Category: Product Management

Inside My Pricing Playbook: Building Value-Based Packaging That Balances Growth and Profit

Pricing looks deceptively simple from the outside; inside, it’s anything but. Over the years, I’ve learned that every price tag is really a strategic statement about value, priorities, and the future we’re building toward.

At Fin, pricing and packaging (P&P) is more than a finishing touch. It’s a research problem, a forecasting challenge, a commercial decision, and ultimately, a strategic statement, requiring deep cross-functional work. We must balance the needs and wants of our customers, the value delivered by our product, and the broader vision we are building towards.

Our approach keeps evolving as our product and market mature. I treat it as a living system—continuously informed by research, GTM learning, and customer behavior, never "set and forget."

Here’s how I run the process in practice, especially when we launch something new that needs to be monetized, like Fin, our AI Agent. The work moves from qualitative discovery to quantitative validation to commercial modeling, with tight partnership across product, research, data science, finance, GTM, and engineering.

Step 1: Foundational research

I start by talking to buyers to understand their mental models of value. How do they define ROI? What pricing models do they expect in this category? What feels intuitive, and what feels off? This early discovery shapes two crucial choices: the pricing model and the pricing metric.

The pricing model is the overall structure; value-based, usage-based, access-based, fixed fee, and so on. With Fin, we chose a value-based model: you only pay when Fin delivers value. Our research clearly showed that buyers don’t want to pay for usage, they want to pay for results.

The pricing metric is the unit of value within that model, the unit we anchor pricing to. For Fin, the pricing metric is “outcomes.” An outcome is defined by Fin successfully handling a customer service query.

Small definitional changes can dramatically alter how customers perceive value, so I obsess over details. Buyers rarely hand us the “right” model; they reveal how they evaluate value, and I translate that into a model and metric that align with their goals and expectations.

Throughout, I loop in execs, finance, GTM, and engineering to ensure alignment before proceeding. Pricing choices cut across the business; they can’t be made in isolation.

Step 2: Willingness to pay

Once we have a model and metric, I quantify what the market will bear. This is where rigorous willingness-to-pay (WTP) research comes in, grounded in the language we validated through the qualitative work.

Here’s the kind of framing I use in surveys to keep things concrete and consistent with our model and metric:

You would only pay when Fin delivers an outcome (→ the model). An outcome is counted when the AI Agent resolves a customer query with no further help needed (→ the metric). Would you be willing to pay $X per outcome for Fin?

The foundational qual is so important as a first step. It helps us decide what we should be asking about before we start asking how much people will pay. Without the qual ground work, you risk building a very convincing answer to the wrong question.

The goal isn’t to find a perfect price. That doesn’t exist. The goal is to ground our discussions in the reality of the market.

I use methods like Gabor-Granger and Van Westendorp to understand WTP and to shape a demand curve that informs strategy, not just a single number.

This chart shows us what percentage of the market is willing to buy the product at various price points. The demand curve shows that 69% of buyers were willing to pay for the product at $0.86 per outcome, whereas only 39% were willing to pay at $1.42.

The dashed line shows the price point at which revenue for the business would be maximized (by multiplying adoption by the dollar amount).

This allows us to debate knotty questions like: What’s the right balance between growth and revenue? How sensitive is demand to price changes? At what price do we start losing the market? If we wanted to increase adoption, would lowering our prices by $X make a meaningful difference?

Those conversations help me weigh customer value and business outcomes side by side. At this stage, decisions feel more tangible, but I don’t finalize a price until I’ve modeled the operational realities.

Step 3: Modeling

By now I have a validated model, a clear metric, and a strong WTP signal. Next I translate theory into a commercially workable plan—this is where data science and finance are indispensable.

I start with a list price aligned to our strategy and commercial goals. Then I adjust for likely discounting to estimate realized price. Next, I analyze beta usage to project outcomes per customer by segment and derive average ARR. I combine usage projections with WTP to model attach rates across conservative-to-optimistic scenarios. Finally, I connect the dots in our long-range plan—logos, ARR, margins—iterating until the numbers and narrative cohere.

The modeling step is important because willingness-to-pay data is somewhat theoretical. It reflects intent, not behavior. Modeling helps us bridge that gap.

The goal of this step is to land on a price point recommendation, alongside forecasts for ARR and adoption. It allows us to understand the real business impact of the decisions we’re making.

Alongside all of this, we need to ensure any decision we make falls in line with our pricing principles and broader business objectives.

Step 4: Sign-off and execution

With the analysis complete, I consolidate everything into a clear P&P recommendation for executive approval. Once approved, the real work begins: enabling sales, communicating changes to customers, instrumenting ROI proof points, and monitoring performance so we can learn and iterate.

Do we run the full process every time?

Not always. This is the ideal process, and I apply it end-to-end for the most material decisions. In reality, time and resource constraints require judgment; rigor should mirror impact. When uncertainty crops up midstream, I run scrappier, targeted research rather than forcing a linear path.

The ongoing challenge

As Fin’s breadth has expanded, our pricing system has had to evolve, too. For a while, modular pricing worked well—each product had its own logic tied to a crisp outcome. As we add more products, more Agent capabilities, and more outcomes, the question shifts from “what is the right P&P for this one product?” to “how does everything fit together into a coherent pricing system?”

We must recognize that pricing isn’t something you set once and leave alone. As products evolve, especially in a world where AI is rapidly changing how value is created and delivered, it’s important to regularly step back and review the bigger picture, not just the component parts.

For example, outcome-based pricing has served us well, particularly when our products were tightly tied to clear, measurable outcomes. But as our products become more varied, and as we continue building toward a broader platform, it becomes less straightforward to apply a single model cleanly everywhere.

The challenge becomes less about replacing one model with another, and more about continually looking up and asking: what pricing philosophy best reflects the value we’re delivering today? And how do we deliver that philosophy in a way that still feels right for customers?

In short, there is no finish line, pricing is never “done” – and that’s exactly how it should be.

Why this work matters

Pricing and packaging is often noticeable only when it goes wrong. A confusing model, a bad metric, or a price that feels disconnected from value. And we hear about those quickly.

When pricing is done well, it becomes nearly invisible—but it still does a lot of work. It shapes how people perceive value, clarifies what they’re paying for, and makes the product easier to sell, easier to buy, and easier to scale. Most importantly, it forces us to be honest about what the product is really worth. That’s why I take it so seriously—and why I treat pricing as a product in its own right.

Inspired by this post on The Intercom Blog.

May 20, 2026
Stop Asking AI Anything: The 3 Outcome-Based Prompts That Unlock Real Product Insights

Too often I watch teams ping a global agent with vague AMAs and then wonder why they get generic summaries instead of decisive guidance. When I lead product reviews, I push the team to treat AI like a partner in decision-making, not a trivia engine. That simple mindset shift transforms how quickly we move from questions to confident action.

AI isn’t built for AMA (ask me anything). Get recommendations for outcome-based questions for the best results with Amplitude AI.

In practice, outcome-based prompting means I don’t ask an agent to “analyze the data.” I ask it to help me reach a specific product decision, grounded in behavioral analytics and connected to our outcomes vs output OKRs. To make that concrete, I always frame my prompts around three things.

First, I state the outcome and metric. I name the business goal and the exact measure in Amplitude analytics that will validate success—activation rate, funnel conversion from A to B, or 8-week retention. I’ll reference the relevant events, segments, or driver trees so the agent has a crisp target. This is where product strategy meets measurement discipline.

Second, I define the context and constraints. I specify the user cohort, the timeframe, and the surface area I care about—new self-serve signups in the last 30 days, first-session behavior on web only, or EU traffic where data governance rules apply. On a unified analytics platform, this context lets an agentic AI narrow its search to the highest-signal slices of behavioral analytics rather than pattern-matching across noise.

Third, I declare the decision and deliverable. I tell the agent exactly what I will do next and the format I need to act: a ranked list of levers for an A/B testing plan, a recommended prompt engineering template for in-app guides, or a one-page brief I can hand to the growth team. Clear decisions lead to clear outputs; vague intents lead to vague answers.

Operationally, I turn these three elements into reusable prompt templates, and I track their performance with Agent Analytics. I review traces to see which inputs drive the best recommendations, and I refine prompts the same way I iterate on product copy. For LLMs for product managers, this is the craft: small, testable improvements that compound into outsized impact.

Here’s a quick example. When I needed to lift user activation, I asked for a prioritized set of friction points blocking first-value within 24 hours for new self-serve accounts, based on last month’s data. I defined activation as completing event X within Y hours, asked the agent to analyze top drop-offs in the funnel, and requested an action plan with two experiment ideas and success thresholds. The response mapped behaviors to interventions, connected to retention analysis, and gave me a prompt engineering snippet for the onboarding nudge we shipped the same week.

If your AI workflow still starts with “What does the data say?”, you’ll keep getting broad narratives. Start with outcomes, sharpen the context, and specify the decision you will make. That’s how Amplitude analytics, paired with agentic AI, stops being interesting and starts being indispensable.

Inspired by this post on Amplitude – Perspectives.

May 19, 2026
Level Up: May 26 Claude Code Show & Tell + Final Product Discovery Fundamentals Cohort

I’m excited to share two opportunities this season to uplevel your craft, connect with peers, and leave with practical, repeatable techniques you can apply immediately to your product work.

We will be doing another round of Claude Code: Show and Tell on May 26th at 9am PDT. These community-driven sessions are hands-on and fast-paced—we swap proven workflows, compare prompts, and pressure-test approaches together. You’ll see how product teams are operationalizing AI workflows in real contexts and walk away with ideas you can adapt for your own roadmap and experimentation pipeline. Invites will go out to Supporting Members and CDH Members tomorrow. If you'd like to join us, keep an eye on your inbox for the invite.

I love these Show & Tell sessions because they translate tacit knowledge into clear, reusable playbooks. Whether you’re refining evaluation loops for LLMs, streamlining discovery synthesis, or standardizing prompts for consistency, the shared rigor and camaraderie make it a high-signal hour for any product leader invested in AI workflows.

I also want to share that I'll be teaching our June 4th – July 9th cohort of Product Discovery Fundamentals. This is the last time I'll be teaching this cohort in its current format. If you've been thinking of enrolling in this program, and want to take it with me, this is your last chance. Register here.

Across this cohort, we’ll practice continuous discovery habits—framing opportunities, tightening assumptions, running lean experiments, and aligning product trios on evidence-backed decisions. If you want a rigorous, repeatable system for turning customer insight into confident prioritization and compelling product strategy, I’d be thrilled to have you in the room.

Inspired by this post on Product Talk.

May 18, 2026
Stop Chasing Churn: How Behavioral Analytics Powers Proactive Retention in SaaS

Churn is a lagging indicator—and by the time I see it in a dashboard, the moment to change a customer’s mind has usually passed. At HighLevel, I’ve learned that durable retention starts long before a cancellation ticket, with product-led growth habits, customer success partnerships, and a clear view of user behavior that flags risk early and often.

Stop chasing SaaS churn after it happens. Learn how proactive product and service experiences, powered by behavioral analytics, help reduce churn before users leave.

My operating model is simple: treat retention as a design problem, not a rescue mission. I anchor our strategy in behavioral analytics and retention analysis, translating leading indicators—activation milestones, time-to-first-value, depth of feature adoption, and expansion intent—into outcomes like Net Recurring Revenue (NRR) and cohort-based retention. When these inputs move in the right direction, churn becomes the exception, not the trend.

To get there, I start with rigorous journey mapping and continuous discovery. We define the exact “aha” moments that signal value realization, instrument events across the funnel, and segment cohorts by persona, plan, and use case. Tools in a unified analytics platform (e.g., Amplitude analytics or Pendo) help us pinpoint where engagement decays, which features predict stickiness, and which friction points block activation. This evidence replaces hunches and lets us prioritize the highest-leverage work.

From those signals, I build a transparent risk score that anyone can use. It blends usage momentum (DAU/WAU), core feature frequency, anomaly detection on key behaviors, billing and payment health, and support sentiment. When the score crosses a threshold, we trigger plays—inside the product and through customer success—so we’re helping users before they drift, not pleading after they’ve left.

On the product side, I favor lightweight, contextual interventions: in-app guides tailored to stalled tasks, checklists that shorten time-to-value, adaptive product tours, and tooltip design that clarifies the next best action. We A/B test these experiences with a clear minimum detectable effect (MDE), watching both local metrics (feature completion, error rate) and global metrics (activation, retention). The goal is precision—right nudge, right user, right moment—without adding cognitive load.

On the service side, we run consultative support and customer success plays keyed to the same behavioral triggers. A sudden drop in core usage may prompt a quick diagnostic call; repeated failed integrations can route to solutions engineering; stalled accounts get value reviews or QBRs focused on outcomes, not feature checklists. Because product and service draw from the same data, customers experience a single, coherent journey.

Proactive retention also depends on smart packaging and pricing. When value metrics mirror how customers win, plan boundaries reinforce the right behaviors and reduce “silent churn” caused by misaligned tiers. Outcome-based pricing and clear upgrade paths can turn potential risk into expansion rather than attrition.

Operationally, I keep a weekly retention review with product trios and customer success leaders. We walk driver trees from inputs (activation, engagement depth, support friction) to outputs (NRR, churn), review session replay where confusion spikes, and commit to small, measurable experiments. This cadence compounds learning and keeps us honest about what’s moving the needle.

If you’re starting fresh, begin with four moves: define an activation milestone tied to value; instrument the few events that prove users are on track; build a basic risk score from those events; and craft three plays—one in-product, one lifecycle message, one success outreach—triggered by that score. You’ll create a flywheel where insights power interventions, and interventions feed better insights.

Churn will always exist, but it doesn’t have to be a cliff. With behavioral analytics guiding both product and service experiences, we can make retention the natural outcome of how we build, communicate, and support—long before a customer ever thinks about leaving.

Inspired by this post on Amplitude – Perspectives.

May 18, 2026
Turn Customer Data into Real Experiences: What I Look For in a Brussels Strategy Partner

I focus every day on turning raw customer signals into meaningful product experiences that create measurable outcomes. Human37 is a Brussels-based customer data strategy agency helping organizations turn data into real customer experiences. That statement sets a useful standard for the kind of partner I look for: one that helps us move beyond reports and into shipped value customers can feel.

What matters most to me is the bridge between discovery and delivery—how insights inform product strategy and roadmaps without slowing execution. The strongest partners operationalize behavioral analytics within a unified analytics platform, connect qualitative learning with quantitative evidence, and make journey mapping a living artifact rather than a slide. Tools like Amplitude analytics can accelerate this work, but the real differentiator is the operating model that converts data into decisions and decisions into outcomes.

When I evaluate a customer data strategy partner, I look for five things: rigorous data governance and privacy-by-design; clean event taxonomy and robust identity resolution; clear experimentation workflows that tie to activation and retention analysis; practical enablement for product teams (not just analysts); and a bias for product-led growth rooted in real user behavior. If a partner can’t articulate how insights ladder to user activation and long-term value, they’re not ready to guide the roadmap.

Here’s how I sequence the work to turn signals into experiences: first, define the outcomes that matter and the driver trees behind them; second, instrument events and unify identities to power trustworthy behavioral analytics; third, map critical paths with journey mapping to expose friction and moments of delight; fourth, run focused experiments linked to product strategy, not vanity metrics; finally, scale what works with in-product experiences and lifecycle messaging that compounds retention.

The payoff is speed and clarity: faster time-to-insight, more confident bets, and fewer handoffs between data teams and product builders. If you’re exploring European partners, a Brussels-based agency with a sharp customer data strategy capability can help you move from analysis to action. The litmus test is simple—can they help your team ship experiences that customers notice and your metrics confirm?

Inspired by this post on Amplitude – Perspectives.

May 18, 2026
Proven 3-Step Playbook to Quantify AI Agent ROI: Boost Revenue, Cut Costs, Reduce Risk

AI agents are only as valuable as the measurable outcomes they deliver. In my role leading product strategy at HighLevel, I’ve learned that the fastest way to earn executive trust is to translate agent performance into clear revenue impact, cost savings, and risk reduction. The challenge isn’t enthusiasm for AI; it’s creating a disciplined, repeatable way to prove business value.

Here’s the three-step playbook my teams and I use to quantify the value of agentic AI, align stakeholders, and scale what works.

Step 1 — Define value outcomes and success criteria. Start with a driver tree that ties agent outcomes to company-level goals. For revenue, target conversion lift, average order value, and expansion (e.g., trial-to-paid, self-serve upsell). For cost, focus on containment/deflection rate, reduced handle time, and lower cost to serve. For risk, measure error rates, hallucinations, security/policy violations, and customer complaint rate. Convert these into outcomes vs output OKRs, set baselines, and pre-commit to thresholds for launch, scale, or rollback. This ensures the team is accountable to business KPIs, not vanity metrics.

Step 2 — Instrument comprehensively and establish baselines. Instrument the full journey: prompts, responses, human-in-the-loop events, escalations, feedback, and downstream conversions. Capture both leading indicators (time-to-first-value, containment rate, self-serve completion) and lagging outcomes (NRR, churn, LTV/CAC). Use behavioral analytics, session replay, product tours, and in-app guides to contextualize what users do before and after agent interactions. Baselines matter—freeze a control period so improvements are truly incremental.

Increase revenue, cut costs, and reduce risk with Pendo’s Software Experience Management platform. Optimize the entire software experience to drive adoption and improve engagement.

Step 3 — Experiment, attribute, and risk-adjust. Treat every agent capability like a hypothesis. Run A/B tests or holdouts with a precomputed minimum detectable effect so you can ship confidently. Attribute outcomes to the agent by linking events to conversions and support deflection, and calculate ROI as (incremental revenue + cost avoided – total operating cost, including model/API, labeling, and oversight). Apply AI risk management by tracking false positives/negatives, escalation rate, and policy breaches; adjust ROI with a risk score so the “cheapest” agent isn’t inadvertently the riskiest. This is eval-driven development in practice: define success, measure, iterate.

Operationalizing the playbook requires crisp reporting. Stand up Agent Analytics dashboards in your unified analytics platform that roll up per-agent KPIs, funnel performance, cohort trends, and experiment results. Review them in QBRs and with frontline teams to connect numbers to lived customer experience. When metrics improve, amplify with product-led growth motions—targeted in-app guides and lifecycle nudges to get more users into high-value agent flows.

What does this look like in the real world? Early on, we celebrated “tickets deflected” and missed that some conversations quietly increased churn risk. After we adopted this three-step approach, we saw the full picture: a modest dip in deflection quality was offset by a larger lift in expansion revenue and a meaningful drop in time-to-resolution. The risk-adjusted ROI was unambiguous, and the CFO greenlit broader rollout.

If you’re building or scaling AI agents, anchor on outcomes, instrument ruthlessly, and insist on experimentation. With the right measurement discipline, you’ll know exactly which agents deserve more investment, which need redesign, and which should be retired. The result is a portfolio of agents that reliably drive adoption, engagement, and durable business value.

Inspired by this post on Pendo – Best Practices.

May 15, 2026
Amplitude Heatmaps Rebuilt: Rock-Solid Screenshots, Precise Placement, Smarter Scrollmaps

When a platform as foundational as Amplitude refreshes a core feature, I pay close attention. Heatmaps are where qualitative intuition meets quantitative scale, and reliability and precision determine whether teams trust what they see. The latest update meaningfully raises the bar for product analytics teams who depend on crisp visual evidence to guide experiments, diagnose friction, and accelerate product-led growth.

Here’s the essence of the change, in Amplitude’s own terms: “more reliable screenshot capture, selector-based placement, automatic device detection, and a redesigned scrollmap.” That combination tackles the two biggest historical pain points with heatmaps—stability in dynamic interfaces and confidence that clicks are attributed to the right UI elements across devices and layouts.

First, more reliable screenshot capture improves the fidelity of what I’m analyzing. When screenshots consistently mirror the live UI state, I can compare sessions across releases without worrying about rendering quirks or timing artifacts. That boosts trust in behavioral analytics, shortens feedback loops with engineering, and makes heatmaps a dependable companion to A/B testing and session replay.

Second, selector-based placement is a pragmatic step toward precision. In modern, componentized front ends where elements shift with personalization, localization, or responsive design, stable selectors dramatically reduce misattributed interactions. In practice, this means cleaner insights for funnel drop-off analysis, clearer readouts for micro-conversions (e.g., CTA vs. secondary actions), and more confident iteration on UX copy and layout—without constant re-instrumentation.

Third, automatic device detection aligns insights with the actual context of use. Patterns on mobile often diverge from desktop, and blending them can mask critical signals. Accurate device-specific readouts help me tailor experiments, refine activation paths, and decide when to prioritize mobile-first optimizations versus desktop refinements.

Finally, the redesigned scrollmap matters because attention is a finite resource. Knowing how far users scroll—and where they pause—helps me position value propositions, trust elements, and calls to action where they’ll be seen. Combining scroll insights with session replay and event data gives me a sharper picture of what’s above the fold, what’s ignored, and where copy or layout needs a rethink.

How I’d operationalize this update: validate key selectors with engineering and design for critical templates; compare pre- and post-update heatmaps to establish new baselines; segment by device to isolate diverging behaviors; map scroll depth to conversion micro-moments; and feed prioritized findings into backlog grooming and product roadmapping. This keeps heatmaps directly connected to outcomes rather than just interesting visuals.

Bottom line: these improvements make heatmaps a more trustworthy lens for discovery and optimization. With sturdier screenshot capture, precise selector-based placement, automatic device detection, and a redesigned scrollmap, I can move faster from observation to decision—reducing analysis ambiguity, tightening experiment cycles, and turning behavioral analytics into measurable product strategy.

Inspired by this post on Amplitude – Best Practices.

May 15, 2026
Scaling AI-Powered Customer Experience: Cross-Org Playbooks to Drive Product Impact

Customer experience is now a core product strategy lever, not a downstream support function. In my work leading product teams, I’ve seen that the fastest path to durable growth is aligning CX strategy with product, data, and go-to-market—especially when we’re building AI-powered solutions that must scale responsibly.

Amanda Sime is the Customer Experience Strategy Lead at Amplitude. She shapes CX strategy and partners across orgs to design and scale AI-powered solutions.

That mandate captures what high-performing organizations are doing well: connecting behavioral analytics, product discovery, and customer success into a unified operating system. When CX leaders partner tightly with product and data teams, we turn insights into action—using Amplitude analytics to identify friction, journey mapping to prioritize moments that matter, and a unified analytics platform to close the loop from hypothesis to measurable outcomes.

Practically, the playbook looks like this in my teams: start with rigorous journey mapping and retention analysis to pinpoint where value realization lags; run targeted A/B testing to validate interventions; and deploy in-app guides and product tours to accelerate user activation. Layer in session replay and behavioral analytics to understand intent, then operationalize learnings into repeatable workflows that improve time-to-value and customer success. This is how we make product-led growth concrete rather than aspirational.

AI Strategy adds both leverage and responsibility. We design AI-powered experiences with privacy-by-design, clear value propositions, and eval-driven development so we can measure lift, not just ship features. Cross-functional partners—from support to solutions engineering—become critical here, ensuring we scale responsibly while improving the signal-to-noise ratio of feedback flowing back to product roadmapping.

The outcome I aim for is simple: faster cycles from insight to impact. With tight cross-org alignment, a shared metrics framework, and disciplined experimentation, we can transform CX from reactive problem-solving into a proactive growth engine. If your team is ready to operationalize this approach, start with one high-friction journey, build a sharp driver tree, and let data, not opinions, guide the next iteration.

Inspired by this post on Amplitude – Best Practices.

May 15, 2026
Operator Unleashed: The AI Agent That Transforms Customer Ops across Fin and Intercom

Today I’m introducing Operator, an Agent that works across both Fin and the Intercom helpdesk to help you manage your customer operations.

In practical terms, Operator manages help content, builds automation, does the ongoing work that determines how well Fin performs, and runs the operational work your human team doesn’t have time for. That combination is precisely what modern support teams need to move from reactive firefighting to proactive, consultative support.

Why does this matter? Running a customer operation means managing AI and humans simultaneously, and doing this well requires more capacity than most teams realistically have. I’ve felt that strain firsthand—competing priorities, constant context switching, and a never-ending queue that blurs strategic focus.

On the AI side, Fin’s performance is largely influenced by what surrounds it: the accuracy of your help content, the quality of your Fin configuration, and how well you understand what’s working and why. When product teams ship daily, keeping your help center current means finding every affected article before customers notice the gaps. When Fin gets a conversation wrong, diagnosing it requires reading through what happened, identifying the root cause at the configuration level, making the fix, and verifying it worked. Analyzing why your resolution rate dropped means pulling conversations, finding patterns, and tracing the cause back to something actionable. And beyond individual fixes, there’s the ongoing question of what to automate next – what your human reps are still handling repetitively, whether it’s worth building a Procedure for it, and how to test it before it goes live.

On the human side, the demands are just as continuous. When an incident hits, someone needs to identify every affected customer, draft the right response, and send it before the problem compounds. Team leads need visibility into rep performance across hundreds of conversations to coach effectively and prep for 1:1s. Reps need to know what to prioritize without spending the first part of their day figuring it out. In fast-moving environments, that operational overhead wastes energy you should be investing in better customer outcomes.

Meet Operator, the agent that explains your customer conversations. This Synthesia testimonial shows how simply asking Operator reveals what happened and makes refining Fin faster for support and enablement teams.

Too often, the work outpaces what teams can manage, so it happens reactively, or not at all. Operator was built to change that, giving teams a new way to understand, manage, and improve their customer operations. Here’s how I put Operator to work across AI workflows and human-led processes.

First, I use Operator to ask my data anything. Your support operation generates more useful data than most teams have time to process. Operator gives you direct access to it. You can ask it any question about what’s happening in your operation (why a metric changed, what’s driving escalations, how the team performed last week) and it returns structured answers with charts, breakdowns, and the ability to dig further. It analyzes samples of real conversations on the fly to surface patterns and identify root causes. If your head of product wants to know what customers are saying about a new release, you can ask Operator rather than spending half a day pulling a report together. It also works across your entire operation, analyzing Fin’s performance, your human reps’ performance, and customer sentiment.

Crucially, I don’t start from scratch every time. Give Operator ongoing work, like analyzing your automation rate every Monday, flagging anything that needs attention, and posting the report in your Fin workspace. It’ll run the analysis, write the report, and deliver it without you having to go looking for it. That’s the kind of agentic AI leverage that compounds week after week.

Second, I keep the knowledge base current without writing a single article. Your knowledge base is only as useful as it is accurate. When product teams ship fast, keeping pace with content updates is a substantial, ongoing job. Give Operator a brief about anything, from a new feature or policy change to release notes, and it finds every article in your help center that needs updating, drafts the edits in your tone of voice and style, identifies content gaps, and drafts new articles to fill them. It even handles localized versions. Every change is formatted as a proposal (Operator’s version of a pull request) for you to review, edit, and approve before anything goes live. It feels like adding several knowledge managers to the team overnight, without the ramp time.

See why teams choose Fin Operator for customer operations: accurate analysis, trend insights, and conversation debugging—going beyond basic LLM connectors. A Raylo testimonial spotlights daily, real-world impact.

Third, I build, test, and ship improvements to Fin directly through Operator. When Fin gets a conversation wrong because of a content gap or misconfigured rule, Operator can debug it by reading through the conversation, identifying what caused the problem, proposing a fix, and running simulation tests to verify it before you approve. You see what changed and why before anything goes live. Beyond debugging, Operator has deep knowledge of every Fin feature and capability, so you can ask it directly to help you configure whatever you need. If you need a Procedure for a specific query type, describe the outcome you want and Operator builds it, including triggers, multi-step instructions, edge case handling, and a simulation test, all from a single prompt. The same applies to configuring Guidance rules, data connectors, monitors, and workflows. You don’t need to know which feature solves your problem or how to configure it; you just describe what you want.

For teams looking to increase their overall automation rate, Operator can handle that strategically too. Ask it to analyze where your biggest automation opportunities are and it surfaces them by volume, along with an estimate of the weekly team time each one is consuming. Pick one, and it builds the solution for you to approve. That’s consultative support, productized.

Finally, I use Operator to effortlessly manage the human side of support. When an incident hits, Operator identifies every affected conversation, drafts targeted responses, and sends them proactively, turning what would normally be hours of reactive triage into minutes of review and approval. For ongoing management, a team lead prepping for 1:1s can ask Operator to pull each rep’s metrics, flag outliers, and surface what’s worth digging into. A rep coming back from a meeting can ask what to focus on next and get a prioritized queue based on urgency, customer value, and wait time. And because Operator sees patterns across everything your human team is handling, it can surface the conversations they’re still resolving manually, flagging your next automation opportunity before you’ve had time to go looking for it.

Here’s why this works. Operator isn’t a general-purpose AI model given access to your data. It’s built on a library of purpose-built tools that encode expertise specific to support operations, like how to pick the right attributes for a given analysis, search a knowledge base semantically, debug Fin’s reasoning in a specific conversation, or write and test a Procedure that will actually work. That specialized toolkit is what makes its recommendations trustworthy and its execution reliable.

Elevate customer service with Operator. The bold headline and vivid knot logo introduce a modern AI platform that streamlines workflows, speeds resolutions, and scales support operations without extra headcount.

The proposal (pull request) system makes this possible. When Operator updates content, adjusts configuration, or modifies how Fin behaves, it creates a proposal – a structured diff of what’s changing and why. You review it, edit if needed, and approve before it takes effect. Operator does the cognitive work; the human stays in control of what goes live.

More than 200 early users are already trying Operator, and every one of them is finding new use cases. It’s a genuine step change in capability, and I expect it will change the way support teams run their operation. We’re working towards a vision of Operator being increasingly agentic, expanding across every new role Fin takes on.

Operator is available in early access now. If you’re ready to transform your customer operations across Fin and the Intercom helpdesk with agentic AI, start here: https://fin.ai/operator.

Inspired by this post on The Intercom Blog.

May 14, 2026
Our Operating Model Is the Product—Why We Built Product Partners to Accelerate Outcomes

I’ve learned that customers don’t just buy features—they buy the way we discover, decide, build, ship, and support. In other words, the operating model is the product. That realization has shaped how my team and I at HighLevel translate product strategy into tangible, repeatable outcomes that show up in quality, reliability, onboarding, and consultative support every single day.

We created Product Partners to codify that operating model and scale it with discipline. It’s a blueprint and operating rhythm that unifies product strategy with go-to-market strategy, customer success, and solutions engineering—so empowered product teams can move faster without sacrificing clarity, governance, or customer trust.

First, we anchored on continuous discovery. Product trios work shoulder-to-shoulder with customer-facing teams to run customer interviews, journey mapping, and A/B testing, then validate insights with session replay and behavioral analytics. We use driver trees and opportunity solution trees to connect problems to outcomes, ensuring prioritization is evidence-based and aligned to product-market fit—not just output.

Second, we elevated delivery excellence. Our practices emphasize CI/CD, feature flags, observability, SRE-informed incident management, and DORA metrics to shorten feedback loops while raising the bar on stability. Privacy-by-design, data governance, and regulatory compliance are built into our workflows, and we make deliberate build vs buy decisions to protect platform scalability and long-term velocity.

Third, we integrated go-to-market alignment from day one. Solutions engineering and customer success shape requirements early, so launches include in-app guides, product tours, onboarding paths, and consultative support that accelerate user activation. We tie outcomes vs output OKRs to stakeholder management rituals, ensuring sales-led and product-led growth motions reinforce each other instead of competing for focus.

Finally, we closed the loop with a unified analytics platform. Activation, retention analysis, and Net Recurring Revenue (NRR) sit alongside qualitative signals from customer interviews and support. This single source of truth helps us refine product positioning, sharpen value propositions, and improve roadmapping and sprint planning with clear, testable hypotheses.

What does this mean for our partners and customers? Faster time-to-value, fewer handoffs, clearer expectations, and a shared lens on the metrics that matter. Product Partners isn’t a side program; it’s how we operationalize trust—through transparency, consistent rituals, and a bias toward learning that compounds.

If this resonates, you’ll feel it in how we discover, build, and support together. I’ll continue to share our playbooks—covering continuous discovery, onboarding, and outcome-based planning—so we can keep raising the standard for product management leadership and product-led growth, one operating rhythm at a time.

Inspired by this post on Product School.

May 14, 2026
How AI-Designed Enzymes and Agentic AI Could Finally Make Plastic Truly Recyclable

Only 10% of the plastic we manufacture gets recycled. For a century, we have relied on mechanical and chemical methods that were never designed to close the loop. As a product leader, I look for step-change technologies that break through entrenched ceilings, and biology—specifically engineered enzymes—has emerged as that missing piece.

Recently, I dug into the work of Rhea's Factory and spoke with their founders, Arzu Sandıkçı (co-founder and CEO) and Mert Topcu (co-founder). Arzu brings deep expertise in molecular biology and enzyme engineering. Mert brings 20 years in tech, including a decade at Google as a product manager. Their combined perspective—domain science plus product rigor—shows up in every design choice.

Rhea's Factory has built an AI platform that uses protein language models, multi-step agentic pipelines, and proprietary wet lab data to design novel enzymes that deconstruct plastic polymers into their original monomers—selectively, at low temperatures, and at industrial scale. That stack matters: it layers foundation models with domain-specific constraints and real-world data to systematically explore, evaluate, and scale candidates.

Here’s the crux: traditional recycling mostly just chops polymer chains into shorter fragments. Enzymatic recycling, by contrast, breaks plastic all the way back to its original monomers. Think of a necklace and pearls analogy—mechanical methods snip the chain; enzymes cleanly return the pearls. The result is true circularity: you can remake high-quality plastic without downcycling.

Selectivity is the superpower. Enzymes can target specific plastic types even in mixed waste streams, operating at low temperatures in a controlled, low energy reactor process. That combination of precision and energy efficiency is why this approach can be both greener and economically competitive.

The field accelerated after the discovery of a plastic-eating bacteria in Japan, which opened the door to enzymatic recycling. Advances in protein structure prediction—“AlphaFold” and the Nobel Prize in Chemistry—transformed what’s possible in enzyme engineering, and created space for AI-native design loops to flourish.

On the AI side, the team evolved from a human-orchestrated pipeline to an agentic AI scientist. Problem statements serve as inputs, multi-step protein generation builds on foundation models, and guardrails at each pipeline step keep the AI pointed in the right direction without limiting exploration. It’s a textbook example of agentic AI applied to a highly constrained, safety-critical domain.

Crucially, wet lab feedback closes the loop. Why wet lab data—even just hundreds of proprietary data points—can be enough to train a powerful domain-specific prediction model is a reminder that quality and relevance can trump sheer volume when you’re operating in a narrow, high-signal domain. The team measures success in the lab first, then scales what works.

I appreciated their take on exploration: there are moments when Mert sometimes wants the model to hallucinate. Running high temperature settings helps explore the full enzyme design space, and the guardrails ensure those forays remain productive rather than random. In other words, controlled creativity beats blind search.

The business constraint is unambiguous: enzymatic recycling must compete economically with cheap, oil-based plastic production. That framing forces disciplined choices around energy use, throughput, and yield—factors that directly determine unit economics and the path to industrial reality and cost parity.

What’s next is equally compelling: a process agent to optimize end-to-end system performance, a 5,000-ton demo plant in California to validate scale, and enzymes for new plastic types. I’m especially intrigued by enzyme blends for mixed plastics and the practical insight into why clamshells aren’t recyclable—precisely the messy corner cases that decide whether circularity works outside the lab.

From a product management lens, several patterns stand out: define clear problem statements as inputs to the agentic orchestration; use eval-driven development to enforce stage-by-stage quality; build a proprietary data moat with wet lab results; and tie milestones to industrial metrics (conversion, selectivity, energy per ton) rather than vanity outputs. This is AI Strategy in action—aligning model capability, data leverage, and operational design to deliver outcomes, not just demos.

Most of all, the ambition to explore an enzyme design space that “makes everything nature has ever evolved look like a tiny dot” captures the promise of this approach. Pairing agentic AI with rigorous lab validation doesn’t just make plastic circularity plausible—it makes it programmable.

Inspired by this post on Product Talk.

May 14, 2026
No More Accidental Agents: How We Engineered Global Agent’s Helpful, Curious Personality

Most teams ship AI agent personalities by accident—emergent quirks, brittle prompts, and uneven behavior. We refused to let that happen. From day one, we treated personality as a first-class product surface, one that should be designed, instrumented, and iterated with the same rigor as any core capability.

Learn how we designed Global Agent’s personality and fine-tuned its inquisitiveness and helpfulness using Agent Analytics.

In my role leading product at HighLevel, Inc., I framed our approach around agentic AI and conversation design: personality is not “flavor text”; it is the control system for how an agent interprets context, asks questions, and decides when to act. Our product strategy prioritized clarity, empathy, and consistency—so the agent would be curious enough to resolve ambiguity without becoming interrogatory, and helpful enough to move work forward without overstepping.

We made that intent measurable. Using behavioral analytics, we defined operational signals such as clarification-question rate, resolution-path efficiency, and escalation quality. We combined eval-driven development with targeted A/B testing to compare prompt patterns and tool strategies, ensuring each change had a clear hypothesis and measurable outcome.

To calibrate inquisitiveness, we mapped decision points where the agent should ask follow-ups versus proceed autonomously. Prompt engineering codified those thresholds, while a retrieval-first pipeline reduced unnecessary questions by improving context completeness up front. When the agent did ask, we constrained tone and cadence to keep queries concise, respectful, and progress-oriented.

To enhance helpfulness, we prioritized precise action-taking and unambiguous guidance. Context window management preserved relevant facts without diluting intent, and guardrails aligned with AI risk management principles ensured the agent stayed within policy, privacy, and compliance boundaries. The result was an assistant that resolved more tasks end-to-end, with fewer stalls and clearer handoffs when human help was warranted.

Agent Analytics became our nervous system. We instrumented every dialog turn to attribute outcomes to design choices, then used driver trees to connect micro-behaviors to macro results like time-to-resolution and customer satisfaction. This closed-loop view let us ship confidently, knowing which levers improved helpfulness, which sharpened curiosity, and which merely added noise.

Process mattered as much as tooling. Product trios ran continuous discovery with customers to surface edge cases—ambiguous intents, multi-intent turns, and sensitive scenarios—while our engineering partners operationalized experiments with clean rollback paths. We favored small, testable changes over sweeping rewrites, building momentum and trust with each iteration.

The payoff is a personality that feels consistent across use cases: curious when clarity is missing, decisive when action is obvious, and transparent when limits are reached. Users experience fewer dead ends, faster resolutions, and a brand voice that shows up the same way every time—because it was defined, measured, and improved on purpose.

If you’re building agentic AI, don’t leave personality to chance. Treat it like a product: set clear outcomes, instrument deeply with Agent Analytics, and iterate with eval-driven development and A/B testing. That’s how curiosity becomes a feature, helpfulness becomes a habit, and your agent becomes reliably, intentionally excellent.

Inspired by this post on Amplitude – Best Practices.

May 13, 2026