scenarios, failure modes, and edge cases. I include golden examples, adversarial prompts, and domain-specific queries. Metrics cover task success, factuality, safety, latency, and cost. This is where prompt engineering and retrieval quality are tuned; if I’m using a retrieval-first pipeline, I evaluate evidence quality separately from generation so improvements are attributable and reproducible.

Online evals follow to validate real-world performance. I roll changes out behind feature flags and use A/B testing to compare variants under production conditions. I track conversation outcomes, tool success rates, fallbacks to human support, and user satisfaction. These online signals close the loop on whether an offline improvement actually compounds value in the product—critical for product-led growth.

Connecting evals to product outcomes is non-negotiable. I map quality signals to a driver tree: from per-turn scores (helpfulness, safety, latency) up to session-level outcomes (task completion, deflection, revenue intent), and finally to product KPIs (activation, retention, NRR). With this structure, I can set thresholds for launch gates, prioritize roadmap items that move the biggest levers, and build dashboards that leadership understands at a glance.

A few lessons learned. Start with a minimal but durable test set and grow it as you discover new failure modes. Version everything—prompts, tools, and datasets—so you can reproduce wins. Beware metric drift when you swap models or update prompts. Blend human review where the cost of error is high. Above all, make evaluations part of your AI workflows and sprint rituals so quality improves continuously, not sporadically.

If you’re just getting started, begin with traces and a small offline suite, add LLM judges for scale, then prove impact with a focused online experiment. Within a few cycles, you’ll have a living evaluation system that guides decisions, accelerates delivery, and gives your team—and your customers—confidence in every AI release.

Inspired by this post on Amplitude – Perspectives.

June 5, 2026
Director of Product, Growth & AI at Amplitude: My Playbook for Viral Growth and Engagement

I see the Director of Product, Growth & AI at Amplitude as a mandate to operationalize "viral and core growth strategies, user acquisition, and product engagement" with precision. From my vantage point, that means building a rigorous, metrics-first operating system grounded in Amplitude analytics and product-led growth principles, then layering in an AI Strategy that personalizes experiences without sacrificing control or safety.

I start by defining a clear North Star Metric and mapping a driver tree to expose causal levers across acquisition, activation, engagement, retention, and monetization. With behavioral analytics and cohort analysis, I quantify which user behaviors correlate with long-term value. I operationalize rapid experimentation through A/B testing with sensible minimum detectable effect (MDE) thresholds, guardrail metrics, and sequential testing to ensure we move fast while preserving measurement integrity.

For "viral and core growth strategies," I lean on durable growth loops more than one-off hacks. Viral loops might include collaboration invites, user-generated content, and shareable artifacts that make the product more valuable as it spreads. Core growth centers on frictionless activation: guided onboarding, in-app guides, product tours, progressive disclosure, and judicious tooltip design that connects users to the ‘aha’ moment quickly. Session replay and funnel instrumentation help isolate friction and systematically remove it.

On user acquisition, I connect performance channels and go-to-market strategy tightly to in-product activation. Rather than optimizing for clicks, I optimize for post-signup behaviors that predict retention. This includes improving landing page-message-product congruence, refining qualification (so top-of-funnel aligns with downstream value), and orchestrating lifecycle messaging that nudges users toward key activation milestones.

To deepen product engagement, I focus on leading indicators of retention and feature adoption. I segment by jobs-to-be-done and intent, then personalize in-app prompts to surface the right capability at the right moment. Retention analysis, pathing, and funnel breakouts inform which nudges to deploy and where—whether that’s smarter checklists, contextual education, or lightweight in-product interventions that turn sporadic usage into reliable habits.

AI raises the ceiling on what’s possible here. With a thoughtful AI Strategy, I use gen ai to personalize onboarding flows, recommend next-best actions based on behavioral signals, and summarize complex activity patterns into actionable insights for the team. I maintain strict measurement: every AI intervention ships behind feature flags, is evaluated through controlled experiments, and adheres to privacy-by-design principles. The outcome is a system that learns continuously while staying aligned to business and user outcomes.

Execution is where strategy becomes real. I rely on empowered product trios, continuous discovery with customers, and outcome-focused roadmaps that tie directly to the driver tree. This keeps the organization moving in sync: engineering prioritizes the highest-signal experiments, design accelerates comprehension and task success, and product ensures each release strengthens the core loop rather than adding ornamental features.

Ultimately, the blueprint is simple and disciplined: anchor on "viral and core growth strategies, user acquisition, and product engagement," quantify what matters with behavioral analytics, and iterate through well-instrumented experiments. Combine that with targeted AI augmentation, and you create a compounding growth engine that is both measurable and resilient.

Inspired by this post on Amplitude – Perspectives.

June 5, 2026
A Game-Changing Leap in Voice AI: Fin Voice 2, Apex Flash, and a Live Demo You Can Trust

In competitive markets, I see two options: try to win the game competitors set, or choose to play a different game. In the "Customer Agents" category, I’ve watched too many glossy, fabricated demos—especially around voice—mask the real challenges. Voice is just extremely hard. We all know the future of customer experiences will be Agent-driven voice, yet most of us haven’t actually spoken with a modern AI Agent when calling a business because the tech hasn’t been truly ready in the wild. Today, the bar moves.

What changed? There’s a live, public demo of cutting-edge voice tech you can stress test yourself—no smoke, no mirrors. I recommend taking it for a spin: https://fin.ai/voice. It’s fast, natural, and, yes, very, very good.

For context, yesterday brought Apex Flash, their newest and fastest model, built for the unique demands of low latency channels like voice. Today comes Fin Voice 2, a major upgrade to Fin Voice with over 20 new features, and the first product built on Apex Flash.

Here are the three things that stood out to me—and why they matter for customer support AI strategy and product strategy.

First — thanks to Apex Flash, Fin Voice 2 is now the fastest, most natural Agent for phone, with higher resolution rates and customer satisfaction scores than ever before. Apex Flash is trained on millions of customer experience interactions, fine tuned for customer service, and can be configured to understand all your knowledge and follow all your policies. The result is higher resolution at significantly lower latency—the best of both worlds for voice AI agent performance.

Speed and naturalness here aren’t accidental. Most voice AI products are slow because they convert speech to text, send it to a general model, get a text answer, and then convert it back to speech. Fin Voice 2 was designed to work differently, separating the real time layer that handles speech processing, and the layer that generates answers. That architecture is purpose-built for the demands of customer service on voice.

Powered by Apex Flash, Fin Voice 2 raises the bar on quality and speed—boosting resolution rates and guidance following while cutting time to first audio and semantic search latency, with a lift in CSAT too.

Second — Fin Voice 2 can handle complex queries end to end: taking actions in external systems, verifying callers’ identities, processing refunds, booking appointments, and more. Phone is a high-stakes channel, and Fin adapts to customers across emotional states, clarifies when needed, and confirms key details before taking action. Most of the time, Fin can resolve the query in full, and when it can’t, it seamlessly hands off to the human team, maintaining full customer context and history. You also get multiple improvements to call quality, plus proactive outbound calls to follow up on unresolved issues—all orchestrated by robust AI workflows.

Third — Fin Voice 2 gives you total control with industry-leading tools to configure and manage how Fin behaves. You get rich, detailed insights into call behavior and quality, the most common topics of calls, and one-click recommendations to improve. As with everything in Fin, you can fully self-serve and then manage it all with ease, without requiring professional services. Many vendors only let you set up their voice agent under supervision; with Fin, you get everything you need to iterate fast.

If you haven’t tried the demo yet, go check it out: https://fin.ai/voice. If you prefer to wait, don’t be surprised when you end up speaking with it at a favorite brand soon.

From a product management lens, this is what matters: latency is a feature customers feel; transparency builds trust in enterprise AI; and control is non-negotiable for CX leaders. The combination of a purpose-built, agentic AI architecture, measurable gains in resolution and CSAT, and true self-serve configuration signals that voice is moving from prototype theater to production reality. That’s the different game I want our industry to play.

Inspired by this post on The Intercom Blog.

June 4, 2026
Crafting Beloved Tech Brands: My Moonshot Marketing Playbook for the Post-LLM Era

I spend a lot of my time asking a deceptively simple question: what does excellent marketing actually look like in 2026? From the vantage point of product leadership, the answer isn’t a spreadsheet or a channel plan—it’s a feeling. Beloved tech brands earn the benefit of the doubt, create gravity around their roadmap, and make customers proud to belong. That kind of momentum is not an accident; it’s a system.

Here’s the hard truth I’ve learned building and scaling products: giving teams different goals creates dysfunction. When brand, demand gen, product marketing, and comms run on fragmented OKRs, you manufacture internal headwinds. “Marketing is one engine – not separate pieces.” One strategy, one narrative, one set of outcomes—expressed through different craft disciplines and time horizons.

That unity of purpose clarifies executive roles, too. The real difference between an SVP and a CMO is scope and narrative ownership. A great CMO architects the whole system—portfolio allocation, brand architecture, integrated go-to-market strategy, and the bar for creative taste—while refusing to get dragged into decisions they should never be making (for example, approving every headline or micromanaging channel tactics). Leaders should decide the outcomes, standards, and constraints; teams should control the craft.

On portfolio design, I run marketing like a portfolio of moonshots. You need a healthy mix: proven programs that compound, emergent bets that learn fast, and a small set of true moonshots that can change the slope of the curve. The point isn’t bravado; it’s risk-balanced exploration. If everything ships safely, you’re under-investing in differentiation. If everything is a swing for the fences, you’re not building a repeatable growth engine.

This is where taste becomes a strategic advantage. “Ubiquity is the opposite of cool.” If you want to be beloved, you cannot treat every channel, audience, and moment as equal. Early on, selective distribution, distinctive creative codes, and tight community loops create status and meaning. Later, you scale without sanding off the edges that made the product special.

Why do a few companies build a flywheel of momentum while others stall? They align story, product, and distribution. The product earns trust, the narrative creates aspiration, and the go-to-market strategy ensures the right customers experience both at the right time. Then perception cycles kick in—the Silicon Valley clock turns—and irrational optimism or skepticism can amplify signals. The antidote is compounding proof: consistent product shipping, community advocacy, and creative that makes people care.

Scaling taste across an organization is teachable. I codify brand principles, narrative guardrails, and examples of “right” versus “almost right.” I replace abstract feedback with decision rubrics—what we keep, kill, or revise and why. I run recurring creative reviews with a small cross-functional council, so judgment compounds. Taste can’t be fully automated, but it can be operationalized: shared references, a story bible, and a high bar for craft that’s explicit, not mystical.

In a post-LLM world, the fundamentals haven’t changed—but the frontier has. Generative tools supercharge iteration and research, yet the artistry never really left. You still need a point of view, a tension worth resolving, and a value proposition that’s felt, not just stated. Can taste be encoded in software? Parts of it—pattern libraries, style constraints, data-driven feedback—absolutely. But the spark that makes work unforgettable remains human: judgment, risk tolerance, and the courage to ship something that might not fit the playbook.

That’s why telling an optimistic, yet realistic story about AI matters. Over-automation drains humanity; under-automation wastes potential. The best work pairs AI Strategy with craft leadership: LLMs for rapid exploration, humans for narrative decisions and ethical judgment. Your message should show how AI expands customer agency, not just efficiency.

The brand-versus-growth debate is a false choice. The right story accelerates pipeline, and the right demand programs reinforce the brand. Look at Apple’s discipline around product truth and design codes, or Google Chrome’s “The Web Is What You Make of It (Dear Sophie)” for proof that emotion and utility can co-exist. Notion, Pinterest, Square, HubSpot, and Harley-Davidson show how community, identity, and product-led growth interlock when the company knows exactly what it stands for.

When it comes to launches, I’ve learned that announcement videos full of humans, lack humanity. Overproduced gloss often dilutes the truth customers seek: what problem does this solve, how quickly can I feel the value, and why does it matter now? Real users, real context, and a crisp arc from problem to promise will outperform most theatrics.

Practically, I architect my week to protect taste and outcomes. Early-week for strategy, portfolio reviews, and cross-functional alignment; mid-week for deep creative and product marketing work; late-week for decision clears and postmortems. I time-box “disruptive energy”—space to chase non-obvious ideas—and I guard it like any critical meeting. Without protected cycles for exploration, the urgent will always suffocate the important.

If there’s a single takeaway: playbooks are obsolete, but the fundamentals are not. The channels change; the psychology doesn’t. Run one engine. Allocate a true portfolio. Scale taste with rigor. In the AI era, make people care. That’s how beloved tech brands are built—and how they endure.

June 4, 2026
Stop Support Tickets Before They Start: How AI Unsticks Users and Lifts Conversions

Every moment of friction in a product carries a hidden cost: attention drifts, motivation wanes, and the next click becomes a support ticket—or worse, silent churn. Over the years, I’ve learned to treat “stuck” as an urgent product signal, not just an operational nuisance. When we unstick users in the flow, we protect revenue, brand trust, and the momentum that powers product-led growth.

Learn how Amplitude’s Global Support team uses AI Assistant to reduce support tickets, prevent user churn, and increase conversions.

I reference that line often because it captures a proven pattern: meet users where confusion peaks and resolve it instantly. In my practice, the formula is straightforward—pair behavioral analytics and session replay with a just-in-time AI Assistant, routed by clear driver trees. This transforms support from reactive firefighting into a proactive, in-product experience that accelerates onboarding and boosts user activation.

Here’s how I operationalize it. First, I use Amplitude analytics and behavioral analytics to surface high-friction steps—pages with elevated drop-off, loops, or rage clicks. Session replay clarifies the “why” behind the numbers, while cohort and retention analysis reveal who’s most at risk. Then I deploy targeted in-app guides and tooltip design to preempt known pitfalls, while an AI Assistant handles real-time questions with context from our knowledge base and product docs.

The AI Assistant is more than a chatbot. With well-structured AI workflows, it detects intent, pulls precise snippets from docs-as-code, and handles routine issues instantly. When complexity spikes, it executes a graceful handoff to consultative support via Intercom or a Zendesk integration—preserving conversation history and sentiment cues—so humans spend time where judgment matters. This hybrid model keeps response times low without sacrificing quality.

To de-risk changes, I lean on A/B testing and feature flags. I measure time-to-value, activation rate, and funnel conversion as leading indicators, while tracking ticket deflection, CSAT, and NRR as trailing indicators. The goal isn’t just fewer tickets; it’s faster learning loops and a compounding improvement in user outcomes. When we see activation curves steepen and onboarding friction flatten, we know the system is working.

Practically, I start with the top three friction points in onboarding, implement narrow in-app guides, and deploy the AI Assistant with strict guardrails and clear escalation paths. Weekly reviews align product, customer success, and solutions engineering around shared telemetry—so we tune prompts, content, and UI patterns together. Over time, I’ve seen ticket volume decline meaningfully, while conversion and retention rise as users experience fewer dead ends.

If you’re evaluating where to begin, identify the moments where confusion compounds—pricing configuration, integrations, and data mapping are common culprits. Then introduce targeted, context-aware help right where users hesitate. You’ll not only prevent “every stuck user” from turning into a ticket—you’ll convert friction into confidence, and confidence into growth.

Inspired by this post on Amplitude – Best Practices.

June 1, 2026
How I Champion Platform Excellence: Lessons in Analytics, Scalability, and Product-Led Growth

I’m continually inspired by platform specialists who champion their analytics platforms end to end. When I study their work, I look for the connective tissue between strategy and execution—how behavioral analytics informs decisions, how a unified analytics platform reduces tool sprawl, and how great documentation and enablement convert insights into habit across product, engineering, and go-to-market teams.

What consistently stands out is the rigor behind the scenes: clear data governance, privacy-by-design, and instrumentation standards that keep events trustworthy as products evolve. Platform scalability isn’t just about throughput; it’s about guardrails—naming conventions, schema versioning, and lineage—that let teams move quickly without sacrificing integrity. These are the unsung details that make insights reliable and repeatable at scale.

I also pay close attention to how experimentation gets operationalized. Thoughtful A/B testing, well-scoped feature flags, and crisp definitions of “minimum detectable effect (MDE)” ensure that experiments produce signal instead of noise. Driver trees, opportunity solution trees, and continuous discovery keep teams anchored on outcomes, while retention analysis translates curiosity into durable growth. This is the backbone of product-led growth: small, fast bets tied to measurable behavioral shifts.

Reliability and insight quality go hand in hand. Observability for event pipelines, anomaly detection to surface data drift, and targeted session replay help teams debug both product experience and analytics instrumentation. Paired with Web Vitals and clear ownership models, these practices shorten feedback loops, reduce blind spots, and keep platform credibility high—because trust is the real KPI behind every dashboard.

In my own practice, I translate these lessons into roadmaps that balance discovery with delivery, and align solutions engineering, product, and design around the same north-star metrics. The result is a culture where platform champions don’t just advocate for tools—they enable outcomes. If you’re scaling an analytics stack or elevating your product strategy, these principles will help you move faster, with confidence, and make every insight count.

Inspired by this post on Amplitude – Best Practices.

June 1, 2026
How I Build High-Impact Experimentation Programs with Amplitude: Proven Practices at Scale

I build experimentation programs to drive measurable outcomes, not just dashboards. In my product leadership work, I’ve seen how the right operating model turns experimentation into a reliable growth engine—especially when paired with the analytical depth of Amplitude. My goal is to help teams move from ad-hoc tests to a disciplined system that compounds learning and impact.

Rigor starts with clarity. I translate strategic goals into testable hypotheses using driver trees, then structure A/B testing with a defined minimum detectable effect (MDE), guardrail metrics, and pre-registered decision criteria. This reduces p-hacking, shortens debate cycles, and makes outcomes auditable. I’m equally deliberate about risk: we monitor sample ratio mismatch, use feature flags for safe rollouts, and align on outcomes vs output OKRs so we celebrate business impact, not vanity wins.

Amplitude analytics is my backbone for behavioral analytics at every step. I instrument clean event taxonomies, build funnels and cohorts to track user activation and retention analysis, and centralize experiment readouts in a unified analytics platform. This lets product trios quickly see how treatments shift behavior, where friction hides, and which moments matter most for product-led growth. The result is a trusted, shared source of truth that accelerates continuous discovery.

At enterprise scale, governance matters as much as math. I often point to lessons inspired by Peacock’s experimentation program: standard naming conventions, centralized QA, CI/CD integration, and an active community of practice. Those practices keep velocity high without sacrificing validity, and they make wins repeatable across teams and surfaces.

Operationally, I anchor the program in clear roles (data, engineering, design, product), templates for hypotheses and readouts, and a tight feedback loop from deploy to decision. With Amplitude, solutions engineering partnerships, and disciplined experiment hygiene, teams learn faster, ship safer, and build products customers love. That’s how experimentation becomes a strategic capability—not a side project.

Inspired by this post on Amplitude – Perspectives.

June 1, 2026
Turn Clicks into Revenue: How I Connect Behavior to Conversions with Persisted Properties

Every revenue story starts with a behavior: a tap, a scroll, a search, an “aha” moment. My job is to make sure we don’t just see those moments—we connect them directly to purchases so marketing, growth, and product can act with confidence.

"Learn how Amplitude’s persisted properties and session analytics help marketing and growth teams connect behavioral data to purchase outcomes without engineering support." That sentence captures the promise I look for in a modern analytics stack: attribution that endures across sessions and analysis that moves at the pace of experimentation.

Here’s how I frame it. Persisted properties let me carry forward the critical context behind a user’s journey—campaign touchpoints, audience attributes, and key in-product actions—so when a conversion happens, I can see the exact trail of behaviors that preceded it. Instead of losing signal between anonymous exploration and account creation, I keep the connective tissue intact and attribute outcomes to the interactions that truly mattered.

Session analytics completes the picture. By understanding how users navigate within each visit—where they hesitate, what they repeat, and which micro-conversions predict success—I can link behavioral analytics to revenue outcomes with far greater precision. In practice, this means better funnels, smarter cohorts, and faster iteration cycles inside Amplitude analytics. When appropriate, I’ll also pair findings with session replay for qualitative context, but the core decision loops are driven by quantifiable behavior patterns.

My operating rhythm is straightforward: I start by defining the purchase outcome clearly, then identify the minimal set of properties that must persist to tell the full attribution story. From there, I instrument events and validate that each persisted property is captured reliably across the journey. With clean inputs, I build conversion funnels, use cohorts to isolate high-intent behaviors, and apply driver analysis to separate correlation from causation. That’s how I isolate the behaviors that consistently generate qualified leads and high-value activations.

The impact is both strategic and immediate. Marketing can test offers and channels with a unified analytics platform and know which touchpoints lift conversion, not just clicks. Growth can optimize user activation flows based on the behaviors that truly predict upgrade. Product can prioritize the moments that drive retention analysis instead of chasing vanity metrics. Most importantly, teams move from opinion to evidence without waiting in an engineering queue.

In my experience, the real unlock comes when we use persisted properties to bridge pre-signup exploration with post-signup intent. That’s where product-led growth takes off: we can trace the first meaningful action to a downstream expansion event, tie it to a specific campaign or in-app guide, and then double down confidently. The result isn’t just better dashboards—it’s a tighter feedback loop between hypothesis, experiment, and measurable revenue impact.

If you’re aiming to connect behavior to outcomes with clarity and speed, lean into persisted properties and session analytics. You’ll empower teams to discover the “moments that matter,” attribute them accurately to conversions, and iterate toward a repeatable growth engine—without slowing down your roadmap or depending on engineering for every new question.

Inspired by this post on Amplitude – Best Practices.

May 28, 2026
Building AI-Era GTM and Analytics That Make Tough Calls Simple: A Product Leader’s Playbook

I build "GTM and analytics products for the AI era—tools that make hard calls simple." That guiding principle shapes how I design systems, prioritize roadmaps, and lead teams: we earn speed by engineering clarity. My north star is straightforward—turn noisy signals into trusted insights that move the business, without adding friction for customers or chaos for teams.

In practice, this starts with behavioral analytics. Whether you're using Amplitude analytics or a homegrown stack, the goal is the same: a unified analytics platform that captures clean events, enforces a clear taxonomy, and maps behaviors to outcomes. I focus on journey mapping, activation and retention analysis, and honest attribution so that every GTM motion ladders to real product usage, not vanity metrics.

Decisions should be testable and reversible. I operationalize experimentation with A/B testing, feature flags, and guardrailed rollouts. Minimum detectable effect, power analyses, and anomaly detection aren’t academic exercises; they’re the foundation for credible learnings. When a result is unclear, we tighten hypotheses, shrink blast radius, and iterate quickly—biasing for learning while protecting the customer experience.

AI changes the surface area of product work, but it doesn’t change the discipline. I treat LLMs for product managers as a capability, not a shortcut: eval-driven development, clear success criteria, and human-in-the-loop feedback remain non-negotiable. Privacy-by-design and data governance shape what we build; responsible prompts, retrieval strategies, and safety checks shape how it behaves in the wild. When the model is uncertain, the product should be honest about it—and offer a graceful fallback.

Great GTM is a system, not a launch day. I connect product strategy to go-to-market strategy through product-led growth loops: in-app guides that meet users where they are, onboarding that accelerates time-to-value, and signals that identify true qualified intent. Driver trees tie adoption to monetization so that marketing, sales, and success work from the same picture—making trade-offs visible and reversible.

Execution is where clarity compounds. Continuous discovery with product trios keeps problems crisp and solutions grounded in user truth. Product roadmapping and sprint planning follow outcome-first principles: fewer projects, clearer intents, stronger accountability. When teams can trace every backlog item to a metric that matters, they move faster with less oversight—and deliver results that stand up to scrutiny.

When we do all of this well, decisions feel simple because the work behind them is rigorous. That’s the promise of modern GTM and analytics in the AI era: no theatrics, just dependable systems that turn possibilities into predictable progress.

Inspired by this post on Amplitude – Best Practices.

May 28, 2026
Inside Growth Engineering at Amplitude: My Playbook to Accelerate Product-Led Growth with Analytics

I’m often asked how leading growth teams turn insights into compounding business results. Few organizations illustrate this better than the Growth Engineering team at Amplitude. Drawing from their example and my own experience, I’ve distilled a practical playbook that any product organization can use to move faster, learn smarter, and scale impact.

At the core is a disciplined blend of behavioral analytics and rapid experimentation. Amplitude analytics, as part of a unified analytics platform, enables precise event instrumentation, cohorting, and funnel analysis that surface where activation and retention truly break down. When I combine those signals with qualitative insights, I can prioritize fewer, higher-leverage bets that directly improve user activation and long-term retention.

My growth loop always starts with clearly stated hypotheses, success metrics, and A/B testing power considerations, including a defined minimum detectable effect (MDE). I pair feature flags with staged rollouts to de-risk changes and accelerate iteration without compromising stability. This cadence turns every release into a learning opportunity, compounding knowledge across teams and time.

Cross-functional execution is non-negotiable. I rely on tight “product trios” collaboration—product, engineering, and design—so we can ship small, measurable changes quickly, observe outcomes, and then widen scope with confidence. The Growth Engineering mindset keeps us grounded in real user behavior, not assumptions, and ensures our roadmap is fueled by evidence rather than opinion.

Consider onboarding. Instead of a single redesign, I prefer a series of targeted experiments—tweaking progressive disclosure, refining tooltip design, and adding in-app guides where users predictably stall. Each test is instrumented end to end, from first action to activation event, and validated via retention analysis to confirm that short-term lifts turn into durable habit formation.

When prioritizing, I map ideas to driver trees tied to our North Star metric. Behavioral analytics tell me which levers—time-to-value, depth-of-use, or frequency—will yield the biggest gain. That clarity focuses engineering effort on interventions that actually shift outcomes, not just outputs.

If you’re building your own Growth Engineering capability, start with three moves: instrument ruthlessly so you can trust your signals, adopt feature flags to speed safe experimentation, and hold teams accountable to measurable, user-centric outcomes. Do this consistently and you’ll feel the compounding effect—faster learning cycles, stronger product-market fit signals, and a durable engine for product-led growth.

Inspired by this post on Amplitude – Perspectives.

May 27, 2026
Speed-to-Lead Is Dead: How AI Agents End the Wait and Rebuild a High-Velocity Sales Org

A prospect lands on our site, skims pricing, watches a demo, and clicks “contact sales.” For years, that’s where momentum died. They waited, and we built entire sales motions around managing that delay.

We optimized for “speed-to-lead,” made it the hallmark of a high-performing sales development org, hired more SDRs, tuned routing rules, added shift coverage, and stared at response-time dashboards. Typical SLA targets were one hour for best-fit leads, four hours for core MQLs, forty-eight hours for everyone else. Those were considered good numbers.

No one questioned the premise because the lag felt structural—shift scheduling, routing delays, and humans working 9–5. The fastest teams could only shrink the gap; nobody could remove it.

An AI Agent closes it completely.

When a prospect arrives today, the conversation can begin immediately. That single change reshapes how I design a sales org—how we staff it, what our team prioritizes, and the metrics we hold ourselves accountable for.

Step outside our dashboards and look at the buyer experience. We spend heavily to drive traffic, then push visitors into forms and queues that add friction precisely when purchase intent peaks.

Intent is highest the moment someone seeks out our product. If an SDR follows up two or three hours later, that buyer’s in another meeting, the urgency has faded, and the moment is gone. We still call it a lead; the buyer has already moved on.

What AI changes

Agents eliminate the structural constraints that made speed-to-lead a problem—shift scheduling, routing delays, CRM batch processing, the SDR being on another call. None of it applies anymore because every single lead can be engaged immediately, at any hour and in any language.

The impact goes beyond response time. When an Agent engages at peak intent, qualification, discovery, and even an initial demo moment can unfold in a single, continuous conversation. The gated funnel collapses. There’s no reason to qualify someone today, schedule discovery for Thursday, and demo the following week when the conversation is already happening.

The constraint the industry built around simply isn’t there anymore. We’re already seeing it with Fin, a Customer Agent. As sales leaders, we need to frame this differently.

If speed-to-lead is no longer the constraint, the knock-on effects reach every part of the org.

Introduce Fin for Sales to your team with this clean hero banner: bold headline, signature blue spiral, and a clear 'Start free trial' call to action—inviting readers to explore an AI customer agent built for revenue.

SDRs focus on moving deals forward. Instead of frontline triage, they double down on phone-based selling and relationship building, complex deal navigation, and multi-threaded engagement across stakeholders—the high-leverage work that used to get crowded out by the inbox.

Pipeline gets more relevant. The old model rewarded volume: capture as many form fills as possible, respond fast, and sort quality later. When an Agent engages at the moment of intent, it qualifies during the conversation. Low-fit leads get filtered out before they reach the team, and high-fit prospects arrive with context—needs, timeline, stakeholders—instead of just a name and email.

You measure outcomes, not response time. When first response is instant, different metrics matter. I anchor on three questions:

1) Is the Agent doing the work? Completion rate, qualification rate, and contact capture rate indicate whether conversations reach clear outcomes and produce usable handoffs to the team.

2) Is the work producing pipeline? Meetings booked and pipeline created through Agent-handled conversations are the leading indicators of revenue, not how fast someone followed up.

3) Are buyers having a good experience? Conversation-level satisfaction matters more than ever because the Agent is the first interaction prospects have with your company. The experience it delivers is the first impression you make.

These three questions reveal whether the motion is working. Time-to-first-response can’t.

Sales orgs built hiring plans, workflows, and performance metrics around beating intent decay. That made sense when the lag was unavoidable. It isn’t anymore.

An Agent is always on. It engages the moment a prospect arrives on your site, qualifies them in real time, and routes them to the right outcome without waiting for someone to be free. The lag the industry built itself around doesn’t exist when the conversation starts immediately.

The companies leaning into this are investing in what happens after the conversation starts: how well the Agent qualifies, where it creates pipeline, and what SDRs should actually spend time on. What matters now is not how fast you respond, but what the conversation produces.

Speed-to-lead made sense when the delay was structural. It isn’t anymore. If you’re re-architecting go-to-market, instrument Agent Analytics, revisit SDR charters, and tighten CRM integration so every qualified handoff is instant, traceable, and revenue-linked.

Inspired by this post on The Intercom Blog.

May 26, 2026
Is Technology Still Net Positive? A Product Leader’s Reckoning and Playbook for Humane Growth

I’ve spent my career building products on top of the internet, championing social media, and now scaling AI. Lately, I keep returning to an uncomfortable but necessary question: are we still building a net positive future—or have we drifted into something else entirely?

A recent long-form conversation in my podcast queue challenged me to do a deeper self-audit. If you want to hear the debate that sparked this reflection, you can listen on: Spotify | Apple Podcasts. What follows is my synthesis as a product management leader: the hard truths, the hopeful paths forward, and the practical actions I’m taking with my teams.

The moment that hit me hardest was a family member’s blunt assessment that the internet has become “net negative.” That phrase landed like a wake-up call—a reminder that those of us inside tech often operate in an echo chamber. We see our roadmaps, our metrics, our progress; the rest of the world experiences the second-order effects. As a leader, I have to seek out those outside-in perspectives with the same rigor I apply to any product discovery practice.

Another truth I can’t ignore: somewhere along the way, parts of our industry slid from “make people’s lives better” to “extract maximum value at any human cost.” You can see it in incentives that prioritize growth at all costs, in waves of layoffs that treat people as an expense line, and in platform behaviors that resemble a modern tycoon era. This isn’t just a moral critique—it’s a product strategy risk. Extractive models erode trust, weaken retention, and invite regulatory and reputational headwinds that no amount of optimization can out-execute.

The loneliness crisis is real, and technology has too often replaced human connection instead of augmenting it. Spend a week in San Francisco and you’ll notice what I call “isolation by design”—QR-code menus, autonomous Waymos, frictionless everything, but fewer genuine human moments. It’s efficient, yes, but alienating. No algorithm can substitute for physical touch, care, and community. As builders, we should design products that create on-ramps to real-world connection, not cul-de-sacs of infinite scroll.

We still have agency. “Don’t be evil” shouldn’t be a nostalgic slogan; it should be a minimum bar. Responsible product management means being a citizen of the ecosystems we influence: naming trade-offs clearly, instrumenting for externalities, and building AI risk management into our operating cadence. It also means stepping outside the industry narrative to ask neighbors, parents, teachers, and small business owners how our products actually land in their lives.

One idea that gives me hope is “mom and pop tech”: AI-enabled, hyper-local tools crafted for specific neighborhoods and communities. Think “inch wide, mile deep”—software that solves a real problem for a defined community rather than chasing a horizontal total addressable market. Consider ride share. The extractive platform playbook maximized liquidity but squeezed drivers and frayed local fabric. A community-owned alternative could optimize for safety, fair wages, and neighborhood vitality over blitz-scaled margins. That’s civic tech with a viable product strategy.

I’m also watching how social norms evolve. At a recent Elternabend at a German primary school, parents collectively agreed to delay smartphones until age 11 or 12—a striking shift from just five years ago when many 7–8 year olds had devices. Culture moves, sometimes faster than we expect. Product-led growth that ignores cultural momentum (or ethical guardrails) is fragile growth.

So what do we do on Monday morning? First, rebuild our discovery muscles outside the echo chamber: continuous discovery with the people most affected by our products, not just our power users. Second, measure what matters: add well-being, community impact, and qualitative trust signals to the same dashboards that track activation and retention. Third, resist technology FOMO—choose fewer bets and go deeper, especially where AI can be applied responsibly to unlock real-world value. Fourth, cultivate communities of practice that normalize responsible experimentation, privacy-by-design, and transparent communication. Finally, narrate the change: as product people, we are educators as much as we are builders; our stories shape what teams believe is possible.

If you’re looking for frameworks to anchor this work, revisit classics like Bowling Alone: The Collapse and Revival of American Community for context on social capital, and pair that with modern conversations on local resilience and community spaces. The future isn’t written yet. With clear principles, careful incentives, and the courage to narrow our scope in service of depth, we can still build technology that strengthens the bonds that make life worth living.

I’d love to hear how you’re approaching this in your organization—especially examples of “mom and pop tech,” AI Strategy in service of community, or product strategies that trade a little scale for a lot of human good. Join the conversation in the comments.

Inspired by this post on Product Talk.

May 26, 2026