I build and scale analytics platforms with a product mindset, and the work starts with the "middleware and compute systems that power analytics at scale." In platforms like Amplitude analytics and other unified analytics platform architectures, that foundation is what makes everything else possible.
Day to day, I oversee the "APIs behind charts, cohorts, and metrics—driving performance, reliability, and platform scalability." When those APIs are fast and resilient, every product team—from growth to customer success—can trust the insights they use to ship, learn, and iterate.
From an engineering leadership standpoint, I partner closely with SRE to define SLOs and error budgets, wire CI/CD pipelines for safe deploys, and track DORA metrics so we improve speed without compromising quality. This combination reduces incident management toil and shortens MTTR while keeping data freshness and query latency within strict thresholds.
From a product management leadership lens, the goal is clarity: crisp APIs, predictable contracts, and transparent stakeholder management across data, engineering, and GTM teams. That alignment empowers product teams with reliable cohorts and metrics, accelerates experimentation, and de-risks roadmaps.
If you’re scaling analytics, invest first in the platform layer: middleware and compute, schema governance, caching strategies, and cost-aware compute. Do that well, and the visible experience—charts, cohorts, and metrics—feels effortless, even as you grow to serve billions of events with confidence.
Inspired by this post on Amplitude – Best Practices.
I’ve seen time and again that when content is as data-driven as the product, adoption accelerates. Partnering closely with a data-driven content marketing manager and Amplitude power user, I watched how precise storytelling—grounded in Amplitude analytics—can unlock user activation and retention at scale.
Previously, she managed all customer identity content at Okta.
We started by translating product strategy into measurable moments in the customer journey: activation events, aha moments, and retention cohorts. Using Amplitude analytics, we built funnels and segmentations to isolate high-signal behaviors, ran A/B testing on messaging and in-app guides, and turned retention analysis into an editorial roadmap that spoke to specific use cases and jobs-to-be-done. This unified analytics platform approach ensured the content engine and product telemetry were speaking the same language.
From there, we aligned go-to-market strategy with lifecycle communication—product tours, onboarding sequences, and contextual education that made the value proposition unmistakable. Through continuous discovery and product discovery rituals with product trios, we iterated messaging to sharpen product positioning and reduce time-to-value. The result was content that didn’t just describe features—it moved outcomes.
To keep us honest, we instrumented outcomes vs output OKRs tied to activation rate, expansion intent, and long-term retention. We watched leading indicators (setup completion, power-user actions) roll up into lagging results (weekly active usage and cohort retention), and refined our bets in tight feedback loops.
If you’re building a product-led growth motion, pair your roadmap with a content leader who treats telemetry as a design material. When an Amplitude power user brings the same rigor to narrative that engineers bring to code, the compounding effect on adoption, engagement, and retention is unmistakable.
Inspired by this post on Amplitude – Perspectives.
I rely on disciplined product benchmarks to turn strategic intent into measurable progress. In B2B technology, benchmarks give me and my team the clarity to prioritize what truly matters, align executives around shared outcomes, and course-correct before small gaps become growth-stalling problems.
Discover exclusive data and strategies from our Product Benchmark Report. Compare the B2B technology industry’s performance across key product metrics.
When I assess product health across a portfolio, I start with a core set of product benchmarks: activation rate, onboarding completion, time-to-first-value, weekly and monthly active accounts, feature adoption, cohort-based retention, expansion and contraction revenue, and support deflection. Together, these metrics show where the product creates value, where users get stuck, and which levers most efficiently drive product-led growth.
Benchmarks are only powerful if they inspire action. I instrument reliable analytics (Amplitude analytics) to capture consistent event data, pair that with in-app guides and product tours (Pendo, Intercom) to test friction hypotheses, and run A/B testing to validate changes with statistical rigor. From there, I translate insights into outcomes-based OKRs, so roadmapping and sprint planning focus on the few bets most likely to move our key product metrics.
I’ve seen this approach pay off repeatedly. When peer benchmarks revealed our user activation lagged, we simplified onboarding, clarified value propositions with sharper UX writing, and launched targeted in-app nudges. We didn’t just ship features—we improved the experience against a clear yardstick, and the subsequent lift in activation and early retention validated the strategy.
Cross-functional alignment is critical. I partner with customer success to interpret retention analysis by segment, with marketing to ensure messaging supports time-to-value, and with engineering to keep quality and reliability high. While product metrics lead, I also keep an eye on complementary signals like incident management and DORA metrics to ensure we’re not trading speed for stability.
If you’re leading a product organization, use benchmarks to calibrate ambition and create focus. Start by identifying the one or two metrics most predictive of long-term retention, set peer-informed targets, and iterate with continuous discovery. The result is a product strategy that is evidence-based, resilient to opinion cycles, and capable of compounding gains over time.
Ultimately, benchmarks aren’t about vanity; they are about velocity. With a shared view of where you stand against the B2B technology industry, you can make sharper bets, accelerate product-market fit, and turn your roadmap into a reliable engine for growth and customer value.
Inspired by this post on Amplitude – Perspectives.
I’m constantly asked by SMB owners: What if your small business could have a full marketing team—automated content calendars, customer segmentation, and channel-specific posts—without the headcount? That question is no longer hypothetical; it’s precisely the promise behind Mowie, and the way they got there is a masterclass in practical AI product development.
I recently listened to Chris O'Connor (CEO) and Jessica Valenzuela (Co-Founder) of Mowie, an AI marketing platform built for small and medium-sized businesses in restaurants, retail, and e-commerce. Their story starts with a concierge marketing service—doing the work by hand for overwhelmed owners—and evolves into a fully automated AI product.
They walk through their "document hierarchy" approach: how Mowie crawls the web to build a "dossier" about each business, infers customer segments and marketing pillars, and generates quarterly content calendars with channel-specific posts. As a product leader, this is the kind of retrieval-first pipeline that consistently outperforms naive prompt chaining because it builds durable context before generation.
They also unpack the technical challenges of structuring unstructured data and the evolution from rigid schemas to loosely structured markdown. In my experience with LLMs for product managers, markdown becomes a flexible intermediate representation that’s easy to diff, trace, and feed back into models without brittle parsing.
Equally important, they use customer feedback—from calendar approvals to regeneration requests—as their primary evaluation signal. That’s eval-driven development in practice: close the loop with lightweight evals that reflect genuine user intent, not proxy metrics.
The planning model is elegant: the three mini-calendars—public events, business-specific events, and recommended campaigns—roll up into a coherent plan that eliminates the blank-page problem and enables steady, predictable execution.
Crucially, they’re building traceability so customers can see which context documents influenced their content. This kind of transparency increases trust, accelerates edits, and supports governance in regulated categories where auditability matters.
Onboarding and data collection stay pragmatic: let the system crawl first, ask humans only for deltas, and progressively profile over time. It’s a pattern I advocate in continuous discovery and AI workflows—keep humans in the loop without overwhelming them, and make the right action the easy action.
Early on, they used Simon Sinek's Golden Circle framework to validate demand and sharpen messaging. Framing the "why" before the "what" helps teams maintain a crisp value proposition and tighten their go-to-market strategy.
Performance measurement goes beyond vanity metrics by connecting marketing performance back to point-of-sale data for attribution. The ability to tie campaigns to revenue events is the bridge from clever content to accountable outcomes.
What’s next is equally compelling: deeper attribution, omnichannel expansion, and digital out-of-home displays. For SMBs, that points to a unified analytics platform spanning email, social, and in-store touchpoints—exactly where modern marketing is headed.
My takeaways for builders: invest in a retrieval-first pipeline with a resilient document hierarchy; prefer loosely structured markdown over rigid JSON when dealing with messy inputs; design human-in-the-loop controls that double as evals; and always connect activity to business outcomes. That’s how you turn an idea into a repeatable system that scales.
If you want to explore further, start here: Mowie AI — AI marketing platform for SMBs. For early validation and storytelling, revisit Simon Sinek's Golden Circle.
I’ve spent countless cycles guiding teams through the maze of dashboards, SQL pulls, and ad‑hoc analyses—only to watch truly meaningful patterns emerge far too late. Automated insights are the next frontier in product analytics: a shift from manual exploration to AI that proactively surfaces what matters most. When we let the system do the heavy lifting, we accelerate discovery, reduce bias, and give product trios the clarity to act.
Finding causal connections in product data involves exhaustive searches and tests. We trained our AI to find “aha” moments in minutes instead of weeks.
Here’s what that means in practice for product management: the platform continuously scans events, cohorts, and segments; prioritizes signals linked to activation, conversion, and retention; and highlights likely causes behind meaningful movements in your core KPIs. Instead of sifting through endless funnels and cohorts, I get ranked hypotheses I can validate with targeted A/B testing and minimum detectable effect (MDE) guardrails.
This approach turns analytics into action. Automated insights reduce time-to-learning, tighten our discovery loops, and make continuous discovery tangible—especially when we’re aligning roadmaps, designing experiments, and refining onboarding. Whether you’re using tools like Amplitude analytics or instrumenting a unified analytics platform, the value is the same: faster, clearer paths to customer impact.
I’ve seen teams unlock retention analysis breakthroughs by spotting counterintuitive patterns—like a specific feature combination or an overlooked step in onboarding—well before they would have surfaced through manual analysis. With AI workflows scanning the noise and elevating the signal, we can focus on decisions: ship or iterate, scale or sunset, double down or pivot. That’s empowered product teams in action.
If you’re building for product-led growth, this is the leverage you’ve been waiting for. Automated insights transform how we prioritize, test, and communicate strategy—bringing us from gut feel and lagging indicators to explainable, causal narratives we can stand behind. The outcome is simple: more confident bets, less waste, and a faster path to durable product-market fit.
Inspired by this post on Amplitude – Best Practices.
I’ve been working to remove the friction between product questions and product answers. The most impactful step so far: connecting Amplitude analytics directly into ChatGPT via OpenAI’s MCP. This turns everyday conversations into decision-grade insights—no dashboards to hunt, no SQL to write, and no analytics queue to wait on.
Connect Amplitude data directly to the tools your team uses every day. OpenAI’s MCP connector eliminates traditional barriers to product data.
In practice, this means I can ask ChatGPT natural-language questions like, “Where are users dropping in our activation funnel this week?” or “Which cohorts are driving retention lift post-onboarding?” and get grounded answers from Amplitude—fast. It’s a step-change for product-led growth because the insights live where we already think and plan.
Here’s how I apply it day to day: I’ll prompt ChatGPT to compare week-over-week activation for new SMB signups across regions, diagnose drop-offs by step, and summarize A/B testing outcomes with guardrails like minimum detectable effect considerations. When we’re shaping strategy, I’ll pull a retention analysis and cohort breakdown to inform bet sizing and roadmap tradeoffs—all without pulling the team into a BI bottleneck.
Governance remains non-negotiable. I scope the MCP tools to a least-privilege data slice, apply privacy-by-design rules to exclude PII, and log every query for auditability. Clear data governance and AI risk management policies ensure we maintain trust while accelerating discovery. Tight context window management keeps prompts focused and reduces noise.
Operationally, the setup is straightforward: define the MCP tool spec for Amplitude, map canonical events and metrics (activation, retention, conversion, and product-qualified lead stages), and test with a retrieval-first pipeline so responses reliably cite the right source of truth. We standardize metric definitions across product, growth, and customer success to avoid semantic drift.
The impact on empowered product teams is immediate. Continuous discovery becomes a daily habit rather than a quarterly ritual; questions move from “I’ll get back to you” to “Let’s check right now.” For product managers working with LLMs, this is the connective tissue that makes ChatGPT a true ChatGPT connector for analytics—an on-demand, unified analytics platform that supports faster iteration and sharper decision-making.
If you’ve been waiting to make analytics truly ambient, this is the moment. Start small with a single funnel or cohort, validate governance, and expand to your core lifecycle metrics. The payoff is a shared understanding of what’s working, what’s not, and where to focus next—delivered in the flow of work.
Inspired by this post on Amplitude – Best Practices.
My writing process used to be messy. Even in my role leading product strategy, I’d start strong and then stall because I hadn’t clarified what I truly wanted to say.
I’d begin with a brain dump—everything swirling in my head. I’d try to shape it into an outline, lose patience, and just start writing. A few paragraphs later, I’d realize I didn’t know where I was going, stop, and return to the outline. It was a tortured loop between writing and structuring.
Now I do it differently. When I get stuck, I don’t start writing. I ask Claude for help.
Claude reviews my outline and helps me fill in gaps. It often suggests things that I don’t like. This is good. It helps me figure out the core of what I want to say. Instead of writing my way to what I think, I discuss my way to what I think.
Claude isn’t just a sounding board. I also use it to help me brainstorm headlines, explore outline alternatives, critique each section as I write, conduct supporting research, act as my thesaurus and dictionary, make SEO recommendations, and so much more. As a result, I am writing way more.
I didn’t design this workflow in one sitting. I built it iteratively, the same way I build products: by asking, "How can Claude help with this?" and evolving from there.
If you haven’t been following along, I’m deep in a series about Claude Code and how it helps me work better. Here’s what we’ve covered so far: Claude Code: What It Is, How It’s Different, and Why Non-Technical People Should Use It, Stop Repeating Yourself: Give Claude Code a Memory, How to Use Claude Code Safely: A Non-Technical Guide to Managing Risk, and How to Choose Which Tasks to Automate with AI (+50 Real Examples).
This week, I’m diving into how to design personal AI workflows. I’ll use my writing workflow to illustrate each step, and I encourage you to follow along with your own process so you end with something tangible.
Claude breaks down an AI workflow article and suggests three paywall points, weighing trade-offs to guide conversion strategy. A clear, structured example of planning content and automation steps with Claude Code.
Designing AI workflows looks a lot like designing product solutions. I lean on "discovery" habits—clarifying outcomes, mapping the journey, and testing assumptions—to make the work both reliable and repeatable.
This series is inspired by my personal usage of Claude Code. I have not received any compensation from Anthropic for writing this series. And you can trust that if that ever changes, I will disclose it. This is not only required by the FTC here in the US, but I strongly believe it is the right thing to do. You can count on me to do so.
First, I map out what I do to complete the task. Once you’ve identified the AI workflow you want to create, start by mapping exactly what you do when you do it yourself. If this feels hard, do the task a few more times and jot down each step as you go.
Here’s what I do when I write a blog post: I choose a topic; I write down everything I can think of related to that topic; I structure it into an outline; I do some research to fill in gaps; I write each section; I edit each section; I think about SEO tactics; I brainstorm headlines; I decide what images to add; and I send it to my editor.
If this looks a lot like story mapping, that’s because it is. Instead of mapping what a customer has to do to get value from a solution, I’m mapping what I do to complete a task. The benefit is the same: I can see what must happen and ask, "Where can AI help?"
From here, I focus on four moves: choose one step to automate or augment with AI; decide on the right automation (or augmentation) strategy—code vs. LLMs; prototype the first workflow with detailed instructions; and test and iterate until it meets my bar for quality and speed.
My goal is to give you enough guidance that you can follow along and end with a draft of your first AI workflow. If you apply continuous discovery to your own process, you’ll not only accelerate output—you’ll improve the clarity and quality of your thinking along the way.
I’m drawn to builders who choose decades over exits. The story behind Meter—providing full-stack networking infrastructure as a service for businesses—captures that ethos with unusual clarity. From day one, the strategy hinged on vertical integration, business model innovation, and committing to a multi-decade horizon. As a product leader, I see this as the rare combination that compounds: patient R&D, an earned right to own the stack, and a commercial model aligned with customer outcomes.
Why think in 25-year horizons? In entrenched, often monopolistic markets like networking, short-term optimization simply doesn’t move the needle. Incumbents such as Cisco and Meraki shape expectations around procurement, installation, and support. If you want to reset the standard, you can’t iterate around the edges—you have to re-architect the experience end-to-end and give yourself the time to do it right. That’s the difference between building a product and building a company.
I also share the contrarian stance on planning. Rituals can easily masquerade as rigor. “We don’t do OKRs” doesn’t mean don’t align; it means don’t confuse activity with progress. I prefer crisp narratives, simple success metrics, and a cadence that keeps teams close to customers. Planning without over-planning lets you steer with first principles: what problem are we solving, for whom, and how do we know it’s working?
On that note, I relentlessly track unhappy customers. Satisfaction scores and dashboards are lagging indicators; the real signal is in the gaps, escalations, and stuck use cases. Building a habit of surfacing and resolving those moments creates the operational muscle you need later when you scale. It’s also how you find “seller-market fit” and sharpen your go-to-market motion.
The origin story matters. Meter spent four-plus years in heads-down R&D, even scrapping a year of OS work during the process. That discipline—killing good work to unlock great work—is the hallmark of teams that play the long game. Shenzhen accelerated progress by compressing feedback loops between design, manufacturing, and iteration, a reminder that sometimes geography itself is a strategy choice.
Getting to a sales-ready product requires intentional sequencing. Own the interfaces, the telemetry, the install experience, and the service envelope—not just the code. In networking, that means controlling the full stack so performance, reliability, and support converge into one promise. The surprising thing you should innovate isn’t only the feature set—it’s the business model. Turning networking into a service aligns incentives, reduces complexity for customers, and creates durable revenue with clear SLAs.
Avoiding the one-trick pony trap is also central. The best teams design for adjacent expansion from day one: new sites, new form factors, new service layers. The secret to finding an excellent market is to look where switching costs and frustration are both high; that’s where a superior end-to-end experience can pry open demand. That’s also why Meter didn’t sell via traditional channels—a direct motion builds intimacy with the customer problem, strengthens pricing power, and helps validate “seller-market fit.”
Resilience is the throughline: surviving COVID, Apple’s M1 transition, and “a thousand bad days.” In those stretches, pace and patience matter more than theatrics. I’ve learned to decouple management from authority, reduce meta-work, and tackle performance issues quickly—“when the person is the problem,” clarity and speed are an act of care for the whole team. There’s inherent value in going slowly when it preserves quality, trust, and optionality.
For founders and product leaders, the takeaway is simple: build a company you’ll want to run for as long as possible. Focus on first principles decision making, empower product teams, and choose the few metrics that truly reflect customer value. Resist the comfort of templates; adopt only the practices that raise your odds of learning faster than the market evolves. Owning the full stack, rethinking the model, and extending your time horizon can transform even the most entrenched categories.
This is how I aim to run product: fewer rituals, tighter feedback loops, and a relentless bias toward long-term compounding. When you commit to decades, you earn the right to define the category—one thoughtful release, one delighted customer, and one resolved escalation at a time.
I’ve spent the last few years weaving AI into core product workflows, and the pattern is clear: when we pair disciplined product thinking with pragmatic AI Strategy, growth compounds. The question I hear most isn’t if AI can help, but where to begin and how to de-risk the journey while moving fast.
AI for business growth starts with one of these six strategies. See how companies use AI to unlock revenue, cut costs, and scale smarter and faster.
1) Revenue acceleration with unified customer intelligence. I start by connecting behavioral analytics and CRM integration to a unified analytics platform, then layer a retrieval-first pipeline so large language models can surface high-intent accounts, churn signals, and next-best actions. With Amplitude analytics and A/B testing, we validate AI-driven playbooks for upsell, cross-sell, and win-back—turning insights into measurable lift rather than novelty.
2) Cost reduction through targeted automation. Not all automation yields the same outcome. I look for repetitive, high-volume processes where quality is easy to verify—customer support ai strategy with AI-assisted deflection, accounts payable automation, and security workflows like threat detection and response. Combining agentic AI with clear guardrails reduces handle time, frees teams for higher-value work, and keeps error rates within acceptable thresholds.
3) Faster time-to-market via eval-driven development. Speed without signal is noise. I lean on eval-driven development to instrument models, measure drift, and tighten CI/CD loops. We track DORA metrics like deployment frequency while using gen ai for product prototyping to compress discovery and delivery. Frameworks and tools such as Claude Code help engineers iterate safely behind feature flags so we can ship learning, not just code.
4) Personalization that drives activation and retention. Growth sticks when onboarding is contextual. I use in-app guides, product tours, and thoughtful tooltip design powered by LLMs for product managers to tailor the first-run experience. With retention analysis and outcomes vs output OKRs, we align personalization with the moments that matter—activation, habit formation, and expansion.
5) Trust-by-design to scale responsibly. AI risk management, privacy-by-design, and data governance are not afterthoughts; they are growth enablers. By defining policy, red-teaming prompts, and practicing context window management, we reduce rework, limit incident management, and maintain compliance across markets. Clear review gates make it easier to say yes to more AI use cases without compromising customer trust.
6) Voice and agent experiences that feel like product, not add-ons. When prompt engineering for voice and voice AI agent patterns are integrated into the core journey—guided onboarding, smart handoffs, proactive notifications—engagement rises. Agent Analytics turns conversations into product signals we can act on in roadmapping and sprint planning, closing the loop between user intent and product improvement.
My playbook for getting started is simple: pick one revenue and one efficiency use case, define success upfront, and ship a narrowly scoped MVP with robust analytics. Use continuous discovery with product trios to refine prompts, data sources, and experience design. Then scale what works, retire what doesn’t, and let evidence—not hype—set the roadmap.
If you’re evaluating where to apply gen ai next, these six lanes offer fast paths to impact without sacrificing governance or customer trust. The companies I’ve seen win treat AI as a capability within the product, not a separate project—and they measure it with the same rigor they use for any critical feature.
I recently listened to Role of Leadership in Transformations – All Things Product Podcast with Teresa Torres & Petra Wille, and it crystallized a pattern I’ve seen across multiple transformations: teams often get trained in continuous discovery, but nothing changes because leadership habits stay the same. If you want to move from projects to true product thinking, “train your leaders first” isn’t a catchy mantra—it’s a prerequisite.
The episode digs into why discovery training can be stellar while adoption still stalls. I’ve witnessed this firsthand: teams return excited to interview customers and test ideas, but leaders continue to manage via features, roadmaps, and approvals. The result is predictable—discovery fades. When leaders evolve how they evaluate work, talk about outcomes, and shape rituals, discovery sticks. Without that shift, even energized, empowered product teams drift back to output.
What resonated most was how organizational dynamics kick in the moment teams start bringing real customer evidence to the table. Discovery uncovers conflicts. Sales, account management, stakeholders, and executives all feel the impact when the old “my job is to tell teams what to build” mindset collides with evidence-driven practices. Hierarchy also clashes with modern product practices—because in discovery, “all ideas come equal.” Product culture isn’t an accident; it must be intentionally created through norms, expectations, and systems that prioritize outcomes over output.
I’ve also seen the leadership skills gap up close. Many product leaders never learned continuous discovery themselves, so they aren’t equipped to coach it, critique it, or celebrate it. This is where great product management leadership shows up: the ability to assess discovery quality, reinforce outcomes vs output OKRs, and run cadences that create momentum. Leaders who invest in building these muscles—often through communities of practice and structured coaching—transform the operating environment for product trios and cross-functional teams.
The episode’s discussion of pilot teams is spot-on. Start small to surface hidden blockers—the corporate “immune system”—before going broad. Pilots expose decision bottlenecks, misaligned incentives, and policy friction that standard training never reveals. Tools like the Product Leadership Wheel help set clearer expectations for the craft of product leadership, while a coherent Product Operating Model makes the path from pilots to full transformation explicit and durable. I’m particularly excited about resources like the Discovery Habits Toolbox because they give leaders practical ways to coach continuous discovery without reverting to feature policing.
Here are the big takeaways I’m carrying forward. Skills training isn’t enough—if leaders still manage through feature requests and static roadmaps, teams will abandon discovery even if they loved the training. Leaders need training too—they must know how to evaluate discovery work, talk about outcomes, and create rituals that reinforce new habits. Discovery will surface conflicts—plan for stakeholder management, alignment with sales and account teams, and executive sponsorship. Product leadership is a craft—seniority alone doesn’t create clarity, systems, or culture. And transformations should start with leaders and pilot teams—because that’s where the real blockers live.
If you want to go deeper, listen to this episode on Spotify: https://open.spotify.com/episode/5cBTEbYX1YW3BF6icAPXzi or Apple Podcasts: https://podcasts.apple.com/kh/podcast/role-of-leadership-in-transformations/id1794203808?i=1000740342572. It’s a concise masterclass on why leadership behaviors—not just team skills—determine whether continuous discovery thrives.
For further exploration, I recommend these resources. Follow Teresa Torres: https://ProductTalk.org. Follow Petra Wille: https://Petra-Wille.com. Product Talk Academy’s Train Your Team by Teresa Torres: https://learn.producttalk.org/train-your-team. Melissa Perri’s “Train leaders first, not last.” Linkedin post: https://www.linkedin.com/posts/melissajeanperri_train-leaders-first-not-last-most-product-activity-7380927349732839424-sqBJ/. Coaching for Product Leaders/Executives by Petra Wille: https://www.petra-wille.com/coaching-packages. Product Leadership Wheel by Petra: https://www.petra-wille.com/plwheel.
To get hands-on with discovery skills, check out Story-Based Customer Interviews: https://learn.producttalk.org/course/story-based-customer-interviews. For visual management, see An idea board—do we see enough potential?: https://images.squarespace-cdn.com/…/idea_board3.png and Four Taskboards in a simple illustration: Idea Board, Product Overview Board, Product Discovery Board and Development Team Board: https://images.squarespace-cdn.com/…/boards.png. Opportunity Assessment: Do We Want to Invest in Discovering This Idea?: https://www.petra-wille.com/blog/opportunity-assessment-do-we-want-to-invest-in-discovering-this-idea?rq=taskboard.
If you’re preparing your organization to adopt a product operating model, read Is Your Organization Ready to Adopt the Product Operating Model?: https://www.producttalk.org/organizational-readiness/ and The Product Operating Model Explained: From Pilot Teams to Full Transformation: https://www.producttalk.org/the-product-operating-model/. Communities of practice can accelerate leadership growth: Community of Practice by Petra: https://www.petra-wille.com/community-of-practice. For foundational texts, see TRANSFORMED: Moving to the Product Operating Model: https://www.svpg.com/books/transformed-moving-to-the-product-operating-model/ and EMPOWERED: Ordinary People, Extraordinary Products: https://www.svpg.com/books/empowered-ordinary-people-extraordinary-products/.
I’d love to hear how you’re enabling continuous discovery in your context. What leadership behaviors have made the biggest difference? Where does your corporate immune system show up, and how are you addressing it with pilot teams, clearer expectations, and a consistent product operating model? Share your perspective—I read every comment.
When I sit down with our product trios to shape the next quarter’s roadmap, I rely on The Kano Model to cut through the noise and focus on what actually moves the needle for customers and the business. It gives me a rigorous, human-centered lens for separating baseline expectations from differentiators and sustained value creation.
Learn how the Kano Model prioritizes the product features that matter by categorizing them into must-haves, satisfiers, and delighters.
Here’s how I think about each category in practice. Must-haves are the non-negotiables—if they’re missing or broken, no amount of innovation will save the experience. Satisfiers scale linearly with user happiness; do them better, and customers feel the improvement immediately. Delighters surprise users with unexpected value that elevates the product’s perceived quality and creates memorable moments that fuel advocacy.
In continuous discovery, I mix quantitative Kano surveys with qualitative interviews to validate which capabilities land in each bucket for specific segments. We ask both functional and dysfunctional questions (e.g., “How would you feel if this feature existed?” and “How would you feel if it didn’t?”) to avoid false positives and to distinguish true delighters from nice-to-haves. This approach de-risks assumptions and keeps our product discovery anchored in real customer voice.
Translating insights into action starts with outcomes vs output OKRs. Must-haves protect core outcomes like reliability, trust, and activation. Satisfiers inform product roadmapping and sprint planning by tying investment to measurable improvements such as speed, accuracy, or completion rate. Delighters earn a deliberate share of the roadmap to strengthen competitive differentiation and to refresh our value proposition before market expectations shift.
Kano also sharpens product-led growth motions. By aligning satisfiers with key activation steps and running retention analysis on cohorts exposed to delighters, we can see where excitement features become habit-forming behaviors. When a delighter consistently correlates with improved retention or expansion, it graduates into the backbone of our product positioning.
Stakeholder management gets easier with a shared framework. I present the portfolio as a balanced mix: must-haves that protect reputation, satisfiers that demonstrate continuous improvement, and delighters that signal vision. This narrative connects short-term reliability with long-term strategy and helps leaders understand why some high-effort ideas are best sequenced behind critical must-haves or high-yield satisfiers.
A quick caution: delighters decay. What delights today often becomes tomorrow’s must-have. I schedule periodic re-reads of our Kano results, especially after major releases or market shifts, to recalibrate where features sit. Combined with A/B testing and usage analytics, this habit prevents us from over-investing in fading differentiators and ensures our roadmap stays crisp and customer-centered.
If your roadmap feels crowded or your team debates priorities without resolution, bring The Kano Model to your next planning session. It adds structure to product discovery, clarifies trade-offs, and helps us deliver a roadmap that not only works—but wins.
Support teams in Spain just got the clearest signal yet that the old way of doing things won’t cut it anymore. As I look at the details, I see more than a regulatory hurdle—I see a blueprint for the modernization many of us have been pushing toward for years.
The signal arrives in the form of one of the most ambitious customer service regulations in Europe—a law designed to strengthen consumer protections and set clear expectations for fair, transparent, and personalized customer service. Among its measures: new protections against spam calls, stronger transparency requirements, safeguards around personalized interactions, and measurable standards for speed, accessibility, and complaint handling within customer support.
It’s a significant shift, especially for large enterprises and essential-service providers. While the initial reaction might be anxiety about audits and penalties, the larger opportunity is hard to ignore: this law compels us to build modern, resilient support operations that scale, perform, and earn trust.
Spain is often an early mover in consumer-protection regulation, and this shift could signal what future standards across the EU might look like. For EMEA leaders, this is a moment to reevaluate operating models, invest in automation thoughtfully, and ensure customer experience improvements directly support regulatory compliance.
Below, I break down what the law requires, what it means in practice, and how AI Agents like Fin can help teams meet regulatory expectations while delivering faster, more personal support at scale.
The law applies in full to providers of regulated services, including water, energy, passenger transport, postal services, pay-audiovisual media, and electronic communications, and also to any company (or group) that meets certain size and turnover thresholds, even if their core business falls outside those sectors.
Large companies (those with more than 250 employees and over €50 million in turnover) also hold additional obligations, particularly around multilingual support in Spain’s co-official language regions.
While the law is still moving through its final approval stages, the direction is clear: a broad set of obligations will apply to reinforce consumer rights, ensuring they can: Reach support quickly. Speak to a human when needed. Get clear information during outages or service disruptions. Have complaints handled promptly and on time.
1. 95% of support calls must be answered within three minutes
This raises the bar significantly for responsiveness, especially during spikes, outages, billing cycles, or seasonal surges. Most support systems are not built for this level of agility. In my experience, you can’t hire your way to this metric sustainably—you have to design for it.
2. Customers must be able to speak to a human on request
Automation is allowed, but it cannot be the only option. At any point during a call, a customer must be able to transfer to a human if they ask for one. Companies cannot trap customers in automated loops. The practical implication: every workflow needs a reliable, audited escape hatch to a person.
3. Support lines must be free of charge
Premium-rate numbers are prohibited. Customer service cannot generate revenue for the business, nor may it be used to upsell products. This cleanly separates service from sales and reduces consumer friction.
4. Essential services must offer 24/7 support for continuity issues
Electricity, water, gas, telecoms, and transport providers must always be reachable at all hours when customers need to report service interruptions. That means coverage, triage, and routing must be always-on.
5. Complaints must be resolved within 15 days – or within five days for undue charges
This halves the previous general complaint window of 30 days and adds a much faster path for billing-error complaints. Companies must maintain records, assign tracking numbers, and ensure timely follow-up. Your case management discipline will make or break this requirement.
6. No spam calls or unwanted commercial pressure
Companies must identify business calls with a designated prefix, and customer -service calls with a different one. Telecom operators will be required to block calls that do not use these codes. Additionally, contracts obtained via unsolicited calls will be legally null and void, protecting consumers from being pressured into commitments they never intended to make.
7. Companies must maintain a unified complaint-tracking system
All complaints, claims, and incidents must be recorded in a centralized system to ensure traceability. If your data is fragmented across tools, this is a call to centralize and standardize intake.
8. Companies must pass annual external audits
These audits assess whether customer service processes are meeting the required standards. In practice, that means consistent processes, measurable outcomes, and reliable evidence.
9. Better linguistic and accessibility rights
Large companies operating in regions with co-official languages must be able to provide support in those languages. They must also ensure their customer service is accessible for vulnerable consumers, such as those with disabilities or older adults. Multilingual and accessible by design is the new default.
10. Fairer contract renewals
Companies must provide customers with 15 days’ notice prior to automatic renewal of online subscriptions and make cancellation simple. This is both a compliance and customer trust win.
Most support systems weren’t built for this level of speed or operational rigor. But the steps required to comply are the same ones that make service better for customers—and better for the teams delivering it. That’s why I view AI as an essential capability, not a bolt-on.
With the regulatory expectations clear, the question becomes: what does a modern, compliant support operation look like? For me, it blends human empathy with intelligent automation, proving auditability without sacrificing experience.
This is where AI plays a meaningful role. Not as a replacement for humans, but as a reliable front line that can handle a wide range of queries, including the most complex ones that require real depth, while keeping queues under control.
Adopting an AI Agent like Fin helps teams build a support model that meets regulatory expectations and improves customer experience across all your channels. Here’s how.
Many organizations will struggle to meet the three-minute standard during normal times, let alone during spikes or busy seasons, without unsustainably scaling their teams. Fin can help by reducing the number of calls that reach your phone lines and Fin Voice will ensure the ones that do are handled quickly.
Reducing avoidable call volume before it reaches the queue
Many of the queries teams receive are predictable: outage updates, billing questions, account changes, and other repeatable issues. Fin can resolve these instantly across several channels, including live chat, SMS, email, and WhatsApp, using the content and processes your team already maintains. I’ve seen this alone cut peak-time pressure dramatically.
Answering the phone immediately
For customers who do call, Fin Voice can pick up straight away. It provides natural, conversational responses based on your existing knowledge and helps your team stay responsive during busy periods.
Making it easy to reach a human easier during spikes
When queues build up, Fin can capture the reason for the call, gather details, and prioritize the most urgent issues. If you offer callback options, Fin can help schedule them quickly so customers avoid long wait times, which is key for staying compliant during peak periods.
The law requires customers to reach a real person whenever they request one. Fin supports this by keeping the path to a human clear and dependable: every interaction includes an option to speak to a person, and that option is accessible until the issue is resolved; when chosen, Fin hands over full context so human teams don’t start from scratch; if you show team availability or wait times, Fin can surface that information for customers; escalations can be prioritized to ensure faster pickup; alerts can notify on-call staff when urgent issues arise. On the phone, Fin Voice follows the same principle. Callers can request a transfer at any moment, and Fin routes the call to the right team with context intact.
Essential-service providers must be reachable at any hour when customers need to report service interruptions. Fin can help you meet this requirement without building a full overnight staffing model.
Always-on answers and triage
Fin provides first-line support at any hour of the day or night. Fin Voice brings this capability to the phone, giving callers immediate help even when your human team is offline. Fin can also direct customers to the latest updates you’ve published, such as outage information or status pages.
Routing urgent issues to the right people
When an issue requires human judgment, Fin gathers the necessary details and routes it to the appropriate on-call team using your existing after-hours processes. Teams can set up notifications so urgent issues are seen quickly.
Proactively surface what matters most
With AI Insights, Fin can also monitor for emerging patterns in customer conversations through Trending Topics. This means that if there’s a sudden spike in reports about a specific outage or a recurring question about a new process, Fin can flag these trends in real time. Your team is alerted to what’s top-of-mind for customers, so you can prioritize updates, publish targeted FAQs, or escalate critical issues, ensuring your support stays relevant and responsive, even overnight.
Complaints and outages often create the biggest spikes in volume, and the new law increases pressure to respond quickly, keep customers informed, and maintain complete records. This is exactly where structured AI intake adds value.
A more structured complaint intake
Fin can recognize when a customer is lodging a complaint, gather required information, and initiate a record in your existing system with a clear ID assigned from the outset.
Clear ownership and deadline alignment
Your team can then use your case-management tools to apply the 15-day resolution timeline (or five says for undue charges). Fin’s structured intake helps ensure that ownership and next steps are visible, rather than buried in unstructured notes.
Faster, more consistent outage communications
During service interruptions, Fin can share the latest published information, provide estimated fix times when available, and direct customers to live updates. On the phone, Fin Voice can triage incident-related calls quickly so callers aren’t waiting for a human agent just to receive basic information.
While multilingual support is only mandatory for large companies operating in co-official language regions, it remains essential for meeting consumer expectations. Fin helps by supporting multilingual, natural language interactions across voice and other channels; operating within channels that support accessibility features, like channels compatible with screen readers or commonly used messaging apps; and offering “request a call” paths and collecting the necessary information up front so teams can follow up quickly for customers who prefer phone support.
The law prohibits customer service interactions from generating additional revenue or being used to offer new products. With Guidance, you can set Fin up to stay firmly within these boundaries by shaping how it responds, which topics it should avoid, and what it should prioritize when a customer is seeking help or lodging a complaint.
The law raises expectations around documentation and audit readiness. Fin helps by making customer interactions more structured and consistent: when a conversation involves a complaint, Fin can ensure the required information is captured and a clear ID assigned; that ID can follow the interaction so it remains easy to trace; consistent intake gives you better visibility into key metrics regulators care about, like response times, time to first human contact, escalation volume, and whether complaints are resolved within required timelines; transcripts, summaries, and metadata can be retained until cases are resolved, supporting audit requirements; many organizations maintain internal compliance playbooks outlining processes and owners. Fin’s structured intake helps keep these practices reliable; leverage Insights to identify trending topics, optimize processes and measure service quality.
Spain’s new customer service law raises the bar on speed, access, and accountability. It’s natural to worry about how your team will cope, especially if your support operation has grown organically across tools and regions. I’ve seen how quickly burnout and chaos can set in when expectations rise faster than capacity.
The reality is that meeting these expectations through people alone would put unsustainable pressure on already stretched support teams. The risk of burnout and operational chaos is real, which is why an AI Agent like Fin can bring welcome relief.
By handling everything from high-volume, repetitive questions to many of the deeper, more involved issues customers raise, Fin keeps queues manageable and prevents the strain from falling entirely on your human team, helping everyone stay above water as expectations rise.
For companies operating across the EU, adapting early to Spain’s stricter expectations can build resilience for whatever comes next—whether that ends up being driven by regulation or customer demand. Now is the time to align compliance, AI strategy, and customer experience into a single, measurable operating model.
