Category: Product Management Leadership

Implementing AI Agents That Scale: My Playbook for One‑Person Departments with Amplitude

Over the past few years, I’ve led cross-functional teams to deploy agentic AI in production, and I’ve learned that success rarely hinges on the model alone. It comes from methodically designing the right workflows, instrumenting every step, and building a feedback loop that compounds. Learn how companies like Replit are consolidating workflows, creating one-person departments, and building systems for scale with Amplitude.

When I talk about AI agents, I’m describing software that behaves like a focused teammate—owning a clear job to be done end-to-end. In practice, that means consolidating fragmented tasks into a single accountable “one-person department,” then giving it the context, tools, and analytics to perform reliably. This is how agentic AI moves beyond demos into durable business impact.

I start with outcomes, not algorithms. I map a driver tree from business goals (e.g., lower response time, higher activation, better retention) to the specific moments an agent can influence. This outcome-first alignment keeps scope tight, informs guardrails, and grounds the value proposition in measurable change instead of vanity metrics.

Next, I define the workflow the agent will fully own. I look for high-volume, rules-adjacent processes—think lead qualification, support triage, or billing inquiries—where clear decision criteria already exist but human time is the bottleneck. I document triggers, inputs, decision points, and handoffs, then design the ideal-state flow the agent will run autonomously, with transparent escalation paths to humans.

On architecture, I favor a retrieval-first pipeline to keep responses accurate and current. I scope the knowledge base, implement context window management, and standardize tools the agent can call (search, CRM actions, ticket updates). For teams new to this, I coach “LLMs for product managers” fundamentals so we make sensible trade-offs between speed and reliability rather than chasing model-of-the-week headlines.

Instrumentation is where the system becomes self-improving. I use Amplitude analytics and an Agent Analytics schema to track intent detection, tool usage, resolution rate, time-to-resolution, deflection, and escalation causes. A unified analytics platform lets me connect agent outcomes to core product metrics—activation, retention, and conversion—so we can see the real revenue and experience impact, not just local efficiency gains.

To validate impact, I run A/B testing when traffic allows, setting a minimum detectable effect (MDE) upfront to avoid inconclusive reads. In lower-volume scenarios, I lean on eval-driven development: curated test sets for edge cases, scenario-based regression suites, and error taxonomies that accelerate iteration. Feature flags let us stage capabilities safely (shadow mode, assistive, autonomous) while we monitor deltas before full rollout.

Reliability and trust are designed in from the start. I apply AI risk management practices—privacy-by-design, data governance, and policy-aligned prompt templates—paired with observability to trace decisions. Clear escalation policies, incident management runbooks, and human-in-the-loop checkpoints ensure the agent fails safe, not silently.

Shipping cadence matters. I use CI/CD to increase deployment frequency, keep prompts and tools versioned, and gate risky changes with targeted rollouts. As patterns stabilize, we scale horizontally to new use cases, sharing core capabilities (retrieval, analytics, guardrails) as a platform. This is how “one-person departments” multiply without multiplying overhead.

Change management closes the loop. I partner with product trios and frontline teams to co-design prompts, set acceptance criteria, and define what “good” looks like in plain language. In-app guides and product tours introduce the agent’s role and limits, and structured feedback channels feed directly into our discovery and iteration rhythm.

The throughline of this playbook is simple: treat agents like real teammates with a job description, operating procedures, and performance reviews. With disciplined workflow design, a retrieval-first pipeline, and outcome-level instrumentation in Amplitude, agentic AI stops being a science project and starts compounding into durable product-led growth.

Inspired by this post on Amplitude – Perspectives.

February 18, 2026
Go From 3 Customer Interviews to a High-Quality Opportunity Solution Tree—In Minutes

Most product teams—and especially well-run product trios—know they should be interviewing customers. More teams than ever are actually doing it. That’s the good news.

The bad news? Many teams still struggle with what comes next. Turning raw recordings into a structured opportunity space that truly guides product discovery can feel overwhelming.

In my experience, interview synthesis is cognitively demanding work. You have to extract the key moments from each conversation, translate those moments into clear opportunities, and then organize those opportunities into a coherent view of your opportunity space. It’s no surprise I hear teams say, "We need to stop interviewing so we can catch up on what we’ve already learned." Too often, they pause—and never start again.

Recordings pile up. Maybe there are scattered notes. But nothing gets turned into an opportunity solution tree. The team hasn’t synthesized what they’ve learned, so the research isn’t actionable. That’s the gap I want to help close.

What if you could go from 3 interviews to a draft OST in minutes?

My AI goals are straightforward: 1) build tools that help you learn discovery and 2) build tools that help you do discovery. The learning tools are coming through on-demand courses. Today, I’m excited to share the first big step on the "do" side.

I’m excited to see an expanded partnership with Vistaly—the opportunity solution tree tool many of you already use—to bring AI-powered discovery tools directly into their platform.

Great synthesis happens in two steps: first, you synthesize each interview separately; then you synthesize across interviews. Most AI tools skip the first step and jump straight to cross-interview analysis—exactly how teams lose the nuance and context that make research actionable.

This approach does both. You upload three interviews for the same product outcome. The AI extracts the key moments and opportunities from each one separately. Then it synthesizes across those interviews and generates a first draft of your opportunity solution tree for you. Three interviews in. A draft OST out.

Here’s what this is—and what it isn’t. You’ve probably heard criticism of tools that promise "one-click opportunity solution trees." Those tools ask you to describe your market, click a button, and get a tree. The point of an opportunity solution tree is not to have one—it’s to synthesize what you’re learning from real customers so your team can align on the best path forward. A one-click tree built from made-up data is useless.

Turn interviews into insights in minutes with Vistaly. This hero screen invites you to enroll in beta and showcases an opportunity solution tree that maps outcomes to opportunities and actionable solutions.

This approach is fundamentally different. It starts with your real customer interviews. The AI does the heavy lifting of extracting key moments and opportunities from those conversations and organizing them into a draft opportunity solution tree. But it’s a draft—you review it, refine it, and reorganize it. You bring your judgment and context to the work.

My vision for AI-aided cross-interview synthesis is simple: AI identifies common opportunities across interviews, suggests a tree structure, and facilitates the team’s review. Historically, it’s been hard to give AI access to an opportunity solution tree in a way that preserves structure and context. The integration with Vistaly solves that problem by building this capability directly into the tool where your tree already lives.

In my own experiments using Claude, the AI surfaced opportunities I missed—and I caught things it missed. The highest-quality synthesis came from combining both perspectives. Research (see here and here) backs this up: Experts working with AI outperform both experts working alone and AI working alone. That’s the model we’re building toward—AI generates the draft, you bring the expertise.

I have mixed feelings about AI doing discovery work for us because there is real value in doing the synthesis yourself. But I also know that a draft OST you actually refine is better than a perfect process you never get to. This is about raising the floor—helping more teams get to a structured opportunity space, even if they aren’t doing every step manually.

We’re looking for a small group of alpha partners to help shape this product. To apply, sign up for a free Vistaly account and upload three customer interviews for the same outcome or product space.

We’ll select alpha partners from the applicants. We want a range of interview styles, experience levels, and product spaces. Selected partners will get access to the AI-powered synthesis tools and will work closely with the team to shape the product. Even if you aren’t selected for the alpha, your application puts you at the front of the line when we enter beta.

A few things to know as you apply: Your three interviews should be for the same outcome, goal, or product space, so the tool can generate a meaningful OST. You don’t need to be a Vistaly user today—the account is free. You don’t need to be an expert interviewer either; we’re looking for a range of experience levels, though we’re particularly interested in story-based customer interviews.

This is just the beginning. The vision is a full AI-powered discovery suite inside Vistaly—from interview analysis to complete interview snapshots to opportunity solution trees and beyond. We’ll learn alongside our alpha partners and share what we discover as we go.

If you’ve been looking to bridge the gap between your customer interviews and your opportunity space, this is your chance to help shape how that works. Apply for the alpha today.

Inspired by this post on Product Talk.

February 18, 2026
What I Learned Scaling Analytics: Candid Lessons on Product Strategy and Product-Market Fit

I write from a place many product leaders know well—the moment when the data you need to make decisions simply doesn’t exist, and you have to build the capability from the ground up. That firsthand experience with gaps in analytics shaped how I think about product strategy, product discovery, and the relentless pursuit of product-market fit lessons.

In my work, I lean on continuous discovery to surface the most meaningful problems, then translate those insights into outcomes vs output OKRs that keep teams focused on impact. When we anchor roadmaps to real user behavior and business results, we avoid vanity metrics and create a durable plan that compounds learning over time.

Execution matters just as much as insight. I rely on rigorous A/B testing, clear minimum detectable effect (MDE) thresholds, and retention analysis to separate signal from noise. This discipline ensures that every iteration—whether it’s a small UX nudge or a bold bet—moves us closer to measurable value for customers and the business.

None of this works without empowered product teams. I build around product trios that partner tightly across design, engineering, and product, and I foster a product-led growth mindset so we earn activation, engagement, and expansion through the experience itself. The goal is to create a system where learning is fast, ownership is clear, and the user’s job-to-be-done stays front and center.

On the tooling side, I favor a unified analytics platform so insights are consistent from discovery to deployment. Whether I’m instrumenting funnels with Amplitude analytics or stitching together qualitative and quantitative inputs, the principle is the same: give teams trustworthy, real-time visibility so they can make better decisions, faster.

If you’re looking to operationalize these practices, you’ll find practical playbooks, decision frameworks, and real-world examples here—built for leaders who want clarity, speed, and confidence in how they discover, ship, and scale products.

Inspired by this post on Amplitude – Best Practices.

February 17, 2026
Build a Support System That Scales: How Product Leaders Maximize Impact with Delegation and AI

I hear the same refrain from product leadership peers everywhere: we’re overwhelmed. Shrinking headcount, constant AI disruption, economic uncertainty, and relentless context switching make it feel like we’re carrying two jobs—setting strategy while shielding our teams. I recently listened to an episode of All Things Product that zeroes in on what a real support system for product leaders looks like, and it resonated deeply with my day-to-day.

Want to listen to the conversation yourself? Find it on Spotify or Apple Podcasts.

Here’s the core tension I see (and felt early in my own leadership journey): product leaders tend to underinvest in themselves. We hold onto work because it feels faster, safer, or “just easier if I do it.” But that pattern quietly taxes strategy, slows learning, and caps team throughput. The hidden cost of “doing it all yourself” is real.

Early in my tenure leading product, I tried to keep every plate spinning—roadmap reviews, stakeholder prep, user research, executive updates—while protecting my team’s focus. I was busy and useful, but not maximally valuable. The turning point came when I started building a lightweight support stack: a few hours of executive assistant help each week, targeted research support for bet sizing, and a personal cadence with a leadership coach. The result wasn’t just more time; it was better time.

One provocative point that landed hard: product leaders rarely have executive assistants—and that’s a problem. If your calendar is your operating system, an EA is an extension of your leverage. Mine now handles scheduling, meeting hygiene, prep packets, and post-meeting artifacts. That shift moved me from “calendar triage” to “strategic curation.” It also reinforced a core principle: delegation is a leadership skill, not a weakness. When I delegate outcomes (not just tasks), my team learns, ownership grows, and we ship decisions faster.

Support for strategy work shouldn’t stop at the calendar. Research and data enable better bets. Lightweight research ops, access to product analytics, and brief synthesis sprints keep me anchored in evidence without drowning in artifacts. Paired with a strong community of practice, I get a steady stream of comparative patterns—how other leaders delegate, scope advisory boards, or run decision reviews—which short-circuits trial-and-error.

Coaches were framed as shortcuts for clarity, accountability, and skill-building—and I agree. A good coach compresses cycles, sharpens decision quality, and holds the mirror up when you drift into doer mode. Two quotes captured the mindset perfectly: “You are a pro athlete. It makes sense to think about how you scale your impact without adding more to your calendar.” — Petra Wille. “As you get busier, it becomes more important to focus on the value only you can bring.” — Teresa Torres.

There’s also a helpful nudge to let go of perfectionism: “80% done by someone else is 100% awesome.” — Dan Martell (quoted). In practice, that means I accept great drafts from others, then add the 10–20% only I can contribute—context, narrative, and the sharp edges of the decision.

What about AI? The conversation hits a practical middle ground I share: use AI where it compounds leverage—meeting summaries, research synthesis starters, doc outlines, and backlog triage. But keep humans where judgment, alignment, and context truly matter—strategy framing, stakeholder management, and the final decision-making loops. In other words, apply an AI Strategy that respects product leadership’s uniquely human work.

Key themes I took away: why product leaders struggle to scale themselves; the true cost of “doing it all yourself”; why not having executive assistants limits impact; delegation as a core leadership capability; how to identify and protect the work only you can uniquely do; using research and data to inform strategy; coaches as accelerators for clarity and accountability; communities of practice as a force multiplier; adopting a “professional athlete” mindset; when AI helps—and when humans still matter; and the liberating mantra that “80% done by someone else is 100% awesome.”

If you’re wondering where to begin, start small and practical. Audit your time: what work truly requires you? Experiment with small amounts of support (even a few hours a week). Delegate outcomes, not just tasks. Keep the hands-on work you love—but be intentional. Use peers, coaches, and communities to learn how others delegate. Don’t wait until burnout to build your support system.

Resources mentioned if you want to go deeper: Follow Teresa Torres: https://ProductTalk.org. Follow Petra Wille: https://Petra-Wille.com. Petra’s Coaching for Product Leaders: https://www.petra-wille.com/coaching-packages. Dan Martell’s book Buy Back Your Time: https://www.buybackyourtime.com.

I’m curious: what’s one outcome you’ll delegate this week, and what support would make it stick? Share your thoughts in the comments—your playbook might be exactly what another product leader needs right now.

Inspired by this post on Product Talk.

February 17, 2026
Deeper AI Integration, Clearer ROI: How Mature Deployments Redefine Support Economics

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

System improvement: Every resolved query makes the system smarter.

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

Organizational agility: You scale service without scaling headcount.

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

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

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

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

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

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

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

Inspired by this post on The Intercom Blog.

February 13, 2026
Why “Figma Is Not the Source of Truth”: My Playbook for Design Leadership That Scales

I keep a simple mantra front and center: Figma is not the source of truth. The customer is. In practice, that means the only thing that truly counts is what we ship, how it performs, and whether users come back for more. Mockups are hypotheses; production usage is evidence. When my teams adopt this lens, velocity improves, judgment sharpens, and quality rises where it matters most.

So what does design actually do in a software company? At its best, design builds leverage for the whole system—engineering, product, and marketing—by clarifying problems, raising the quality bar, and making complex decisions legible. The standard I hold is ancient and still essential: products must be useful, usable, and desirable — and above all, used. When we calibrate around “used,” debates about pixels give way to outcomes, and cross-functional partners feel the difference.

I often trace the roots of our craft back well beyond the digital era. The lineage from industrial design to software is real; constraints, ergonomics, affordances, and systems thinking didn’t start with screens. If you’ve ever mapped delight, performance, and reliability in a Kano Model, you’ve touched this lineage. The translation to software is simple: design the full journey, not just the interface—prioritize what improves time-to-value, reduces cognitive load, and earns habitual use.

One lesson I’ve learned the hard way: why design leaders who stop designing stop leading. I still sketch flows, write UX copy, and prototype when it unblocks the team or sets a decisive quality bar. The altitude changes constantly—one hour I’m in a strategic roadmap review, the next I’m in a critique or poking at a prototype. Great design leaders jump up and down in altitude to connect vision to details without becoming a bottleneck.

Over time, I’ve come to rely on four pillars every design manager must master: craft (raising taste and execution), product strategy (clarifying choices and trade-offs), people leadership (coaching, feedback, and hiring), and systems (processes, rituals, and design ops that scale). Neglect any one of these and either quality, speed, or team health will eventually falter.

Perfectionism is a double-edged sword. Over-indexing on quality can paralyze decision-making, but lowering the bar indiscriminately is worse. I’ve seen moments where relaxing standards to “go faster” actually cost the business—rework piled up, trust eroded, and customer value stalled. The answer is principled delegation: I define what “must be true” at each milestone, delegate ownership with clear guardrails, and reserve my veto power for moments where product integrity is genuinely at risk.

Measuring success as a design leader starts with outcomes vs output OKRs. I care about activation, retention, time-to-first-value, NPS verbatims tied to key journeys, and the operational metrics that earn the right to build the next thing. Design output is visible; design outcomes are durable. When trade-offs are needed, I optimize for the smallest shippable surface that still proves the core value proposition, then expand with data.

Scaling judgment is the multiplier. I build it through pattern matching—studying enduring product systems from companies like Airbnb, Amazon, Apple, Asana, Notion, Stripe, Nest, and others—to distinguish where polish compels usage versus where it’s ornamental. Strong opinions matter, but so does being easy to convince with new evidence. I encourage designers to articulate the pattern they’re invoking, why it fits the job-to-be-done, and how we’ll know it worked.

Operating cadence matters. My week is anchored around recruiting, crits, and staff meetings that actually make decisions. In critiques, I use the Do/Try/Consider framework to give actionable direction without micromanaging. On one-on-ones, the question isn’t “Should one-on-ones exist?” but “What are they for right now?”—coaching, performance, or clearing execution blockers. If a meeting doesn’t increase clarity or commitment, it gets redesigned or removed.

Execution-wise, I’ve taken inspiration from Rippling’s operating system—especially its emphasis on speed, precise ownership, and hard commitments. The lesson is timeless: go fast on the right things, make clear promises, and instrument your work so you can see reality quickly. When speed is paired with crisp decision rights and observable outcomes, momentum compounds rather than frays trust.

Hiring your first design leader? Look for someone who can set standards, scale judgment, and ship. They should be able to zoom from company narrative to interaction copy in a single afternoon, coach product trios, and build rituals that make taste and trade-offs explicit. Above all, they should have a point of view on where quality moves the business and where speed is the quality.

Here’s how my team’s approach differs from many: Figma is not the source of truth. We design in Figma, but we learn from production. We pair designers with engineering early, prototype in code when it reduces risk, and wire telemetry into every critical path. Product trios use discovery to validate “useful, usable, desirable — and used,” then commit to outcomes with clear, testable definitions of success. The result is faster iteration, fewer surprises, and experiences customers actually adopt.

If you want to deepen your own pattern library, study products and practices from leaders like Airbnb (https://www.airbnb.com/), Amazon (https://www.amazon.com/), Apple (https://www.apple.com/), Asana (https://www.asana.com/), CrossFit (https://www.crossfit.com/), Figma (https://www.figma.com/), Honeywell (https://www.honeywell.com/), Nest (https://store.google.com/category/google_nest), Notion (https://www.notion.so/), Retool (https://retool.com/), Rippling (https://www.rippling.com/), and Stripe (https://www.stripe.com/). Pay attention to how they balance versatility with clarity, defaults with flexibility, and speed with trust.

The throughline is simple and demanding: design for reality, not for the board. Keep your standards where they create business value, scale judgment with explicit patterns, and instrument everything so learning never stops. When teams embrace that, the work gets better, customers feel it, and the roadmap starts to pull you forward.

February 12, 2026
Reinventing Product Management Workflow: The AI Upgrade I Use to Ship Faster, Smarter

The most valuable upgrade I’ve made to my product management workflow isn’t a new framework or a shiny dashboard—it’s an AI-first operating model that compresses discovery-to-delivery cycles while increasing confidence in every decision. I built this approach to reduce context switching, remove toil, and keep the team relentlessly focused on outcomes over output. The result is a faster, clearer, and more reliable path from insight to shipped value.

Here’s how I run an AI-powered product workflow end to end: continuous discovery, opportunity sizing, solution shaping, planning, execution, and iteration—each step instrumented with automation, retrieval, and evaluation so we learn faster without compromising rigor.

Intake and triage start with a retrieval-first pipeline that unifies customer feedback, support tickets, sales notes, research transcripts, and usage analytics. I use embeddings to cluster themes, de-duplicate signals, and surface the most representative examples. This gives me an instant, always-fresh view of customer jobs, pains, and opportunities without manually combing through noise.

For discovery, I rely on “LLMs for product managers” to accelerate the hard parts without replacing judgment. I generate interview guides, summarize transcripts, extract entities, and tag moments of friction. Prompt engineering and context window management ensure the model sees the right evidence at the right time. I keep all sensitive data governed by privacy-by-design and data governance controls.

Opportunity sizing is where I connect insights to business impact. I map problems to a driver tree, quantify potential lift, and align to outcomes vs output OKRs. When relevant, I apply the Kano Model to balance performance, basic, and excitement attributes. To maintain rigor, I use eval-driven development on my prompts and heuristics so prioritization is repeatable, not anecdotal.

Solution shaping is a collaborative exercise with product trios. I draft problem narratives and PRDs, generate acceptance criteria, and create first-pass UX flows. For speed, I use gen ai for product prototyping to explore alternatives quickly, then gate final choices through usability feedback and feasibility checks. Where uncertainty is high, I define a minimum detectable effect (MDE) and design A/B testing plans upfront.

Planning ties strategy to execution through product roadmapping and sprint planning. I break work into sequenced bets, enable feature flags for controlled exposure, and wire quality signals into CI/CD. DORA metrics—like deployment frequency and change failure rate—help me keep the system honest. Observability ensures we see the “why” behind behavior, not just the “what.”

Execution is instrumented with in-app guides, Intercom messaging, and Pendo to shape onboarding and activation. I connect Amplitude analytics to measure habit formation, retention analysis, and feature adoption. When experiments run, I monitor leading indicators in near real time while protecting against peeking and p-hacking. The point isn’t to prove we’re right; it’s to learn fast enough to get right.

Iteration closes the loop. I use a unified analytics platform to compare expected vs actual outcomes, harvest qualitative feedback, and push new evidence back into discovery. The system improves with each cycle because the retrieval-first pipeline and eval harness both get smarter as data grows.

Governance is non-negotiable. AI risk management, cybersecurity, and regulatory compliance sit alongside model evaluations to prevent drift, leakage, or bias. I document decisions, model versions, and test artifacts so we can audit how we got to a call—especially when trade-offs are nuanced.

If you’re standing up this AI workflow from scratch, I recommend a 30/60/90 rollout. In the first 30 days, audit your data sources and build a retrieval-first pipeline. In days 31–60, pilot two high-leverage workflows—continuous discovery and PRD drafting—backed by eval-driven development. By days 61–90, scale to prioritization and experiment design, then thread the outputs into your planning and CI/CD rhythms.

Common pitfalls I watch for: over-automation that blurs context, lack of evaluation frameworks, ungoverned data that undermines trust, and vanity metrics that celebrate activity over outcomes. The antidote is simple but disciplined—clear decision criteria, measurable hypotheses, and automated evaluations that run as guardrails, not bottlenecks.

This AI upgrade doesn’t replace the craft of product management; it amplifies it. By combining judgment, clear strategy, and reliable automation, we ship value faster, reduce risk, and make better calls under uncertainty. The payoff is durable: compounding learning velocity and a team that spends more time solving the right problems—and less time wrestling the process.

Inspired by this post on Product School.

February 11, 2026
Build CX Scores You Can Defend: My 5-step playbook for transparent, trustworthy AI metrics

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Inspired by this post on The Intercom Blog.

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

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

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

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

First, the macro picture: 2,470 global support professionals across industries were surveyed to understand current AI usage, challenges, and the 2026 opportunities. The headline is clear—AI investment is now table stakes. Eighty-two percent of senior leaders say their teams invested in AI in the past year and 87% say they plan to invest in 2026. Those investments are already paying off: Over three-quarters of CS teams (77%) say AI is meeting or exceeding expectations, delivering faster response and resolution times, always-on coverage, cost savings, increased capacity, and multilingual support that scales globally.

And yet, only 10% of organizations say they have reached a "mature" level of deployment, where AI is fully integrated into operations and working at scale. That’s the tell: most teams are skimming the surface and leaving meaningful performance gains on the table.

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

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

The results follow. Of the teams that have reached mature deployment, 43% report higher quality and consistency across support—nearly double the rate of those still in the initial deployment stage. That quality shift is how support evolves from a cost center to a value driver. Great experiences don’t just prevent churn; they create advocacy and become a reason customers choose you. The more you trust your AI Agent with meaningful work, the more it creates the conditions for higher-quality, more consistent support.

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

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

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

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

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

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

Rethink how you approach support. If you were building from scratch today, you’d design around AI from day one. As Grant Lee, CEO of Gamma, puts it: "If you want to unlock the real value of AI, you have to design for it, not retrofit around it." Treat AI as infrastructure, not a feature. That shift impacts your org design, workflows, and what “good” looks like.

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

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

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

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

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

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

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

Inspired by this post on The Intercom Blog.

February 5, 2026
Mastering 30,000-Foot Vision and Ground-Level Execution: Systems That Decide Without You

Executive function, for me, is the art and discipline of building systems that make high-quality decisions without my constant involvement. The real unlock isn’t personal heroics; it’s institutionalizing judgment. When I do my job well, teams move faster, ambiguity shrinks, and the organization compounds learning even when I’m not in the room.

Operating simultaneously at 30,000 feet and ground level is the defining muscle of executive leadership. I deliberately switch altitudes. At 30,000 feet, I obsess over strategy, architecture, and resourcing. On the ground, I validate core assumptions with firsthand data, listen for weak signals, and spot process cracks before they widen. Altitude changes are not random; they’re triggered by variance from plan, critical customer moments, or leading indicators that deviate from expected ranges.

The leap from frontline manager to manager of managers is where many rising leaders stall. As a manager of managers, my primary value shifts from personal execution to system design. I move from answering questions to installing mechanisms that ensure questions get answered well by others. This includes clear decision rights, shared metrics, and repeatable, lightweight rituals that scale across teams.

What is an executive actually accountable for? Outcomes over output, talent density, and the clarity of the operating system. That means defining strategy, aligning resources, creating a cadence of review that exposes truth, and ensuring incentives reward the behaviors we want. My barometer: if I step away, do priorities hold, do metrics behave as expected, and do tradeoffs land where I would have landed?

Knowing when to dive deep versus when to step back is a craft. I dive deep when risks are existential, when metrics have no credible owner, or when narrative and numbers diverge. I step back when leaders demonstrate consistent judgment, metrics sit inside control limits, and learnings are documented. The principle I return to again and again: context is everything. Senior leaders operate on context, not control.

To scale judgment, I teach people how I think. I externalize my mental models: how I construct decision trees, how I stress-test assumptions, and how I weigh time horizons. I rely heavily on driver trees for metrics because they force causal clarity. If we can’t map how a top-line goal decomposes into controllable levers, we’re managing by hope, not design.

Creating a shared language across the business is a force multiplier. I standardize definitions for our core metrics, codify what “good” looks like, and make it easy to repeat the system. We align around outcomes versus output, and we use cadences like MBRs and QBRs to unify narrative and numbers. Shared language makes decisions legible across functions and reduces rework.

My COO playbook emphasizes owning the full customer experience end to end. When marketing rolls up under a COO in certain stages, the upside is coherence: one narrative from awareness to activation to expansion, one set of metrics, one growth engine. The point isn’t org charts; it’s removing seams customers can feel.

Demanding and supportive is not a contradiction. I set ambitious, unambiguous bars and back them with coaching, resourcing, and fast feedback. The combination builds trust: expectations are clear, and help is immediate. I expect leaders to bring problems paired with proposed solutions and to escalate early, not perfectly.

Inside my executive interview process, I’m assessing altitude agility, operating cadence, and taste in metrics. I use structured interviews and live case workshops to see how candidates frame ambiguous problems, build driver trees, and prioritize tradeoffs. The best prompts are simple and revealing: design the operating system for a 3x scale scenario; diagnose a broken funnel with incomplete data; align two teams with conflicting incentives. The workshop prompts that reveal everything surface thinking speed, humility, and the instinct to make context legible.

The common thread in failed executive hires is a mismatch between the company’s operating system and the leader’s default mode. Some leaders can’t stop doing the work themselves. Others stay too abstract and never build mechanisms. I look for demonstrated ability to change systems, not just run them—leaders who can both author and evolve the playbook.

On metrics, I practice the driver tree philosophy. I begin with the North Star, decompose it into controllable levers, instrument each node, and assign single-threaded owners. We design review cadences where deviations trigger targeted diagnostics, not thrash. Each tree has documented assumptions, data sources, and thresholds that prompt action. This is how teams learn to anticipate, not react.

High-functioning executive teams are visibly collaborative. We clarify decision rights, disagree and commit quickly, and conduct post-decisions to harvest learnings without blame. My favorite litmus test is simple: can 30 people operate as one team when it matters? When we get this right, information flows, execution accelerates, and customers feel consistency.

One of the most counterintuitive leadership lessons is working yourself out of a job. If the system cannot run without you, you have a key-man risk, not a leadership strength. I aim to build successors, codify judgment, and design mechanisms that make good decisions the default state. That’s how you create durable, compounding advantage.

And the review feedback you can’t unhear? Mine was brutally honest: my bar was high, but my mechanisms were implicit. Once I wrote them down—how I decide, what I expect, where I dive deep—the organization moved faster, and I actually became less central. If there’s a throughline to extraordinary leadership, it’s this: make your judgment teachable and your systems inevitable.

February 5, 2026
From Coaching to Co‑Pilots: How AI Elevates Product Owners and Feature Teams

After two decades of coaching product teams, I’m making a deliberate shift in how I guide leaders and practitioners. The destination hasn’t changed—great products, empowered product teams, and durable outcomes—but the route has. AI is now a practical, compounding advantage, and it demands we evolve our product coaching model.

In my day-to-day as a VP of Product Management at HighLevel, I’ve watched AI move from novelty to necessity. Large language models, agentic AI, and streamlined AI workflows now accelerate how we discover opportunities, test hypotheses, and communicate decisions. This is not about replacing product judgment; it’s about augmenting it with a disciplined AI Strategy.

For years, I’ve raised the alarm about the gap between execution and strategy among “product owners and feature team product managers.” The intent was never to pile on more process. It was to strengthen product discovery, sharpen product strategy, and clarify outcomes vs output OKRs so that teams ship what matters. AI finally gives us the leverage to make that shift unavoidable—and repeatable.

Here’s the new coaching stance: treat AI as a co-pilot, not an answer engine. I coach teams to build an AI product toolbox they can trust—prompt engineering patterns, eval-driven development to measure model quality, and a retrieval-first pipeline for institutional knowledge. When combined with continuous discovery, this creates a tight loop between insight, iteration, and impact.

Practically, this means elevating core rituals. In product trios, we start discovery with AI-assisted opportunity mapping, then pressure-test problem framing with user evidence. We generate multiple solution sketches with LLMs for product managers, annotate assumptions, and use A/B testing with a minimum detectable effect (MDE) to validate the riskiest bets. The result is faster learning without skipping the hard thinking.

On the governance side, I set clear guardrails: privacy-by-design, data governance, AI risk management, and explicit criteria for acceptable model behavior. We treat prompts and evaluation datasets as versioned assets, and we pair product managers with forward deployed engineers to operationalize insights in production safely.

Coaching also extends to measurement. We anchor product outcomes in the customer journey and watch leading indicators for activation, adoption, and retention. On the delivery side, we look at deployment frequency and the health of the feedback loop between support signals and roadmap choices—because empowered product teams win when they learn faster than the market shifts.

The most profound cultural change is mindset. Instead of asking AI for answers, we ask it for alternatives, counterexamples, and structured ways to explain tradeoffs to stakeholders. That makes product positioning clearer, decision narratives stronger, and the path from insight to execution shorter.

If you’re responsible for developing talent, reframe coaching as enablement plus guardrails. Build the AI muscle into everyday discovery and delivery, not as a side project. When we do this well, we transform good practitioners into strategic operators—people who pair judgment with leverage and consistently ship value.

The bottom line: AI doesn’t replace the craft; it amplifies it. Our job as leaders is to harness that amplification responsibly and turn it into a durable competitive advantage.

Inspired by this post on SVPG.

February 4, 2026
How We Built Rock-Solid AI Infrastructure: Lessons From Scaling AI Visibility and Reliability

Scaling AI Visibility pushed me to rethink what “reliable” really means for AI infrastructure. As my team expanded usage across more datasets, models, and workflows, we uncovered unexpected sources of report failure and built the guardrails, observability, and processes that now anchor our stability strategy.

In practice, the surprising failure modes were rarely the loud ones. We saw report failure triggered by small schema drift from non-deterministic LLM outputs, silent permission changes in upstream data sources, token-limit truncation that broke downstream parsing, third-party API rate limits that surfaced only under bursty load, and clock skew that confused idempotent writes. Individually these issues looked minor; together they created reliability debt.

Our first move was deep observability. We instrumented the end-to-end pipeline with structured logs, distributed tracing, and high-signal metrics mapped to SLOs and error budgets. That visibility let us separate symptom from cause, quantify impact by segment, and prioritize fixes that moved business outcomes, not just vanity thresholds. It also gave product managers and SREs a shared, real-time view to make tradeoffs explicit.

Next, we hardened the runtime with resilience patterns: circuit breakers on flaky dependencies, timeouts tuned to p95 behavior, retries with jittered backoff, idempotent processing for at-least-once delivery, and backpressure-aware queues. We enforced schema contracts at ingestion with JSON validation and added feature flags to decouple deploys from releases, so we could roll forward or back within minutes when signals degraded.

On the product side, we adopted eval-driven development for model and prompt changes, shifting risky modifications behind canaries and staged rollouts. CI/CD gates required evaluation baselines to hold or improve before promotion. We tracked DORA metrics to keep deployment frequency high without sacrificing change failure rate, and we used P95 latency and budget burn as the forcing functions for prioritization.

Culture mattered as much as code. We formalized incident management with clear ownership, lightweight runbooks, and blameless reviews that produced crisp, automatable actions. We partnered early with SRE on SLO design, integrated privacy-by-design and PII scanning into the pipeline, and treated AI risk management as an ongoing product constraint rather than a checkbox.

The net effect: fewer flaky reports, faster recovery when things do break, and far more confidence to ship improvements to AI Visibility at pace. If you’re scaling similar capabilities, start with observability, make resilience patterns non-negotiable, and let SLOs guide your product roadmap. Reliability is not a phase—it’s the product.

Inspired by this post on Amplitude – Best Practices.

February 4, 2026