Tag: gen ai

Break the Headcount Ceiling: How AI Agents Create Net-New Pipeline at Scale

I’ve been through enough planning cycles to know the impossible math sales leaders juggle. Every year, we’re asked to deliver more pipeline, and the expectation is that the team will somehow hit the target—whether headcount follows or not. In a good year you close some of the gap, but the underlying constraint remains: your pipeline ceiling is tied to your headcount. The ask gets bigger, but the resources rarely keep pace. There’s never been a convincing answer to “how do I grow pipeline by 30% without 30% more people?”

For the first time in my 20-year sales career, there’s a real answer, and it comes from how we’re using our Customer Agent—internally nicknamed “Fin”—for inbound sales. What changed my perspective wasn’t faster execution on the same tasks; it was recognizing that an Agent can generate its own pipeline, consistently and at scale.

Most conversations about AI in sales focus on efficiency—do the same work, just faster. That’s helpful but incomplete. In practice, the Agent is producing net-new, attributable pipeline. It’s not simply an efficiency layer inside the SDR team; it’s a distinct source that deserves its own targets, its own owner, and clear visibility in our pipeline analytics.

Here’s how we run it. Fin has dedicated performance metrics but is held to the same outcomes as any rep: meetings booked, pipeline created, and revenue generated. On live chat, we track qualified, disqualified, and dropped conversations, then follow those cohorts through to opportunity and close. When you fold the Agent’s numbers into the team’s aggregate, you lose the crucial signal of what the Agent is actually doing. Reframing this with explicit attribution changes the boardroom conversation from “efficiency gains” to “a new, incremental source of pipeline.” Last month was our highest pipeline month from Fin to date—stronger than when live chat was handled by humans alone.

The template for this transformation came from customer service. Before we operationalized AI for sales, I partnered closely with our support organization. They built the organizational architecture we’re applying today: clear ownership of the AI motion, Agents and humans running in parallel, and a continuous optimization loop that treats the Agent as a living system, not a set-and-forget tool. The workflows in support and sales are more similar than people expect—qualify the need, guide to the right solution, and move decisively toward an outcome.

“The right benchmark is matching a high-performing rep on that channel, consistently and at scale”

When the Agent reliably meets that benchmark, the gains compound. The team wins back time for work where relationships truly matter—multi-threading across stakeholders, tailoring value narratives, and navigating complex buying processes. That is where human judgment shines.

The most common question I hear is what this means for SDRs. If the Agent owns the frontline, what are SDRs actually doing? The answer is: higher-leverage work. The Agent handles frontline inbound—engaging instantly, qualifying, routing high-intent prospects to the right team, and keeping lower-intent visitors warm by directing them to self-serve resources or remembering their context until they’re ready for a real conversation. It does this 24/7, across languages, without the capacity constraints that come with a human-only model.

What changes is where SDRs’ time goes. For us, that’s phone-based qualification, where we still see the strongest conversion. It’s also deeper relationship-building across multiple stakeholders in an account—the kind of multi-threaded engagement that takes time and judgment. Trials are a great example: rather than treating a trial as a conversion mechanism, SDRs can help prospects get real value from it through guided setup and outcome-oriented check-ins.

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

“That’s work they rarely have capacity for right now, because too much of their time goes to the frontline. Fin changes that”

I want to be direct about one thing: replacing your SDR function entirely with AI is a mistake. SDRs are the talent pipeline for closing teams. The reps who become your best AEs are, more often than not, people who came up through an SDR role. That’s where they learn to qualify and build relationships at speed. Eliminating that function to reduce cost creates fragility further up the funnel that can take years to surface.

Across the market, many sales organizations are still early in this journey. Startups and smaller teams are ahead—they’re building AI-first motions from the ground up and deliberately designing to avoid scaling headcount in the traditional way. Larger, more established sales development functions are mostly still running standard workflows. That makes sense—transforming a mature org is harder than building anew—but complexity isn’t a reason to wait. Momentum is building, and the gap is widening between teams leaning in and those holding back.

What’s emerging now is dedicated AI ownership within sales. It requires someone with program-level responsibility for how the Agent actually performs, rather than bolting AI tools onto an existing job description. We created that role – it’s called “AI SDR program lead.” This role owns the strategy, implementation, and optimization of Fin within the inbound SDR motion, ensuring it drives pipeline growth and integrates well across our systems and workflows. It’s a new career opportunity that came directly from the AI motion, with one of our existing managers moving into it.

The long-held assumption that pipeline growth requires proportional headcount growth is no longer a fixed law. AI-generated pipeline is real, measurable, and improvable with the same rigor we apply to any other part of the function. Treating it as its own source—with explicit targets, attribution, and dedicated ownership—is the difference between marginal efficiency gains and truly breaking the link between pipeline growth and headcount.

The constraint hasn’t disappeared; it has moved. It’s no longer just about how many people you can hire. It’s about how well the Agent understands your product, your customers, and your qualification logic—and how quickly your team can iterate the workflows, knowledge, and guardrails around it. For the first time, the pipeline ceiling can be higher than your headcount allows.

If you’re standing up this motion now, start with three moves: give the Agent its own KPIs and attribution, put a single owner in charge of performance and iteration, and reorient SDR time toward high-conversion conversations and multi-threaded account development. That’s how you scale pipeline with AI Strategy and sales-led growth—without scaling headcount in lockstep.

Inspired by this post on The Intercom Blog.

April 28, 2026
What’s New with Amplitude Agents: Faster Releases, Smarter Insights, and Must‑Try Upgrades

I’ve been deep in the work of turning agentic AI from a promising idea into reliable, measurable outcomes. Today, I want to share a concise, practitioner’s update on what’s new with Amplitude Agents—and, more importantly, how to get real value fast using proven product management techniques.

We launched AI Agents a few weeks ago. We’ve been shipping pretty fast since then, so we wanted to loop you in on what’s new and what’s worth trying.

Rapid releases only matter if they translate into user value. My approach is to treat every agent improvement as a learning opportunity: instrument it, set clear success metrics, run controlled experiments, and iterate. This eval-driven development mindset keeps us honest about what’s truly working in the wild.

If you’re trying Amplitude Agents now, start with a narrowly scoped, high-signal workflow where success is unambiguous—think a single journey with a clear “done” state. Connect the experience to your unified analytics platform so you can see the full picture across events, funnels, and cohorts. In practice, I lean on Amplitude analytics and Agent Analytics to make this visibility effortless.

Define how you’ll measure impact before you ship. Identify activation and completion events, baseline them, and then A/B test your agentic AI flow against the status quo. Behavioral analytics will show whether users are discovering the agent, sticking with it, and returning for more. When the story in the data is clean, it’s much easier to scale the win.

Hardening matters as much as headlines. As you expand use, apply sensible guardrails—input validation, clear prompts, and transparent handoffs to deterministic flows when confidence is low. Pair this with observability so you can spot anomalies early and recover gracefully. These practices reduce risk while preserving the speed and creativity that make AI workflows powerful.

Once the basics are working, dig into adoption patterns: segment by cohort, study user activation paths, and run retention analysis to find where the agent is truly changing behavior. These insights shape roadmap priorities and help you invest in the moments that drive durable value.

We’ll keep shipping quickly and sharing practical guidance. If you have feedback, experiments to showcase, or questions about instrumentation, send them our way—I use that signal to refine our next set of improvements and learning agendas. Expect more short, focused updates and deeper dives on evaluation frameworks, prompt strategies, and rollout playbooks.

In short: keep it scoped, instrument everything, test deliberately, and let the data guide your next move. That’s how Amplitude Agents becomes not just new, but indispensable.

Inspired by this post on Amplitude – Best Practices.

April 28, 2026
AI Data Security for Product Teams: Protect Sensitive Product Data Without Slowing Innovation

Protecting product data has never felt more urgent. Every week, my teams experiment with gen ai prototypes and LLM-powered capabilities, and I’m accountable for ensuring our innovation never compromises cybersecurity, privacy, or customer trust. The goal is not to slow down—it's to build in the right guardrails so speed and safety reinforce each other.

Understand AI data security risks in product teams, what product data is most exposed, and how to use AI tools responsibly without slowing innovation.

When I assess AI risk with product managers, I start with how data moves. The biggest threats usually come from prompt and context leaks, unsafe logging of sensitive inputs or outputs, permissive access controls, unmanaged third-party model usage (shadow AI), and unclear data-retention policies. For LLMs for product managers, I emphasize that every step in AI workflows—from collection to processing to storage—must assume adversarial conditions.

In my experience, the product data most exposed includes customer PII and payment identifiers, internal strategy documents and roadmaps, analytics and behavioral telemetry tied to users, feature flags and configuration values, embeddings and vector stores that can reveal sensitive patterns, and the prompts or contexts themselves. Even “harmless” evaluation datasets can contain inferred identities. Treat all of this as high-value assets in your data governance model.

I apply privacy-by-design from the first discovery conversation: minimize data by default, redact or tokenize before any external model call, and separate identities from content wherever possible. A retrieval-first pipeline helps keep raw customer data within our boundary while still enabling relevant context. We combine deterministic safeguards (policy-based redaction, allow/deny lists) with runtime observability to detect anomalous prompts, outputs, or access patterns.

To keep velocity high, we operationalize risk rather than debate it ad hoc. A lightweight risk scoring rubric classifies each capability (e.g., internal-only, customer-facing, regulated data adjacent) and dictates controls: redaction requirements, human-in-the-loop thresholds, eval-driven development gates, and incident response readiness. These controls live in CI/CD so product teams get fast, automated feedback without waiting on meetings.

Partnership is essential. I bring Security, Legal, and Data partners into the product trios early to align on regulatory compliance and threat modeling while scoping solutions that meet outcome goals. We maintain a shared catalog of approved providers and architectures, document data flows, and version our policies just like code—so everyone can see what changed and why.

Vendor diligence is non-negotiable. I ask LLM providers about data retention and training usage, encryption at rest and in transit, key management, regional data controls, audit posture (SOC 2, ISO 27001, HIPAA where needed), and support for private networking. We restrict scopes with least-privilege access and instrument robust observability for threat detection and response across the full path, not just the API call.

Culture makes the biggest difference. I coach teams on prompt hygiene, secret handling, and context window management; we publish redaction patterns, approved libraries, and clear do/don’t examples. When incidents happen, we treat them as learning opportunities, run blameless reviews, and update our playbooks, guardrails, and training materials accordingly.

The outcome I aim for is confidence with speed: we ship AI features that customers love while protecting the data they entrust to us. With a clear risk model, strong data governance, and embedded controls, product teams can innovate boldly—without compromising on security or trust.

Inspired by this post on Product School.

April 27, 2026
Fin for Sales: Instantly Engage, Qualify, and Close High‑Intent Leads with an AI Customer Agent

Today, I’m spotlighting Fin for Sales, a new role for Fin Customer Agent that runs your inbound sales motion end-to-end. From my vantage point leading product management and collaborating closely with revenue teams, this is a meaningful evolution in how we capture, qualify, and convert high-intent demand with precision and speed.

The promise here is simple and powerful: a single Customer Agent with shared context, memory, and business goals that supports the entire journey from first touch to close. Fin for Sales brings Fin to the start of the customer journey so it can engage prospects, guide them through your funnel, and ensure the best opportunities reach your sales team without delay.

At a high level, here’s what stands out to me in practice. Fin engages every prospect instantly at the moment intent is highest. It runs discovery like your best rep with clear pricing guidance, product education, and objection handling. It qualifies and routes in real time using your playbook and syncs full context to your CRM. And it closes deals while you sleep by booking meetings, starting trials, and steering buyers to the right next step—boosting MQLs, pipeline, and early close/win rates.

Fin engages every prospect instantly. It starts the right conversation when interest peaks, re-engages before prospects go cold, and works on every channel, in every language, 24/7. In my experience, that immediacy is the difference between a lead that converts and a lead that disappears.

Introducing Fin for Sales, a conversational assistant that qualifies prospects in real time. The chat compares Free vs Pro, spotlights reporting and Salesforce integrations, and invites users to book a call.

Fin runs discovery like your best rep. It explains pricing, guides product discovery, handles objections, and personalizes each interaction based on who the prospect is and what they care about. This is where thoughtful conversation design and consistent playbook execution really compound.

Fin qualifies and routes in real time. Using your playbook, it collects and enriches data about your prospects, sends qualified leads to your sales team or down self-serve paths, while syncing full context to your CRM. Your team never works the wrong lead. That’s operational rigor revenue leaders crave.

Fin closes deals while you sleep. It can book meetings, start trials, and guide buyers to the right next step. Early customers are already seeing impressive results, increasing MQLs, growing pipeline and seeing close/win rates of nearly 50% in the first month. That’s the kind of lift that reshapes go-to-market strategy and forecasting confidence.

Fin for Sales links customer agent insights with Salesforce, turning live conversations into rich profiles and lead scores. View key details, intent and opportunity signals, and guided next steps like booking a meeting.

Why this matters: most online sales experiences still rely on forms, queues, and follow-ups—exactly when prospects want clarity and momentum. Hiring enough reps to cover every time zone, channel, and hour is unrealistic, and even the best teams burn cycles on leads that were never going to convert. I’ve watched high-intent demand slip through the cracks simply because the response wasn’t fast, consistent, or contextual enough.

Revenue leaders need a system that meets every inbound interaction immediately, without sacrificing quality, and routes only the right opportunities to sales. Incremental automation doesn’t fix the core issue; an agentic approach does. Fin for Sales closes that gap by pairing instant engagement with disciplined qualification and crisp handoffs.

How it works in the moment: when a prospect is actively exploring your site, any delay—a form, a queue, a “we’ll get back to you”—erodes intent. Fin engages in real time through the Spotlight Messenger, a new interface built specifically for sales conversations. It can proactively start a conversation based on context like the page someone is on or how they’re browsing, and it offers smart suggestions to kick-start engagement.

Fin for Sales schedules meetings directly in chat. A sleek widget shows a March 2026 calendar with selectable time slots and a clear Confirm booking CTA, streamlining lead capture and speeding up sales follow-ups.

Prospects who might have waited—or never reached out—now get answers immediately. Fin also works across channels including messenger and email, so buyers can engage however they prefer. Whether someone is browsing your pricing page at 2am or comparing features during a lunch break, Fin responds instantly and relevantly so no lead is left behind.

To move prospects toward a decision, Fin guides personalized discovery conversations that clarify needs and accelerate choices. Four pillars make this consistent and trustworthy. Playbook: you brief Fin in natural language on desired outcomes and scenarios; it follows your rules, handles objections with approved guidance, and stays on track. Knowledge: it draws from your product knowledge base to answer pricing, features, and plan fit, and can reuse what you’ve already trained for customer service—no duplicate setup. Enrichment: once Fin learns a user’s email or name, it enriches that data with outside sources to improve qualification, personalization, and routing. Memory: if Fin recognizes a returning visitor, it remembers context so the buyer never starts over.

As conversations progress, Fin surfaces the opportunities most likely to close. It qualifies like your best SDR—asking about use case, budget, fit, and timing—and applies your existing playbook to identify the strongest opportunities. Details captured in conversation, plus enrichment, produce a complete picture that’s structured and synced into your CRM for immediate sales action. And when a lead isn’t a fit, Fin gracefully disqualifies or redirects to self-serve resources, ensuring your pipeline stays focused.

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

When a lead is ready to act, Fin closes. It books meetings via tools like Chili Piper or Calendly, guides qualified buyers into trials or subscriptions, and routes opportunities to your sales team with full context. Crucially, it passes the full conversation history and an AI-generated summary so reps pick up exactly where the buyer left off—no repeated questions, no lost nuance. For self-serve motions, Fin can guide prospects from discovery to trial signup or even paid conversion, automatically assigning the right path.

Real results underscore the model’s value. Fin is already delivering measurable results for early customers across different company sizes, sales motions, and go-to-market models. Attio, an AI CRM built for scaling go-to-market intelligently, deployed Fin to replace their traditional form-and-wait inbound flow with real-time conversational engagement. In three months, Fin handled over 1,600 conversations with website visitors, qualified more than 50 leads for sales, and routed over 30 applicants into their startup program. One returning prospect engaged with Fin, had their questions answered in real time, and converted to a paying customer at six times Attio’s average contract value.

Fellow, an AI-powered meeting assistant and management platform, started by deploying Fin overnight, a window where no human was online and prospects waited up to 18 hours for a reply. In January alone, Fin booked 18 meetings the team would never have reached, converting at around 48%. Importantly, the human team maintained its booking rate while Fin added net-new meetings—proof that automation layered on top of strong human coverage can be additive, not cannibalistic.

Fin for Sales is built on the same AI platform that powers the highest-performing Agent in customer service, which keeps the end-user experience consistent. If a prospect asks a support question mid-sales conversation, Fin can handle it—no handoffs to other vendors, no lost context. It shares knowledge and memory across its platform, always knows whether it’s talking to a prospect or a customer, and moves between roles as needed. Setup follows the same Fin Flywheel: Train, Test, Deploy, Analyze. Describe your sales playbook, qualification criteria, and routing rules in natural language; test in preview; deploy live; and use Analyze to understand performance and iterate quickly.

Fin for Sales is available today, and there’s more coming. I share the conviction that the future is a single Customer Agent, vertically integrated down to the model layer, orchestrating customer experience across the entire lifecycle. If you want to see it in action, go to fin.ai/sales and talk to Fin—then imagine that instant, high-quality engagement running across your inbound sales engine, every hour of every day.

Inspired by this post on The Intercom Blog.

April 22, 2026
Forget Crystal Balls: How Scenario Planning Helps Me Ship Smarter in the Age of AI

AI headlines are everywhere—and many claim they know exactly what’s coming next. In product management, I’m often asked to make single-point predictions about gen ai and LLMs for product managers. I resist that temptation because confident forecasts are seductive—and usually wrong.

Listening to Teresa Torres and Petra Wille unpack why certainty fails reinforced what I practice with my product trios: scenario planning. Instead of betting on one future, I explore several plausible ones, define the signals that would confirm or disconfirm each, and translate those insights into product strategy and product roadmapping and sprint planning we can adapt as evidence evolves.

Their argument mirrors what I see with customers and stakeholders: people are bad at predicting the future, and overconfidence creates fragility. Early adopters don’t represent everyone, so when we extrapolate from enthusiasts to the mainstream, we waste time and erode trust by building the wrong things.

Here’s how I apply this to avoid technology FOMO and make sharper AI Strategy decisions. I treat every bold claim as one possible future, then ask, “what else could happen?” I push extremes—AI everywhere vs. AI as invisible utility; GUIs vanish vs. GUIs evolve; centralized vs. edge compute—and hunt for the needs that stay true across scenarios. Those invariants anchor empowered product teams to outcomes, not outputs, and they help us stage bets responsibly.

Listen to this episode on: Spotify | Apple Podcasts

My key takeaways: Confident predictions are often wrong. Early adopters don’t represent everyone. Treat predictions as one possible future. Scenario planning > trying to be right. Focus on patterns, not hype.

In short: We’re in a period of change—but no one can predict exactly how it plays out. Strong predictions often ignore uncertainty.

A better approach in practice: Treat every prediction as a scenario. Ask: what else could happen? Use multiple futures to guide decisions.

As you evaluate roadmaps, watch for traps like “My experience = everyone’s future” thinking, over-indexing on early adopters, and ignoring real-world constraints like budgets, compliance, and change management.

Tactically, we run quick scenario exercises, push ideas to extremes to explore implications, and extract the underlying insight (not the exact prediction). This complements continuous discovery and helps us write outcomes vs output OKRs that are resilient to uncertainty.

00:00 – The problem with future predictions

04:00 – Why experts get it wrong

06:00 – Scenario planning explained

12:00 – Early adopters vs. reality

20:00 – AI, GUIs, and extreme takes

27:00 – Using scenarios in product work

34:00 – Final thoughts

Resources & Links:

Follow Teresa Torres: https://ProductTalk.org

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

Mentioned in this episode:

Claude Code

What did I miss—or what scenarios are you considering for your team? Leave a comment below and let’s compare notes.

Inspired by this post on Product Talk.

April 21, 2026
From Brain Dump to Done: How Todoist’s Ramble Captures Tasks in Real Time with AI

Turning a rambling stream of consciousness into a clean task list while someone is still talking has been a longtime product dream of mine. With Ramble, Todoist brought that dream to life by using live audio AI to capture tasks in real time—no transcription step required. The result is a voice-to-task flow that feels natural, fast, and surprisingly disciplined.

As I listened to the Doist team—Ernesto Garcia (Front-end Product Engineer), Thomas Jost (Backend Software Engineer), and Hugo Fauquenoi (Product Manager)—walk through their approach, I heard a blueprint for building pragmatic GenAI features. What began as a two-to-three month AI exploration became one of their most technically deliberate releases: a “Gemini-powered pipeline that makes tool calls while the user is still speaking, surfacing tasks on screen in real time without any text output from the model.”

The breakthrough started with user research. People weren’t merely dictating tasks; they were doing a “brain dump” first—often into pen and paper or even ChatGPT voice—and only then committing items to Todoist. Meeting users where they already are reframed the problem: don’t force structure upfront; capture fluid thought and translate it into actionable tasks instantly.

That insight led to a bold architectural choice: skip transcription entirely and process raw audio directly with a Gemini live audio model. By removing the brittle middleman of text, the team reduced latency and kept the model focused on one job—turning intent into structured actions. It’s a crisp example of AI workflows designed for reliability over novelty.

The real magic is in the real-time “tool calls.” As the user speaks, the model triggers add task, edit task, and delete task operations immediately. For high-friction contexts like driving, they paired visual task cards with subtle sound effects as confirmation cues. It’s thoughtful conversation design that respects attention and safety without sacrificing speed.

Teaching the model to capture tasks literally—without over-interpreting or trying to complete the work—required careful prompt engineering for voice and temperature tuning. Drawing a bright line between “capture versus do” kept the experience trustworthy. In my own AI Strategy work, I’ve found that establishing explicit agentic guardrails early prevents unintended autonomy later.

Dates were the sleeper challenge. The team had to inject the current date, normalize to days vs. months, and always output dates in English for the natural language parser—while preserving the user’s original language for everything else. If you’ve ever shipped date handling across locales, you’ll appreciate how many edge cases hide in “Taming Dates and Time.”

Quality didn’t hinge on intuition alone. They built an LLM-judge eval system using real employee recordings from 100+ people across 35 countries in 20+ languages to catch prompt regressions. That’s eval-driven development done right: representative data, repeatable scoring, and tight feedback loops as models and prompts evolve.

For project and label matching, they chose direct context injection over RAG. Instead of building a retrieval pipeline, they injected the full project/label list into the system prompt. With smart context window management and a sharply constrained task schema, this was both simpler and more accurate. Sometimes the fastest path to product-market fit is removing moving parts, not adding them.

One product principle stood out: easy correction beats perfect first-time accuracy. Natural language interfaces earn trust when users can fix misfires in a tap or two. That bias toward quick recovery over false precision is how you ship AI that feels useful from day one.

Looking ahead, the roadmap is compelling: multimodal task capture from images and text blobs, Apple Watch support, and automation integrations. As voice AI agent patterns mature, this “tool-only architecture” sets a solid foundation for going from capture to coordinated execution—without losing the simplicity that makes Ramble shine.

If you want to hear the full conversation, you can listen on Spotify or Apple Podcasts. It’s a masterclass in building focused GenAI features that trade cleverness for clarity—and still delight.

Resources & Links: Todoist • Doist • Google Vertex AI (Gemini)

Inspired by this post on Product Talk.

April 16, 2026
Inside Amplitude’s AI Platform: Powerful Lessons for Product Leaders Shaping Analytics

Every so often, a single line captures the essence of platform thinking at scale. "Vinay is a Staff AI Engineer at Amplitude. He builds the foundational AI platforms that empower internal innovation and help define the future of AI analytics." That statement crystallizes the mandate many of us share: create durable AI capabilities that compound value across teams, products, and customers.

When I think about "foundational AI platforms" in the context of Amplitude analytics and behavioral analytics, I see more than infrastructure. I see a product strategy choice: invest in a unified analytics platform that lowers the cost of experimentation, increases the trustworthiness of insights, and speeds time-to-learning for empowered product teams. That’s the engine behind sustainable product-led growth.

For me, the platform blueprint starts with three layers: high-quality data foundations (schema design, governance, lineage), model lifecycle rigor (evaluation, observability, versioning), and safe, self-serve interfaces that meet teams where they work. Without strong data governance and clear accountability, even the smartest gen ai features struggle to gain adoption. With them, platform scalability and reliability become a competitive advantage—not just an operational checkbox.

Empowering internal innovation requires thoughtful constraints. I’ve seen the best teams pair self-serve tooling with guardrails: templates for use cases, bias and risk checks, and well-documented pathways from prototype to production. This balance turns AI Strategy from a slide into a system—one that helps teams decide when to build vs buy, how to measure value, and how to retire what no longer serves the roadmap.

Looking ahead, the future of AI analytics is about making intelligence ambient. That means stitching together event data, product usage, and customer context so insights surface exactly when decisions are made. It also means bringing gen ai responsibly into the workflow—summarizing behavior, explaining anomalies, and suggesting next best actions—while maintaining transparency and auditability.

My practical takeaways: invest early in shared components that everyone can use (feature stores, evaluation harnesses, data contracts); standardize interfaces so teams ship faster with fewer handoffs; and measure platform outcomes with product metrics, not just infrastructure metrics. Done well, this approach compounds: faster cycles, higher confidence, and a steady drumbeat of wins that reinforce a culture of learning.

In short, building the right AI foundations is how we unlock scale, create leverage for every team, and keep our edge in a dynamic market. That one line about building foundational AI platforms isn’t just a role description—it’s a north star for any product leader serious about shaping the next era of analytics.

Inspired by this post on Amplitude – Perspectives.

April 15, 2026
Never Lose Your AI Superpowers: How I Sync Context and Skills Across Every Device

I spend a meaningful portion of my week helping teams operationalize AI workflows, and one theme comes up over and over: how to share context files and skills seamlessly across devices and with colleagues. Hosting Claude Code office hours has only reinforced it—sharing context and skills is the single biggest blocker to reliable, repeatable outcomes.

I hear from leaders driving AI adoption who have built robust, high-signal context systems and carefully crafted skills. Their challenge isn’t creating value—it’s distributing it. They need a way to make the same trusted workflows available to teammates and to keep everything in sync across laptops, desktops, and phones.

I hit the same wall myself. I work across multiple devices (a Mac Mini for day-to-day, a MacBook Air on the road, and an iPhone) and I collaborate with a full-time admin. I wanted my context and skills to be consistent everywhere, for both of us. In this piece, I’ll share my setup—what I store where, how I share it across devices and with my team, the trade-offs of each option, and how I keep everything current. We’ll cover four different syncing services: git/GitHub, Obsidian Sync, Dropbox and iCloud.

If you’re new to this series, this is the eighth installment. Earlier pieces provide foundational context: Claude Code: What It Is, How It's Different, and Why Non-Technical People Should Use It; Stop Repeating Yourself: Give Claude Code a Memory; How to Use Claude Code Safely: A Non-Technical Guide to Managing Risk; How to Choose Which Tasks to Automate with AI (+50 Real Examples); How to Build AI Workflows with Claude Code (Even If You're Not Technical); How to Use Claude Code: A Guide to Slash Commands, Agents, Skills, and Plug-ins; and Context Rot: Why AI Gets Worse the Longer You Chat (And How to Fix It).

The day it really hit me was right before my interview with Claire Vo on How I AI. I was staying in an AirBnB with only my laptop, and I planned to demo my /today command along with my context file structure. Minutes before the session, I realized the latest version of my /today command wasn’t on that machine. I was able to remote into my Mac Mini and grab it—crisis averted—but it was a wake-up call. I needed a more reliable, shareable approach for syncing context and skills across devices and with my admin.

I started by testing the tools I already used—Dropbox, iCloud, and GitHub—to see what might fit. Each got me partway there, but each also introduced friction that mattered in daily use.

First, absolute file paths don’t travel well. I began with Dropbox but quickly ran into cross-linking headaches. Good context systems rely on rich interlinking—index files point to other context files, and those context files link to each other. When Claude creates a link from one context file to another, it tends to use the full file path: /Users/ttorres/Library/CloudStorage/Dropbox. That worked on my Mac Mini and MacBook (same user name), but not on my phone—and not for my admin. I tried to force relative links (~/Dropbox), but couldn’t get Claude to do it consistently, which led to broken links. This isn’t unique to Dropbox; Claude prefers full paths because they’re reliable on a single machine, but they’re brittle across devices and useless when sharing with colleagues. Claude is trained to use relative file paths when working within a git repository, but I struggled to get it to work reliably in Dropbox.

Second, skills live in a user directory by default. By default, skills live in ~/.claude/skills. Most sync services aren’t designed to share your ~/ folder. iCloud is the exception, but then you’re limited to Apple devices—no Windows or Android. There is a workaround: set up a claude folder in Dropbox and create a symlink from ~/.claude to your synced claude folder, so all skills, commands, and settings live in Dropbox. Then, on each device (yours or a colleague’s), you set up a symlink to that folder so Claude can find the files. This works, but I was running into another limitation that made Dropbox a poor fit.

Third, Obsidian on iOS doesn’t sync cleanly with Dropbox. I rely on Obsidian’s file browser alongside my notes to navigate context quickly. Storing vaults in Dropbox gave me parity across my Mac Mini and MacBook Air, but I couldn’t get the iOS Obsidian app to reliably load my Dropbox vaults. That friction was a dealbreaker for on-the-go work.

At that point, I explored git/GitHub. GitHub is cloud storage for git repositories. A git repository is a folder of shared files used so engineers can collaborate on the same code base. Each person clones a local copy, works locally, then pushes changes back to the hosted repo on GitHub; others pull to update. Git’s merge and conflict tooling is excellent. Git is the powerhouse of file syncing and version control. It easily handles syncing context and skills, Claude behaves better with relative links in a git repo, and I can open the repo in my IDE with a clean file browser. For me, that checked all the boxes—until I factored in my admin. Git has a learning curve, requires manual pull/push hygiene, and often assumes an IDE workflow. That overhead was too heavy for a non-technical collaborator.

The turning point was Obsidian Sync. A colleague suggested it, and it ended up being the sweet spot. Obsidian is a markdown reader; files are stored locally in a normal folder you can open in Finder or File Explorer. There’s no proprietary format—you can read files with any text editor, and Claude can access them via bash commands. Obsidian Sync is simpler than git: open a note and it syncs in the background. I can access the same vaults across my Mac Mini, MacBook Air, and iPhone, and I can share a vault with my admin so we can both create and access notes.

Because we’re in different time zones and rarely edit the same note simultaneously, limited conflict handling hasn’t been an issue. Obsidian’s internal link notation also means one note can link to another and those links just work across devices. Claude can follow these links, so the brittle file path problem disappears.

Here’s where I landed. After a lot of trial and error, I have a setup that works across my devices and for my admin, who uses both a Windows desktop and a Mac laptop. I keep my core context in Obsidian vaults synced with Obsidian Sync, which preserves portability, link integrity, and ease of use. For skills, I avoid scattering files in machine-specific locations and instead centralize what Claude needs to reference in shared, human-readable folders. If you require advanced version control with branching and reviews, git/GitHub is excellent. If your priority is low-friction, cross-device access for non-technical teammates, Obsidian Sync is a practical, reliable choice. And if you must use Dropbox or iCloud, consider symlinks and be vigilant about relative paths—just know that absolute paths won’t travel well.

Inspired by this post on Product Talk.

April 15, 2026
Inside the Most Politically Dangerous C‑Suite Role: Hard Truths on Culture, Layoffs, and Leadership

I’ve long believed the people function is a strategic engine, not a support lane. That conviction was only reinforced in a recent deep dive with Katie Burke, now COO at Harvey after joining as Chief People Officer. Before Harvey, she spent 11 years in HR leadership at HubSpot, helping build one of tech’s most distinctive cultures. In this piece, I unpack what resonated most for me as a product leader: a marketing-minded approach to HR, deliberate hiring from hospitality, and the non-negotiable case for culture as a core business strategy.

The first principle is simple and often overlooked: HR leaders should think like marketers. Employer brand is a product; your candidate and employee journeys are funnels; and your programs deserve the same rigor we bring to product—segmentation, positioning, channels, and continuous A/B testing. When we treat onboarding, performance, and manager enablement like iterative product launches—complete with activation metrics, retention curves, and NPS—we stop guessing and start compounding results.

One line has become a north star for how I approach executive leadership: “Don’t ask for a seat at the table. Build the table.” In practice, that means codifying the operating system—decision rights, principles, cadences, and accountability—so the organization isn’t improvising strategy in every meeting. Product, People, and Finance should co-own this OS; that’s how you scale clarity faster than headcount.

Transparency is the tax we pay for alignment, and it compounds trust. After an IPO, the impulse can be to close ranks. The better move is radical transparency with context: what changed, why it matters, and how decisions get made now. On my teams, that looks like publishing decision records, sharing tradeoffs explicitly, and using written docs to reduce rumor velocity—core muscles in stakeholder management as complexity grows.

I also loved the counterintuitive hiring bet: prioritize hospitality backgrounds alongside traditional corporate pedigrees. People who’ve thrived in service environments bring customer empathy, operational resilience, and a bias for proactive care—traits that elevate everything from onboarding to incident response. In product terms, they’re culturally accretive hires with high signal on service quality and consistency.

The trickiest part of the Chief People Officer role isn’t process—it’s politics. You are the executive team’s own HR business partner, which requires coaching, candor, and conflict mediation at the highest stakes. The goal is to “Be the Michael Jordan of your exec team”—the teammate who elevates standards, makes others better, and chooses the hard right over the easy familiar.

Layoffs create a culture debt that accrues interest. Expect a “2.5-year cultural hangover after a layoff”—in many companies, an inevitable two-year layoff hangover—unless you actively repay it. That repayment plan includes narrating the why with specificity, rebuilding trust through manager enablement, and re-anchoring on performance and values. Measure leading indicators (manager effectiveness, time-to-decision, psychological safety) alongside lagging ones (regretted attrition) to track the true recovery arc.

People leaders also need to create “graceful exits.” Doing this well preserves dignity for the person, protects the team’s morale, and safeguards the company’s brand. The bar is straightforward: clear rationale, fair process, useful feedback, generous support, and alumni pathways. A graceful exit signals that even when business realities bite, respect is non-negotiable.

Expectation-setting matters. Two truths cut through the noise: “The workplace shouldn’t be Disneyland” and “Our job is not to make you happy every day.” The promise is not perpetual happiness; it’s meaningful work, fair standards, growth opportunities, and leaders who tell the truth. When we set that contract clearly, engagement becomes an outcome of purpose and progress—not perks.

On feedback, I use the protein vs. sugar rule for employee feedback. Sugar feedback is pleasant and perishable; protein feedback is specific, sometimes uncomfortable, and growth-driving. Great cultures build a taste for protein—clear role expectations, crisp examples, and written follow-ups. Mechanically, that looks like structured 1:1s, decision retros, skip-levels, and manager training that demystifies “what good looks like.”

Being a Chief People Officer isn’t for the faint of heart. The role must be demanding by design—on executive hiring quality, performance management courage, and values enforcement. Moments like “Berry-Gate” are reminders that small symbolic issues can balloon when feedback loops are unclear. Close the loop fast, publish the rationale, and ensure there’s a predictable path for concerns to be heard and resolved.

When hiring, beware patterns that predict friction. That’s why “frequent flyers” are a new-hire red flag. Movement can signal adaptability—but weather-vein pivots and blame-shifting often repeat. Probe for ownership, learning moments, and sustained impact; you want people who compound value, not just sample it.

Clarity on scope prevents leadership whiplash. Which company decisions fall to the Chief People Officer? Think leveling frameworks, compensation philosophy and bands, performance calibration, manager standards, ER policies, and org design guardrails—always in lockstep with Finance and the CEO. Escalate when there are values collisions or systemic risks; otherwise, push decisions to the right altitude and owner.

Scaling exposes the same few failure modes on repeat: fuzzy decision rights, a thin manager bench, brittle processes that don’t flex, and inconsistent leveling that erodes trust. The antidote is an operating model that pairs clear principles with lightweight mechanisms—documented roles, regular calibration, and reviews that audit for both outcomes and operating behaviors.

Comparing a scaled SaaS like HubSpot with an AI-native company like Harvey surfaces important differences. The former optimizes for durable systems, predictable cadences, and governance; the latter optimizes for rapid learning loops, emergent org design, and a higher tolerance for ambiguity. The art is porting the right controls at the right time without crushing velocity.

AI is already changing the people function. GenAI can draft job descriptions, summarize performance notes, classify themes from engagement surveys, and power AI workflows that resolve common HR tickets. The human-in-the-loop remains essential for judgment, context, and ethics—especially around data governance and privacy-by-design. A pragmatic AI Strategy here frees HRBPs for higher-order coaching and organizational development work.

One practice I recommend widely: share your own performance reviews. Modeling openness normalizes growth and turns feedback into a shared craft, not a secret ritual. It also builds trust when you later ask the organization to lean into sharper, protein-rich feedback.

Finally, disagreements with the CEO are inevitable—and healthy. Handle them with pre-briefs, crisp written proposals, explicit tradeoffs, and a shared decision record. Argue like scientists, not politicians; once a call is made, disagree and commit. That combination of candor and alignment is what keeps executive teams high-trust and high-velocity.

The people leader’s chair may be the most politically dangerous role in the C-suite—but it’s also one of the most leveraged. Build the table, tell the truth, design for standards and dignity, and treat culture like the product that powers everything else.

April 10, 2026
Beat AI FOMO: A Product Leader’s Playbook to Choose Tools, Stay Focused, and Learn Deeply

Lately, it feels like every morning brings a new AI launch, a dazzling demo, or a must-try tool. I love the pace of innovation, but the constant stream can trigger counterproductive FOMO if I’m not intentional. As a product leader, I’ve learned to turn that anxiety into a disciplined learning system—one that keeps me curious without letting novelty hijack my focus.

That’s exactly why this conversation with Petra Wille and Teresa Torres resonated with me. They explore how to stay experimental in the AI era without chasing every shiny object. Their perspective aligns closely with my own operating cadence: start with real problems, go deep on a small set of tools, and create explicit boundaries between work, learning, and play.

Listen to this episode on: Spotify | Apple Podcasts

Here’s the mindset I apply. I don’t start with tools—I start with problems. When I encounter concrete friction in a workflow or see a credible opportunity to improve an outcome, that’s my trigger to explore a new capability. This mirrors the continuous discovery habit of prioritizing opportunities over solutions, and it’s how I avoid performing “innovation theater.”

To keep exploration healthy, I time-box my learning. I block recurring windows specifically for experiments, reading, and hands-on trials so they don’t overrun my core product work. During these blocks, I’ll set a clear question, run a tight test, and capture what I learned. No rabbit holes, no endless tinkering.

I also separate “interesting” from “actionable.” Plenty of inputs are worth awareness, but very few deserve immediate action. I bookmark the rest for later. This simple filter reduces cognitive load and keeps my backlog—from ideas to proofs of concept—well-governed.

Social media can amplify technology hype cycles, so I establish boundaries. I batch consumption, mute low-signal channels, and prioritize practitioner communities over performative threads. The goal isn’t to be first; it’s to be right for my customers, my team, and our strategy.

When choosing what to try next, I use a practical rubric. Does the tool target a real friction I’ve seen in discovery or delivery? Can it plug cleanly into our AI workflows without unsustainable glue work? Do we have a safe, compliant way to test it? Is there a plausible path from trial to compounding value? If the answer isn’t a confident yes to most of these, I wait.

Depth beats breadth. I’d rather take one promising tool into a real use case, instrument it, and measure outcomes than skim ten trending demos. That tighter loop produces sharper intuition, clearer product bets, and better partner decisions. A quick opportunity solution tree helps me connect user pain to outcomes before I let any solution onto the field.

In the episode, Petra Wille and Teresa Torres talk candidly about managing FOMO, deciding which tools to explore, and designing intentional learning systems. They discuss why starting with a problem is more valuable than starting with a tool, how social media amplifies technology FOMO, and why going deeper with fewer tools can lead to better learning. If you’ve ever felt like you’re falling behind because you haven’t tried the latest AI tool yet, this conversation will help you rethink how you approach learning and experimentation.

If you’re curious about what came up, here are some of the tools and communities mentioned: Claude Code, OpenClaw (formerly Clawdbot, Moltbot), NotebookLM, Product Talk, ElevenLabs, Lenny’s Newsletter Community, and even a nod to Bridgerton for a touch of levity.

My takeaway is simple but powerful: curiosity doesn’t require constant experimentation. The best product managers cultivate a balanced system—grounded in product discovery, energized by focused experiments, and protected by clear boundaries—so we can learn faster while staying pointed at outcomes that matter.

Discussion Question: How do you decide which new tools or technologies are worth exploring—and which ones you can safely ignore?

Resources & Links: Follow Teresa Torres: https://ProductTalk.org | Follow Petra Wille: https://Petra-Wille.com

Full transcripts are only available for paid subscribers.

Have thoughts on this episode? Leave a comment below.

Inspired by this post on Product Talk.

April 7, 2026
Inside Banani: How a Canvas-First AI Designer Elevates UX and Accelerates Product Teams

I believe the future of product design isn’t about replacing designers—it’s about giving every team access to one. That’s why Banani grabbed my attention. It’s an AI product designer that doesn’t just generate code—it generates design. For solo founders, stretched design teams, and early-stage startups, that shift matters: it raises the design floor without lowering the creative ceiling.

I spent time with Vlad Solomakha (CEO & Co-founder), Vova Kovalchuk (CTO & Co-founder), and Vlad Ostapovats (Founding Growth) to unpack how they took Banani from a Figma plugin proof-of-concept to a canvas-first AI design tool generating hundreds of thousands of designs per week. Vlad brings a decade of design experience and a precise north star: AI should produce beautiful, tasteful design rather than average, undifferentiated UI.

The architectural choices stood out. They engineered their agent to handle parallel screen edits, manage per-screen context across canvases with hundreds of frames, and make surgical edits without regenerating entire screens. This is the kind of agentic AI work that product leaders have been waiting for: concrete advances in context window management, tool orchestration, and prompt engineering that translate into higher throughput without sacrificing quality.

Equally important is how they addressed the "gulf of specification"—the mismatch between how designers think visually and how agents understand text. Banani’s canvas-first approach acknowledges that design is spatial, hierarchical, and iterative. Rather than forcing a chat-first UX, they center the canvas and let the agent do production work while keeping the designer firmly in control. In practice, this narrows intent ambiguity, speeds up iteration, and preserves taste.

The team made another pivotal bet: Why Banani doesn’t compile running applications — just HTML/CSS mockups — and how that shapes everything. By decoupling the design artifact from runnable code, they optimize for velocity, taste, and exploration. In my experience, this separation is the right product strategy for early discovery and gen ai for product prototyping—move fast on aesthetics and flows, then converge on implementation once you’ve validated the direction.

I also appreciated their pragmatic evaluation approach. Instead of traditional evals, they spin up 10 screens from one prompt to compare models. It’s hands-on, outcome-based, and aligned with eval-driven development in real product environments. They’re relentlessly discerning about when to work around model limitations versus when to wait for the models to improve—an essential discipline when building at the edge of what’s possible.

Under the hood, context engineering and specialized agent tools do the heavy lifting. Per-screen history with shared project context enables precise, reversible changes across large canvases. The result: fewer destructive regenerations, more reliable design intent preservation, and a workflow that feels like collaborating with a strong mid-level designer who’s exceptionally fast and consistent.

If you want a quick tour, I recommend jumping to a few highlights: 20:13 Product Tour Canvas First AI, 33:40 Gulf of Specification, 42:54 Agent Architecture Under Hood, 48:48 State History Context Tricks, and 56:04 Navigating Busy Canvases. Each segment reveals a different layer of the system design and product thinking behind Banani’s canvas-first UX.

For product leaders, this is a compelling blueprint for raising the design floor while protecting the last mile of craft. It aligns with empowered product teams, continuous discovery, and LLMs for product managers who need leverage without losing judgment. If you’re exploring agentic AI in design, this is a thoughtful, execution-focused model worth studying and trialing on your next product tour or redesign.

Resources worth exploring: Banani and TL Draw. To hear the full conversation, you can listen on Spotify or Apple Podcasts. Then, pressure-test the approach inside your own product development lifecycle and see how a canvas-first AI designer reshapes your team’s velocity and quality bar.

Inspired by this post on Product Talk.

April 2, 2026
How I Safely Deploy Amplitude AI in Healthcare: Governed Analytics, PHI-Safe Workflows, Real ROI

Healthcare leaders ask me the same question every week: how do we unlock AI-driven insights without risking patient trust or regulatory missteps? My approach is pragmatic and proven—connect business goals to measurable behavioral analytics, wrap everything in clear governance, and keep protected health information (PHI) out of the analytics layer by default. In other words, we earn the right to scale by making safety, compliance, and transparency visible in every step of the workflow with Amplitude AI.

At the core, I anchor our rollout on "governed analytics"—curated events, certified metrics, and role-based access that make audits straightforward and decision-making fast. When product, data, security, and compliance share a single source of truth in Amplitude analytics, we reduce rework, eliminate ambiguous definitions, and ship improvements with confidence. This is where AI Strategy meets operational excellence: a unified analytics platform that balances velocity with verification.

From there, I establish "PHI-safe workflows" by drawing a hard boundary around what data enters analytics. Behavioral signals flow in; identifiers stay in clinical systems. I lean on privacy-by-design, data minimization, and clear data governance so we can demonstrate regulatory compliance before a single end user is exposed to a new AI-powered experience. That alignment builds trust with legal and security, shortens review cycles, and operationalizes AI risk management without slowing innovation.

Insights must be "trusted insights"—reliable enough to drive care pathways, staffing decisions, and patient communications. I emphasize repeatable instrumentation, observability of data quality, and transparent lineage so teams can trace outcomes back to inputs. In practice, that means we agree on event contracts, enforce change control, and verify that behavioral analytics reflect real-world adoption and efficacy across patient and provider journeys.

To move decisively from legal review to production, I run a two-speed rollout. First, we validate in a sandbox with synthetic or de-identified data to pressure-test prompts, dashboards, and alerting. Then we graduate to controlled pilots with strict guardrails, documented data flows, and pre-agreed risk mitigations. By the time we scale, stakeholders have evidence, not just assurances—accelerating approvals and reducing last-minute scope churn.

One pattern I rely on is connecting AI outcomes to product metrics that matter: activation, time-to-first-value, task completion rates, and variance in outcomes across segments. With Amplitude analytics, we can spot drop-offs, attribute improvements to specific design or model changes, and quantify impact in language that resonates with executives and clinicians alike. That rigor is what transforms AI from a promising prototype into a dependable operating capability.

Success looks like faster time-to-insight, fewer compliance iterations, and audit-ready documentation built into normal workflows. It also looks like teams who are confident enough in their data to run A/B testing and continuous discovery—because they know their dashboards reflect reality. When governance, safety, and clarity are designed in, product-led growth becomes compatible with healthcare’s unique regulatory and ethical obligations.

"See how to adopt AI in healthcare safely with Amplitude, using governed analytics, PHI-safe workflows, and trusted insights that help teams move from legal review to real usage." That’s the journey I guide teams through—measurable, compliant, and humane—so we can deliver AI that clinicians trust, patients respect, and leaders can scale.

Inspired by this post on Amplitude – Perspectives.

March 25, 2026