Category: Product Management

4 Costly Misconceptions About AI Agents—and What Product Leaders Must Do Instead

Building AI agents looks deceptively simple right now. After leading multiple agentic AI initiatives, I’ve learned that the difference between a demo and a dependable product comes down to disciplined product discovery, ruthless scoping, and a clear AI Strategy that aligns with business outcomes. Here are four common misconceptions I correct early with stakeholders—and the practices I use to avoid expensive detours.

Misconception 1: “An LLM plus a few prompts is a production-ready agent.” In reality, production-grade agents require orchestration and rigor: tool-use and retrieval, memory design, state management, deterministic fallbacks, and continuous evaluation. I instrument Agent Analytics from day one to trace tool calls, latency, error codes, and cost per task; then I use A/B testing with a clear minimum detectable effect (MDE) to validate improvements before broad rollout. This is where product roadmapping and sprint planning matter—sequencing capabilities so we avoid building speculative features that don’t move outcomes.

Misconception 2: “More autonomy is always better.” The right autonomy level is contextual and risk-adjusted. For high-stakes workflows, I design for human-in-the-loop and role-based guardrails, grounded in privacy-by-design and data governance. Policies like least-privilege access, audit logs, and reversible actions reduce operational risk while still delivering leverage. In practice, this hybrid approach also controls cost: narrower scopes, clearer prompts, and bounded tool access reduce hallucination surface area and improve reliability—key to AI risk management.

Misconception 3: “If we build it, users will adopt it.” Adoption is earned with thoughtful onboarding and in-app guidance, not promised by a feature launch. I pair agent launches with targeted product tours, contextual tooltips, and progressive disclosure to drive user activation and product-led growth. Increase revenue, cut costs, and reduce risk with Pendo’s Software Experience Management platform. Optimize the entire software experience to drive adoption and improve engagement. Whether you use Pendo or a comparable solution, the principle stands: instrument the experience, run experiments, and iterate quickly based on evidence, not intuition.

Misconception 4: “Security, compliance, and governance can wait.” Deferring controls is a false economy. I embed AI risk management from day zero: prompt injection defenses, PII redaction, DLP, grounding and citation strategies, and threat detection and response. Clear data retention policies, vendor diligence, and model evaluation standards keep leadership, security, and legal aligned. This is the crux of building trust—and it’s far easier to design up front than to retrofit under pressure.

How I execute in practice: start with a tightly framed use case tied to a measurable outcome; define outcomes vs output OKRs; build a slim vertical slice to validate feasibility; instrument Agent Analytics from the first commit; ship behind feature flags; and operationalize learning loops across support, success, and GTM. The result is a durable path to product-market fit for agentic AI—one that compounds learning while minimizing blast radius.

The leaders who win with AI agents won’t be the ones who move fastest in a demo. They’ll be the ones who manage risk transparently, learn in public with their users, and turn continuous insight into competitive differentiation. If you’re planning your next agent milestone, align the roadmap to outcomes, treat governance as a feature, and make adoption your North Star.

Inspired by this post on Pendo – Best Practices.

October 25, 2025
Design Four High-Impact Lifecycle Journeys with Pendo Orchestrate to Drive Retention

I’ve spent my career building product-led growth motions that deliver value fast and build durable retention. The most consistent pattern I’ve seen is simple: When we orchestrate timely, contextual guidance inside the product, customers discover value sooner, adopt core workflows more completely, and return more often. That’s exactly where Pendo Orchestrate shines for my team.

From first click to lifelong retention, you’ll deliver the right message at the exact right time, every step of the way. With Pendo Orchestrate, you can design those kinds of moments with intention. And in this blog, we’ll show you how.

At a high level, I map the customer lifecycle into four journeys—onboarding, activation, retention, and expansion—and align each to clear outcomes. Using targeted in-app guides and product tours, behavioral triggers, and segment-specific messaging, I can optimize each stage without overwhelming users. What follows is how I approach each journey to maximize time-to-value and retention.

Onboarding: I design progressive onboarding that adapts to a user’s role and first-run actions. Instead of a single, long product tour, I use short, contextual nudges that appear exactly when a user reaches a relevant screen or performs a key event. This reduces cognitive load, shortens time-to-value, and sets up a reliable path to initial success. When needed, I A/B test different sequences and measure impact on activation rate to ensure we’re improving the real user experience, not just adding more guidance.

Activation and habit-building: After first value, I focus on reinforcing the behaviors that correlate with long-term retention. Here, lightweight tooltips, celebratory moments when users reach the “aha” action, and just-in-time prompts for adjacent features help form habits. I track cohort-level activation metrics and use retention analysis to see whether these nudges translate into sustained product usage. If a segment stalls, I adjust copy, timing, or the sequence to better match user intent.

Retention and re-engagement: Not every customer stays on a steady path. For at-risk cohorts—users who haven’t completed a critical workflow or whose usage is declining—I trigger helpful, empathetic in-app guides that remove friction and offer a direct path back to value. I also solicit lightweight feedback to understand obstacles. The goal isn’t to interrupt; it’s to make it effortless to recover momentum.

Expansion and upsell: When users demonstrate readiness—mastery of core features, frequent usage, or role-based signals—I introduce advanced capabilities with targeted product tours and clear value propositions. Timing is everything; I prefer unobtrusive prompts that appear at the exact moment their workflow benefits from an upgrade. By matching message to milestone, expansion feels like a service, not a sell.

Operationalizing these journeys starts with crisp definitions of success (activation, adoption depth, and retention), thoughtful segmentation, and a cadence of experimentation. I keep the loop tight: instrument key events, launch small, measure outcomes, and iterate. Over time, the orchestration becomes a durable system—consistently delivering the right guidance to the right user at the right moment, and continuously compounding product impact.

If you’re looking to scale product-led growth, these four journeys provide a pragmatic blueprint. Start with the stage that’s hurting most (often onboarding), prove the lift, then expand. As outcomes improve, your users feel supported, your product experience feels intuitive, and your business earns the retention and expansion it deserves.

Inspired by this post on Pendo – Best Practices.

October 25, 2025
WTF is MCP? The powerful protocol giving enterprise AI agents real-world autonomy

I get asked this constantly by boards, CIOs, and product teams: WTF is MCP, and why does it matter for enterprise AI? Here’s my straightforward take from the trenches of rolling out agentic AI across complex, regulated environments—and why it changes how we design, govern, and scale autonomous capabilities.

“Model Control Protocol gives your AI agents arms and legs to go do stuff with your data.” That framing resonates because it’s both simple and accurate. MCP turns passive “chatbots” into active agents that can safely take action within defined guardrails.

In practice, MCP is the connective tissue between models and the tools, systems, and workflows we trust. It standardizes how agents request permissions, execute tasks, and report outcomes—so enterprises can move from demos to durable operations. The benefit isn’t just autonomy; it’s autonomy with accountability, aligned to our AI Strategy and data governance obligations.

When I pilot agentic AI in production, I start with a narrow scope: which systems the agent touches (for example, CRM integration via HubSpot), what actions it can take (read, write, or propose), and what evidence it must log (inputs, outputs, and approvals). That discipline keeps us compliant with privacy-by-design while unlocking real business impact.

Great MCP use cases emerge where read-write actions compress time-to-value. Think: pulling Amplitude analytics cohorts to personalize outreach, auto-generating Pendo in-app guides based on feature adoption, or triggering customer support workflows with predefined playbooks. Each action is observable, reversible, and measured—because in the enterprise, repeatability beats novelty.

From a product management leadership perspective, I treat MCP-enabled agents like any other product surface. We define clear outcomes, not outputs: success rate per task, mean time to resolution, quality score, and safety incidents. We validate uplift with A/B testing and a minimum detectable effect (MDE) before scaling. Then we feed results into an Agent Analytics dashboard, just as we would for product-led growth funnels.

Governance is where MCP earns trust. I enforce least privilege, time-boxed credentials, environment isolation, and tamper-evident audit logs. Every tool call is tied to a business purpose, owner, and SLA. We integrate with existing threat detection and response processes so cybersecurity teams see the same telemetry they’re used to—no shadow AI, no surprises.

There’s also an adoption playbook that works: start with a contained domain, ship a sandboxed agent, require human-in-the-loop approvals, then progressively relax controls as accuracy and alignment improve. Document the boundaries in plain language, and instrument everything from day one. This is how we de-risk AI risk management while accelerating impact.

The most exciting shift is cultural: teams move from asking “Can the model do this?” to “What outcomes should the agent own—and what guardrails make that safe?” That mindset unlocks empowered product teams, clearer ownership, and faster iteration. MCP is simply the operational backbone that lets those choices stick.

If you’re evaluating where to start, pick one workflow with high frequency, clear rules, and measurable outcomes. Wire it to MCP with tight scopes, ship it to a friendly cohort, and learn aggressively. Autonomy isn’t the end goal—reliable, governed value is. MCP just makes that scalable.

Inspired by this post on Pendo – Best Practices.

October 25, 2025
4 Proven Ways GTM Teams Drive Explosive Growth with Pendo’s HubSpot Integration

In my role leading product management, I’ve learned that the most reliable path to product-led growth is aligning product signals with the systems our go-to-market teams use every day. That’s exactly where Pendo’s HubSpot integration shines—by merging behavioral insights with CRM context so sales, marketing, customer success, and product move in lockstep.

See how customer behavioral data can help sales, marketing, customer success, and product teams create a better, more engaging customer experience.

First, I use the integration to create a single source of truth that blends in-app behavior with account and contact data. When product usage, feature adoption, and intent signals flow into HubSpot, lead scoring becomes smarter, pipeline quality improves, and our go-to-market strategy gets more precise. Reps prioritize the right accounts, marketing tunes messaging to demonstrated needs, and we operate as a unified analytics platform instead of scattered tools.

Second, I activate lifecycle journeys directly from HubSpot using in-app guides and product tours. By targeting experiences based on CRM stage or persona, onboarding accelerates, trial conversion increases, and time-to-value drops. The ability to personalize onboarding without engineering work gives marketing and customer success a powerful lever to deliver exactly the right guidance at the right moment.

Third, I orchestrate customer success playbooks that reduce churn and expand revenue. Health scoring improves when retention analysis is informed by real product usage, not just survey sentiment. When usage dips below a threshold, HubSpot workflows trigger save-plays; when product engagement surges, we operationalize expansion motions across self-serve upgrades and account-based upsell. The result is a tighter feedback loop between product adoption and revenue outcomes.

Fourth, I close the loop between sales, product, and marketing to refine product positioning and roadmap priorities. Signals from Pendo in HubSpot highlight which features correlate with win rates and renewals, so we double down on the value proposition that actually converts. Those same insights inform targeted campaigns, sharper messaging, and a continuous learning cycle across GTM and product teams.

To make this work in practice, I start with clear event taxonomies, privacy-by-design data governance, and tightly scoped use cases that we can measure within a quarter. We iterate with small A/B tests, compare outcomes to baselines, and socialize wins across sales, marketing, and customer success to build momentum. The integration becomes more than a data pipe—it’s an operating system for coordinated growth.

When product signals meet CRM workflows, teams stop guessing and start executing with confidence. That’s the power of Pendo’s HubSpot integration: it operationalizes product-led growth across the entire customer journey, from first touch to expansion.

Inspired by this post on Pendo – Best Practices.

October 25, 2025
Pendo Admin Power Checklist: 4 Proven Practices to Drive Adoption, Clarity, and Trust

Overseeing complex platforms like Pendo is where product leadership comes to life. I rely on four disciplined practices to keep our instrumentation clean, our in-app experiences on-brand, and our analytics credible enough to guide high-stakes decisions. If you’re setting up or tuning your instance, this checklist will help you build trust with stakeholders and accelerate product-led growth.

Learn best practices that every Pendo admin should know.

1) Standardize tagging and taxonomy. I start by defining a clear naming convention for feature tags, page tags, and track events (for example, feat:[area]:[action]). This taxonomy lives in a shared document, aligns to our product roadmapping and sprint planning, and includes ownership, definitions, and “do/don’t” examples. In practice, this reduces duplicates, improves segment reliability, and makes funnels, paths, and retention analysis far more actionable. I also schedule quarterly hygiene to retire stale tags and revalidate critical measures tied to OKRs.

2) Segment deliberately and manage access with intention. Meaningful segments—role, lifecycle stage, plan tier, and account health—unlock precise targeting for in-app guides and stronger insights. On the admin side, I enforce least-privilege access with SSO/SCIM, audit changes to tags and guides, and keep visitor and account ID strategies consistent across environments. This combination strengthens data governance and privacy-by-design while reducing operational risk.

3) Operationalize a guide lifecycle. In-app guides are powerful, but only when they’re coherent and governed. I maintain a style system and reusable templates for tooltips, walkthroughs, onboarding checklists, and the Resource Center so the UX feels intentional, not noisy. Every guide goes through QA in staging, frequency capping, sunset dates, and an owner accountable for outcomes. I measure impact with clear success metrics—adoption lift, funnel completion, or onboarding time—to ensure guides serve the product strategy, not just add UI clutter.

4) Build an analytics cadence that leaders can trust. I treat Pendo as a decision system, not just a dashboard. That means SDK updates are part of our release checklist, known key events are smoke-tested after deployments, and weekly insight reviews turn funnels, paths, and retention analysis into clear actions. Where appropriate, I pair experiments with A/B testing guardrails and tie findings back to outcomes vs output OKRs. Finally, I publish a simple “what we learned” summary to keep stakeholders aligned and focused on the next best move.

Your 5‑minute checklist: confirm a shared tagging taxonomy; align segments to roles, lifecycle, and plans; apply least-privilege access and SSO/SCIM; standardize guide templates and QA; set metrics for every guide; and establish a recurring analytics review tied to OKRs. With these four practices in place, your Pendo instance becomes a flywheel for onboarding, product adoption, and continuous discovery—without sacrificing governance or customer trust.

If you’re scaling quickly, start small: pick one product area, instrument it cleanly, launch a targeted in-app guide, and run a focused funnel review the following week. Momentum builds when teams see crisp insights and customers feel helpful guidance at just the right moment.

Inspired by this post on Pendo – Best Practices.

October 25, 2025
SXM vs. the Rest: My High-Impact Playbook for Today’s Software Experience Tools and PLG

I spend a lot of time reviewing how customers move through our product and where their momentum stalls or accelerates. The tools you use to build and optimize software experiences are evolving. That simple truth reshapes our strategy every quarter, from the analytics we trust to the in-app touchpoints we design and the experiments we run to improve product-led growth.

When I say SXM, I’m talking about a comprehensive software experience management approach that unifies analytics, experimentation, in-app guides, messaging, and feedback loops. SXM vs. the rest is the real-world choice between an integrated platform and a patchwork of point solutions. I’m not married to one path; I’m obsessed with outcomes—speed to learning, lower friction for teams, and compounding retention gains.

The foundation is a unified analytics platform and a clean, consistent event schema. From there, I pair behavior analytics with in-app orchestration: tools like Amplitude analytics for deep behavioral insights and Pendo for targeted in-app guides, product tours, and contextual nudges. I instrument rigorous A/B testing with a clearly defined minimum detectable effect (MDE) and follow through with retention analysis to validate whether an uplift sticks beyond vanity metrics. Great UX writing and thoughtful tooltip design often make the difference between a nudge that converts and a prompt that gets ignored.

I choose between best-of-breed and platform consolidation using first principles decision making. If a point solution unlocks a capability that meaningfully advances our product discovery or activation work, I adopt it. If multiple tools converge on the same points of parity, I consolidate to streamline governance, reduce integration overhead, and accelerate delivery. The goal is not more software; it’s faster, clearer learning that informs product positioning and drives customer value.

AI now sits at the center of this stack. I apply gen ai and agentic AI to accelerate hypothesis generation, automate cohort detection, draft UX microcopy, and suggest next-best actions inside the product. That said, AI risk management, privacy-by-design, and data governance are non-negotiable. I won’t trade trust for tempo; we can have both by putting guardrails around training data, access controls, and evaluation criteria.

Operating rhythm matters as much as tooling. Product trios set outcomes vs output OKRs, then test and iterate—starting with onboarding, activation, and the moments that trigger value realization. We build in measurable in-app guides, run A/B testing with tight feedback cycles, and keep our go-to-market strategy aligned so every nudge, message, and feature release supports product-led growth.

My playbook is simple: clarify the outcomes, instrument the journey end-to-end, choose the smallest toolset that can answer the biggest questions, and learn faster than the market. Map critical paths, standardize taxonomy, and make experimentation a habit—not a project. Then double down where signal is strongest and retire anything that adds minimal lift to retention or expansion.

SXM isn’t a buzzword; it’s a disciplined way to build software that feels intuitive, responsive, and valuable from the first click. With the right blend of analytics, in-app guidance, experimentation, and AI—grounded in strong product management leadership—we can turn insights into momentum and momentum into durable growth.

Inspired by this post on Pendo – Best Practices.

October 25, 2025
The 5 Stages of Software Experience Maturity: What to Fix First to Unlock Growth

I’ve led product teams through chaotic launches, painful plateaus, and breakout growth, and one truth keeps showing up: software wins when the experience is intentionally designed, measured, and continuously improved. To make that work repeatable, I rely on a simple maturity framework that aligns our product strategy, analytics, and in-app experience work across the organization. Find out where you stand—and what to fix first—with this maturity framework. Why “software experience” and not just “features”? Because activation, adoption, and retention depend on how clearly users understand value in their first sessions, how seamlessly they complete key workflows, and how consistently they succeed over time. That’s where empowered product teams, product-led growth, and outcomes vs output OKRs come together to create durable results. Stage 1 — Ad Hoc: At this level, teams ship features without a clear sense of who benefits, how success is measured, or how UX writing and onboarding shape outcomes. If this is you, fix this first: define your activation events, instrument the core funnel, and write concise, in-product copy that reduces friction. Even a lightweight retention analysis will reveal where value drops off. Stage 2 — Instrumented Awareness: You’ve added basic analytics and can see signups, activations, and drop-offs, often via tools like Amplitude analytics or a unified analytics platform. What to fix first: translate raw metrics into hypotheses and prioritize a small set of A/B testing experiments. Use a minimum detectable effect (MDE) to size tests, and start tracking leading indicators tied to adoption—not vanity metrics. Stage 3 — Guided Journeys: Onboarding, in-app guides, product tours, and contextual tooltips now clarify value and reduce time-to-first-value. What to fix first: build a guided path to activation for your top two personas, then test microcopy and sequencing. Pair qualitative insights from user feedback with cohort-based retention analysis to ensure your guides create durable behavior change, not just clicks. Stage 4 — Outcome-Driven Execution: Teams set outcomes vs output OKRs, run disciplined experiments, and connect learnings to roadmap decisions. What to fix first: standardize an experimentation playbook with clear guardrails for MDE, sample sizing, and stop rules. Align quarterly bets with a value proposition narrative that ties product discovery to measurable, customer-centric outcomes. Stage 5 — Predictive and Proactive: You anticipate user needs with tailored experiences, automate nudges at the right moments, and systematize continuous discovery. What to fix first: unify data across product, support, and lifecycle channels to personalize experiences without eroding privacy-by-design. Invest in scalable governance so insights flow to product trios and forward deployed engineers quickly and safely. How to use this framework: honestly score your current stage across analytics, onboarding, guidance, experimentation, and decision-making. Then pick the single change that removes the biggest bottleneck to the next stage—often a measurement gap, not a feature gap. Make improvements visible through product roadmapping and sprint planning, and celebrate progress to reinforce empowered product teams. In practice, maturity is not a badge; it’s a habit. When we pair rigorous analytics with thoughtful in-app experiences and clear strategic outcomes, we compound learning and unlock growth. If you’re unsure where to begin, start small: instrument activation, improve one critical guide, and run one high-quality experiment. Momentum follows.

Inspired by this post on Pendo – Best Practices.

October 25, 2025
6 Hard Questions Your AI Agents Must Answer to Win: Performance, Risk, and Real ROI

“Do you know how your AI agents are performing?” I ask this question in every review because it exposes whether we’re managing by outcomes or by anecdotes. Too often, teams point to latency, token counts, or completion rates and call it a day—useful signals, but not the story.

In my role, shipping agentic AI into production means I need decision-quality evidence, not vibes. That starts with Agent Analytics built on a unified analytics platform and instrumentation that lets me trace behavior, quantify value, and manage risk. Below are the six questions I use to separate novelty from durable impact.

1) What outcome are we optimizing for—and how do we measure it? If we can’t map the agent’s work to outcomes vs output OKRs, we’re optimizing noise. I anchor on task success rate, time-to-resolution, containment rate (no human handoff), cost per successful outcome, and downstream business impact (retention, conversion, NPS/CSAT) to keep us honest.

2) Are the right guardrails in place for AI risk management and data governance? I expect documented policies for prompt injection defenses, PII redaction, access control, and auditability. Every tool call should be permissioned, every data boundary explicit, and every failure mode observable. If we can’t demonstrate compliance by design, we’re scaling risk instead of value.

3) Can I explain every decision the agent made? Agentic AI needs traceability: prompts, intermediate reasoning, tool calls, retrieved context, and final outputs. I route key events into Amplitude analytics so product, engineering, and risk can slice behavior end to end. If we can’t reconstruct the path to an answer, we can’t debug, improve, or trust it.

4) What is the true cost per successful outcome? Raw token spend is misleading. I model total cost of ownership across retries, tool usage, escalations, and human review time—then benchmark against a consumption SaaS pricing lens. If cost per resolution trends up as volume grows, we haven’t built a scalable system; we’ve built a demo.

5) How does the agent learn without breaking what already works? My bar is a disciplined experimentation loop: offline evals, online A/B testing with clear guardrails, and a rollback plan. We predefine a minimum threshold for improvement before rollout and track regressions by persona, task type, and channel so we can localize fixes quickly.

6) Where is this agent creating durable differentiation? I look for capabilities competitors can’t easily copy: unique data advantages, superior tool orchestration, or workflows that compound learning. If the edge is just a base model prompt, the moat will evaporate; if it’s embedded in product workflows and proprietary signals, we’re building advantage.

Answering these six questions turns agentic AI from a novelty into a managed system. With Agent Analytics feeding a unified analytics platform, we can tie behavior to business outcomes, enforce governance, and make portfolio trade-offs grounded in evidence. The result is a product management leadership motion that prioritizes real ROI over vanity metrics—and scales with confidence.

If you’re not satisfied with the answers today, start by instrumenting the journey end to end, aligning metrics to OKRs, and setting clear risk thresholds. The compounding effects show up quickly when every iteration is measurable, explainable, and accountable.

Inspired by this post on Pendo – Best Practices.

October 24, 2025
How I Scale Revenue with Pendo Predict: Cut Costs, Reduce Risk, and Drive Product Adoption

When my team and I set out to accelerate growth without ballooning costs, we leaned into Pendo Predict as a keystone of our product-led growth strategy. Predict gives us a practical, data-driven way to focus on the right users at the right moments, align teams around measurable outcomes, and turn product usage signals into revenue impact.

“Increase revenue, cut costs, and reduce risk with Pendo’s Software Experience Management platform. Optimize the entire software experience to drive adoption and improve engagement.” That statement maps exactly to how we operate: we use the platform to understand user behavior, guide users through high-value actions, and instrument the experience so we can learn, iterate, and scale with confidence.

To scale revenue, we identify high-intent segments based on product behaviors and run targeted in-app guides and product tours that shorten time-to-value and boost conversion. Predict helps us surface which features correlate with expansion and retention, so our onboarding flows nudge users into those paths. This approach compounds: better activation drives stronger engagement, which fuels a healthier pipeline for cross-sell and upsell.

On the cost side, we reduce support load with contextual guidance—tooltips, checklists, and just-in-time education—so customers self-serve through common friction points. We consolidate insights in a unified analytics platform, enabling product, success, and go-to-market teams to work from the same source of truth. The result is fewer reactive escalations, tighter prioritization, and more engineering time invested in features that move retention and revenue.

Risk reduction comes from visibility and control. With predictive signals and retention analysis, we spot churn risk early, intervene with timely in-app messaging, and de-risk launches by rolling out features to targeted cohorts while monitoring adoption and engagement. We pair this with disciplined experimentation and A/B testing to validate changes before scaling broadly.

If you’re considering a similar motion, a simple playbook works: define your adoption and engagement metrics, instrument key workflows, create predictive segments, ship focused in-app guides, and measure impact against outcomes—not just outputs. Over time, this turns your product into a durable growth engine that consistently improves user experience and business performance.

Inspired by this post on Pendo – Best Practices.

October 24, 2025
How AI Is Supercharging Product Design: Faster Prototyping, Smarter Testing, and Better UX Outcomes

AI has fundamentally changed how I lead design and testing, not by replacing craft, but by compounding it. When my teams pair generative models with time‑tested product management practices, we move faster, learn sooner, and ship with more confidence—without compromising privacy-by-design or quality. The result is a tighter loop from product discovery to product-market fit lessons. Learn how Pendo’s product design team is using genAI and traditional tools to speed up design and development. That single line captures my own operating model: blend genAI with established toolchains to accelerate, not shortcut. In practice, I treat AI as a force multiplier for product trios—PM, design, and engineering—so empowered product teams can explore broader solution spaces while staying anchored to outcomes vs output OKRs. In discovery, genAI helps me synthesize qualitative inputs at scale—interviews, support threads, and in-app behaviors—into testable opportunity statements. I triangulate those insights with a unified analytics platform and Amplitude analytics to spot friction, then use in-app guides and product tours to target learning, recruit the right cohorts, and validate problems before we overbuild. For prototyping, gen ai for product prototyping is a game-changer. I generate multiple UX writing variants, microcopy, and flows in minutes, then narrow the set using heuristics and stakeholder feedback. Before any A/B testing, we precompute the minimum detectable effect (MDE) and sample size, making sure our experiments are powered to detect meaningful differences, not noise. In testing, I combine classic A/B testing with AI-assisted analysis to surface patterns faster. GenAI drafts experiment summaries, flags anomalous segments, and proposes follow-up tests, while my team makes the final calls. We deploy targeted in-app guides to onboard users into trials, monitor adoption via event telemetry, and iterate quickly until the value proposition is unmistakable. Execution depends on rigor and guardrails. We codify AI risk management and data governance policies, keep humans-in-the-loop for critical judgments, and log model prompts and outputs for auditability. This lets us move with speed and integrity, aligning stakeholder management, product roadmapping and sprint planning, and go-to-market strategy around measurable outcomes. The payoff is material: shorter cycle times, clearer value narratives, and stronger product-led growth curves. By fusing genAI with traditional practices, we preserve the craft of design while scaling our capacity to learn. That’s how we differentiate—through faster insight generation, smarter testing, and experiences that feel unmistakably intuitive.

Inspired by this post on Pendo – Best Practices.

October 24, 2025
SaaS + AI Is Here: How Our Summer 2025 Release Builds an Intelligent Foundation to Win

Leading product at HighLevel, I’m watching the convergence of SaaS + AI reshape how we build, price, and scale software. The winners will combine a sharp AI Strategy with disciplined product management leadership to ship real outcomes, not just demos. That’s why my team and I have been focused on giving you pragmatic ways to move fast without breaking trust. Give your company an intelligent foundation for the SaaS + AI era with our Summer 2025 Release. When I set priorities for this release, I optimized for three things: speed with quality, responsible AI, and measurable business impact. Practically, that means enabling agentic AI and gen ai workflows where they actually create leverage, unifying analytics so teams can make decisions from a single source of truth, and hardwiring data governance and privacy-by-design into every layer. If you’re wondering how to keep up, here’s what’s working for us and our customers: tighten product roadmapping and sprint planning around clear outcomes, not outputs; align teams with simple, observable OKRs; and empower product trios to run lean product discovery loops. These practices reduce cycle time while raising confidence, especially when introducing AI into core experiences. On the go-to-market side, I’m doubling down on product-led growth—shipping value into the product with in-app guides, thoughtful product tours, and frictionless onboarding. Pair that with rigorous retention analysis and A/B testing, and you’ll see which AI-powered moments actually move activation, adoption, and expansion. Don’t overlook the fundamentals either: smart SaaS pricing (including consumption models where it fits) can unlock the economics that sustain AI investments. My goal is to give you a foundation that is both ambitious and accountable—a platform you can trust to scale responsibly while your teams iterate quickly. If you’re planning your 2H roadmap, this release is built to help you ship faster, de-risk AI, and create outsized customer value in the moments that matter most.

Inspired by this post on Pendo – Perspectives.

October 24, 2025
Prioritize, Build, and Measure AI with Confidence: Lessons I Apply from PendomoniumX NYC

AI is moving faster than any product wave I’ve seen in my career, and that urgency demands rigor. At HighLevel, I anchor our AI Strategy around measurable outcomes, responsible delivery, and pragmatic execution—principles that a recent PendomoniumX NYC customer discussion reinforced for me. “Three product leaders sat down with Pendo to discuss how they’re balancing AI investments, building their AI roadmap, and measuring success.” When I decide what to fund, I start with outcomes vs output OKRs. If an initiative cannot tie to a defensible customer outcome—time-to-value reduction, revenue expansion, retention lift, or cost-to-serve efficiency—it doesn’t make the cut. From there, I pressure-test feasibility and risk through data governance and AI risk management lenses: model choice, training data readiness, privacy-by-design, security posture, and responsible use guardrails. Building the roadmap is where discipline meets speed. I use empowered product teams—product trios across PM, design, and engineering—to run tight discovery sprints. We validate desirability and viability with gen ai for product prototyping, then graduate concepts into delivery using product roadmapping and sprint planning habits that prioritize smallest shippable value. I’ve found the try do consider framework helpful to stage bets from low-risk utilities to higher-impact, agentic AI workflows. Measuring impact is nonnegotiable. I define success up front with a minimum detectable effect (MDE), then instrument adoption and behavioral change via Pendo and Amplitude analytics. A/B testing gives me causal confidence, while retention analysis tells me if AI features are durable value, not novelty. If we can’t attribute improvement to a metric that matters, we iterate or retire. Governance is a product requirement, not an afterthought. We maintain data governance standards, threat detection and response controls, and clear model evaluation criteria before anything reaches customers. That operating model helps us move quickly without compromising trust—a cornerstone in any product-led growth motion. For go-to-market and adoption, I rely on in-app guides, product tours, and contextual tooltips to shorten the learning curve. We measure feature discovery, task completion, and ongoing engagement to ensure the experience is intuitive. The goal is to make AI feel like a natural extension of the workflow, not a science project bolted onto the product. My simple playbook: prioritize by customer outcomes and risk posture, build with validated learning and smallest shippable value, and measure with rigorous analytics and OKRs. Repeat that loop, and AI stops being a buzzword—it becomes a compounding advantage.

Inspired by this post on Pendo – Perspectives.

October 24, 2025