Tag: data governance

Own Your AI: 4 Essential Roles to Supercharge Support and Prevent Performance Drift by 2026

AI doesn’t fail because the model is bad, it fails because ownership is missing.

When someone truly owns your AI, everything changes. Resolution and automation rates climb, the system self-improves, and the customer experience transforms in ways a dashboard alone will never show you.

This is part three of our five-part series on customer service planning for 2026. We’ll be sharing all five editions on our blog and on LinkedIn.

If you’d rather have them emailed to you directly as they’re published, drop your details here.

Last week, we introduced the four roles that make AI actually work in a support organization. These roles are already showing up inside the teams who are scaling AI the fastest, and this week, we get closer to the ground.

Here’s what these roles look like in practice — what they do, how they work, and why your AI performance will inevitably drift without them.

AI operations lead — owns AI performance, every day. I think of this person as the air-traffic controller for our AI Agent. I treat the AI as a living system that needs ongoing supervision, evaluation, and tuning. This role is accountable for what leaders care about most: quality, reliability, and continuous improvement.

The AI ops lead sees the whole picture: conversation quality, missing knowledge, flawed assumptions, unexpected failures, new opportunities for automation, and the subtle signals that the system is beginning to drift. In practice, that vigilance is the difference between steady gains and slow decline.

Day-to-day, here’s what I expect from this role.

1. Reviews AI conversations and surfaces performance patterns. The AI ops lead monitors the AI Agent’s behavior — the tone shift after a product launch, a sudden dip in resolution for a specific intent, or conversation clusters revealing new customer behavior. They scan for anomalies, trends, and early warnings, with an emphasis on what’s happening right now, not last week. Without this intentional ownership, I’ve watched a 2% dip turn into a 10% drop in days.

2. Prioritizes fixes and improvements. Once patterns emerge, they triage fixes like a product team handles bugs. Missing or incorrect content? They route it to the knowledge manager. Behavioral issues? They adjust guidance and guardrails. Action or system issues? They partner with the automation specialist. This connective tissue turns individual fixes into compounding improvements.

3. Defines and maintains AI guardrails. Leaders everywhere worry about AI doing things it shouldn’t. This role answers that fear by establishing clarification logic, escalation rules, “never answer” policies, and safety boundaries. The goal is predictable behavior that protects customer trust — an essential pillar of any AI Strategy and AI risk management practice.

4. Aligns reporting with leadership. The AI ops lead reports on resolution rate, CX Score, CSAT, automation coverage, and hours saved — making the economic impact visible. That visibility is a foundational step in any credible customer support ai strategy.

Why this role exists now. AI systems are dynamic and require constant tuning. A small dip in quality quickly becomes an operational issue, and no existing role naturally owns that. When someone does, teams feel the benefit almost immediately.

Knowledge manager — builds and maintains the structured knowledge AI depends on. I hear the same thing from leaders again and again: AI is only as good as the content you give it. This role is rapidly evolving from classic knowledge management into knowledge strategy — part content designer, part systems thinker, part information architect. Their job is to build the knowledge scaffolding that lets AI answer accurately, consistently, and safely.

Here’s how the knowledge manager creates leverage.

1. Writes, maintains, and improves support knowledge — continuously. After every product change, they update articles, remove duplication, resolve contradictions, and pay down “knowledge debt” that quietly erodes accuracy. The upkeep is shaped by AI performance; when patterns expose gaps, they fix the source.

2. Structures knowledge for AI, not for browsing. Traditional help centers are for humans skimming pages. AI needs clean intent signals, crisp formatting, and clearly structured language. The knowledge manager designs that structure as intentionally as the content itself.

3. Works hand-in-hand with AI ops. Many performance issues stem from missing or unclear knowledge. When the AI ops lead surfaces recurring misunderstandings or low-resolution categories, the knowledge manager resolves the root cause at the source.

4. Ensures accuracy and compliance at scale. As AI handles more sensitive situations, the knowledge manager safeguards correctness, currency, and compliance — critical for data governance and regulatory alignment.

5. Develops a cross-functional knowledge strategy. The role creates a canonical, cross-functional source of truth that product, engineering, product marketing, go-to-market, and support (AI and human) can all rely on.

Why this role exists now. This is one of the highest-leverage positions in an AI-first support org. Teams like Rocket Money and Anthropic are hiring knowledge managers because AI accuracy depends on the quality of knowledge feeding it. Without this role, resolution rate caps out early and never climbs.

Conversation designer — designs how the AI speaks, clarifies, and interacts. AI isn’t just a tool customers use; it’s a representative they interact with. Tone, clarity, pacing, and conversational structure matter, especially in voice. Every word affects perceived expertise, trustworthiness, and brand. The conversation designer ensures the AI feels human-friendly without pretending to be human — the sweet spot that builds trust without misleading customers.

In my experience, staffing conversation design early accelerates results. It changes not only how we tune AI, but how we understand the end-to-end customer experience.

Here’s what great conversation design looks like.

1. Shapes the AI’s tone, voice, and communication style. This role refines phrasing, tunes politeness, adjusts how confusion is handled, and shapes micro-interactions that determine whether customers feel cared for or dismissed. On voice channels, natural cadence is make-or-break.

2. Designs flows for high-value conversations. They design how the AI clarifies intent, branches, communicates uncertainty, verifies details, escalates, hands off, and returns to the main thread without feeling mechanical — treating customer experience as a product with language as the interface.

3. Translates procedures and complex workflows into natural language and logic. As AI runs structured procedures and actions, this role becomes a conversational system architect, translating SOPs into conditional logic with exceptions and fallbacks. For example, in Intercom, our conversation designer uses Simulations to run simulated conversations to see where the AI Agent gets confused, over-confident, or awkward, and refine flows until the interaction feels effortless end-to-end.

4. Ensures transitions to humans feel smooth and respectful. Handoffs should provide clear context to the human agent and maintain continuity so customers never feel dropped.

Why this role exists now. As AI becomes the primary interface, conversation design directly influences trust, brand perception, and operational outcomes. It’s a core competency for any Generative AI and LLMs for product managers program.

Support automation specialist — builds the backend actions that allow AI to do real work. If the conversation designer shapes expression, this role shapes capability. They transform AI from an answering machine into an outcome engine by bridging AI and the systems it must safely and deterministically act on.

Support teams increasingly expect AI to do what a human would do: refund a charge, adjust a subscription, verify an identity, update an account setting, or pull relevant data. That expectation creates a new technical role at the edge of support, ops, and engineering.

What I rely on this specialist to deliver.

1. Creates and maintains backend workflows the AI executes. This includes building and maintaining: Fin Tasks. Fin Procedures with embedded steps. Action flows that call internal and external APIs. Automations that span billing systems, user identity layers, CRM objects, subscription entitlements, refund tools, and more. They ensure the AI can act compliantly and predictably — the playbooks that turn intent into action.

2. Owns the integrations required for advanced automation. Many problems require data elsewhere — billing platforms, internal databases, systems of record. The specialist ensures the AI can retrieve, validate, and use that information safely, often partnering closely on CRM integration and internal services.

3. Partners closely with product and engineering. Some workflows require new endpoints, permission layers, safety gates, or deterministic fallbacks. This role drives those changes across the stack.

4. Ensures reliability and safety at every step. Guardrails, validation logic, exception handling, safe execution paths — all are essential. They confirm that the AI has access to the correct data, the action matches policy, edge cases are accounted for, risky flows have deterministic constraints, and every action is auditable and reversible.

Why this role exists now. Customers don’t want answers, they want outcomes. AI can now deliver those outcomes, but only with the right backend scaffolding. This role modernizes operational architecture and unlocks end-to-end automation.

How these roles work together — the new operating loop. These roles aren’t silos; they’re interdependent parts of one system. The AI ops lead identifies patterns and performance gaps. The knowledge manager resolves inaccuracies or missing content. The conversation designer improves clarity, tone, and flow. The automation specialist expands the system’s ability to take action. Each improvement compounds the next, moving you from early automation to transformational resolution rates through continuous refinement.

This loop is what separates teams that plateau early from teams that scale AI into a reliable, high-performing system — the essence of a durable AI Strategy.

How to get started (even if you can’t hire all four roles today). Most teams phase into this model: assign partial ownership, formalize responsibilities, then specialize as AI volume grows. Here’s the progression I recommend.

Phase 1: Assign ownership. Give each role’s core responsibilities to someone who can devote five to 10 hours weekly. Early on, support ops, enablement, senior ICs, and technically inclined teammates can anchor the work.

Phase 2: Formalize the responsibilities. As AI resolves more queries, optimization becomes core operational work. Formalizing ownership prevents performance drift and knowledge debt.

Phase 3: Specialize and hire. Once AI handles 50–70% of incoming volume, these responsibilities become full-time roles. Investing in specialization becomes essential infrastructure for the next scale stage.

The bottom line. AI changes the shape of your support team. These four roles — AI operations lead, knowledge manager, conversation designer, and support automation specialist — form the backbone of the AI-first support organization. They bring order to a constantly changing environment and enable AI to deliver the outcomes leaders and customers expect heading into 2026.

Next week, we’ll continue the 2026 planning series with a deep dive into org design models for AI-first support teams — how to structure people, workflows, and accountability in a world where AI resolves most conversations before a human ever sees them.

To follow along with the series and have each new edition emailed to you directly, drop your details here.

Inspired by this post on The Intercom Blog.

December 2, 2025
Marketing Analytics in 2026: Bold, Data-Driven Predictions to Outperform Your Market

I’m stepping into 2026 with a practical playbook for marketing analytics—one forged at the intersection of product management, go-to-market strategy, and AI Strategy. My lens is simple: connect data to decisions, decisions to outcomes, and outcomes to revenue. If you’re serious about product-led growth, this is the year to turn your unified analytics platform into a true competitive advantage.

Start 2026 off with a bang with exclusive insights and predictions from some of marketing analytics’ most influential voices. See what they have to say.

The biggest shift I expect is from channel-centric dashboards to journey-centric systems that stitch together product usage, CRM integration, and campaign performance. When Amplitude analytics or Pendo data sits alongside HubSpot pipeline metrics, we stop arguing about attribution models and start instrumenting the full revenue motion. That’s how marketing, product, and sales align around one truth: activation, engagement, and expansion drive sustainable growth.

I’m betting on deeper adoption of A/B testing with a rigorous minimum detectable effect (MDE) discipline and cohort-led retention analysis. Vanity metrics won’t cut it. Teams that operationalize outcomes vs output OKRs and tie experiments to LTV, CAC, and payback will outperform. The win is not more tests—it’s better tests that translate into compounding user activation and retention.

Gen AI will supercharge analysis, but not replace analytical thinking. I see LLMs for product managers accelerating root-cause analysis, surfacing anomalies, and explaining drivers behind conversion shifts. The craft moves from “pulling reports” to “asking higher-quality questions,” then validating with sound statistical methods. The highest-leverage teams will pair gen ai with strong taxonomies, clean event schemas, and clear definitions of North Star metrics.

Data governance becomes a growth enabler, not a compliance cost. With privacy-by-design, consented data, and well-documented schemas, your models become more accurate and your campaigns more resilient. When governance is strong, personalization sharpens, lookalike models improve, and executive confidence in the numbers rises—unlocking faster, bolder bets.

Product-led growth analytics will mature from “feature usage” to “value moments.” I’m focusing my teams on measuring time-to-value, depth-of-use, and expansion signals embedded in in-app guides, product tours, and contextual tooltips. The companies that make value visible earlier—and measure it precisely—will see outsized improvements in trial-to-paid and expansion.

Operationally, I expect tighter cadences between discovery and delivery. Product trios will partner with marketing to run continuous discovery on messaging, onboarding friction, and pricing signals. When insights flow directly into campaign creative and in-product experiments, learning cycles compress and the cost of delay drops.

If you’re building your 2026 roadmap, here’s my short list: consolidate tools into a unified analytics platform, standardize event taxonomies across web, product, and CRM, formalize MDE for every A/B test, and align OKRs to activation and retention milestones. Do this, and you’ll turn fragmented data into a durable growth engine—one that compounds every quarter.

Inspired by this post on Amplitude – Perspectives.

November 25, 2025
Mastering Data Governance in the AI Era: Move Fast, Reduce Risk, and Unlock Trusted Insights

Every week, I’m in conversations with product leaders, engineers, and security teams who are trying to ship AI features faster without compromising trust. The tension is real: stakeholders want velocity, customers want transparency, and regulators want accountability. That’s exactly where modern data governance earns its keep.

New AI pressures are redefining what good governance takes. Learn how to build better frameworks, move fast with confidence, and keep your data from being a black box.

In my role leading product management, I’ve learned that robust data governance isn’t a compliance checkbox—it’s a strategic capability. When we treat governance as a product, we architect for clarity, safety, and speed. That means aligning AI Strategy with day-to-day delivery so teams know what they can ship, when, and why.

Here’s the practical blueprint I rely on. First, establish ownership and a shared language. Create a living data catalog, lineage maps, and clear data classifications so teams know which assets are sensitive, regulated, or eligible for training LLMs. Second, harden privacy-by-design and least-privilege access. Bake PII detection, secrets management, and role-based policies directly into your workflows. Third, bring quality and observability to the forefront: instrument data contracts, monitor drift, and track model performance across environments. Finally, implement model governance end to end—dataset cards, model cards, bias testing, human-in-the-loop review, and a repeatable evaluation harness.

To move fast with confidence, make governance invisible and automated. Treat policies as code in CI/CD, gate deployments with pre-merge checks, and fail builds that violate data contracts. Log prompts and outputs responsibly, route unsafe patterns to red-teaming, and use a retrieval-first pipeline to anchor models on verified sources rather than fragile context stuffing. This is how we scale AI product development while keeping audit trails complete and costs in check.

Avoiding the black-box problem starts with transparency. Document assumptions, training data sources, and known limitations—then expose explanations where it matters in the product experience. Pair this with a unified analytics platform to tie telemetry, feature flags, and user feedback to model changes. When something goes sideways, your observability, incident management playbooks, and threat detection and response processes should make root-cause analysis fast and defensible.

If you’re building your program from scratch, use a 30-60-90 approach. In the first 30 days, inventory systems, classify data, and map high-risk use cases. By day 60, formalize RACI for governance, deploy access controls, and set up your evaluation pipeline with golden datasets and measurable acceptance thresholds. By day 90, operationalize incident response, conduct tabletop exercises, and wire governance outcomes into OKRs—think time-to-approval for high-risk changes, reduction in production incidents, and model evaluation pass rates.

This playbook pays off in board conversations and with customers. You can articulate your AI risk management posture, show measurable progress on regulatory compliance, and demonstrate how governance accelerates—not hinders—delivery. Most importantly, your teams gain the confidence to experiment, knowing there’s a safety net that protects users, the brand, and the business.

If your organization is wrestling with how to balance innovation and control, start small, codify what works, and scale with intent. With the right foundations in data governance, AI becomes an engine for durable advantage—not a source of sleepless nights.

Inspired by this post on Amplitude – Perspectives.

November 21, 2025
High-Quality Data, High-Velocity AI: My Product Playbook for Governance, Trust, and Scale

Every breakthrough we ship in AI reinforces a simple truth I live by: "Companies that prioritize data quality, governance, and structure will accelerate their AI initiatives the fastest." That statement captures the difference between flashy demos and durable, scalable products. In my experience, the strongest AI Strategy starts with the discipline to treat data as a product, not an afterthought.

When teams rush to production with generative AI or LLMs, the first issues rarely come from the model itself—they come from the data. Poor lineage leads to hallucinations, inconsistent schemas inflate costs, and weak access controls erode trust. For LLMs for product managers, this is the gap between a compelling prototype and a reliable system customers depend on every day.

Let me clarify what I mean by data quality, governance, and structure. Quality is completeness, accuracy, freshness, and consistency across sources. Governance is policy, ownership, and accountability—privacy-by-design, regulatory compliance, and AI risk management built in from day one. Structure is the architecture: clear data contracts, standardized schemas, metadata and lineage, and role-based access that keeps sensitive signals protected while enabling speed.

Here’s the product playbook I use to operationalize this. First, map critical sources and define data contracts at the edges so producers and consumers can move independently. Second, standardize schemas and entity resolution to eliminate ambiguous joins. Third, enforce privacy-by-design with policy-as-code and automated redaction. Fourth, converge analytics into a unified analytics platform so definitions, freshness, and observability are shared. Fifth, instrument end-to-end lineage and quality SLAs with alerting. Finally, close the loop with human feedback and labeling to continuously improve model performance.

For generative AI workloads, a retrieval-first pipeline is essential. Unify trusted sources (product analytics, CRM, support, docs), embed and index them with guardrails, and focus on context window management to keep prompts lean, relevant, and cost-effective. This approach improves response quality, reduces token spend, and makes updates near-real-time—without retraining the base model every week.

Measure what matters. Tie model outcomes to product metrics through rigorous A/B testing, and size experiments with minimum detectable effect (MDE) so you can ship confidently. Use product analytics to verify that better data actually improves activation, retention, and support deflection. When teams can trace an AI improvement back to a specific data-quality fix, they invest in governance with conviction.

Culture closes the gap. Empowered product teams and product trios (PM, design, engineering) make crisper decisions when data stewards are embedded and accountable. Clear ownership, shared definitions, and transparent dashboards reduce friction with security and compliance while speeding up delivery. This is how product management leadership sustains velocity without trading away trust.

The bottom line: if we want faster, safer, and more scalable AI, we start with the data. Build strong foundations, treat governance as enablement, and structure every step so improvements compound. With that in place, Generative AI stops being a science experiment and becomes a durable competitive advantage.

Inspired by this post on Amplitude – Perspectives.

November 19, 2025
AI at Home, Impact at Work: Experiments That Supercharged My Product Leadership

I recently tuned into an insightful All Things Product episode featuring Teresa Torres and Petra Wille on how experimenting with AI in everyday life sharpens how we build AI-powered products at work. The core premise resonated deeply with my AI Strategy: low-stakes, personal experiments accelerate confidence, clarify limitations, and build an AI product toolbox we can bring into the office with rigor.

If you want to dive in, you can listen on Spotify or Apple Podcasts. I found the conversation especially relevant for product trios and anyone shaping LLMs for product managers in high-stakes environments.

The idea is simple but powerful: when I prototype with AI at home—where the stakes are low—I learn faster, make safer mistakes, and internalize critical product patterns. Over time, those patterns transfer directly to work: tighter context management, sharper bias awareness, clearer human-in-the-loop guardrails, and a more nuanced view of when to use AI as a thought partner versus when to consider agentic AI.

In my own practice, I’ve mirrored many of the scenarios discussed: using ChatGPT by OpenAI to plan meals, analyze public data sets like school budgets, and even sanity-check real estate evaluations. These seemingly mundane tasks are fertile ground for learning about context window limits, hallucination (artificial intelligence), AI bias, and privacy-by-design trade-offs. Each experiment helps me craft better prompts, structure data for clarity, and decide when a human review step is non-negotiable—core habits for AI risk management.

At work, I treat AI as a thought partner for writing, research synthesis, and contract review. I also explore when and how to responsibly evolve toward agentic AI for repeatable workflows. The distinction matters: a thought partner augments judgment; an agent automates execution. Building the right scaffolding—data governance, auditability, constraints, and escalation paths—ensures we unlock speed without compromising safety.

Three lines from the episode stayed with me: “I’m trying to write things that only I can write — that’s my guiding writing light right now.” — Teresa. “The more we use AI, the more we learn what it’s good at, what it’s not good at, and where context becomes a limitation.” — Teresa. “It’s a safer playground — we can build our toolbox at home before bringing those lessons to work.” — Petra. These are practical north stars for product management leadership in the GenAI era.

For anyone getting started, here’s what worked for me: begin with “low-stakes” personal experiments, write down your prompts and outcomes, and reflect on failure modes. Treat each activity as product discovery: What problem am I solving? What outcome matters? What data and context does the model need? Which decisions must stay human-in-the-loop? This discipline builds an AI product toolbox you can confidently apply to real customer problems.

I also keep a running toolkit of references and tools that inform my practice: Context window as a concept helps me size and sequence information. Visual and video tools like Midjourney and Sora expand how I think about multimodal experiences. I rotate between Claude by Anthropic and ChatGPT by OpenAI depending on task fit, and I’ve used Claude Code when I need structured assistance with code review. For knowledge capture and workflow, Readwise and Ghost help me structure insights and ship content.

If you want more structured learning paths, I found Josh Seiden’s Learn AI With Me, A 30-Day Sprint to be a practical primer, and the broader community conversation at Product at Heart Conference is invaluable. For a deeper grounding in risk, I recommend reviewing topics like Hallucination (artificial intelligence), AI bias, and Agentic AI—and revisiting the complementary episode, Context is King.

I’d love to hear how you’re experimenting: Where have you seen AI meaningfully reduce toil? Where does it still struggle? How are you balancing creativity, data safety, and compliance as you scale? Drop a comment below and let’s compare notes—especially on patterns that help product trios move faster without sacrificing trust.

Bottom line: start small at home, carry lessons into the office, and build with curiosity and intentionality. That’s how we level up our product discovery, sharpen our value proposition, and lead teams confidently through the GenAI transition.

Inspired by this post on Product Talk.

November 4, 2025
9 Corporate Innovation Trends Redefining Business—and How I’m Turning Them into Wins

Corporate innovation isn’t a side project anymore—it’s the operating system for how we build, scale, and win. In my product leadership work, I’ve watched the pace of change accelerate across every function, from engineering and data to go-to-market and customer success. The companies pulling ahead are the ones translating trends into execution with clarity, speed, and measurable outcomes.

We researched corporate innovation to reveal top trends, types, and examples that can spark growth and keep your business ahead.

Here’s how I’m seeing that play out right now—and the nine trends I’m actively using to guide roadmaps, prioritize bets, and ship value faster.

Trend 1: Generative AI is moving from pilots to products. Teams are evolving beyond demos into durable capabilities powered by gen ai, LLMs for product managers, and agentic AI patterns that automate workflows end-to-end. The winners pair bold AI Strategy with AI risk management, privacy-by-design, and clear value propositions so customers trust what we ship and can see its impact on outcomes, not just outputs.

Trend 2: Product-led growth is becoming the default go-to-market motion. I’m doubling down on onboarding, in-app guides, product tours, and activation loops that reduce time-to-value. We back this with disciplined A/B testing, well-chosen minimum detectable effect (MDE), and retention analysis to prove what actually moves the needle. PLG isn’t a tactic—it’s a cultural shift toward continuous learning and self-serve experience design.

Trend 3: Unified analytics and experimentation are the new backbone. A unified analytics platform, instrumented with tools like Amplitude analytics, Pendo, and CRM integration via HubSpot or Intercom, gives us a single source of truth from acquisition through expansion. I push teams to connect user journeys to revenue and to operationalize insights into roadmapping and sprint planning—not monthly reports that sit on a shelf.

Trend 4: Outcome-driven operating models are replacing feature factories. We align on outcomes vs output OKRs, empower product teams, and structure product trios to balance customer insight, technical feasibility, and commercial impact. First principles decision making helps us cut through noise, set sharper points of parity, and focus on differentiation that customers will pay for.

Trend 5: Velocity and reliability matter more than ever in engineering. Continuous delivery via CI/CD, healthy deployment frequency, and DORA metrics are my leading indicators for a team’s ability to learn fast. I’ve seen forward deployed engineers and thoughtful developer evangelism tighten the feedback loop with customers and speed up iteration without compromising quality.

Trend 6: Data governance and security are strategic differentiators. Trust is a product feature. I prioritize data governance, cybersecurity, and threat detection and response alongside usability. Privacy-by-design isn’t a compliance checkbox; it’s table stakes for enterprise adoption and a durable moat when paired with transparent controls and auditability.

Trend 7: Pricing and packaging innovation is unlocking growth. We’re testing SaaS pricing models, including consumption SaaS pricing, to align value delivered with value captured. Clear articulation of the value proposition and thoughtful packaging reduce friction in sales and support product-led expansion. Pricing experiments belong in the product backlog—not just in finance spreadsheets.

Trend 8: Customer-in-the-loop discovery is the fastest path to relevance. I treat product discovery as a continuous practice, weaving QBR-style business reviews into roadmaps and using stakeholder management to align incentives across sales, success, and product. Customer support ai strategy helps surface high-signal insights from tickets and conversations, turning support into a discovery engine.

Trend 9: Open platforms and ecosystems amplify innovation. From API-first thinking and ChatGPT connector patterns to integrations that meet customers where they work, ecosystems drive stickiness and reduce time-to-value. The strongest roadmaps combine a focused core with extensibility that partners and customers can build on.

How to act now: I recommend a simple try do consider framework. Try one high-conviction AI use case with clear guardrails. Do instrumented experiments across onboarding and activation to fuel product-led growth. Consider pricing and packaging tests tied to measurable outcomes. With disciplined learning cycles and empowered teams, these trends stop being headlines—and start becoming compounding advantages.

Innovation favors teams that ship, learn, and adapt. If these trends are on your roadmap, align them to outcomes, measure obsessively, and keep customers in the loop. That’s how we turn momentum into durable growth.

Inspired by this post on Product School.

November 3, 2025
Global Product Manager Playbook: Build Borderless Products, Align Teams, Win Every Market

Products without borders are exhilarating—and unforgiving. In my role leading product strategy, I’ve learned that “global” isn’t a launch plan; it’s a system. It’s the discipline of creating one product vision that flexes to many markets without breaking the core experience, the roadmap, or the business.

Here’s what a Global Product Manager does, key skills, tools, challenges, and how to grow into this high-impact role.

At its heart, the Global Product Manager role orchestrates product-market fit in multiple regions simultaneously. I translate a unified value proposition into localized realities—aligning product positioning, go-to-market strategy, pricing and packaging, and compliance—while keeping the platform cohesive. That means partnering closely with product trios, regional leaders, sales, customer success, and marketing to drive outcomes vs output OKRs that actually move the business.

Operationally, I start with deep product discovery across segments and geographies: what pains are universal, and where do we need regional nuance? From there, I map points of parity we must maintain globally and the differentiators we’ll localize—copy, workflows, payments, support models, and integrations. The art is delivering a consistent core with flexible edges so we can scale without fragmenting the codebase or the customer experience.

Trust is the non-negotiable. I build privacy-by-design into the product and roadmap, and I collaborate early with legal and security on data governance, data residency, and evolving regulations like GDPR. The right guardrails reduce rework later and enable faster regional launches—because compliance is a feature customers feel, even when they don’t see it.

On the commercial side, I partner on consumption SaaS pricing, product-led growth motions, and country-level market entry. Some markets need lighter onboarding and in-app guides; others demand concierge support or partner-led distribution. I use retention analysis to identify fit and inform sequencing, then adjust messaging and activation flows to shorten time-to-value and improve user activation by region.

My analytics and enablement stack is intentionally boring—and ruthlessly consistent. A unified analytics platform with Amplitude analytics gives us comparable funnels across countries. For experimentation, I run A/B testing with a clear minimum detectable effect (MDE) and disciplined rollout plans. Pendo powers product tours and in-app guides tailored by locale, while Intercom and CRM integration with HubSpot help me close the loop with GTM and support teams. The outcome is a learning system, not just a dashboard.

The hardest part isn’t translation—it’s alignment. Time zones, competing priorities, and matrixed ownership test even strong cultures. I rely on stakeholder management, crisp decision records, and product roadmapping and sprint planning rituals that respect regional input without derailing the global plan. When tension rises, I return to first principles decision making and the try do consider framework to make trade-offs transparent and repeatable.

If you’re growing into this role, start by owning a multi-region initiative end to end: lead localization for a critical workflow, run market-specific A/B testing with clear MDE, and publish a country launch plan that ties discovery insights to OKRs and resourcing. Build your credibility by shipping outcomes, not artifacts—then scale your impact by mentoring peers and creating shared templates for pricing, positioning, and experimentation. That’s how you shift from capable PM to trusted global operator.

Ultimately, a Global Product Manager is a force multiplier. We reduce complexity for the organization while increasing resonance for customers. If “products without borders” is your mandate, build the systems—analytics, governance, enablement, and decision-making—that make borderless execution reliable, repeatable, and fast.

Inspired by this post on Product School.

November 3, 2025
RAG for Product Managers: Transform Strategy, Speed Discovery, and Win with Confidence

I’ve watched Retrieval-Augmented Generation (RAG) shift from a buzzword to a practical advantage that changes how my team discovers insights, makes roadmap bets, and competes. When I ground large language models in our own product, customer, and market data, I make faster decisions with more confidence—and I spend far less time debating opinions and more time shipping outcomes.

Think RAG for product managers is just AI hype? Wait until you see the use cases and ways it’s reshaping your work and product strategy.

RAG connects the power of LLMs with the credibility of your internal knowledge: user research, support tickets, win/loss notes, specs, QBRs, and analytics. Instead of generic answers, I get contextual, citeable responses that reflect our reality. That means cleaner product discovery, sharper product positioning, and a clearer value proposition grounded in customer truth.

Day to day, I use RAG to accelerate product discovery by synthesizing interviews and feedback across channels; to de-risk roadmapping by surfacing evidence behind feature requests; and to power go-to-market strategy with crisp messaging that maps to points of parity and true competitive differentiation. It’s equally effective for onboarding new PMs, increasing stakeholder alignment, and unblocking empowered product teams when signals are noisy or fragmented.

Execution still matters. I treat RAG like any critical system: prioritize data governance, privacy-by-design, and AI risk management. I integrate with our CRM and support stack so the model learns from live customer context, and I instrument everything with product analytics to track impact. When the outputs are measurable, RAG moves from novelty to operating system.

To start, I focus on a narrow, high-signal slice of the workflow—like summarizing support patterns or synthesizing discovery for a single segment—then iterate. I pair PMs with design and engineering in tight product trios, define quality criteria up front, and review answers with subject-matter experts. As quality rises, I scale to roadmapping and product-led growth experiments, always validating with users before I automate.

The payoff is real: faster decisions, clearer narratives, and fewer surprises. RAG won’t replace the craft of product management, but it will amplify it—giving us an edge in both speed and accuracy. If you’re serious about LLMs for product managers and want results you can defend, RAG is a strategic bet worth making now.

Inspired by this post on Product School.

November 3, 2025
Beyond Digital: How AI Transformation Builds Adaptive, Intelligent Organizations That Win

Digital transformation rewired our systems; AI transformation rewires how we learn, decide, and compete. “AI transformation goes beyond automation to create adaptive, intelligent organizations. Discover why it’s the next imperative and how to measure success.” That statement captures what I experience daily: we’re moving from scripted workflows to living systems that improve with every interaction.
When I talk about AI transformation, I’m not describing a tool rollout. I’m describing an operating model where data, models, and product strategy converge to create compounding advantage. In practice, that means agentic AI orchestrating tasks, robust data governance and privacy-by-design from day one, and empowered product teams that ship, measure, and iterate at high tempo.
The imperative is strategic, not merely technical. Markets are compressing cycle times, and customers now expect intelligent experiences by default. Organizations that master AI Strategy and product-led growth will set the pace—using AI for competitive differentiation rather than feature parity.
This shift changes how I build teams and backlogs. I lean on product trios, forward deployed engineers, and tight product discovery loops to reduce uncertainty early. We design for resilience and learning: human-in-the-loop feedback, clear escalation paths, and telemetry that turns every interaction into a hypothesis test.
Governance is a first-class feature. AI risk management, data governance, and threat detection and response sit alongside performance metrics in the same dashboard. We codify guardrails—policy, provenance, and permissions—so innovation scales safely and sustainably.
Measurement is where transformation becomes real. I anchor on outcomes vs output OKRs tied to customer value and revenue impact. At the product layer, I track activation, time-to-value, retention, and adoption by persona. For ML quality, I monitor precision/recall, coverage, hallucination rate, and model drift. In experimentation, A/B testing with a thoughtful minimum detectable effect (MDE) prevents false wins, while Amplitude analytics, Pendo, and Intercom instrumentation expose where guidance or UX writing can unlock activation.
The fastest wins often start in service and sales. A customer support ai strategy can deflect tickets with high-resolution answers while escalating edge cases to humans with full context. CRM integration with HubSpot and a ChatGPT connector enables reps to generate next-best-actions, summarize calls, and personalize outreach—measurably lifting conversion and lowering cost-to-serve.
On the build side, LLMs for product managers and gen ai for product prototyping accelerate discovery cycles. I use CustomGPT workflows to validate value propositions quickly, then harden successful flows with engineering. Throughout, product positioning and a crisp value proposition ensure that what we ship is understandable, differentiated, and priced to match ROI—consumption SaaS pricing when usage scales value.
If you’re getting started, begin with a single, high-frequency journey, instrument it deeply, and publish transparent OKRs. Pair empowered product teams with clear governance, and iterate toward agentic AI experiences. The payoff isn’t a one-time launch; it’s a continuously learning system—and a culture—that compounds advantage release after release.

Inspired by this post on Pendo – Perspectives.

October 25, 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