Category: Product Management Leadership

Stop Selling Your Roadmap: Win Stakeholder Trust by Showing Your Work, Not Conclusions

I’m seeing the same pattern in product orgs everywhere—inside HighLevel and across my network: everyone is racing to add AI to the roadmap, and every stakeholder has a strong opinion about what to build next. Delivery has never been faster, which makes it dangerously easy to confuse speed with progress.

When we chase features without grounding in continuous discovery, we drift back into a feature factory. We ship more, but we ship the wrong things faster. The antidote is simple and hard at the same time: recommit to product discovery, validate with assumption testing, and let the evidence steer our AI Strategy—not the hype.

Of course, that only works if we can bring our stakeholders along. In the AI moment, it’s deceptively easy to get to a slick prototype and painfully hard to harden it for production. Early demos make almost any idea look promising. That’s precisely why stakeholder management must evolve from pitching solutions to showing our work.

In practice, stakeholder management is about alignment with the people who influence our product decisions—executives, sales, marketing, customer success, engineering leadership, and sometimes legal or finance. Some have veto power; others have input. Knowing who can block versus who can shape is crucial for where we spend our time. Even in empowered product trios, the best discovery can derail if we reveal only conclusions at the end.

I’ve tried every mapping framework—power-interest grids, RACI matrices—and they help. But the real challenge isn’t identifying stakeholders. It’s figuring out how to bring them along so that our product roadmapping and sprint planning decisions stick.

Identify who shapes your product decisions. This visual groups stakeholders into three tiers—those with veto power, key influencers, and audiences to inform—so teams can align, communicate, and reduce delivery risk.

Here’s the most common trap I see (and have fallen into): focusing stakeholder reviews on the roadmap, release plan, or prioritized backlog. That invites an opinion battle. And stakeholders have their own conclusions—usually shaped by the last customer call, a board meeting, or a market headline.

This is how the HiPPO dynamic gets created. HiPPO stands for the “Highest Paid Person’s Opinion,” and the saying goes, “The HiPPO always wins.” When we present conclusions without the journey, we set ourselves up to lose. In the gen ai rush, the chorus of “everyone is doing AI” makes that opinion even harder to counter.

So I don’t try to win opinion battles. I bring new information—fresh customer interviews, clear opportunity mapping, and results from assumption tests. The gap between what the market hypes and what customers actually need is often enormous. Our edge is evidence.

The strategy that consistently works for me is simple: show your work. If you’re practicing continuous discovery, your opportunity solution tree isn’t just a thinking tool—it’s your strongest stakeholder management asset. It helps you build confidence in your decisions, and it can help your stakeholders build the same confidence.

Avoid the stakeholder trap of selling conclusions. This visual shows how anchoring on solutions invites HiPPO battles—and how to shift the conversation by sharing discovery evidence, insights, and data.

Step 1 — Start with the outcome. I open every conversation by restating the shared goal and asking whether anything has changed. Anchoring on outcomes vs output OKRs reframes hot-button solution debates (like “we need an AI feature”) back to what will move the needle on the outcome we agreed to pursue.

Step 2 — Share the opportunity space. I show how we mapped customer needs, pain points, and desires. Then I ask, “What did we miss?” Stakeholders often surface opportunities we haven’t seen yet—signals from the field, market shifts, or partner feedback. I capture their input and commit to validating it in upcoming customer interviews.

Step 3 — Walk through prioritization. Using the tree’s structure, I explain why we prioritized one branch over another. Then I ask where they might have chosen differently. This turns debate into collaboration and lets me leverage their expertise without ceding the discovery framework.

Step 4 — Go deep on the target opportunity. Before we talk solutions, I make the customer’s problem vivid and real. Interview snapshots help stakeholders empathize and see what matters most. Once the opportunity is crisp, solution discussions become dramatically more objective.

Show your work, not just your conclusions. This infographic guides product teams through seven steps to build stakeholder confidence—align on outcomes, map opportunities, prioritize, test assumptions, and repeat.

Step 5 — Share solutions and invite theirs. I present our solution set and explicitly ask for additional ideas. If their suggestions diversify our set, we include them. Solution ideas are cheap; the opportunity is what matters. This is where product trios can benefit from leadership’s pattern recognition and industry context.

Step 6 — Share your assumption tests and results. I walk through our story maps, high-risk assumptions, and what we’ve learned so far. I invite stakeholders to add assumptions—this is where their knowledge shines. If we have data, we share it; if we’re pre-data, we share the plan to get it and ask for feedback.

Step 7 — Repeat. I don’t batch this into a big reveal. I keep a steady cadence and tailor depth to each audience: weekly for my manager, monthly highlights for marketing, and concise updates for executives. Continuous discovery pairs with continuous stakeholder management.

Showing your work doesn’t mean drowning people in detail. It means tailoring the signal to the audience. My rule of thumb is outcome, opportunity, solution, evidence—walk the lines of the tree at the right altitude for each stakeholder.

Show your work the right way for each stakeholder. Use a smart filter to turn discovery noise into clear signals—weekly journeys for your manager, focused monthly highlights for marketing, and a 30-second CEO pitch.

In a 30-second update with a CEO, it might sound like this:

“Our goal is to reduce time-to-first-value for new users. We’ve been interviewing customers and learned that onboarding is where most people get stuck—specifically, they don’t know which features to try first. We explored a few approaches and tested them. The most promising one is a guided setup flow that adapts based on the user’s role. In early tests, new users completed onboarding 40% faster.”

That pattern works across channels—Slack updates, monthly reviews, or quarterly planning. The format flexes, the structure doesn’t: outcome, opportunity, solution, evidence.

As you adopt this approach, watch for four anti-patterns that quietly erode trust.

Avoid the traps that erode stakeholder trust. This infographic guides product teams to show their work, welcome ideas, provide frequent updates, and prioritize results over ideology to build alignment and credibility.

Anti-pattern 1 — Telling instead of showing. The curse of knowledge makes our conclusions feel obvious to us and opaque to others. The fix: slow down, start at the top of the tree, walk the decisions, and let stakeholders reach the conclusion with you.

Anti-pattern 2 — Shooting down stakeholder ideas. As you build a library of validated assumptions, it’s easy to spot flaws in a suggestion and say “no” too quickly. Instead, place their idea within your discovery framework. If it maps to a different opportunity, say, “That idea has promise—we’ll consider it when we address that opportunity.” If it rests on risky assumptions, story map the idea together, list the assumptions, and share what you’ve already learned. People accept the evidence they help generate.

Anti-pattern 3 — Saving everything for a big reveal. Infrequent, comprehensive updates invite opinion battles because stakeholders have formed their own conclusions in the dark. Short, frequent updates build alignment as the work unfolds.

Anti-pattern 4 — Fighting the ideological war. Sometimes a more senior stakeholder will overrule you. Don’t turn it into a debate about how product decisions “should” be made. Focus on the decision at hand, do the best work within constraints, and let results—not ideology—prove the value of discovery over time.

Shift from selling to showing. This co-creation guide invites stakeholders into discovery, taps their expertise, and turns relationships from obstacles into partnerships for smarter product decisions.

Here’s the mindset shift that changes everything: stakeholder management is a co-creation opportunity. When we show our work with artifacts like an opportunity solution tree, experience maps, and interview snapshots, we’re not just communicating—we’re inviting collaboration. We’re leveraging stakeholders’ expertise, context, and connections to make better product decisions.

When stakeholders have walked the path with us, they don’t need to be sold on the destination. They become allies. Engagement stops being a status ritual and starts being real partnership—the kind that moves outcomes and builds durable trust.

Try this in your next review: don’t start with your roadmap. Start at the top of the tree. Reaffirm the outcome. Share the opportunity space. Explain your prioritization. Show what you’re learning. Invite contribution. You might be surprised how quickly alignment—and confidence—follow when you stop selling conclusions and start showing your work.

Inspired by this post on Product Talk.

March 4, 2026
From Chaos to Clarity: My Proven Playbook to Scale an Analytics Taxonomy That Sticks

I’ve stepped into too many product reviews where teams argued over numbers that should have been obvious. Three names for the same “signup” event, properties scattered across tools, and no shared definitions—classic analytics chaos. As VP of Product Management at HighLevel, I’ve learned that scaling an analytics taxonomy isn’t just a data exercise; it’s a leadership mandate that unlocks decision velocity, alignment, and confident product bets.

Learn best practices our professional services team has compiled in helping customers move from scattered events to a scalable, user-friendly data structure.

Why does this matter so much? A robust taxonomy powers a unified analytics platform across Amplitude analytics, Pendo, and our CRM stack, reduces rework, and strengthens data governance. When events are clear and consistent, product-led growth accelerates: onboarding becomes measurable, activation is trackable, and retention analysis turns into a weekly ritual rather than a quarterly scramble.

I always start with outcomes, not events. We define a North Star metric and use driver trees to map how user behaviors ladder up to that outcome. Then we ground the plan in journey mapping: what signals mark activation, aha moments, and long-term engagement? This ensures our taxonomy mirrors real user intent, not just engineering convenience.

Next comes naming conventions and structure. We standardize on a readable, durable pattern (for example, actor_action_object), apply consistent property naming, and document required vs. optional properties. We version events deliberately, so we can evolve without breaking dashboards. Most importantly, we align events to product strategy—tracking less, but better.

Governance makes it scale. We establish a clear DRI for the tracking plan, a lightweight review process for changes, and a schema registry that serves as the single source of truth. Privacy-by-design is non-negotiable: we treat sensitive fields deliberately and audit access. Observability closes the loop—schema validations and alerts catch drift before it confuses teams.

Tooling and process turn good intentions into muscle memory. We keep the tracking plan “as code” in a repository, run CI/CD checks to validate events, and use feature flags to roll out new instrumentation safely. Pendo helps us annotate in-app experiences, while Amplitude provides the exploratory lens for cohorts, funnels, and retention. Together, these systems reduce guesswork and speed up discovery.

Migrations are where many teams stall, so I de-risk them with a clear, time-boxed plan. We audit the current event surface, map scattered events to the new taxonomy, and deprecate duplicates with guardrails. We communicate changes broadly, provide easy-to-scan documentation, and pair enablement sessions with hands-on examples from live dashboards. The goal is confidence, not just compliance.

We measure success like a product. Are we answering critical questions faster? Are duplicate events trending down? Are activation and retention questions easy to answer in under five minutes? When the taxonomy is working, stakeholders stop asking, “Do we trust this?” and start asking, “What should we build next?”

One of the most rewarding shifts I’ve seen: product trios moving from ad-hoc analyses to repeatable, weekly rituals. With crisp definitions, onboarding flows become testable, PLG motions are predictable, and leadership reviews focus on outcomes, not definitions. That’s the moment analytics transforms from a cost center into a growth engine.

If you’re staring at a wall of scattered events, start small: clarify outcomes, align your journey map, set conventions, and ship a minimum viable taxonomy to one critical flow. Iterate quickly. The compounding payoff—clarity, speed, and trust—will be obvious to every team you partner with.

When we do this well, analytics becomes a strategic asset. Our teams spend less time reconciling numbers and more time building what matters. That’s the real meaning of moving from chaos to clarity.

Inspired by this post on Amplitude – Best Practices.

March 3, 2026
Lost in the Woods: 5 Survival Patterns Every Product Leader Must Master Now

Ever feel like your product team is “lost in the woods”? I’ve certainly been there—when strategy gets fuzzy, outcomes drift, or constraints aren’t clear. What helped me reframe the chaos was borrowing “lost person” patterns from search-and-rescue and mapping them to product strategy, product discovery, and team behaviors. The result is a practical playbook for product management leadership that keeps empowered product teams moving toward outcomes—not just outputs.

Listen to this episode on: Spotify | Apple Podcasts

Here are the five patterns I see most often—and how I turn each one into forward motion: settle in place (freeze), chase shortcuts, follow the first visible path, use your own navigation (intuition/taste), and retrace your steps. Each of these has a smart, minimal move that helps teams reorient fast without abandoning continuous discovery or product strategy discipline.

Settle in place (freeze). Sometimes the smartest move is to stop. When my team lacks context or authority, I pause delivery work and escalate instead of improvising fixes. This prevents thrash, protects focus, and creates the air cover we need to realign outcomes vs output OKRs.

Chase shortcuts. Shortcuts can be brilliant—or overconfident. I’ve learned to pressure-test whether the “road” is where we think it is before we commit. That means lightweight experiments, clear exit criteria, and the humility to pivot. Think about big bets like Spotify podcasts: compelling vision, but you still have to validate assumptions step by step.

Follow the first visible path. The obvious option isn’t always the best one. My job as a product leader is to make multiple paths visible before we choose. I lean on opportunity solution trees and KPI trees (or driver trees) to surface alternatives, align stakeholders, and keep empowered product teams focused on customer impact and product-market fit—not just the loudest idea.

Use your own navigation (intuition/taste). Judgment matters, especially for product trios making fast calls—but it’s not a replacement for evidence. When my “compass” conflicts with what we observe, I anchor back to customer interviews, rapid tests, and discovery loops. Intuition should guide where we look, while data validates how we proceed.

Retrace your steps. When we’re drifting, I go back to what used to work: principles, quality practices, and discovery habits as feedback loops. Returning to fundamentals—clear problem statements, crisp value propositions, and disciplined outcomes—rebuilds momentum fast.

Team prompt to try: If your team is “lost” right now, which pattern are you defaulting to—and what’s the smallest move you can make this week to get oriented (escalate, test a shortcut, map options, validate intuition with evidence, or retrace to a principle)? I use this question in weekly reviews to keep us grounded in continuous discovery and product strategy.

Resources & Links:

Follow Teresa Torres: https://ProductTalk.org

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

Mentioned in the episode:

Lost Person Behavior: A Search and Rescue Guide on Where to Look – for Land, Air and Water

Robert J. Koester

Examples referenced: Xerox, Nokia, Kodak, Volkswagen emissions scandal, Spotify podcasts, large-org tooling contexts like Oracle and SAP

Opportunity Solution Trees: Visualize Your Discovery to Stay Aligned and Drive Outcomes

KPI Trees: How to Bridge the Gap Between Customer Behavior, Product Metrics, and Company Goals

Let's Read Continuous Discovery Habits Together (January 2026) for Continuous Discovery Habits (and the idea of habits as feedback loops)

Shifting from Outputs to Outcomes: Why It Matters and How to Get Started

I’d love to hear how your team navigates these patterns. Which small move will you try this week? Leave a comment below and let’s compare notes on product discovery, stakeholder management, and product roadmapping that actually drives outcomes.

Inspired by this post on Product Talk.

March 3, 2026
March CDH Book Club: Master Experience Mapping to Align Teams and Accelerate Discovery

I’m thrilled to invite you to our March session of the CDH Book Club. Continuous Discovery Habits turns five this year. And to celebrate we are reading the book together. I’ve seen firsthand—leading product trios and empowered product teams—that sharpening our discovery habits is the fastest way to better outcomes vs output OKRs, tighter team alignment, and more confident product strategy.

Each month, I am releasing an in-depth reading guide that includes:

The chapters we will be reading

A preview of the most important concepts we'll be learning about

Short videos you can share with friends and colleagues to help spread the ideas

Individual and team discussion questions to help you absorb and engage with the reading

Team exercises to help you put the ideas into practice

Additional reading to help you go deeper on the core ideas

We’ll be discussing each month’s reading in the comment section and we’ll gather quarterly to discuss on a live call. I’ll be there to trade notes, compare experience maps, and share what’s working across product discovery practices.

Joining late? No problem. I monitor the comments on each reading guide throughout the year. Start with the current month or go back to January—whatever works for you. You can ask for help, share what’s working, and connect with other readers at any point.

If you want to participate, grab a copy of the book (or dig up your old copy), share the "Spread the Love" videos, reserve some time to do the team exercises, and register for the community sessions. Let’s do this!

This Month’s Reading

Chapters:

Chapter 4: Visualizing What You Already Know

Estimated reading time: ~14 minutes

This chapter will introduce you to:

Why starting individually—rather than as a group—is the fastest path to unlocking your team’s collective intelligence

How drawing (even badly) forces you to get specific in ways that words never will

The strategic choice of setting your experience map’s scope—too narrow and you miss opportunities, too broad and you lose focus

How diverse perspectives become your team’s secret weapon when you know how to synthesize them

Why your first experience map isn’t truth—it’s a hypothesis you’ll test and evolve with every customer conversation

Need a copy? Grab the book.

Share the Love with Friends and Colleagues

We learn best in community. Use the following short videos to share the key concepts from this chapter with friends and colleagues. Invite them to participate in the book club with you. In my teams, these quick hits help us align faster before we co-create an experience map or opportunity solution tree.

Visualize your thinking – To bring others along

Unlock team alignment – With visualizations

Reflect & Discuss What You Read

When we reflect and discuss what we read, we absorb more of the material. It helps us put what we learn into practice. Don’t skip this step. In my own practice, the real unlock came when I treated mapping as a living artifact that shapes customer interviews, not a one-off deliverable.

Most of us believe we work collaboratively, but we’ve never truly experienced what it means to build shared understanding from diverse perspectives. This chapter challenges you to get uncomfortable—to draw when you’d rather talk, to work alone before working together, and to see your maps as living documents rather than one-time deliverables.

Individual Reflection

Think about the last time your team tried to align on what you know about your customers. Did everyone start by creating their own perspective first, or did you jump straight into a group discussion? What happened as a result?

When was the last time you drew something at work? What stops you from using drawing as a thinking tool—is it discomfort with your drawing skills, lack of time, or something else?

Look at your current work. If you were to create an experience map right now, what scope would you choose? How does your desired outcome help you determine what to include and what to leave out?

Team Discussion

As a trio, each person should identify one unique perspective they bring to your team’s understanding of your customer. How might these different viewpoints create blind spots if you only relied on one person’s view?

When your team disagrees about what customers need or want, how do you typically resolve it? Do you debate until someone wins, defer to the most senior person, or test your different hypotheses?

Does your team have a current experience map? If so, when was the last time you updated it based on what you’re learning from customers? If not, what’s preventing you from creating one?

Put It Into Practice

Understanding why experience maps matter is different from actually creating one that drives your discovery work. These exercises will help you practice the discipline of starting individually, synthesizing diverse perspectives, and using your map to guide customer conversations. My suggestion: timebox, embrace imperfect drawings, and let the artifact lead your next interview script.

Exercise: Create Your Individual Experience Maps

Time: 20 minutes individually, 45–60 minutes with your team

Do this: Individually first, then share with your trio

Start by agreeing on the scope of your experience map based on your current outcome. Each member of your trio should then independently create their own experience map using pen and paper (or your favorite digital drawing tool).

Focus on drawing the customer’s experience, not your product’s features. Where do they get stuck? What goes wrong? How do they work around problems? Don’t worry about drawing well—boxes, arrows, and stick figures are perfectly fine.

Once everyone has created their individual maps, schedule time to share them with each other. As you explore each person’s perspective, ask questions to understand their thinking. Pay particular attention to the differences between maps—this is where the richest insights emerge.

Exercise: Co-Create Your Shared Experience Map

Time: 30 minutes with your team

Do this: With your product trio

Bring your individual experience maps together and work to synthesize them into a single shared map. Start by identifying all the unique nodes (distinct moments, actions, or events) across all three maps. Arrange them in a comprehensive flow.

Collapse similar nodes, but be careful not to overgeneralize. Add links to show relationships and flow between nodes—including loops, error cases, and abandonment points. Finally, add context about what customers are thinking, feeling, and doing at each step.

As you work, avoid getting bogged down in endless debate. If you disagree about details, draw out the difference rather than debating it. This often reveals you already agree or helps you pinpoint exactly where your understanding differs.

Remember: This map is your current hypothesis about your customer’s experience. Use it to guide your upcoming customer interviews and plan to evolve it based on what you learn.

Go Deeper: Additional Reading

If you prefer an audio summary of this month’s reading, including the book chapters and the following resources, I’ve included an audio version for paid subscribers at the bottom of this post.

Supplementary Reading

Why Drawing Maps Sharpens Your Thinking

Core Concept: Collaborative Decision-Making in a Product Trio

Other Voices

To Draw or Not to Draw: Is Traditional Sketching Still Relevant in the Digital Design Era? by Julia Ku

Journey-Mapping Approaches: 2 Critical Decisions to Make Before You Begin by Kate Kaplan

The Visual Language of Comic Books Can Improve Brain Health by Mary Widdicks

Mapping Your User’s Day with the User Clock Sketch by Ben Crothers

Our Live Discussion Schedule

Our live discussion sessions are for paid subscribers. Sessions are not recorded. Invitations will go out to Supporting Members and CDH Members two weeks before the scheduled event. But reserve the time on your calendar now.

Wednesday, March 18, 2026: 9am–10am PDT and 4pm–5pm PDT

Tuesday, June 16, 2026: 9am–10am PDT and 4pm–5pm PDT

Thursday, September 17, 2026: 9am–10am PDT and 4pm–5pm PDT

Wednesday, December 16, 2026: 9am–10am PST and 4pm–5pm PST

Audio Summary

This summary was produced by NotebookLM. The sources supplied were the book chapters as well as all of the additional reading.

Listen here: March — Draw the User Clock to Build Empathy (audio)

This article is part of the CDH Book Club celebrating the five-year anniversary of Continuous Discovery Habits. See all book club posts.

Inspired by this post on Product Talk.

March 2, 2026
From Tickets to Strategy: How AI Is Rewriting Support Careers—and Why Now Is the Moment

To truly transform with AI, I’ve learned it’s never just about the technology—it’s about redesigning how we work. The teams that win don’t bolt AI on; they re-architect around it. That means rethinking roles, workflows, and governance to build a system that sustains and improves AI performance over time.

In The 2026 Customer Service Transformation Report, teams at every stage of maturity describe human agents taking on more proactive work—training AI systems, handling the hardest queries, and owning tasks that demand judgment. Job descriptions are shifting, too, with many organizations explicitly adding AI-related responsibilities.

I’m also seeing a clear rise in dedicated AI specialists. Conversation analysts, knowledge managers, and AI operations leads are fast becoming standard. For support professionals, this opens new, higher-leverage career paths—and creates a talent pipeline that blends service excellence, data fluency, and product thinking.

Support once centered on queue-level activity—ticket triage, routing, translations, and answering FAQs. Now, as AI handles more frontline interactions, our human roles are moving up the stack toward optimization, oversight, and continuous improvement.

According to the latest research, 45% of teams report updating job descriptions to include AI-related responsibilities, with 40% saying their human agents are now more focused on training AI systems. Another 27% report that human agents primarily handle the most complex escalations and edge cases, while a quarter say agents are doing more consultative and strategic work.

Even at the initial deployment stage, 16% of teams report spending less time handling support volume since implementing AI – and among teams who’ve reached maturity, that figure rises to 28%.

When Intercom’s Research, Analytics & Data Science (RAD) team interviewed 166 of our customers, similar themes emerged. Nearly all participants (≈95%) reported meaningful workflow changes, with manual processes being handled by AI, and humans focusing more on monitoring or fine-tuning AI outputs. Eighty-three percent of participants also reported seeing their team’s roles and responsibilities change to become more strategic and supervisory in nature.

AI is reshaping support teams: organizations are adding conversation analysts (32%), knowledge managers (30%), AI operations leads (28%), and support automation specialists (24%). Just 8% report no new AI roles.

It’s not just the work that’s evolving; organizational structures are, too. Some teams are reallocating existing talent into AI-focused roles; others are hiring entirely new skill sets. Many of the most common job titles in this space didn’t exist two years ago.

Consider a Senior AI Knowledge Manager, Beth-Ann Sher, who transitioned from a help center manager role. Like many careers transformed by AI, her work evolved from administrative to strategic. Instead of focusing solely on customer-facing, self-serve content, her mandate expanded to designing and optimizing knowledge inputs that directly improve AI Agent Fin’s performance—work that materially lifts resolution rates.

Or look at a Senior Conversation Designer, Fred Walton, hired specifically for an AI-first function. He focuses on frictionless customer journeys with Fin, smoothing handoffs between automation and human support while keeping customer satisfaction front and center—hallmarks of mature AI workflows and conversation design.

In high-performing organizations, roles like these typically sit within a dedicated AI support team under senior CS leadership. Clear ownership and accountability for AI performance is critical; without it, optimization stalls and trust erodes.

These shifts aren’t isolated. Take Robb Clarke from RB2B. He went from Head of Technical Operations to Head of AI. With Fin, his focus moved from repetitive support questions to managing knowledge and improving the system behind it—freeing him to be proactive about product improvements and fix issues before they hit customers.

Or consider Eric Broulette from Bloomerang, a support leader who leaned into AI and became the VP of Support and Education. By deploying Fin, his team found breathing room to invest in what’s next. Agents stepped into new roles, contributed to meaningful projects, and built skills that had previously felt out of reach. As Eric puts it: “Do not wait to embrace AI. It will unlock more career growth for your teams than you can imagine.”

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

Bringing AI into support will eventually change every agent’s day-to-day work. For leaders at the start of the journey, that can feel daunting. My perspective: the most successful teams treat this as an operating model shift, not a tooling rollout—anchored in AI Strategy, governance, and continuous improvement.

Be transparent about what’s changing, why it matters, and how success will be measured. Define how AI performance will be evaluated (resolution rate, containment, CSAT impact), empower agents to train and improve the system, and communicate how responsibilities will evolve. When teams help build the AI, they’re invested in making it great.

Here’s the playbook I rely on with support leaders: First, reset expectations about time allocation—less time in the queue, more time improving the AI system that serves the queue. Second, elevate knowledge management as a core capability. Prioritize content quality and coverage for your AI Agent, and carve out dedicated “out of the inbox” time so every agent contributes. Third, keep outcome metrics—especially resolution rate—front and center. It gives the team a north star for experimentation and iteration.

Scaling AI is as much a people challenge as it is a technology challenge. As automation takes on more work, support roles become more proactive, strategic, and cross-functional—even early in the journey. Responsibilities expand, new roles emerge, and team structures adapt to concentrate on and amplify AI performance. In the process, support careers are transformed.

If you’re leading this shift, now’s the moment to reimagine your operating model: clarify ownership, invest in knowledge and conversation design, adopt eval-driven development, and build the muscle for continuous improvement. That’s how you move from tickets to strategy—and unlock compounding value for your customers, your business, and your teams.

Inspired by this post on The Intercom Blog.

February 27, 2026
Scaling Enterprise Sales from $0 to $3.5B: CRO Lessons, MEDDIC Mastery, and GTM Truths

I’ve led product organizations through multiple growth chapters, and the pattern is always the same: the tighter the alignment between product, sales, and marketing, the faster you scale. Reflecting on the journey of Chris Degnan — the first sales hire at Snowflake who spent 11 years helping scale the company from zero to $3.5 billion in revenue as its CRO while partnering with four different CEOs — I’m struck by how consistently the fundamentals win. The playbook isn’t mysterious; it’s disciplined execution, ruthless clarity, and a go-to-market strategy that matures with each revenue stage.

At $10M ARR, the CRO role is hands-on and founder-adjacent. You’re close to the product, running point on key deals, pressure-testing messaging, and building credibility with early customers. By $1B+, the job is organization design: segmentation, international expansion, forecast accuracy, enablement, recruiting, and cross-functional orchestration. The shift is from deal quarterback to system architect — standing up repeatable, auditable processes that produce reliable outcomes across regions, segments, and industries.

Sales leaders who can’t sell the product themselves don’t last. Whether you sit in product management leadership or run the field, you need to master discovery, speak the customer’s language, and translate use cases into value. That also means getting fluent in solutions engineering — understanding integrations, data paths, security, and the operational realities buyers live with. I’ve found this hands-on competence to be the fastest way to earn trust internally and externally, and to keep product strategy grounded in market truth.

The MEDDIC methodology is the foundation for every durable sales org — and, frankly, a founder’s best insurance policy. MEDDIC forces alignment on qualification criteria, from Metrics to Economic Buyer to Decision Process and Identifying Pain. When product and sales both operate to this standard, roadmap bets improve, marketing targets sharpen, and win rates climb. It’s not paperwork; it’s pattern recognition at scale.

High-output CROs obsess over the right numbers. Pipeline coverage by segment and stage; conversion rates through each gate; sales cycle length by use case; average selling price and discount discipline; consumption predictability when you have consumption SaaS pricing; and post-sale expansion velocity. The art is deciding which two or three metrics are the organization’s true north at a given stage — then designing enablement, compensation, and operating cadence around them.

On operating cadence, the week in the life at scale is predictable for a reason. Forecast reviews that surface risk early. Deal reviews that coach to MEDDIC depth, not activity theater. Enablement blocks to uplevel managers and ICs. Recruiting time — always. Customer roadshows to refine value proposition and product positioning. And standing meetings with product, marketing, and finance to keep the GTM motion, roadmap, and unit economics in sync.

Compensation is a force multiplier or a silent saboteur. Keep it simple, consistent, and aligned to the current motion. Early on, weight new logo acquisition and land quality; as you mature, balance new business with expansion, multi-product adoption, and healthy consumption. Guardrails matter — cap over-discounting, reward multi-threading, and avoid plans that create end-of-quarter cliff behavior. The best plans reinforce the behaviors you want your culture to scale.

Technical CEOs often underestimate how much narrative, segmentation, and process discipline great GTM requires. The handoff from founder-led GTM to sales-led growth is where many teams stall. My rule: prove one repeatable motion in one segment before you add complexity. Codify the buyer’s journey, instrument the funnel, and make sure product strategy and enablement move in lockstep.

Culture sets the ceiling. You have to find the fakers, manage-uppers, and passengers quickly — people who look busy but don’t move pipeline, who talk big but avoid accountability, or who ride the momentum of others. The mantra that has saved me endless time: “When there’s doubt, there’s no doubt”. Move fast, but with humanity; be clear on expectations, coach hard, and when it’s not a fit, make the change before the team does it for you.

Feedback is the operating system of a high-performing org. Leaders at every level need to be coachable — on message discipline, on forecast rigor, on how they develop people. I’ve benefited from straight talkers who hold a high bar, and I try to pay that forward. The fastest way to raise organizational IQ is to institutionalize feedback loops across sales, product, and marketing — from post-mortems to win-loss analysis to field-sourced roadmap reviews.

What separates exceptional ICs from the rest? Hunger, intellectual honesty, and a builder’s mindset. They qualify hard, align to customer metrics early, multi-thread to power and value, and partner tightly with solutions engineering. They don’t hide from gaps; they surface them, and they know exactly what they need from product, marketing, and leadership to win.

Executive teams that scale share a few traits: crisp segmentation decisions, single-threaded ownership for outcomes, and healthy conflict that resolves into commitment. Dysfunction, by contrast, looks like metrics roulette, opaque decision-making, and a tolerance for exceptions that become precedent. Make the rules explicit and the exceptions rare.

Leaders like Frank Slootman have popularized intensity, speed, and focus — and there’s real power there when paired with clarity and data. The lesson I carry forward: move fast on people decisions, keep the message simple, and measure what matters. Equally important is knowing where that approach can backfire — when speed outruns learning, or when pressure erodes cross-functional trust. The best operators balance urgency with systems thinking.

Most AI companies will face a go-to-market reckoning. Model quality won’t save a weak motion. The winners will articulate a hard-nosed ROI, solve specific workflow pain, address data governance and security head-on, and show measurable lift — not demo dazzle. In other words, the same fundamentals apply; the stakes and scrutiny are just higher.

If you’re building or rebuilding your revenue engine, start here: define your ideal customer profile and segmentation with ruthless clarity; adopt MEDDIC and teach it across product and sales; align compensation to today’s motion; instrument the funnel and inspect it weekly; and cultivate a culture where feedback is fuel. Do that, and the path from $0 to $3.5B stops feeling like mythology — and starts looking like math.

February 26, 2026
Stop Blurring the Lines: Clear Product–Engineering Boundaries to Boost Quality and Prevent Burnout

Where is the true boundary between product and engineering—and what happens when it gets blurry? I’ve led and coached teams through this question many times, and I’ve learned that clarity here isn’t just a nice-to-have; it’s foundational to quality, velocity, and team health.

I’ve seen well-intentioned product managers step in to “help” by taking ownership of bug triage, tech debt prioritization, or even system architecture. At first, it feels productive. Over time, it creates role confusion, slows decision-making, and burns out PMs—while paradoxically lowering engineering quality. The “CEO of the product” myth and legacy IT, project-based mindsets are usually at the root. Treating engineers as “order takers” breaks down in evergreen product environments.

The healthiest collaboration model is simple and disciplined: The product trio owns the “what”; engineering owns the “how”. Product managers are not people managers for engineers—and shouldn’t be accountable for engineering quality. Our job is to frame the problem, align on outcomes, and continuously discover value with customers—not to supervise technical execution.

If quality is a problem, the solution is escalating and fixing the system, not managing individual bugs. In practice, that means surfacing patterns and elevating them to engineering leadership, who can address root causes—staffing, skills, code health, CI/CD gaps, observability, or process design—rather than asking PMs to paper over issues with status updates. This keeps accountability where it belongs and reinforces outcomes vs output OKRs.

One high-leverage move is to remove unnecessary intermediaries. Removing the PM as a middleman creates better flow and clearer ownership. Create direct paths for stakeholders to get bug status without routing everything through product. Use dashboards, shared tools, or Slack channels instead of one-off updates. In my teams, shared Jira views, Slack incident channels, and status pages eliminated handoffs, improved stakeholder management, and gave engineers the space to solve problems end-to-end.

Strong engineering leadership is non-negotiable. What strong engineering leadership should own (and why that matters) is the technical system, quality guardrails, sustainable pace, and the practices that uphold them—incident management, code review rigor, test coverage, and SLOs with SRE. Skilled engineering teams naturally push back when boundaries are crossed—and that’s a good thing. It signals ownership, craft pride, and a pathway to durable execution.

When do I step in as product? Primarily to clarify desired outcomes, sequencing, and trade-offs—bringing customer and business context to the table. I structure product roadmapping and sprint planning around value slices and risks, not task lists. I align on decision rights early: architecture and tech debt strategies live with engineering; product strategy, positioning, and success metrics live with product; discovery and prioritization live with the product trio.

Here are the system-level moves I’ve found most effective: Escalate systemic quality issues to engineering leadership, not individual contributors. Advocate for real engineering leadership if your org expects product teams—not IT teams. Then reinforce a culture of continuous discovery so product, design, and engineering make better upstream decisions together. This is how empowered product teams ship higher-quality outcomes—without burning anyone out.

If you’ve ever found yourself acting as the middleman for bug status or being asked to “own” engineering decisions outside your expertise, you’re not alone. Reset the boundaries, make work visible, and double down on shared outcomes. In my experience, the moment we clarify roles and remove status theater, quality rises, cycle time improves, and everyone does the job they were hired to do—better.

Inspired by this post on Product Talk.

February 24, 2026
Human-in-the-Loop Mastery: Proven Oversight Tactics That Elevate AI Quality and Trust

Human-in-the-loop oversight is the fastest and most reliable way I know to elevate AI quality, build user trust, and reduce risk. At HighLevel, my teams treat oversight as a product feature—not an afterthought—because dependable AI experiences come from deliberate design choices across data, models, and people.

When I say “human-in-the-loop,” I mean a system that blends automation with targeted human judgment at key moments: during data curation, prompt engineering, evaluation, deployment, and post-launch learning. This approach turns “AI workflows” into measurable, repeatable processes and keeps me honest about what’s working, what’s drifting, and where a human safety net must step in.

Architecturally, I start with a retrieval-first pipeline to ground outputs in trusted knowledge, then wrap it in guardrails. Deterministic preprocessing, careful prompt engineering, and post-processing validators catch obvious failure modes. Confidence thresholds and policy checks route ambiguous or sensitive cases to a human reviewer, while clear, auditable traces show why the system chose automation versus escalation. This balance supports reliability at scale while preserving agility for “agentic AI” patterns when they add value.

Quality is only real if I can measure it, so I build with eval-driven development from day one. I maintain golden datasets, rubric-based scoring guidelines, and an automated evaluation harness that runs on every change to prompts, models, or data. Pre-production gates protect against regressions, while production telemetry surfaces drift by segment and use case. When it’s time to run experiments, I use A/B tests sized with a minimum detectable effect (MDE) to avoid overfitting to noise.

Operationally, I optimize for outcomes, not output. I track task success rate, time-to-resolution, safety violation rate, hallucination rate, and cost-to-serve, then connect these to outcomes vs output OKRs. The signal I want is simple: are we reliably solving the user’s job-to-be-done with lower effort and higher confidence? If not, I tighten prompts, refine retrieval, or expand human review where it pays off most.

Risk governance is non-negotiable. I design with privacy-by-design and data governance from the start—role-based access, audit trails, PII redaction, and red-team tests for safety. Clear reviewer playbooks and calibration sessions reduce bias and ensure consistent decisions. These practices aren’t bureaucracy; they’re how I operationalize AI risk management while maintaining velocity.

Teams make or break this model. I empower product trios to own the full lifecycle—discovery, build, and learning—so feedback loops close quickly. In-product feedback widgets, reviewer queues, and incident management playbooks help us respond in hours, not weeks. Over time, human review becomes a targeted scalpel rather than a blanket requirement as the system learns and improves.

Economics guide the level of oversight. I treat each workflow like a portfolio: where the value of accuracy is high and ambiguity is common, I route more to humans; where tasks are simple, frequent, and well-bounded, I automate aggressively. The goal isn’t zero humans—it’s optimal humans, deployed precisely where their judgment compounds ROI.

If you’re getting started, begin with one high-impact workflow, establish your golden set and evaluation rubric, and wire in a simple review queue. Prove the lift, then scale. In the short video above, I walk through the patterns I use to design these loops, measure quality with rigor, and ship AI that teams—and customers—can trust.

Inspired by this post on Product School.

February 23, 2026
Inside Partner Product Marketing: Lessons that Elevate Go-to-Market and Product-Led Growth

I’ve learned that the most effective partner product marketing is less about decks and more about decisions. When I collaborate with partner product marketing managers, we translate complex capabilities from a unified analytics platform into crisp, outcome-led narratives that customers can act on. This is where product positioning and go-to-market strategy intersect to create momentum for product-led growth.

In my experience, the strongest partner product marketing managers operate like solution orchestrators. They align value propositions across partners, clarify the problem-solution fit, and articulate competitive differentiation without drowning teams in feature lists. By anchoring messaging in clear customer pains and measurable gains, they help everyone—from solutions engineering to sales—tell the same story with confidence.

My playbook starts with outcomes. We define the “why” in terms customers care about, then quantify it with retention analysis, user activation, and time-to-value. That evidence shapes positioning, enables tighter points of parity and differentiation, and ensures our value proposition resonates in market. The result is faster alignment and fewer cycles spent debating messaging without data.

Cross-functional execution makes or breaks the strategy. I partner closely with solutions engineering to validate solution patterns, and with sales to balance sales-led motions alongside product-led growth. Strong stakeholder management keeps discovery loops tight: we capture objections early, refine narratives quickly, and reduce friction across the funnel.

On the tactics side, I rely on A/B testing to de-risk bold messaging changes and to optimize in-app guides and product tours. We set a minimum detectable effect upfront, instrument journeys with Amplitude analytics, and iterate quickly. This gives the team statistical confidence while keeping speed high—especially when refining narratives for complex partner solutions.

Ultimately, great partner product marketing illuminates the shortest path from capability to customer value. When we pair disciplined positioning with data-driven learning, we strengthen our go-to-market strategy and build durable competitive advantage. That’s how we turn strong solutions into market-leading stories that win—and keep—customers.

Inspired by this post on Amplitude – Best Practices.

February 21, 2026
Unlock Data-Driven Growth: My Take on Analytics, Experimentation, and Personalization Mastery

I’m sharing a focused set of insights on analytics, experimentation, and personalization designed to help teams ship smarter, reduce risk, and accelerate outcomes. Drawing on years of leading product teams, I translate complex data practices into practical playbooks you can apply immediately to improve user activation, conversion, and retention.

My approach starts with a strong measurement foundation. I lean on a unified analytics platform—often powered by tools like Amplitude analytics—to centralize product, marketing, and customer success signals. With clear event taxonomies, consistent governance, and trustworthy dashboards, teams gain a single source of truth to prioritize the right problems and sequence roadmap bets with confidence.

Experimentation turns insight into evidence. I emphasize A/B testing discipline, including minimum detectable effect (MDE), guardrail metrics, and pre-registered hypotheses. This repeatable system lifts decision quality, shortens feedback loops, and aligns cross-functional partners around what actually moves the needle, not what merely sounds promising.

Personalization compounds the value of experimentation by delivering the right value to the right segment at the right moment. Thoughtful in-app guides and product tours—rooted in behavioral signals—nudge users through friction points and increase the likelihood of early wins. The result is a more intuitive path to first value, stronger user activation, and healthier long-term engagement.

Retention is the ultimate scoreboard. I rely on retention analysis, cohorting, and leading-indicator metrics to connect feature usage to durable outcomes. When paired with product-led growth motions, teams can identify activation thresholds, build habit loops, and scale what works without overextending sales or support capacity.

If you’re getting started, begin with a crisp instrumentation plan, shared definitions, and a lightweight review ritual. Use continuous discovery practices, opportunity solution tree mapping, and driver trees to tie data signals to real user problems. From there, iterate: test small, learn fast, and scale what is proven. Over time, this system becomes a flywheel for product strategy—fewer debates, more evidence, better products.

In this series, I distill the frameworks, templates, and real-world lessons that have consistently improved outcomes for product teams: how to structure experiment backlogs, how to read funnel breakpoints, how to detect false positives quickly, and how to operationalize analytics for day-to-day decisions. Expect practical guidance you can copy, adapt, and run with immediately.

Inspired by this post on Amplitude – Perspectives.

February 20, 2026
How Deep AI Transforms Support Into Proactive, Omnichannel CX—No Extra Headcount Needed

For years, I chased the elusive goal of delivering a perfect customer experience. Today, with AI embedded in our support operations, that standard is finally within reach—and it’s reshaping how we prioritize, design, and scale service.

In “The 2026 Customer Service Transformation Report,” teams report early, tangible wins from AI: faster responses, higher efficiency, and consistent coverage across languages and time zones. Those gains create the capacity we’ve always needed. The more we push the technology, the more quality improvements we unlock.

This marks a fundamental shift. As AI takes on more, our focus can finally move from firefighting to crafting the customer experience. When the AI is working, the measure of success becomes how well it’s working—across accuracy, tone, resolution, and end-to-end journey quality.

I’ve seen this transformation firsthand. Mature AI deployment gives my team “breathing room,” so we can design for consistently excellent outcomes rather than obsess over deflection. That means widening access to support, removing friction on the path to resolution, and anticipating customer needs before they escalate.

In our own support organization, we opened support to trial customers, accelerated first response times, and added consultative sessions during onboarding. We absorbed a 300% increase in total demand without adding headcount—made possible by deep integration of an AI Agent and a disciplined AI strategy.

Teams with mature customer service deployments are nearly three times likelier to say they always meet increasing expectations—27% vs 9% at initial rollout—highlighted by bold orange and gray comparison bubbles.

Across the industry, the pattern is similar. When teams initially deploy AI, only 9% say they can always meet customer expectations. That number triples as teams reach a mature level of deployment. Even as expectations rise, the organizations that deeply integrate AI—complete with clear ownership, robust instrumentation, and continuous improvement loops—are the ones most likely to meet (and exceed) the bar.

Looking ahead to 2026, I expect omnichannel consistency to become a key differentiator. The data shows planned investment is distributed nearly equally across chat, email, and social messaging (36% each), closely followed by phone/voice (31%). The question is no longer “Which channel should we optimize?” but “How do we deliver a consistent, AI-powered experience everywhere our customers are?”

Teams that solve for omnichannel consistency will bridge the long-standing gap between what customers expect and what support can deliver. Every touchpoint becomes an opportunity to exceed expectations and build durable trust.

Consider Clay, a team that scaled support without sacrificing quality. Support is one of their main growth drivers, and as their customer base expanded, ticket volume surged. Early on, they concentrated much of their effort in Slack, cultivating close, transparent community relationships. But relying on a single channel created friction as they grew; customers wanted the flexibility of email and in-app chat, and Clay needed to deliver the same high standard everywhere.

Where AI investment is headed for customer service in 2026: chat, social, and email lead at 36%, with phone/voice close behind at 31%. A bold visual snapshot of shifting channel priorities in CX.

By unifying their support experience with an AI Agent, Clay brought consistency across channels. Today, AI is involved in 90% of all queries and handles half of Clay’s total volume, upwards of 7,000 queries a month. First response rates improved significantly, freeing the team to focus on proactive, high-impact work.

That work includes identifying content gaps for education and content marketing, reaching customers before they need to ask for help, and surfacing feature requests and recurring challenges to product teams. Clay proves that when support is truly great, it becomes a competitive edge.

So how do you build a superior customer experience with an AI Agent? Here are five principles I use when scaling toward mature deployment.

1) Treat customer experience like a product. Treating support as a product means designing, building, and managing the support experience with the same rigor as your core product. You define goals (faster onboarding, higher CSAT or CX Score, lower churn). You map flows (AI starts the conversation, human handovers, proactive nudges). You instrument the journey (track handoffs, drop-offs, success states). You run tests and ship improvements (tone tweaks, fallback paths, training updates). You own the outcomes (gather feedback, measure performance, use insights to continuously improve the system).

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

2) Lead with AI, back with humans. AI isn’t replacing the human touch. It’s redefining when, where, and how it’s most valuable. In a scaled model, AI is the first responder and the end point for most conversations. Humans step in where they add the most value—particularly during high-stakes issues—and those handoffs should feel seamless. Meanwhile, your team focuses on improving AI performance and optimizing the end-to-end journey.

3) Be proactive. Use AI to anticipate needs, guide customers before problems arise, and nudge them toward successful outcomes. This is where customer support AI strategy shines—moving from reactive triage to journey orchestration that protects momentum and builds trust.

4) Build for trust. Many customers still carry the legacy of clunky chatbots that delivered vague answers and dead ends. You earn trust by showing that your system works. Don’t hide your AI Agent behind layers of “choose an option.” Get customers to the AI quickly, demonstrate real problem-solving, and ensure that when a human is needed, they join with full context to resolve complex issues efficiently.

5) Make it feel personal. Your AI Agent represents your brand. The way it speaks, follows policies, and responds matters. Use tone control, fallback logic, and language preferences to align the experience to your standards. Consistency builds trust; personality builds connection and loyalty.

Perfect really is possible. With deep AI implementation, you can scale comprehensive, fast, and personal support across channels—so customers feel supported not just when they reach out, but throughout their journey. That’s the promise of modern AI workflows in support, and it’s what will separate leaders from laggards in the years ahead.

Inspired by this post on The Intercom Blog.

February 20, 2026
90% of CROs Will Fall Behind by 2028: Hard-Learned Lessons to Stay Ahead of GTM Change

I’ve been reflecting on why so many revenue leaders are at risk of falling behind, and the conclusion is stark: fewer than 10% of current CROs will thrive by 2028. That isn’t hyperbole—it’s a wake-up call for how quickly go-to-market strategy, organizational design, and AI-driven execution are evolving. From my seat leading product, I see the pressure building on the CRO role to orchestrate the entire revenue system, not just run a sales team.

One story that crystallizes this reality comes from the journey of Stevie Case, the CRO of Vanta, the trust management platform serving everyone from founders to Fortune 100 CISOs. A former pro-video gamer who stumbled into sales through a mentor’s bet, she exemplifies how unconventional paths can drive unconventional insight. Her trajectory underscores a bigger truth I’ve witnessed across companies: the best revenue leaders aren’t just great sellers—they’re builders who understand product, process, and people at scale.

Why do early revenue hires fail? In my experience, it’s rarely about raw talent. It’s about fit, scope, and time horizon. Early-stage teams often hire coin-operated closers to sprint for this quarter’s number, when what they actually need are long-term builders who can shape ICP clarity, pipeline math, and repeatable motion. The trap is simple: you hire for momentum before you’ve validated the motion. That misalignment shows up at 00:00 Why early revenue hires fail and again at 04:16 Coin-operated sellers vs. long-term builders—two ideas every founder-led GTM team should internalize before the first half-dozen sales hires.

What separates a VP of Sales from a top 1% CRO is scope and systems thinking. A true CRO owns the full revenue engine—marketing, sales, solutions engineering, customer success, pricing, channels, and post-sale activation—not just the new-business line. It’s a role defined by precision around 07:44 Metrics, confidence, and velocity and the courage to decide when to centralize vs. decentralize capabilities as you grow. Should CROs lead sales? At 12:04 Should CROs lead sales?, the nuance is clear: yes, if the motion is still coalescing; not necessarily, once the machine is humming and specialization unlocks scale. My rule of thumb: start consolidated for speed of learning; split functions only when interlocks are provably robust.

There’s a humbling lesson in 16:36 Learning to scale at Twilio and 19:58 Stevie’s scaling mistake at Vanta: copying another company’s operating system, even a world-class one, is an easy way to blunt your edge. Context is king. What worked at Twilio won’t automatically work at a trust management business. That’s why the line at 17:44 “There is no CRO playbook” resonates so deeply. There are principles—org design, segmentation, enablement, compensation, customer activation—but your playbook must be bespoke to your product, pricing, cycle time, and buyer power map.

22:16 Why Vanta stays 100% sales-led is a reminder that not every high-growth motion demands product-led growth. In categories where compliance, security, and risk shape buying behavior, a consultative, sales-led approach builds trust and shortens time to value—especially when solutions engineering, onboarding, and customer success are tightly choreographed. I’ve seen teams chase PLG headlines while ignoring the higher-ROI path right in front of them: nailing the sales-led experience, from first touch to first value.

Top CROs plan 24–26 months ahead. 23:16 The value of planning 24-26 months ahead isn’t about creating perfect forecasts; it’s about designing optionality. That means hiring with stage gates, building enablement before you feel “ready,” instrumenting activation and retention early, and pressure-testing your pricing and packaging quarterly. In my org reviews, I push for scenario modeling: what breaks at 2x volume, what centralizes again at 600 headcount, and what competencies must be grown vs. bought.

On judgment and decision quality, 29:54 When trusting intuition was the wrong call is a familiar leadership tax. Pattern recognition is powerful—until it isn’t. I’ve learned to pair intuition with a data backstop and a lightweight pre-mortem: what would have to be true for this to fail? It’s the same posture I take with AI in GTM. At 30:49 Do humans still have a place in the future of GTM? and AI vs. humans in go-to-market, the answer is yes—but augmented. Humans set narrative, negotiate ambiguity, and build trust; AI accelerates research, writing, discovery, and coaching. The winning motion fuses both.

I’m often asked which tools materially shift outcomes. For revenue intelligence and operational rigor, I look to systems that compound learning: Gong: https://www.gong.io/, Salesforce: https://www.salesforce.com/, and Cursor: https://cursor.sh/. To study benchmark operating models and developer-led growth infrastructure, Twilio: https://www.twilio.com/ remains instructive. And to understand why trust, security, and compliance can define the entire GTM architecture, Vanta: https://www.vanta.com/ is a useful case study.

Leadership non-negotiables matter more as you scale. 33:33 Stevie’s leadership non-negotiables reminded me to be explicit about standards: clarity over activity, customer outcomes over internal wins, and auditability over anecdotes. 36:36 The myth of hiring for industry expertise shows up again and again—I’d rather hire for learning velocity, systems thinking, and builder DNA than narrow domain familiarity. And at 40:00 What stays centralized in a 600-person company, remember: centralize what must be consistent (data, tooling, pricing guardrails, core enablement), decentralize what benefits from speed and context (segment plays, partner motions, field marketing).

If you prefer a structured digest, here’s the operating checklist I use with revenue and product peers: define your ICP and value proposition crisply; hire builders over coin-operated sellers; instrument the first 30 days post-sale (47:09 The hidden leverage of a customer’s first 30 days); align pricing, packaging, and onboarding to activation; model capacity and hiring plans on 24–26 month horizons; decide early what stays centralized; use AI to amplify discovery, coaching, and content while keeping humans front-and-center for trust-building; and cultivate an unvarnished CEO–CRO pact (01:02:30 Unpacking the CEO-CRO dynamic) that aligns on strategy, segmentation, and sequencing.

For those who want a few timeline highlights: 00:00 Why early revenue hires fail; 02:23 Who to hire at $5M in revenue; 05:57 What excellence looks like in the CRO role; 17:44 “There is no CRO playbook”; 22:16 Why Vanta stays 100% sales-led; 23:16 The value of planning 24-26 months ahead; 47:09 The hidden leverage of a customer’s first 30 days; 53:42 Why the CRO role will face enormous changes by 2028; 58:42 What leaders must do now to stay relevant.

The throughline is simple and urgent. 53:42 Why the CRO role will face enormous changes by 2028 isn’t a forecast—it’s a present-tense mandate. 58:42 What leaders must do now to stay relevant: build a revenue system, not a sales team; plan further out while executing faster; let AI handle the mechanical so your people can master the human. Those who internalize this shift will be the fewer than 10% of current CROs who thrive by 2028. The rest will be outpaced by change they could have anticipated—and designed for.

February 19, 2026