Tag: agentic AI

How I Decide What to Automate With AI: A Practical Framework + 50 Real Examples to Boost Productivity

Most mornings start the same way for me: coffee in hand, I sit down, open Claude Code, and type /today. In a few seconds, Claude pulls fresh tasks from my Trello board, compiles a clean today.md with what matters most, and assembles a research digest of the latest academic work across my focus areas.

Scanning that today.md has become my daily ritual. My workload typically spans writing, coding, and administration. I now make a habit of asking Claude, "What's on my to-do list that you can help with?" That simple question keeps me honest about where AI can accelerate my day.

I’m experimenting with a workflow where Claude enriches every task based on what it can take on or accelerate. It’s still early, so we iterate together for a few minutes each morning to tighten the loop and improve the prompts and outputs.

Next up is my research digest. I skim, download the PDFs that look promising, and move on. Tomorrow, Claude will deliver detailed summaries of every paper I saved—so I stay current without burning hours on search and sorting.

For the first few hours, I protect deep work. Today, that means writing this article. My to-do list and draft live side-by-side in Obsidian, so I click directly from the task into the outline, pick up my running conversation with Claude, and get right back into flow. I pair-write: we outline, I draft, and then I ask, "I wrote the intro. What do you think?"

A terminal-based AI helper suggests concrete ways to lighten your workload—draft a blog, plan 2026, launch a course, migrate files, craft a survey, and digest research—so you can pick the next task fast.

Claude gives pointed feedback—what’s working, what needs tightening—and we iterate. This is genuinely how I work now. I pair with Claude on almost everything I do. It didn’t happen overnight; over the past five months, I’ve built a personal AI-enhanced operating system that has fundamentally improved how I operate: more output, faster cycles, and frankly, more joy in the work.

Because it’s made such a difference, I’m sharing the playbook. If you’re new to Claude Code or want to get more from it, start here:

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

In recent office hours, one question came up again and again: Where do I start—what should I automate and what should I have AI augment? Today, I’ll walk through how I decide, share my own workflows, and show how I prioritize what to build next. Next week, we’ll get into how to design and build personal workflows.

This series was inspired by my personal usage of Claude Code. I have not received any compensation from Anthropic for writing this series. And you can trust that if that ever changes, I will disclose it. This is not only required by the FTC here in the US, but I strongly believe it is the right thing to do. You can count on me to do so.

Understanding what AI workflows can do for you

Peek inside a dark-themed writing workspace where a markdown editor displays an article on choosing tasks to automate with AI. The sidebar organizes notes, while the draft outlines pulling Trello tasks, making today.md, and using Claude.

I started with ChatGPT in the browser not long after it launched and quickly began asking, “Can ChatGPT help with this?” As my use cases grew (and my patience for copy-paste vanished), I moved to Claude Code. The philosophy never changed: continuously push the envelope of what LLMs can do today while managing risk.

My default stance is to attempt everything with AI, then decide what becomes a reusable workflow versus a one-off assist. A workflow, to me, is a sequence of steps where some are automated by AI, others are AI-augmented, and some still require me.

Across my setup, clear patterns emerged. I use AI to: (1) do more of what I’m already good at, (2) eliminate friction in frequent tasks, and (3) remove what drains me. The goal is simple: multiply impact without sacrificing quality.

Take writing. I now average about 35,000 words per month—up from roughly 8,000. I’m writing more often and in more depth. I draw more from academic research and include more stories—both my own and those from others. Claude gives me detailed feedback on everything I write, which helps me maintain momentum. It’s remarkable how often a simple nudge—“Ready to write the next section?”—keeps me in the zone. I also spend more time with Claude on structure before drafting, so I discard far less.

Go behind the scenes of creating an AI automation guide: a split-screen workspace pairs the article draft with detailed reviewer notes, revealing a practical, iterative process of outlining, fact-checking, and refining before publication.

Podcast production is another domain where AI shines. I produce two weekly shows: I love connecting with Petra Wille on All Things Product, and talking with product teams building AI-powered products on Just Now Possible. I use Descript to edit, and I rely on Claude Code shortcuts (slash commands) to draft episode titles, descriptions, show notes, chapters, and social posts. I still own the editorial bar—no “AI slop”—but I let AI handle the heavy lifting so I can focus on shaping the final story.

Then there are tasks I fully automate. I love reading across creativity, collaboration, AI efficacy, and more. I do not love searching for relevant papers. So I don’t. Every morning, my automated research workflow finds the newest, most relevant articles and populates my digest. All I do is review.

Choosing your first AI workflows

Classic delegation advice still applies: build awareness of where your time goes; identify what you can delegate; invest your time in the work you’re uniquely equipped to do. That’s a great start for AI workflow strategy, but don’t ignore what you love doing and want to do more of. Augmentation often generates the highest returns—AI helps me go deeper, faster, without diluting my craft.

Peek inside an AI-powered curation flow: a markdown workspace compiles a 'Filtered Research Digest' with criteria, paper counts, and summaries, demonstrating how automation turns raw literature into actionable insights.

To uncover opportunities, I simply ask, over and over: Can AI help with this? As you go about your work today, keep asking yourself: How can AI help with this?

Evaluating if a task is a good candidate for an AI workflow

Through trial and error, I now run new tasks through a quick filter:

• Is this a one-time task or do I do it often?

A clean, workshop-style slide asks the pivotal question: "How can AI help with this?" Use it to spark automation ideas, map steps, and decide where generative AI can accelerate research, drafting, analysis, and repetitive work.

• Do I enjoy doing this task or would I give it to someone else if I could?

• How complex is the task?

• Can I articulate how I would do the task step-by-step?

• Does completing the task require my human judgment?

• Can I define what "done successfully" looks like?

• How much risk is there if the task is not done well?

This checklist takes minutes and pays off quickly. The answers tell me whether to automate, augment, or keep a task human-only for now—and they guide how much process and guardrailing to build around each workflow.

From here, I’ll walk through how to answer these questions in practice, how the answers map to different levels of automation or augmentation, and how I prioritize which workflows to invest in. I’ll also share 41 of my own AI workflows (noting which are automated versus augmented) plus 9 discovery-related workflows currently in development so you can steal shamelessly and ship your first one today.

The rest of this article requires a paid subscription. This publication is reader-supported. If you’ve benefited from my writing, please subscribe today.

Inspired by this post on Product Talk.

December 3, 2025
Build Smarter MVPs with AI: Test Faster, Fail Cheaper, and Accelerate Product-Market Fit

I build MVPs to learn, not to launch—and AI lets me compress those learning loops from weeks into days. When the stakes are high and the clock is ticking, I default to simple architectures, ruthless scoping, and instrumentation from the very first commit. What follows is the practical playbook I use to reduce uncertainty quickly, keep risk contained, and ship value with intent.

This is a practical guide for product people who move with purpose. Build smarter, test faster, fail cheaper. This is how AI reshapes the MVP game.

I start by framing the problem in business terms and picking a single success metric tied to the customer’s core job-to-be-done. I document the riskiest assumptions, define guardrails (quality, safety, latency, cost), and choose a minimum detectable effect (MDE) so my A/B testing has statistical teeth. This forces clarity: What has to be true for this AI MVP to matter?

Then I scope the thinnest, testable slice of the experience—one clear user, one context, one outcome. I write the happy path first, instrument the key events, and resist the urge to boil the ocean. If it can’t be demoed in five minutes and measured in five days, it’s not an MVP.

Data comes next. I adopt privacy-by-design, set up basic data governance, and map inputs and outputs to avoid silent failures. I define an AI risk management checklist (prompt injection, PII leakage, hallucinations) and set budget limits to keep inference costs predictable. Responsible scaffolding early saves me from operational drag later.

On the model strategy, I prefer the simplest option that can win the experiment. I often start with an off‑the‑shelf LLM and a retrieval-first pipeline (RAG) for grounding, plus light context window management to keep prompts lean. If the workflow demands autonomous steps or tool use, I add agentic AI behaviors incrementally; fine‑tuning only comes after I’ve validated repeatable value.

For prototyping speed, I lean on my AI product toolbox: CustomGPT workflows for rapid flows, a ChatGPT connector for quick integrations, and Claude Code for code scaffolding and refactors. I stitch the MVP into the existing stack with pragmatic CRM integration, then layer in in-app guides and product tours so users immediately understand what to try and why it matters.

Measurement is non‑negotiable. I set up Amplitude analytics to track activation and retention, add Pendo for in‑product guidance and usage heatmaps, and wire Intercom for qualitative feedback inside the flow. With A/B testing in place and an agreed MDE, I can make crisp calls on whether the AI feature clears the bar or needs another iteration.

Shipping must stay frictionless. I keep a simple CI/CD pipeline, monitor deployment frequency, and prepare basic incident management with SRE hygiene appropriate to an MVP. Small, reversible releases let me learn safely while protecting user trust.

The learning loop is continuous discovery, not a one‑off demo. I run quick research sprints with product trios, capture edge cases, and turn user feedback into structured prompts, examples, and evaluation sets. As signal strengthens, I harden guardrails, improve retrieval quality, and elevate the value proposition in messaging.

When the metrics move and the experience feels reliable, I scale deliberately: tighten privacy-by-design controls, document outcomes vs output OKRs, and explore product-led growth motions. Only then do I consider pricing experiments, broader go-to-market strategy, and heavier investments like fine‑tuning or bespoke infrastructure.

If you want a simple way to start: day one, define the problem and metric; day two, wire a thin RAG prototype with guardrails; day three, put it in front of real users with analytics and a clear activation path. The goal isn’t perfection—it’s validated learning you can scale with confidence.

Inspired by this post on Product School.

December 3, 2025
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
Unlock AI Product Roadmaps: Essential Tools Every PM Needs to Prioritize and Ship Faster

In my role leading product teams, the AI product roadmap isn’t just a plan—it’s the operating system for how we discover value, prioritize with rigor, and ship with confidence. The pace has changed, the stakes are higher, and the best product managers are now orchestrating AI capabilities, data, and customer insight in near-real time.

Master the evolving art of the AI product roadmap. Prioritize smarter, turn data into direction and insight into action, only much faster.

When I say “AI product roadmap,” I’m talking about a living system that blends strategy, discovery, and delivery. It’s less about dates and more about outcomes, risk reduction, and sequencing learning. In practice, that means combining AI Strategy with product roadmapping and sprint planning, then validating each bet with real customer signals.

For prioritization, I anchor on outcomes vs output OKRs and connect them to measurable signals across the funnel. Continuous discovery keeps insights flowing, while a unified approach to analytics and retention analysis tells me where the lift is. This lets me rank initiatives not just by impact and effort, but by how quickly we can learn, iterate, and compound value.

On discovery, product trios are non-negotiable. We prototype early with gen ai and LLMs for product managers to accelerate concept validation and reduce ambiguity. When customers can co-create through in-app guides or lightweight product tours, we turn vague needs into crisp problem statements and testable hypotheses far faster.

On delivery, I pair tight feedback loops with experimentation. A deliberate cadence of A/B testing and strong instrumentation ensures we’re learning every sprint, not just launching. The goal is to de-risk decisions quickly, keep momentum high, and translate signals into roadmap movement without thrash.

Under the hood, the AI stack matters. I rely on a retrieval-first pipeline to ground models in trusted data, and I’m intentional about privacy-by-design and data governance from day one. As agentic AI patterns emerge, I put evaluation workflows in place so we can ship confidently—and safely—without slowing down innovation.

Finally, alignment is the multiplier. Clear narrative roadmaps tied to customer outcomes help stakeholders see trade-offs, while crisp interfaces with go-to-market and CRM integration close the loop from roadmap to revenue. When everyone can trace a line from AI strategy to shipped value, prioritization becomes easier and trust grows.

If you’re feeling the acceleration, you’re not alone. With the right AI product toolbox—rooted in discovery, grounded in data, and delivered through tight feedback loops—you can move faster, learn smarter, and build products your customers can’t live without.

Inspired by this post on Product School.

December 1, 2025
The Customer Service Roles AI Needs to Thrive: A Practical Playbook for High-Impact Support

When AI Agents resolve the majority of customer conversations, the shape of your support team has to change. I’ve experienced this shift firsthand: the moment AI begins to carry the volume, your people must pivot from answering individual questions to engineering the system that consistently delivers quality outcomes.

The old tiered model built around queue management, handoffs, and volume-based productivity no longer fits. AI now handles the bulk of customer interactions, and that changes the role of your human team entirely. Responsibilities evolve, and success is measured differently. It goes beyond just adding automation to existing ways of working. You’re building an operating model that’s entirely new.

Most teams don’t hire a dedicated AI function from day one. They start by distributing a few critical responsibilities across existing team members, and formalize those responsibilities as AI becomes central to how support works. That’s exactly how I recommend getting momentum without over-hiring too early: prove value fast, name clear owners, and then scale.

Once you have executive support and a clear strategy in place, these are the four foundational roles we believe are key to getting AI off the ground in a meaningful way:

1. AI operations lead

Responsibilities: Owns day-to-day AI performance. Tracks quality. Tunes behavior. Prioritizes fixes. Drives iteration.

Skillset/background: Often promoted from support ops. Deep understanding of workflows, systems, and tooling. Strong analytical and cross-functional coordination skills.

Why you need this: Without clear ownership, performance drifts. This role ensures the AI Agent constantly improves.

AI isn’t replacing support—it’s opening doors. This visual highlights how GenAI is spawning roles in customer success, from digital support engineers to automation success teams, and unlocking clearer, upward career paths.

In my teams, this role becomes the heartbeat of AI performance—instrumenting quality feedback loops, triaging failure modes, and aligning fixes across product, data, and support ops.

2. Knowledge manager

Responsibilities: Owns macros, snippets, and help content. Maintains structured, accurate inputs the AI Agent depends on.

Skillset/background: Often promoted from support ops. Deep understanding of workflows, systems, and tooling. Strong analytical and cross-functional coordination skills.

Why you need this: Without clear ownership, performance drifts. This role ensures the AI Agent constantly improves.

Every generative AI system is only as good as its knowledge. I’ve learned the hard way that inconsistent or stale content erodes trust—both for customers and internal stakeholders. A rigorous knowledge manager prevents that.

3. Conversation designer

Build a winning AI support team with four core roles: an ops lead to drive quality, a knowledge manager to keep content accurate, a conversation designer for tone and flow, and an automation specialist to power customer actions.

Responsibilities: Designs how the AI Agent communicates by focusing on tone of voice, structure, handoff logic, and interaction flow. Tunes how responses feel.

Skillset/background: Background in content design, UX writing, or support enablement. Deep grasp of policy, CX standards, and conversational nuance.

Why you need this: This role ensures the AI Agent speaks like your brand – clearly, helpfully, and in line with customer expectations.

This is your brand’s voice in motion. A strong conversation designer sets the guardrails that keep interactions on-brand, compliant, and empathetic while still efficient.

4. Support automation specialist

Responsibilities: Builds workflows and backend actions the AI Agent can execute.

Skillset/background: Background in support engineering, systems, or tooling. Works closely with product and engineering teams.

AI in customer service thrives with player‑coaches—hands‑on leaders who build, mentor, and iterate with the team. This quote-driven graphic signals a move away from heavy management toward agile, coaching‑first support operations.

Why you need this: Enables the AI Agent to take action – not just respond. This role translates customer intents into business systems.

In practice, this role unlocks the jump from “answering” to “resolving.” They wire up secure actions, map intents to outcomes, and partner with engineering to keep latency low and reliability high.

Introducing new AI-first roles doesn’t mean your existing functions disappear. But they do need to evolve. For AI to scale effectively, every function in your support organization must shift its focus from managing queue-level activity to improving the system’s performance:

Enablement trains human agents to work with the AI Agent: managing handoffs, tuning responses, and understanding how to give feedback that improves the system.

QA evolves from reviewing conversations to reviewing the quality of the customer experience and behavior of the AI Agent: where the AI succeeds, where it falls short, and how the system as a whole performs.

Workforce management plans capacity based on automation coverage, not just inbound volume.

You’ll also need a new kind of leadership to make this model work. The traditional support leader doesn’t map cleanly to an AI-first organization. You need a new layer: leaders who are part strategist, part operator. They roll up their sleeves to analyze the AI Agent’s performance, refine content, and debug handoffs, but they also coach the team through a new way of working.

Customer service is reorganized for the AI era: a VP of Support leads human support, ops and optimization, and a new AI support function—adding conversation design, knowledge management, and systems analysis alongside agents, insights, and WFM.

This is the “player-coach model” – leaders who actively shape both the system and the people within it.

These leaders see the AI Agent as a teammate to manage, not just a tool to monitor. They can’t be purely people leaders or purely systems thinkers. They need to be both, and they’re emerging as a critical hire in support right now.

Some teams are restructuring their organizations around the AI Agent as a core product, not just a support tool. Here are some real-world examples:

At Dotdigital, a dedicated “Fin Ops” specialist role was created to refine content and improve AI performance.

At Clay, a dedicated GTM engineer role has been established as part of the ops team with a focus on making support more efficient at scale using Fin. Additionally, a support engineering function has been embedded directly in the CX organization to help reduce volume by fixing bugs and building internal tools.

Lightspeed created a dedicated Digital Engagement team to manage Fin’s optimization, and formalized a triangular model that brings together technical teams, frontline experts, and content specialists.

In my experience, the most resilient org designs align around three pillars: Human Support, AI Support, and Support Operations and Optimization. Each pillar carries distinct ownership yet shares accountability for AI performance. That structure keeps the team focused on outcomes over output and makes continuous improvement everyone’s job.

AI shouldn’t replace your agents—it should elevate them. This Rocket Money quote highlights a modern support model where automation handles the busywork and people concentrate on high‑value, human moments.

Once AI Agents handle most conversations, your team’s work moves from “answering questions” to “designing and improving the system that answers questions.” They become the force that steers quality, rather than the one that carries the volume.

This is why new roles are important. It’s not because they’re trendy, but because the performance of your support organization now depends on the performance of AI, and no AI Agent succeeds without clear ownership of content, behavior, workflows, and improvement cycles.

That’s the pattern we’ve seen from working with so many teams:

They name owners early.

They distribute responsibilities before they formalize them.

They anchor teams around AI outcomes, not ticket outcomes.

And they hire leaders who can manage both the system and the people.

If you take one thing away from this week’s article, let it be this: if AI is going to handle the majority of your customer conversations, your team needs to be designed to help it do that well.

Your roles, responsibilities, and leadership approach are now part of the architecture of AI performance.

Next week, we’ll go deeper into how these roles actually operate day-to-day – the workflows, responsibilities, rhythms, and collaboration patterns that make an AI-first support organization run.

Inspired by this post on The Intercom Blog.

November 25, 2025
How We Built an AI Sleep Coach: CBTI, Voice AI, and a Product Playbook for Better Rest

What if your morning started with a helpful check-in from a voice AI that actually improves your sleep—using the same core principles that typically cost thousands of dollars and come with year-and-a-half waitlists? That idea energizes me as a product leader, because it blends clinical-grade outcomes with consumer-grade accessibility. Recently, I dug into how the team at Rest built an AI sleep coach inspired by Cognitive Behavioral Therapy for Insomnia (CBTI), and why their method offers a repeatable blueprint for complex, personal AI products.

The origin story is a classic product discovery moment. Rest’s team noticed that a meaningful slice of users in their podcast app were using audio to fall asleep. Although it represented only about 10% of users, that group showed a high willingness to pay. That signal pushed them to explore a dedicated sleep solution, moving from a general audio app to a targeted sleep experience—and eventually toward an AI-powered coach as LLMs matured.

Through jobs-to-be-done research, they identified a clear, underserved segment: “DIY sleep hackers.” These are motivated users who want agency, structure, and results without navigating clinical systems. Choosing CBTI (a clinically proven approach with 80% efficacy) gave the product a strong evidence-based foundation while remaining accessible as a wellness tool. It’s the kind of strategic choice I look for: credible, measurable, and aligned with user motivation.

The product evolution moved in smart, incremental steps. Rest started with a basic text chatbot before graduating to a voice-first experience—using Vapi for voice and OpenAI for reasoning. Voice changed the relationship dynamic: it increased intimacy, lowered friction for daily check-ins, and made behavioral coaching feel human without pretending to be. The team built a memory system that tracks context (like traveling or having a dog) with time-based relevance, which keeps conversations fresh, respectful, and genuinely personalized.

Daily engagement is driven by dynamic agendas that adapt based on sleep data, the user’s stage in the program, and their recent compliance. I love this mechanic: it operationalizes behavior change by sequencing the right intervention at the right time. In parallel, they developed text via OpenAI Assistants while building voice with Vapi, which let them ship value while learning in two modes. They also moved from massive system prompts to RAG for general sleep knowledge, keeping personal user context in the prompt—reducing brittleness while improving scalability.

Because sleep sits close to healthcare, the team drew a firm line between wellness and medical positioning. They implemented clear guardrails: no diagnosis, no medication advice, and strong boundaries on scope. Weekly error analyses with domain experts (sleep therapists) tightened quality and tone, and they adopted LLM-powered evals to enforce safety boundaries. For observability and evaluations, they leveraged Langfuse, and they experimented with Hamming for voice testing to refine the experience end-to-end.

Under the hood, this is a great example of “one bite of the apple at a time” product building in AI. Start with a simple interface, anchor on an evidence-based method, layer personalization with memory, formalize program structure with dynamic agendas, and shift to RAG when general knowledge outgrows prompt engineering. As a product leader, I see strong echoes of agentic patterns here—goal-oriented orchestration, stateful memory, and adaptive planning—shipped in pragmatic increments rather than as a monolithic platform rewrite.

A few takeaways I’m applying with my teams: First, segment deeply and pick a high-intent niche (those “DIY sleep hackers” were the right beachhead). Second, let modality fit the job—voice is not a gimmick when it boosts compliance and empathy. Third, design safety and scope from day one if you’re anywhere near health. Finally, invest early in evals and observability so you can improve with confidence, not hope.

If you want to explore the full conversation and product decisions, you can listen here: Spotify | Apple Podcasts.

Resources & Links:

Rest – AI sleep coach app

Vapi – Voice agent platform Rest uses

Langfuse – Observability and evals platform

Hamming – Voice testing platform

AI Evals Maven Course by Hamel Husain and Shreya Shankar

Bottom line: Rest demonstrates how to take a clinically grounded method like CBTI, translate it into a daily voice-first experience, and ship it with rigor. If you’re building in AI, this is a model worth studying—practical, safe, and deeply user-centered.

Inspired by this post on Product Talk.

November 20, 2025
How I’m Rebuilding Customer Service for 2026: An AI‑First Playbook for Real Impact

Like many support leaders right now, I’m deep in 2026 planning. The more I map scenarios and stress-test assumptions, the clearer one thing becomes: the way work gets done has fundamentally changed, and that change must reshape our customer service organization.

In 2026, you won’t get the full value of AI by keeping your org chart, systems, and operating model the same. You need to think differently about how support is structured, how performance is owned, and how your systems evolve around an AI-first model. That’s the lens I’m using across my team and our cross-functional partners.

To help you do the same, I’m launching a 2026 customer service planning series. Over the next five weeks, I’ll share how I’m approaching roles, skills, organizational design, and an operating model that makes AI the backbone of support—not a bolt-on feature.

We’ll publish each edition here and on LinkedIn. If you’d rather get them by email as soon as they go live, drop your details and I’ll send each edition straight to your inbox.

But before you can make any of those decisions, you need the right mindset and the right internal conditions for change. That’s where I’m starting this week.

Week 1: Start with a mindset shift

If you were building support from scratch today, you’d design around AI from day one. That’s the mindset to carry into 2026—and it’s the mindset I’m using to guide investment and accountability.

Too many teams still treat AI like a feature instead of infrastructure. They tack it onto existing processes, limit scope to tier-one issues, and never evolve the organization or systems around it. I’ve seen that approach stall progress and fragment the customer experience.

Those teams are thinking too small. They chase incremental efficiency, underinvest in the system change required to make AI successful, and get stuck. The result: a reactive team, a choppy customer experience, and value left on the table.

AI Agents are fully capable, end-to-end resolution engines. They fundamentally change the architecture of support.

To plan effectively and get the most value out of the technology, you need to adjust your mental model. Here are the mindset changes I’m prioritizing.

1) Move from ‘AI as a tool’ to ‘AI as infrastructure’

For the past decade, support systems have been the intermediary between customers and human support agents. AI isn’t an intermediary, it’s the first touchpoint (and often the last), the primary resolver, it manages workflows, orchestrates handoffs, and takes real actions.

Planning with the “AI is a tool” mindset leads to small optimizations that don’t move the needle. Planning with the “AI is infrastructure” mindset lets you redesign around the real sources of value creation.

Here’s what I’m designing around in 2026:

• Clear ownership of Agent performance

• A feedback loop that never shuts off

• A shared understanding of when humans step in

• Systems that evolve as AI capabilities expand

This framing sets up every decision that comes later in your planning process.

2) Look at how the work is changing

You need to plan your 2026 support organization around what the distribution of work will be—not what it is today. AI has shifted where volume goes, what humans spend time on, where judgment is needed, how performance is measured, and how the customer experience is designed.

If your planning assumes the current distribution is stable, you’ll design the wrong structure. I’m modeling for the work that’s coming, not just the work on our queue today.

3) Think like a product leader

When customers primarily interact with your AI Agent, support becomes responsible for designing the customer experience—not just managing it.

“Support is becoming a product function, and you are becoming a product leader”

Design your 2026 support org for AI from day one. This Gamma testimonial shows how an AI agent (Fin) resolves 80%+ of inbound requests, letting a small team scale customer service efficiently without increasing headcount.

Support is now a product surface, and support teams act like AI product teams. They:

• Design the customer experience

• Create and curate the knowledge layer that drives AI quality

• Maintain continuous improvement loops and tune system behavior over time

This is a big shift. Your planning—hiring, skills, rituals, and metrics—needs to reflect that evolution.

4) Redefine performance

This is a big mental leap for support leaders. Traditional performance was measured on speed and satisfaction, but AI performance is measured on resolution, impact, and system reliability.

Planning for 2026 means assuming that:

• Humans will handle a smaller % of volume.

• Customer experience will be shaped by AI’s performance, not throughput

• “Support productivity” gets measured differently

When AI handles the bulk of your support volume, you need new metrics for how your team creates value. In practice, that means instrumenting AI and human-in-the-loop workflows with the same rigor you’d apply to a customer-facing product.

5) Understand that your value increases as AI takes on more work

You need to re-orient your team around AI’s performance to get the most value out of it. The more complex work you give it, the higher impact it will have.

Instead of routing complex, messy questions straight to your human team, shift their focus to improving the AI system so it can take on more over time.

Automating low-effort questions reduces noise, but automating complex workflows changes the economics of your entire team. It creates asymmetric returns that compound as AI absorbs the work that once demanded the most time and skill.

6) Plan for adaptability

A big difference between traditional planning and 2026 planning is simple: change will be constant.

“Change is hard, but the teams that adapt will be the ones who get the most out of this opportunity”

AI learns, evolves, and improves continuously. I’m asking, “How do we build an organization designed to adapt fast as the system evolves?” That question is informing everything from team topology to knowledge governance and experimentation cadence.

Food for thought

Heading into 2026, your org chart will look different—and that’s a good thing. Your people will play new, more meaningful roles as designers, curators, and stewards of an AI-first customer experience.

Once you accept that 2026 demands a different way of thinking, working, and planning, you can move to the next stage: designing the support organization that fits this future. I’ll share exactly what that looks like next week, including roles, skills, and ownership models that have worked well in my experience.

Want the full series delivered by email? Drop your details and I’ll send each edition to your inbox as soon as it’s published.

Inspired by this post on The Intercom Blog.

November 18, 2025
Taming 1,000+ Vendor Emails: How Xelix’s AI Helpdesk Delivers Fast, Confident Answers

Chaos in vendor communications is a problem I see across finance operations: sprawling accounts payable inboxes, slow response times, and missed context. That’s why this build caught my attention—not just because it’s GenAI, but because it’s a disciplined product strategy that converts email overload into measurable outcomes.

Accounts payable inboxes can see 1,000+ vendor emails a day. Xelix’s new Helpdesk turns that chaos into structured tickets, enriched with ERP data, and pre-drafted replies—complete with confidence scores.

I dug into the end-to-end approach with the team—Claire Smid — AI Engineer, Xelix; Emilija Gransaull — Back-End Tech Lead, Xelix; Talal A. — Product Manager, Xelix—focusing on how they scoped the problem, iterated fast, and de-risked AI in production.

Their product thesis is refreshingly pragmatic. They prototyped with “daily slices” (Carpaccio-style) and built a retrieval-first pipeline that matches vendors, links invoices, and drafts accurate responses—before a human ever clicks “send.” That framing matters: enrichment and matching take center stage, with the model amplifying precision instead of improvising.

We unpacked the tricky bits that make or break an AI helpdesk at scale: vendor identity matching, Outlook threading, UX pivots from “inbox clone” to ticket-first views, and the metrics that prove real impact (handling time, stickiness, auto-closed spam). The pipeline architecture and email processing choices were grounded in operational realities, not just AI aspirations.

Several takeaways are worth pinning to any AI product roadmap. “Start narrow to win: pick high-volume, high-cost requests (invoice status & reminders).” “Enrichment > magic: accurate replies come from great retrieval/matching, not just a bigger LLM.” “Design for adoption: familiar inbox view helps onboarding, but a ticket-first UI unlocks AI features.” These are the kinds of decisions that drive adoption, trust, and ROI.

Data enrichment challenges dominated early learning curves: stitching ERP context into tickets, handling vendor identification at scale, managing email thread continuity, and calibrating response generation for accuracy. On the generation side, the team emphasized precision over verbosity—clean responses that reflect system-of-record truth—then instrumented the experience to “Evaluate System Performance” with production-grade telemetry.

Trust was treated as a product feature. “Measure outcomes, not vibes: track ‘messages sent from Helpdesk’, % auto-resolved.” And critically, “Confidence builds trust: show match quality and response confidence so humans know when to edit.” By surfacing match quality and confidence scores, they shortened coaching loops and made human-in-the-loop supervision feel natural, not burdensome.

What’s next is equally compelling: “targeted generation, multiple specialized responders, and more agentic routing.” That direction aligns with agentic AI patterns I recommend for operations-heavy workflows—route first, retrieve deeply, then generate with intent. It’s a scalable path from assistive AI to autonomous resolution while maintaining governance and auditability.

If you want a quick map of the journey, the conversation flowed from 0:00 Meet the Team: Claire, Emilija, and Talal, 00:36 Introduction to Xelix and Its Products, 01:08 Understanding Accounts Payable Teams, 01:37 Help Desk Product Overview, 03:11 Challenges Faced by Accounts Payable Teams, 04:03 AI Integration in Help Desk, 05:47 Automating Reconciliation Requests, 07:45 Development Methodology: Carpaccio, 09:11 Prototyping and Beta Testing, 12:00 Manual Tagging and Data Collection, 16:39 Focusing on High-Impact Use Cases, 18:55 User Experience and Interface Design, 24:56 Pipeline Architecture and Email Processing, 28:21 Data Enrichment Challenges, 29:04 Handling Vendor Identification, 33:33 Email Thread Management, 36:15 Generating Accurate Responses, 40:48 Evaluating System Performance, 49:20 Future Developments and Goals.

My takeaway for product leaders: when the domain is high-volume and rules-heavy (like AP), retrieval-first beats model-first. Start with the narrowest, costliest intents; prove lift with “messages sent from Helpdesk” and “% auto-resolved”; then graduate UX from familiar to AI-native (ticket-first) once trust is earned. That’s how you turn vendor chaos into answers—reliably, scalably, and fast.

Inspired by this post on Product Talk.

November 13, 2025
AI Won’t Replace Engineers—Engineers Using AI Will: A Practical Playbook for Your Next Move

Will AI replace software engineers or reshape their roles? Explore risks, opportunities, and alternative career paths in tech.

I’m often asked whether AI will make software engineers obsolete. My short answer: AI is already automating tasks, not eliminating the role. The engineers who learn to orchestrate models, systems, and stakeholders will create more value—not less. The real shift is from keystrokes to judgment, from writing code to designing socio-technical systems that deliver outcomes.

Today’s gen ai assistants—think Claude Code and ChatGPT connector—excel at unit test scaffolding, boilerplate generation, refactoring, docstrings, and code search. When integrated into CI/CD, they can open draft pull requests, annotate diffs, and propose fixes. This lifts developer productivity and frees time for higher-leverage work: problem framing, architecture decisions, and customer discovery.

What changes in the role? We spend more cycles on product discovery, privacy-by-design, and AI Strategy, and fewer on repetitive implementation. We design agentic AI workflows that combine retrieval, tools, and guardrails; we evaluate trade-offs that blend performance, cost, and safety; and we partner with empowered product teams to ship the smallest valuable slice, learn, and iterate.

Measure what matters. If AI is working, DORA metrics should improve: higher deployment frequency, shorter lead time for changes, stable change failure rate, and faster MTTR. Pair that with outcomes vs output OKRs to avoid gaming the system—shaving seconds off a build is meaningless if it doesn’t move activation, retention, or revenue. A unified analytics platform can help connect engineering signals to business impact.

Risk is real—and manageable. AI risk management and data governance are now core competencies, not afterthoughts. Protect IP with robust access controls, context window management, and red-teaming. In production, instrument threat detection and response to catch prompt injection, data leakage, and model drift. Treat this like any other reliability discipline alongside SRE.

If parts of coding get automated, where can great engineers thrive? Several high-impact paths are emerging: platform engineering for LLMs (tooling, evals, observability), SRE for AI-infused systems, developer evangelism and education, product management for AI-native experiences, security engineering focused on model and data threats, and forward deployed engineers who pair with customers to solve messy, real-world problems.

How to upskill fast: build an AI product toolbox and ship small. Prototype gen ai features end-to-end—retrieval, function calling, human-in-the-loop QA—and connect them to your CRM integration or support stack. Use A/B testing with a clear minimum detectable effect (MDE) to validate impact. Leverage CustomGPT workflows for internal enablement and in-app guides or product tours to onboard users safely.

Here’s a pragmatic 90-day plan. Week 0–2: audit your top 10 engineering tasks by time spent; identify 3 that are ripe for AI augmentation. Week 3–6: pilot inside CI/CD with explicit guardrails; track DORA metrics and developer sentiment. Week 7–10: productionize the wins; document runbooks; add incident management paths. Week 11–12: share learnings with product trios, refine your value proposition, and set next-quarter OKRs.

AI won’t replace software engineers; engineers who master AI will outpace those who don’t. If we embrace the shift—toward systems thinking, responsible governance, and customer outcomes—we’ll build better products faster and open new, rewarding career paths. The opportunity is here and compounding.

Inspired by this post on Product School.

November 12, 2025
Stop Falling for Hollywood Demos: The Unfiltered Truth of Live AI Voice for Support

I’ve sat through countless AI demos, and I’ve learned there are really two kinds: the “Hollywood demo,” which is polished to perfection, and the “real-world demo,” which shows the product raw—imperfections and all. The former dazzles, but the latter is where you discover what’s actually ready for prime time.

Hollywood demos look great, but sometimes need a closer look to make sure what you see is what you’ll get. When I’m evaluating an AI Agent for customer service, I always look past the polish. I’m assessing how well it will handle real-world scenarios—the messy, complex conversations your team deals with every day. That’s especially true on voice, the toughest channel to get right.

Voice is one of the toughest tests of any AI system. It’s not just “chat with speech.” An AI Agent needs to be able to listen, respond, and adapt in real time. Timing, tone, and turn-taking are all part of the product, they shape the experience as much as accuracy or reasoning.

An edited video might sound seamless, but it can’t show how a system behaves in a real support environment—like when a conversation takes an unexpected turn or when it pauses briefly to reason or retrieve data. Those small moments—latency, clarifications, interruptions—are when you see what the AI Agent is really capable of. A real-world demo lets you see and hear how the system actually behaves under real conditions, not in a controlled environment that’s been smoothed out with editing.

That’s why the live Fin Voice demo at Pioneer stood out. The team called Fin live on stage to show the real thing (with real latency and interruptions) so people could understand the product they’d be deploying to their own customers. As a product leader, I appreciate that level of transparency because it mirrors how customers will experience the system in production.

When Paul Adams, Chief Product Officer, demoed Fin Voice at Pioneer, the goal was to show the product exactly as customers experience it. In 90 seconds, Fin verified his identity, retrieved account data, managed an interruption, offered options, completed the workflow, and sent a follow-up email. That’s the kind of end-to-end outcome I look for—fast verification, accurate retrieval, natural pacing, and a closed loop.

Latency. You could hear brief pauses while Fin fetched subscription details and checked backend systems. That wasn’t lag—it was work happening in real time. In voice AI, thoughtful latency that signals reasoning is far better than synthetic speed that collapses under real load.

Natural conversation flow. Fin detected when Paul finished speaking, handled interruptions gracefully, and replied in short, human-like turns. That turn-taking behavior is essential for trust and comprehension in voice customer support.

Awareness and tone. Subtle changes in pacing when Paul laughed or hesitated showed sensitivity to context. Tone control is not a “nice to have” in voice—it’s a core UX capability.

Unscripted conversation design. No rigid IVR menus or fixed paths. Paul spoke naturally, and Fin adapted to resolve his query. That adaptability is what differentiates a true AI Agent from a glorified decision tree.

Those details are the real test. A voice AI Agent that performs well in a live demo is one that will perform well for you and your customers too.

Voice has been one of the most demanding, and rewarding, areas of development for Fin. Since launch, we’ve been expanding what it can do so support leaders can customize how Fin sounds, behaves, and aligns with their brand.

Voice and tone customization: Choose from multiple natural voices, set greetings, and fine-tune how Fin communicates with customers.

Escalation and conversational guidance: Teach Fin to use your terminology, ask clarifying follow-ups, and escalate when needed.

Deployment controls: Manage rollouts, test safely in internal environments, and fine-tune before going live.

Flexible integrations: Connect to any telephony system via call forwarding, and link Fin Voice to backend systems or APIs to take action.

Multilingual capability: Fin Voice now supports 28 languages natively.

Alongside these features, we’ve made big improvements to Fin’s answer quality—the foundation of a great voice experience. When people call, they’re looking for accurate, immediate answers they can trust.

So we’ve focused on three key areas: low latency, which is down roughly 30–40% since launch; clarification flow, so Fin asks smart follow-up questions to reduce back and forth and improve resolution rates; and voice-specific answer structure, so Fin delivers information in shorter sentences with pacing designed for listening.

Together, these improvements mean customers get the highest-quality answers as quickly as possible, resulting in more resolutions and better experiences.

Running a live demo always carries risk because things can go wrong. But that’s also why it matters—because that’s how customers experience it too. Support leaders stake their reputation on the systems they choose, so the only way to understand what you’re putting in front of your customers is to see it under real conditions.

When you see Fin in a demo, you’re seeing the same system that runs in production. Real-world demos take more effort and don’t always go perfectly, but they show what’s real—and that’s exactly what you need to evaluate before you deploy voice AI at scale.

Inspired by this post on The Intercom Blog.

November 11, 2025
From Sketch to Clickable Demo: My AI Prototyping Playbook to Build Apps in Hours

I’ve spent much of my career compressing the distance between a napkin sketch and something real customers can touch. At HighLevel, my product teams use generative AI to validate ideas faster, reduce risk earlier, and win stakeholder trust with evidence instead of slides. The goal isn’t to be flashy—it’s to be precise, testable, and repeatable.

Today, you can build it before you pitch it. AI prototyping can turn ideas into clickable demos in hours. Here are some tools to try and steps to follow.

I start every AI prototyping sprint by sharpening the problem statement and the outcome we care about. That means being explicit about the target user, jobs-to-be-done, and the riskiest assumptions. I define a minimum detectable effect (MDE) and tie it to outcomes vs output OKRs so everyone aligns on what “good” looks like before we touch a tool.

From there, I move from sketch to interface. I capture a rough flow (whiteboard, tablet, or even paper) and generate UI variations with my AI product toolbox—tools that translate structure into components and screens. I’ll iterate on information hierarchy and copy until the narrative supports the core job, borrowing techniques from UX writing. For product managers leaning into LLMs for product managers, this phase is about speed to feedback, not perfection.

Next, I wire data and logic. I connect a lightweight backend or spreadsheet, stitch in a CRM integration if needed, and add LLM calls through a ChatGPT connector or Claude Code. If the concept benefits from multi-step autonomy, I introduce agentic AI to orchestrate tasks across APIs. CustomGPT workflows help me encapsulate business rules so the demo behaves consistently in user paths we care about.

Governance is not optional at this stage. I apply privacy-by-design defaults, document data governance decisions, and run a quick AI risk management pass: input validation, prompt safety, rate limits, and fallback responses. This keeps the prototype credible and prevents false positives from polluting stakeholder perception.

With a click-through in hand, I instrument the experience so learning compounds. I drop in Amplitude analytics to track activation, task completion, and drop-off, and set up simple A/B testing when there’s a meaningful design or copy choice. This makes the prototype a learning vehicle, not just a demo.

Then I get it in front of users—fast. Five targeted conversations will beat fifty internal opinions. I run structured product discovery interviews, observe time-to-value, and capture objections. This is where empowered product teams shine: we make changes in real time, re-run the flow, and document what moves the needle for product-led growth.

When speed matters, I use a four-hour cadence: Hour 1 for problem framing and MDE; Hour 2 for sketch-to-UI generation; Hour 3 for data wiring and AI logic; Hour 4 for instrumentation and user walkthroughs. By the end, we have a clickable demo, preliminary analytics, and a clear decision on whether to advance, pivot, or park.

Finally, I translate insights into a concise artifact: the hypothesis we tested, the signal we observed, the trade-offs we made, and the next sprint plan for product roadmapping and sprint planning. The point is not to be right on the first try; it’s to learn precisely, cheaply, and quickly enough to invest with conviction.

If you adopt this approach, you’ll find that stakeholder management becomes easier, team energy rises, and your roadmap earns credibility. Build it before you pitch it, and let real interactions—not wishful thinking—do the heavy lifting.

Inspired by this post on Product School.

November 10, 2025
AI Context Pulling Playbook: How I Make Humans + LLMs Collaborate for Sharper Product Outcomes

Over the last few years, I’ve learned that the fastest path to better product outcomes isn’t “more prompts,” it’s better context. When I combine thoughtful product judgment with disciplined context window management, LLMs become true partners—accelerating discovery, sharpening strategy, and improving execution.

Learn a new way in which product professionals can collaborate with AI to get even better results on their projects.

When I say “AI context pulling,” I’m talking about the intentional process of assembling, structuring, and compressing the right product evidence—customer insights, metrics, constraints, and goals—so an LLM can reason effectively. For LLMs for product managers, the win is simple: by feeding the right inputs and framing the right outcomes, we turn generic AI into a strategic co-pilot for Product Management and AI Strategy.

I start by clarifying intent through outcomes vs output OKRs. Before I ask an LLM to ideate, critique, or plan, I anchor it in the product problem, the measurable outcomes we seek, and the guardrails we cannot cross (risk, privacy, brand). This keeps the collaboration focused and aligned with stakeholder management expectations.

Next, I build a tight “context packet.” I pull customer quotes from discovery notes, usage trends from our unified analytics platform and Amplitude analytics, funnel friction from Intercom transcripts, and commercial constraints from HubSpot data. Then I summarize, deduplicate, and highlight contradictions—so the model gets the signal, not the noise.

From there, I run an agentic AI workflow. In my AI product toolbox, I use CustomGPT workflows with specialized roles: a Summarizer (compress evidence), a Strategist (propose options), and a Skeptic (stress-test assumptions). This agentic AI pattern reduces blind spots and produces artifacts I can share with empowered product teams and executives.

I then bring the insights into a product trios forum (PM, Design, Engineering). We iterate on problem framing, explore solution narratives, and translate options into product roadmapping and sprint planning. The LLM helps us rapidly compare trade-offs, highlight dependencies, and craft crisp decision memos.

Execution still demands rigor. We validate with A/B testing when appropriate, size our minimum detectable effect (MDE), and monitor activation and retention signals. The model helps generate experiment variants and risk checklists, but we own judgment, ethics, and the call to ship.

Governance matters. I treat data governance and privacy-by-design as first-class constraints in every prompt, context packet, and workflow. Clear boundaries make collaboration safer—and paradoxically, more creative—because the LLM spends its cycles inside a well-defined sandbox.

Here’s a simple example: when we explored a new onboarding flow, I fed the model a compressed brief (user segments, friction points, support tickets, and conversion deltas). It returned three viable patterns, each with hypotheses and measurement plans. Our trio refined them, launched a controlled test, and used LLM-powered analysis to summarize learnings for leadership. The result: faster clarity, better decisions, and a tighter feedback loop.

The promise of AI context pulling isn’t that AI replaces product judgment—it’s that it elevates it. With the right structure, LLMs help us think more clearly, decide faster, and build what truly matters. If you’re ready to try this, start small: define an outcome, curate a context packet, and run a single agentic loop with your team. The compounding returns will surprise you.

Inspired by this post on Pendo – Perspectives.

November 6, 2025