Category: Product Management

What I Learned from Trainline’s Agentic AI: Building a Trusted Travel Assistant at Scale

Over the past year, I’ve been shipping agentic AI into production and coaching product teams on what it really takes to make these systems trustworthy in the wild. One story that crystallizes the playbook comes from Trainline’s move to an agentic architecture for travel assistance—an approach that mirrors what I’ve seen work in high-stakes, real-time customer experiences.

Trainline—the world’s leading rail and coach platform—helps millions of travelers get from point A to point B. Now, they’re using AI to make every step of the journey smoother.

I studied how "David Eason (Principal Product Manager) Billie Bradley (Product Manager), and Matt Farrelly (Head of AI and Machine Learning)" approached the build of "Travel Assistant, an AI-powered travel companion that helps customers navigate disruptions, find real-time answers, and travel with confidence." Their work exemplifies the kind of end-to-end thinking required to move beyond demos into dependable, on-the-go assistance.

They share how they: Identified underserved traveler needs beyond ticketing; Built a fully agentic system from day one, combining orchestration, tools, and reasoning loops; Designed layered guardrails for safety, grounding, and human handoff; Expanded from 450 to 700,000 curated pages of information for retrieval; Developed LLM-as-judge evals and a custom user context simulator to measure quality in real-time; Balanced latency, UX, and reliability to make AI assistance feel trustworthy on the go.

I align strongly with their core takeaways: "AI assistants need both scalable reasoning and deep domain context to be useful." "Tool design and guardrails are as critical as prompt design in agent systems." "LLM-as-judge evals make it possible to measure open-ended systems without massive labeling costs." And perhaps most importantly, "Even legacy companies can move fast when they embrace experimentation and tight PM–engineering collaboration."

From an AI strategy perspective, starting "fully agentic" was the right call. When the problem space is dynamic—disruptions, route changes, fare conditions—reasoning loops and orchestration aren’t luxuries; they’re table stakes. Tool selection becomes product design: you need the right retrieval interfaces, constraint-aware planners, and API contracts that are resilient to partial failures. Layered guardrails for safety, grounding, and human handoff reduce hallucination risk while preserving responsiveness—critical when users are standing on a platform waiting for an answer.

The retrieval scale-up—"Expanded from 450 to 700,000 curated pages of information for retrieval"—is a classic inflection point. I’ve seen teams stall here when they treat content growth as a pure indexing problem. The winning move is curation and structure: normalize sources, encode policy-level constraints, and align retrieval chunks to decision boundaries the agent actually uses. That’s how you keep precision high while coverage explodes.

Evaluation is where most open-ended assistants fail quietly, which is why I was encouraged to see "Developed LLM-as-judge evals and a custom user context simulator to measure quality in real-time." In practice, LLM-as-judge gives you scalable, scenario-based scoring without prohibitive labeling, while a user context simulator surfaces regressions tied to persona, itinerary state, and device constraints. The combination closes the loop between model behavior, tool layer changes, and UX outcomes.

On product delivery, the decision to have the system "Balanced latency, UX, and reliability to make AI assistance feel trustworthy on the go" shows mature prioritization. For travel, trust accrues in seconds: fast-enough responses, graceful degradation when upstream data lags, and explicit handoff when confidence dips. This is where guardrails meet UX writing—clear, bounded language signals competence even when the system defers.

Finally, the organizational pattern matters. The teams that win in agentic AI are cross-functional, experimentation-driven, and ruthless about instrumentation. Tight PM–engineering collaboration, explicit safety thresholds, and an eval stack that mirrors real user journeys are what turn promising architectures into dependable products.

It’s a behind-the-scenes look at how an established company is embracing new AI architectures to serve customers at scale.

If you’re building agentic AI in production, borrow these moves: invest early in tool and guardrail design, scale retrieval with curation not just volume, adopt LLM-as-judge plus context simulation for continuous evaluation, and treat latency and reliability as core product requirements—not afterthoughts. That’s how you ship AI assistance that customers trust when it matters most.

Inspired by this post on Product Talk.

October 30, 2025
Context Is King: My Playbook to Prep Product Teams for High-Impact AI Collaboration

Context is king in AI-powered product work—and I felt that deeply while digging into “Context is King – All Things Product Podcast with Teresa Torres & Petra Wille.” The conversation affirmed a truth I see daily: AI becomes a powerful teammate only when we give it the right context, just as we do with empowered product teams. When we treat AI like a colleague joining mid-flight—without our company history, industry nuances, or strategy—we instantly unlock better outcomes.

Listen to this episode on: Spotify | Apple Podcasts

Here’s what stood out and how I’m applying it. First, most AI outputs fail without proper context. That’s not a model problem; it’s a leadership problem. Thinking of AI like onboarding a new intern is the right mental model—start with the minimum viable context, then iterate. Practical first steps matter: decision logs, clear success metrics, and structured documentation. The art is balancing enough context to guide performance without overloading the system. The parallels are striking: the way we create strategic context for product trios and teams is the same way we’ll empower agentic AI systems.

In my teams, we prepare for AI collaboration by operationalizing context. We keep decision logs to capture the why behind choices, use outcome-based success metrics (not just output), and maintain machine-readable documentation that LLMs for product managers can parse reliably. We define guardrails up front—constraints, customer segments, privacy-by-design considerations, and the non-goals that often trip up gen ai. This foundation turns AI from a novelty into a force multiplier for product discovery and product roadmapping and sprint planning.

I use a simple “context pack” to onboard AI agents and teammates alike: 1) business goals and outcomes, 2) constraints and guardrails, 3) canonical artifacts (like PRDs, journey maps, interview notes), 4) domain vocabulary and definitions, and 5) operating procedures (how we make decisions, when to escalate, what good looks like). Start small, then refine as the AI demonstrates capability. This mirrors great onboarding—and it works just as well for agentic AI as it does for humans.

Not all context is helpful. More isn’t better; the minimum effective context is. I resist the urge to dump our entire Confluence on an AI system. Instead, I progressively reveal relevant details—just like I would with a new PM on a complex problem space. This keeps signals high, noise low, and performance measurable against clear success metrics.

If your org isn’t adopting AI yet, don’t wait. You can become AI-ready now by documenting strategic intent, decision rationale, and definitions in structured, searchable, machine-readable ways. Treat this as core AI Strategy work that strengthens empowered product teams—regardless of tooling—while building your AI product toolbox for tomorrow.

For those who want to explore further, these resources and mentions are a strong complement to the episode’s themes.

Follow Teresa Torres: https://ProductTalk.org

Follow Petra Wille: https://Petra-Wille.com

Agentic AI

Teresa’s new podcast, Just Now Possible in Youtube, Apple Podcast, and Spotify

Petra’s Coaching Packages

ChatGPT

Henrik Kniberg’s talk at Product at Heart on treating AI agents like interns

Teresa’s webinars on how she built the Product Talk Interview Coach: Behind the Scenes: Building the Product Talk Interview Coach and How I Designed & Implemented Evals for Product Talk’s Interview Coach

Josh Seiden’s blog series about AI

Teresa’s new blog posts: 15 Ways to Use AI at Home (and Fill Your AI Product Toolbox) and 21 Ways to Use AI at Work (And Build Your AI Product Toolbox)

Petra's new blog post: Why Context, Not Just Data, Will Define AI-Ready Product Teams

Have thoughts on this episode or how you’re preparing your teams to collaborate with AI? Leave a comment below—let’s compare playbooks and level up together.

Inspired by this post on Product Talk.

October 28, 2025
3 Hidden Hurdles Blocking Effective AI Agents—and How I Turn Them into Business Wins

AI agents promise leverage at scale, yet too many proofs of concept stall before they create measurable value. Over the past several launches, I’ve seen the same patterns repeat across IT and operations. The mandate is clear: “Discover three key challenges IT and ops teams face when building and managing AI agents that drive real business wins.” Here’s how I frame the work, where teams get stuck, and the playbook I use to move from demo to durable outcomes.

Hurdle 1: fragmented data and weak data governance. Agentic AI is only as strong as the data it can reliably access. In most organizations, knowledge is scattered across CRMs, ticketing tools, wikis, and data lakes—each with different schemas, permissions, and freshness guarantees. Without privacy-by-design and consistent access patterns, agents hallucinate, miss context, or violate policies. This isn’t a model problem—it’s an information architecture problem.

My approach starts with an integration-first mindset: anchor the agent to authoritative systems via CRM integration, unify retrieval across knowledge sources, and enforce role-based access at query time. I pair this with data contracts, lineage, and content freshness SLAs so the agent never acts on stale or restricted information. A unified analytics platform and strong data governance let me monitor coverage, drift, and security posture as the knowledge footprint grows.

Hurdle 2: reliability, observability, and AI risk management. Even well-fed agents can behave unpredictably without tight control loops. Teams often lack Agent Analytics, standardized evals, and guardrails to catch prompt injection, tool abuse, or subtle regressions. The result is fragile behavior that erodes trust with IT, security, and front-line operators.

I build a reliability stack that looks a lot like SRE for agentic AI: scenario-based evaluations before release, production tracing of every step and tool call, red-teaming for threat detection and response, and policy enforcement at runtime. Hallucination mitigation, input validation, and fallbacks (including human-in-the-loop) are non-negotiable. We track latency, cost, accuracy, and safety incidents in one Agent Analytics view so we can ship confidently and iterate quickly.

Hurdle 3: workflow integration and organizational adoption. The best agent can still fail if it can’t take action in real systems or if change management is an afterthought. Agents must fit the way people actually work—permission models, SLAs, audit trails, and existing approval paths—instead of creating shadow processes that confuse teams.

I integrate agents directly into systems of record and daily tools—ticketing, CRM, knowledge bases—so outcomes are auditable and reversible. I define clear RACI, rollout guardrails, and metrics in product roadmapping and sprint planning (e.g., first-contact resolution, time-to-resolution, deflection, cost per task). We ship narrowly scoped capabilities first, pair them with in-app guides and product tours, and expand privileges as confidence and KPIs improve. This is product management leadership, not just prompt engineering.

In practice, the pattern is consistent. For customer support, we anchored the agent to the CRM, knowledge base, and incident runbooks with strict access controls, then layered policy checks for regulated data. With unified analytics, we measured precision/recall of suggested actions, tracked cost and latency, and flagged risky prompts. The result: higher accuracy, cleaner handoffs, and faster time-to-value without sacrificing compliance.

If your agents aren’t delivering, start here: fix the data plane, instrument the control plane, and design for real workflows. Do this well and you’ll move beyond flashy demos to durable productivity gains and competitive differentiation—while keeping security, governance, and stakeholders on your side.

Inspired by this post on Pendo – Perspectives.

October 25, 2025
Unlock Customer Gold: Securely Access Intercom Data in ChatGPT to Align Every Team

I see customer conversations as a goldmine for every team—yet too often, they’re trapped inside the support platform. That silo makes it harder to make confident, customer-first decisions across product, sales, marketing, and leadership. I’ve felt that pain firsthand, which is why this update matters.

From today, the new Intercom connector for ChatGPT changes this. Intercom customers can now allow all teams to securely access conversations, tickets, and user data directly inside ChatGPT. Without having to switch tools, you can now get all the context you need to put the customer first across every area of your business.

Here’s how I approach it in practice: when frontline insights are accessible in the same workspace where I ideate, plan, and write, my team moves faster with more conviction. It’s the difference between guessing at customer needs and grounding decisions in real conversations.

How to connect Intercom to ChatGPT

Connecting Intercom to ChatGPT is easy:

1. In ChatGPT, open Settings → Connectors.

2. Search for “Intercom” and select it.

3. Sign in with your Intercom account to approve the secure connection.

(The connector is read-only and respects your existing Intercom permissions, so people only see what they already have access to. See more about security and setup details here.)

Once you’re in, you can start exploring your customer data using prompts written in natural language, like:

“Help me prepare for a meeting with customer X by updating me on outstanding issues raised in the last four weeks.”

“Find positive Intercom conversations mentioning our new feature Y, and add customer quotes to my campaign brief in Drive.”

“Build a list of the most common feature requests based on customer inquiries.”

What this unlocks

Connecting Intercom to ChatGPT makes customer feedback available across the company in a usable way. In my own workflow, this turns previously buried signals into actionable inputs for roadmaps, messaging, and enablement—without hopping between tools.

Support tickets contain direct information about what’s breaking, what’s confusing, and what people actually need. Normally, that information stays siloed in the support team. When I can query those conversations in plain language, I get immediate clarity on friction points and opportunities, and I can share that context with cross-functional partners in minutes.

When anyone can query it in plain language, it becomes useful for decision-making across the board. Teams stop working at cross-purposes because they’re looking at different parts of the picture. Now, product can see what’s actually frustrating users. Sales can understand common objections. Marketing can use the language customers actually use. Leadership can spot trends as they’re happening.

My recommendation: establish a lightweight ritual around this data. For example, build a weekly highlights digest sourced from Intercom conversations and review it in your product sync or go-to-market standups. It’s a simple way to align stakeholders and keep customer reality front and center.

We’ll be adding more connectors soon so you can access Intercom data in other AI tools your team already uses.

Inspired by this post on The Intercom Blog.

October 25, 2025
Make Data Work Together: Build a High-Trust, Data-Driven Culture with Amplitude and Slack

Data collaboration isn’t a tool you buy; it’s a culture you build. In my role leading product teams, I’ve learned that the fastest way to better decisions is aligning on a shared language of metrics and weaving insights into our daily rituals. When we do that well, momentum compounds—roadmaps clarify, stakeholder debates get healthier, and teams ship with confidence.

Break down data silos and align teams with Amplitude: define shared metrics, share insights in Slack, and build better habits together.

Here’s how I operationalize that guidance. First, we create a crisp measurement framework—one North Star metric supported by a few input metrics that map to customer value. We document definitions in a living “metrics glossary,” enforce data governance, and design a clean Amplitude taxonomy so events, properties, and user identities are consistent across the product. This is the foundation of a unified analytics platform that everyone can trust.

Next, we make insights unavoidable. Amplitude dashboards are curated by product trios and subscribed into Slack channels so context meets people where they work. I ask teams to pair charts with a one-paragraph narrative: what changed, why it likely changed, and what we’ll try next. This simple habit closes the loop between analysis and action—and it catalyzes product-led growth.

We institutionalize these behaviors in our operating cadence. Weekly insights reviews focus on outcomes vs output OKRs. Sprint planning starts with what the data says, not what we wish were true. In QBRs, we connect customer journeys to retention analysis and A/B testing results, making sure tests are designed with an appropriate minimum detectable effect (MDE). Empowered product teams own decisions; stakeholder management shifts from opinion trading to hypothesis testing.

A few pragmatic enablers make this stick: clean CRM integration to join product usage with lifecycle and segment data; privacy-by-design guardrails; clear ownership for instrumentation; and lightweight documentation that evolves with the product. I also encourage teams to ship in-app guides when we launch a feature so we can measure activation and iterate quickly based on Amplitude analytics.

The cultural side matters just as much. I celebrate learnings (even when metrics dip) and spotlight teams that translate insights into experiments quickly. Psychological safety unlocks better questions, and better questions unlock better products. Over time, this builds the high-trust environment required for durable, data-informed decision-making.

If you’re just getting started, pick one product surface and one customer journey. Define the shared metrics, wire up Amplitude, pipe key dashboards into Slack, and run a single, well-powered experiment. You’ll feel the difference in a sprint or two—and you’ll have a repeatable playbook to make data truly work together across your organization.

Inspired by this post on Amplitude – Best Practices.

October 25, 2025
Turning Community Noise into Action: My Product Lessons from Zencity’s AI That Listens

I’m constantly looking for ways to turn messy, multi-source signals into decisions leaders can trust. Recently, I dug into how Zencity powers government decision-making with community voices—and it’s a masterclass in building AI products that are both responsible and useful.

Noa Reikhav, Head of Product, Zencity; Andrew Therriault, VP of Data Science, Zencity; and Shota Papiashvili, SVP of R&D, Zencity share a comprehensive view of how they designed an AI that listens and acts without sacrificing rigor.

How do you use AI to help city leaders truly hear their residents?

I was struck by the clarity of their platform vision—“They share how Zencity brings together survey data, 311 calls, social media, and local news into a unified platform that helps cities understand what people care about—and act on it.” That single line captures the essence of a unified analytics platform done right.

You’ll hear how the team built their AI assistant and workflow engine by being thoughtful about their data layers, how they combined deterministic systems with LLM-driven synthesis, and how they keep accuracy and trust at the core of every AI decision.

It’s a fascinating look at how modern AI infrastructure can turn noisy, messy civic data into clear, actionable insight.

Here are the takeaways that resonated with me most, and they align closely with how I approach AI Strategy and product management leadership. Data architecture defines what AI can do. Guardrails and transparency matter more than flashy outputs. Agentic systems become powerful when grounded in real, multi-tenant data. AI in the public sector can make democracy more responsive—if built responsibly.

The team’s layered data model is the backbone that enables trustworthy synthesis: raw data → elements → highlights → insights → briefs. As a product leader, I love how each layer introduces meaning and structure while preserving traceability. It’s the difference between a demo-friendly prototype and a durable platform.

Why context is everything when building AI for civic use. That’s not a platitude—it’s a requirement. Community conversations are hyper-local, emotionally charged, and policy-laden. Without context and rigorous data governance, you risk misclassification, bias, and broken trust.

How the team designed their AI assistant using MCP servers to safely negotiate data access. This is a smart pattern for privacy-by-design: let the assistant request access, let the system adjudicate, and make the boundary explicit and auditable. In multi-tenant environments, that clarity is the difference between scaling confidently and shipping risk.

Balancing agentic flexibility with deterministic trust. I’ve found this to be the most practical framing for real-world agentic AI: give the system room to explore, but bind its outputs to deterministic rails where it matters—taxonomy, citations, permissions, and evaluation criteria.

Evaluating accuracy when latency matters: how they think about evals, citations, and model-as-judge systems. I appreciate the pragmatism here. In production, you don’t have the luxury of slow truth-finding. You need tight feedback loops, interpretable citations, and layered evals to keep both precision and speed.

Using workflows like annual budgeting or crisis communication to deliver AI-generated briefs to the right people at the right time. This is where product-market fit shows up: not in features, but in end-to-end workflows aligned to real decision cycles and stakeholders.

Why government workflows are the ultimate “jobs to be done” framework. When the job is a public process—with deadlines, accountability, and high scrutiny—you don’t just need insights; you need timely, contextualized briefs that match the cadence of the work.

From my lens, the magic isn’t any single model. It’s the orchestration: deterministic systems with LLM-driven synthesis, strong guardrails, transparent citations, and an orchestration layer that routes the right brief to the right role at the right moment. That’s how you turn community noise into legitimate signal—and signal into action.

If you’re building AI for regulated, high-stakes environments, take note: invest in your data layers, make context a first-class citizen, embrace privacy-by-design with clear access negotiation, and treat evaluation as a living system. Do that, and you’ll earn the trust that makes your AI assistant—and your organization—indispensable.

Inspired by this post on Product Talk.

October 25, 2025
Go Hard Early: Enterprise AI Lessons That Built Serval’s Magical IT Automation Agents

Go hard early is more than a mantra—it’s a product strategy. When I study the most durable enterprise companies, I see the same pattern: you win by shipping fast, obsessing over the customer’s day-to-day pains, and delivering consumer-quality experiences to business buyers. That lens is exactly why Serval’s recent momentum caught my attention and why the lessons behind it matter for every product and IT leader building in AI.

Jake is the founder and CEO of Serval, an AI-driven IT automation and service management platform that just raised $47M in Series A funding this week. Before founding Serval, Jake spent over five years at Verkada, where he led multiple products from 0-1 and helped scale the company across hardware and software. His years at Verkada taught him that winning in enterprise means delivering consumer-quality experiences to business buyers — a lesson that shapes how Serval turns complex IT automation into something that feels magical.

From my vantage point, the most counterintuitive lesson here is the power of building “in existing categories.” Rather than inventing a new market, the better move can be to redefine expectations inside a known one—where buyers, budgets, and success criteria already exist. That’s how you compress sales cycles, build trust rapidly, and create a wedge for product-led growth without boiling the ocean.

Another playbook thread I admire: turning “hard mode” into a moat. The teams that lean into gnarly integrations, real workflow depth, and enterprise-grade reliability end up compounding an advantage that’s very hard for fast followers to copy. That mindset shows up in Serval’s platform strategy and, more importantly, in how they translate complex IT work into something that feels intuitive on day one and powerful on day 100.

Customer intimacy sits at the center of that strategy. The customer interview question that unlocked the IT buyer’s hidden pain points is the kind of move I try to operationalize across product trios and forward-deployed teams. When you ask not just, “What do you do?” but, “What do you do when everything breaks?” you surface the real constraints: shadow runbooks, brittle scripts, brittle processes, and the political friction that slows down response times. That’s where durable value—and competitive differentiation—lives.

How Serval’s automation builder uses AI to generate code-based workflows is a particularly smart architectural choice. Code-first doesn’t mean hard-to-use; it means source-controlled, interoperable, and shareable across teams—exactly what IT leaders want when automation moves from side project to system of record. Tie that to agentic orchestration and you get reliable automations with clear observability, safety rails, and the ability to scale without collapsing under edge cases.

I’m also a believer in redefining engineering and PM roles with forward-deployed engineers. When engineers partner directly with customers, discovery accelerates, prioritization sharpens, and product bet quality improves. You avoid ping-ponging requirements through layers, and you raise the hiring bar for true product creators who can think in outcomes, not just output.

Keeping the hiring bar high in an AI-native startup isn’t optional—it’s existential. The best teams screen for candidates who can reason from first principles, ship quickly with taste, and articulate the value proposition in plain language. The ultimate hiring litmus test is whether someone can improve the product on day one by clarifying a user journey, simplifying a workflow, or tightening a metric that actually matters.

There’s also Why there’s a “land grab” moment right now in enterprise AI. Incumbents are strong on breadth but often slow to re-architect for AI-native workflows. New entrants that show up with opinionated defaults, pragmatic security, and crisp buyer narratives can establish points of parity quickly while extending into true points of differentiation. That’s the window to seize—especially when building for mid-market and enterprise.

Here are the core themes I took away and how I translate them into practice across product roadmapping and sprint planning, product discovery, and go-to-market strategy.

Why building “in existing categories” can be more powerful than creating new ones. Use the market’s mental models, measure against known alternatives, and win by delivering a meaningfully better experience—not by forcing buyers to invent new procurement paths.

The lessons from Verkada that shaped Serval’s platform strategy. Treat UX polish as a strategic asset, make setup effortless, and let power users go deep without friction. Consumer-grade quality is not a veneer; it’s a trust accelerator in enterprise.

The customer interview question that unlocked the IT buyer’s hidden pain points. Go beyond happy-path discovery. Ask about the 3 a.m. moments, the panic buttons, and the messy handoffs—then design for those first.

How Serval’s automation builder uses AI to generate code-based workflows. Pair AI generation with reviewability, versioning, and safe rollbacks. Make it easy to see, test, and share what the agent is doing under the hood.

Redefining engineering and PM roles with forward-deployed engineers. Collapse feedback loops by putting builders where the problems are. It’s the fastest path to product-market fit lessons and real-world reliability.

Keeping the hiring bar high in an AI-native startup. Look for taste, speed, and ownership. Optimize for people who can both prototype with gen ai and ship production-hardened systems.

Why there’s a “land grab” moment right now in enterprise AI. Move quickly, but anchor on outcomes. Land with a wedge use case, expand with measurable value, and maintain clear points of parity while you deepen differentiation.

If you want to follow or explore the companies and leaders referenced, these links are a useful starting point.

LinkedIn: https://www.linkedin.com/in/jakestauch/

Twitter/X: https://x.com/jakeserval

LinkedIn: https://www.linkedin.com/in/brett-berson-9986094/

Twitter/X: https://twitter.com/brettberson

Website: https://firstround.com/

First Round Review: https://review.firstround.com/

Twitter/X: https://twitter.com/firstround

YouTube: https://www.youtube.com/@FirstRoundCapital

This podcast on all platforms: https://review.firstround.com/podcast

References:

Alex McLeod: https://www.linkedin.com/in/alexmcleodio/

Clay: https://www.clay.com

Cloudflare: https://www.cloudflare.com

Cursor: https://cursor.sh

Filip Kaliszan: https://www.linkedin.com/in/kaliszan/

Hans Robertson: https://www.linkedin.com/in/hansrobertson

Linear: https://linear.app

Okta: https://www.okta.com

Rippling: https://www.rippling.com

Serval: https://www.serval.com/

ServiceNow: https://www.servicenow.com

Verkada: https://www.verkada.com

Workday: https://www.workday.com

Timestamps and topic highlights for easy navigation and deeper study:

(02:25) Lessons from holding different product roles

(07:29) Turning “hard mode” into a moat

(10:49) The early days of Serval

(12:59) Scratching the founder itch

(14:57) Unconventional interview techniques

(17:47) Solving core interview challenges

(21:10) Planning the early product roadmap

(23:03) The surprising power of patience

(26:12) Serval’s impressive technical advantage

(27:35) Disrupting legacy incumbents

(31:13) Building for mid-market and enterprise

(33:35) Serval’s enduring roadmap

(36:08) How to sell to an existing market

(39:16) The evolving role software plays

(43:55) Building for AI that didn’t exist yet

(49:49) Serval’s forward-deployed engineers

(58:31) The hybrid PM-GM

(1:00:27) “You can over-prioritize”

(1:02:48) The unexpected value of panic buttons

(1:04:50) What Serval looks for in new talent

(1:07:01) The ultimate hiring litmus test

(1:13:59) Building out Serval’s go-to-market function

(1:16:31) The evolving IT market in 2025

My bottom line: build where budgets already live, ship with uncompromising UX, embed engineers with customers, and hold the line on talent. Do that, and you won’t just keep up with the enterprise AI “land grab”—you’ll define the standard others have to meet.

October 25, 2025
Urgent Alert: Spot Fraudulent Job Offers Impersonating Pendo—and Protect Your Career

In my role leading product management, I take brand trust and cybersecurity seriously—especially when it affects people’s livelihoods. Over the past few weeks, I’ve seen a troubling uptick in brand impersonation and social engineering targeting candidates. It’s a reminder that protecting our community isn’t just a technical problem; it’s a product management leadership and stakeholder management responsibility.

We want to warn you about recent instances of fraudulent job offers purporting to be from Pendo and/or its affiliate companies.

If you receive an unexpected outreach claiming to be from Pendo with a fast-track offer, requests for payment, or a push to move conversations to informal channels, treat it as a red flag. Scammers often spoof logos, clone profiles, and use vague role descriptions to create urgency. Their goal is to extract personal data, money, or access—classic social engineering tactics that undermine data governance and privacy-by-design principles.

Here’s how I advise candidates to protect themselves while keeping their job search momentum. Validate every opportunity through the company’s official careers page and confirm the recruiter’s identity through corporate channels. Check that email addresses and domains match publicly listed corporate information, and be wary of communication conducted exclusively through messaging apps. Never pay fees, buy equipment up front, or share sensitive data like Social Security numbers or banking information before a formal, verified offer is in place.

If something feels off, pause and verify. Contact the company via the channels listed on its website, ask for a video meeting with the recruiter using an official corporate account, and request written details on the role and interview process. If it’s fraudulent, report it to the company, the platform where the outreach occurred, and—when appropriate—local authorities. Acting quickly helps with threat detection and response and protects other candidates from harm.

From a product and security perspective, this is a cross-functional issue that benefits from AI risk management discipline. Strong signals include clear public guidance on recruiting practices, a dedicated reporting mailbox for suspected scams, and hardened email authentication (SPF, DKIM, DMARC). Pair these with privacy-by-design reviews for hiring workflows, recruiter verification checklists, and ongoing education for talent teams. These measures reduce attack surface while reinforcing brand integrity.

If you believe you’ve shared information with a fraudulent recruiter, take immediate steps: change any reused passwords, enable two-factor authentication, place fraud alerts or freezes with credit bureaus as appropriate, and monitor accounts for suspicious activity. Document all communications; they can help security teams and platforms act faster.

Recruitment fraud is emotionally taxing and can erode confidence in the process. Don’t let scammers slow your momentum. Stay vigilant, verify before you trust, and share this warning so others can avoid similar traps. If you’re ever unsure about a message that appears to come from Pendo, pause, validate through official channels, and prioritize your safety first.

Inspired by this post on Pendo – Best Practices.

October 25, 2025
Pendo’s Summer Release: How I Reimagine Onboarding, Support, and Expansion in the SaaS + AI Era

I’ve been reflecting on How Pendo’s Summer Release reimagines onboarding, support, and expansion in the SaaS + AI era, and it resonates deeply with the product-led playbooks my team and I use every day. The core promise is simple and powerful: “These three best practices aren’t new, but how you achieve them is.” That framing captures the shift I see across high-performing product organizations—same outcomes, radically upgraded execution through AI, in-app experiences, and unified analytics.

For onboarding, I prioritize accelerating user activation with clear product tours, in-app guides, and great UX writing that removes cognitive load. The difference now is how precisely we personalize these moments: segmentation driven by product usage, CRM integration, and experiments (A/B testing with a disciplined minimum detectable effect) help us craft paths that meet users where they are. When onboarding is instrumented this way, it becomes a scalable engine for product-led growth rather than a one-time setup task.

Support is undergoing an equally meaningful transformation. Contextual, in-app help combined with agentic AI can diagnose issues, surface relevant knowledge, and guide users without forcing channel switches. I’m bullish on this, but only when it’s anchored in privacy-by-design, AI risk management, and strong data governance—trust is the prerequisite for any customer support AI strategy. When done right, support shifts from reactive ticket resolution to proactive value delivery.

Expansion, to me, is the earned outcome of consistent product value. In the SaaS + AI era, we can use unified analytics to identify readiness signals—feature adoption, outcomes achieved, and time-to-value—and trigger timely, ethical nudges in-app. The best motions align offers with real customer milestones, whether that’s consumption SaaS pricing upgrades, role-based add-ons, or advanced capabilities unlocked through demonstrated need. This is product-led growth at its most customer-centric.

Underpinning all three motions is measurement discipline. I push for a unified analytics platform that ties together behavioral data, retention analysis, funnels, and cohorts with downstream CRM integration. That allows product trios to make fast, informed decisions and connect activation, support efficiency, and expansion to business outcomes. Whether your stack includes Pendo, Amplitude analytics, or custom pipelines, the principle is the same—one source of truth that informs action.

Execution matters as much as strategy. Empowered product teams working in tight product trios can ship small, valuable increments, run clean experiments, and learn faster than the market shifts. Strong stakeholder management and clear product roadmapping keep leadership aligned on outcomes vs output OKRs, so we’re funding what works and pruning what doesn’t. In my experience, this operational rigor is what turns promising ideas into durable competitive differentiation.

If you’re looking to operationalize these ideas, start by defining activation and expansion milestones that map to your value proposition. Instrument your in-app guides and product tours to support those milestones, and commit to an experimentation cadence with well-defined MDE. Layer in agentic AI carefully—pilot in the support surface where context is rich and stakes are clear—and enforce privacy and governance from day one. Finally, close the loop with unified analytics so every improvement compounds.

Pendo’s Summer Release highlights a broader reality: our industry isn’t inventing new destinations, we’re modernizing the routes. Onboarding, support, and expansion remain the pillars—but AI, in-app experiences, and integrated data make them smarter, faster, and more human. That’s the shift I’m leaning into—and the one customers feel immediately.

Inspired by this post on Pendo – Best Practices.

October 25, 2025
The Only 3 Dashboards Product Executives Actually Use to Drive Outcomes, Alignment, and Growth

I’ve learned the hard way that more charts don’t equal more clarity. One challenge that comes with this is knowing what matters at the right level of leadership. Executives everywhere are busy, and they don’t need the nitty-gritty details to do their jobs well. When I’m operating at the VP level, I rely on just three dashboards that give me fast signal, reduce noise, and keep teams aligned to outcomes—not output.
These dashboards sit on top of a unified analytics platform that connects product analytics (Amplitude analytics or Pendo), CRM and revenue data (e.g., HubSpot), billing, and support signals. Consistent definitions, data governance, and outcomes vs output OKRs ensure we’re making decisions with confidence, not gut feel. The goal is simple: a shared, executive-ready view that ties product strategy to business impact.
Dashboard 1: Outcomes and Strategy Alignment. This is the north star view I use to orient the company. It highlights ARR, NRR, and GRR trends; progress against our outcomes vs output OKRs; our product-led growth funnel; and our primary value proposition metric (e.g., activation-to-time-to-value). I include a 12-month view with quarter-over-quarter deltas, a short written narrative, and the top three strategic bets we’re funding. In board management and QBRs vs OKRs discussions, this keeps focus on what we achieved, what moved, and what we’re changing next.
Dashboard 2: Customer Value, Adoption, and Retention. This is where retention analysis meets product discovery. I track activation rate, time-to-value, feature adoption cohorts (from Amplitude analytics or Pendo), retention curves by segment, and expansion vs contraction signals. Leading indicators include NPS and CES alongside qualitative themes from support and sales. I also monitor funnel drop-offs and in-app guides or product tours performance to see where users get stuck. The intent is to connect behavior to revenue so we can prioritize changes that actually improve customer outcomes.
Dashboard 3: Execution Health and Quality. This helps me assess whether our operating system is working. I look at delivery predictability against product roadmapping and sprint planning, cycle time and throughput, escaped defects, incident volume, and MTTR. I also review experiment velocity and A/B testing readiness (including minimum detectable effect) to ensure we’re learning at pace. Resource allocation across strategic initiatives and a clear risk register support proactive stakeholder management.
I review these dashboards weekly with my product trios and monthly with cross-functional leaders, then synthesize a concise narrative for the executive team and the board. Each dashboard is a decision engine: it has an owner, a single source of truth, clear thresholds, and a list of next actions. By grounding conversations in the same views, we reduce back-and-forth and keep momentum high.
A few implementation rules have served me well: keep the signal dense and the visuals simple; lock metric definitions and ownership; avoid vanity metrics; and instrument privacy-by-design from the start. When data is trustworthy and the story is tight, teams focus on the right problems and progress compounds.
If you find yourself wading through dozens of reports, try consolidating to these three executive dashboards. You’ll spend less time arguing about the data and more time driving product-led growth, accelerating alignment, and delivering customer value at scale.

Inspired by this post on Pendo – Best Practices.

October 25, 2025
Build the Cake, Then the Frosting: 3 Elements of a High‑Performing AI Strategy That Wins

Over the past few years leading product at HighLevel, I’ve watched too many teams rush to demo flashy agents before they’ve built a reliable foundation. The metaphor I use in every AI roadmap review still hits home: “Think of AI readiness as a three-layer cake. Most companies are trying to build the fancy frosting (the agent interface) without bothering to bake the actual cake underneath.” If we want durable impact, we have to bake first, frost later.

When I design an AI Strategy, I anchor on three elements that map directly to that cake: a data and instrumentation foundation, a governance and risk layer, and finally the agent experience itself. This sequence isn’t theory—it’s how we de-risk delivery, accelerate product-market fit, and create competitive differentiation without compromising trust.

Layer 1 — Data and instrumentation: The base of the cake is clean, well-instrumented data flowing through a unified analytics platform. I start with a clear event schema, rigorous data quality checks, and tight CRM integration so we can connect outcomes to users, accounts, and journeys. Privacy-by-design is nonnegotiable: we minimize PII, define retention, and ensure consent flows are explicit. With this in place, gen ai features have the context they need—retrieval works, grounding holds, and feedback loops from production inform continuous improvement.

On top of that, I build measurement in from day one: activation, retention, task success, latency, and satisfaction. Every AI interaction is observable. We run A/B testing with a well-defined minimum detectable effect, pair quant with qualitative review, and feed human-in-the-loop judgments back into ranking and prompt libraries. This is how we avoid “demo-ware” and deliver real, repeatable value.

Layer 2 — Governance and risk: Before scaling, I formalize AI risk management and data governance. That includes model evaluation against safety and quality thresholds, red-teaming for jailbreaks, and threat detection and response for prompt injection and data exfiltration. We establish policy for model and provider selection, versioning, and rollback; we log prompts, responses, and decisions for auditability; and we define escalation paths when the system is unsure. These controls don’t slow us down—they create the confidence needed for faster iteration and board management alignment.

I also align legal, security, and product early on a taxonomy of risks—bias, hallucinations, privacy, IP leakage—so we can write tests and guardrails once and reuse them across features. The result is fewer surprises in customer pilots and a far smoother path through enterprise procurement.

Layer 3 — The agent experience: Only now do we invest in the frosting—the agent interface and workflows. Here I focus on clear jobs-to-be-done, crisp UX writing, and transparent system behavior. We design agentic AI flows that show reasoning steps when helpful, ask for clarification when confidence is low, and gracefully hand off to humans in customer support scenarios. Product tours, in-app guides, and tooltips reduce the learning curve and accelerate user activation.

Crucially, we measure the interface, not just the model. Agent Analytics tracks intents, tool use, fallbacks, and user corrections so we can tune prompts, tools, and policies. This closes the loop from experience back to data and governance, and it directly informs product roadmapping and sprint planning. When the cake is baked this way, go-to-market becomes easier: we can prove ROI with hard numbers, fine-tune pricing, and scale adoption with product-led growth tactics.

If your AI roadmap feels stuck, start with an honest readiness audit against these three elements. Shore up instrumentation and data pipelines, codify governance, then refine the agent interface with real user telemetry. Bake first. Frost last. That’s how we ship AI that customers trust—and keep winning after the first demo high fades.

Inspired by this post on Pendo – Best Practices.

October 25, 2025
Urgent Alert: Spot Fraudulent Job Offers Impersonating Pendo—and Protect Your Career

In my role leading product management, I take brand trust and cybersecurity seriously—especially when it affects people’s livelihoods. Over the past few weeks, I’ve seen a troubling uptick in brand impersonation and social engineering targeting candidates. It’s a reminder that protecting our community isn’t just a technical problem; it’s a product management leadership and stakeholder management responsibility.

We want to warn you about recent instances of fraudulent job offers purporting to be from Pendo and/or its affiliate companies.

If you receive an unexpected outreach claiming to be from Pendo with a fast-track offer, requests for payment, or a push to move conversations to informal channels, treat it as a red flag. Scammers often spoof logos, clone profiles, and use vague role descriptions to create urgency. Their goal is to extract personal data, money, or access—classic social engineering tactics that undermine data governance and privacy-by-design principles.

Here’s how I advise candidates to protect themselves while keeping their job search momentum. Validate every opportunity through the company’s official careers page and confirm the recruiter’s identity through corporate channels. Check that email addresses and domains match publicly listed corporate information, and be wary of communication conducted exclusively through messaging apps. Never pay fees, buy equipment up front, or share sensitive data like Social Security numbers or banking information before a formal, verified offer is in place.

If something feels off, pause and verify. Contact the company via the channels listed on its website, ask for a video meeting with the recruiter using an official corporate account, and request written details on the role and interview process. If it’s fraudulent, report it to the company, the platform where the outreach occurred, and—when appropriate—local authorities. Acting quickly helps with threat detection and response and protects other candidates from harm.

From a product and security perspective, this is a cross-functional issue that benefits from AI risk management discipline. Strong signals include clear public guidance on recruiting practices, a dedicated reporting mailbox for suspected scams, and hardened email authentication (SPF, DKIM, DMARC). Pair these with privacy-by-design reviews for hiring workflows, recruiter verification checklists, and ongoing education for talent teams. These measures reduce attack surface while reinforcing brand integrity.

If you believe you’ve shared information with a fraudulent recruiter, take immediate steps: change any reused passwords, enable two-factor authentication, place fraud alerts or freezes with credit bureaus as appropriate, and monitor accounts for suspicious activity. Document all communications; they can help security teams and platforms act faster.

Recruitment fraud is emotionally taxing and can erode confidence in the process. Don’t let scammers slow your momentum. Stay vigilant, verify before you trust, and share this warning so others can avoid similar traps. If you’re ever unsure about a message that appears to come from Pendo, pause, validate through official channels, and prioritize your safety first.

Inspired by this post on Pendo – Perspectives.

October 25, 2025