Inspired by this post on The Intercom Blog.
Tag: product management leadership
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2026 Support Capacity Playbook: Bold AI Automation, Smarter Staffing, Zero‑Surprise SLAs
Capacity planning has always been a high-stakes exercise in customer service, and when you miss, the signal shows up fast in backlogs and SLAs. I’ve lived that pressure across multiple cycles, and 2026 will reward teams that plan differently. AI fundamentally changes capacity planning because it changes the work. It resolves the bulk of your volume, speeds up execution, and elevates the complexity and value of what humans handle. The consequence is simple: planning models must evolve. This is the final installment in my 2026 customer service planning series, and I’m focusing on the tension every leader feels right now—be ambitious about automation, but avoid the trap of understaffing if your assumptions don’t hold. My goal is to share how AI changes the logic of capacity planning, what I’ve learned implementing these practices with my team and with customers, and the common traps to avoid. Traditional planning rests on relatively stable assumptions: volume grows predictably, work types stay consistent, handle times don’t swing dramatically, and productivity improves slowly with better tools and training. In an AI-first model, none of that is guaranteed, and the fundamentals flip. The mix of work changes as AI absorbs a growing share of simpler conversations, leaving humans with deeper, more time-consuming issues that demand human-to-human connection. Demand can actually increase when you remove friction, so AI can both resolve more and attract more volume. Human time splits differently as teammates solve customer problems and also review AI behavior, give feedback, improve content, and support system-level work. Performance becomes dynamic, not fixed—automation rate isn’t a one-time number; it can rise with care and fall with neglect. If you plan for 2026 using a pre-AI model—assuming similar productivity, similar work mix, and a linear relationship between volume and headcount—you will underestimate what it now takes to run a high-performing support organization. There are many metrics you can track, but the one to put at the center is automation rate (AI Agent involvement rate × AI Agent resolution rate). This single construct tells me what share of total volume AI actually resolves, how much work remains for humans, how much additional demand humans can absorb, and how ambitious I can be with headcount. Early in the journey, I prioritize raising involvement—getting the AI involved in more conversations. Once involvement is high, I shift to resolution on the hardest remaining work, where each additional 1% of automation can represent several people’s worth of capacity. In my 2026 plans, automation rate sits alongside projected inbound volume, average “output” per person for the more complex work that remains, and occupancy—how much time is allocated to customer-facing interactions versus operational and strategic work. Together, those inputs give a realistic picture of how many people you need and where they should spend their time. First, plan boldly on automation, but match it with investment. I do not cap automation assumptions at 40–50% “because AI is new.” Many teams are already modeling 60%, 70%, even 80%+ for 2026—when they invest in AI ownership and content. The investment is non-negotiable: named ownership for AI performance (AI ops, knowledge management, conversation design), clear automation targets by work type (e.g., informational vs. personalized vs. actions vs. deep troubleshooting), realistic expectations for what’s easy to automate and what’s not, and a concrete plan to raise automation over time in monthly or quarterly steps rather than a single jump. To decide where to invest first, I dig into the data. I start with the biggest volume drivers, separate content-led issues from those dependent on data or complex procedures, assume higher resolution potential for content-led topics once the knowledge base is in shape, and set more modest initial resolution expectations for system-dependent flows. Then I stair-step improvements as the systems, data contracts, and workflows mature. In short, bold automation goals only work when paired with the team structure, content, and systems required to reach them—and the discipline to iterate. Second, expect human “output” per person to go down. That’s a mindset shift. Historically, we assumed individual productivity would stay flat or tick up as tools improved. In an AI-first model, humans handle fewer conversations but more complex, cross-functional issues—and create more value despite lower case counts. I model a lower “cases closed per person” than prior-year baselines, explicitly assume the remaining work is more complex and time-consuming, and redefine productivity to include system-level work like AI Agent improvements, content updates, and policy or workflow change management. I also report “capacity created” from automation alongside human outputs, so leadership sees the full picture. Third, rethink occupancy: more time off the queues, on higher-value work. Traditional occupancy splits time between inbox and training, meetings, and breaks. Now there’s an expanding “out-of-inbox” portfolio that directly affects AI performance and overall capacity: reviewing AI-handled conversations, improving AI Agent triaging and handovers, contributing to content and procedures, feeding insights to product and engineering, and supporting system changes that reduce future volume. I set lower inbox occupancy targets than before and make the rationale explicit. People aren’t working less—they’re working differently. In planning, I assume more time spent on improvement and system work, make it visible (for example, X% in inbox and Y% on AI and system improvement), and treat this as critical, not a “nice to have.” If you don’t proactively allocate it, it won’t happen—and your automation and performance targets will suffer. Fourth, work with the finance team early, and treat your plan as a set of assumptions. Capacity planning with AI is a set of bets across automation rate, human output, demand growth, occupancy, and where surplus capacity (if any) goes. I bring finance in early, show that the plan is dynamic and directly tied to AI performance, and label every lever as an assumption with ranges. I commit to a quarterly review cadence with finance to compare assumptions versus reality and adjust headcount, targets, and investment as needed. The risks are real: if automation grows slower than expected and you stop backfilling too early, you’ll be understaffed for months. Hiring and onboarding take time, so course-correcting late creates strain. If you do produce surplus capacity, have a clear strategy to reallocate those teammates to higher-value work—improving systems, feeding insights back to product, supporting new channels, and driving proactive CX—rather than defaulting to reductions. I also set explicit guardrails—if automation rate misses by five points for two consecutive months, we pause planned reductions and revisit hiring gates. If it over-performs, we shift people into backlog eradication, content upgrades, or proactive outreach, so we bank compounding value. To set your team up for success in 2026, anchor your plan on automation rate, be honest that humans will handle fewer but harder conversations, and protect time for system improvements. Partner early and often with finance, avoid shrinking too fast, and design a plan for surplus capacity so you’re never caught flat-footed. If AI is going to handle the majority of your customer conversations, your plan has to be designed to help it do that well and to keep your team set up for meaningful, sustainable work. A 2026 plan built on adaptable assumptions—not fixed predictions—will hold up as your work, your systems, and your customers’ expectations continue to change. If you’d like future editions like this, subscribe and stay close—I’ll keep sharing what’s working, what isn’t, and how to tune your customer support AI strategy in real time.
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Year-End Reflection for Product Leaders: Values, Themes, and the 100‑Wishes Reset
I’ve been closing the year with a deliberate reflection ritual for more than a decade, and this season I found fresh energy for it after listening to an insightful conversation with Teresa Torres and Petra Wille on All Things Product. Their approaches mirror the evolution many product leaders experience: moving from rigid annual goal-setting to values-led themes, longer time horizons, and a healthier respect for spaciousness. In my own practice, that shift has created better focus, less pressure, and far more meaningful outcomes.
Prefer to listen? You can find this episode here: Spotify | Apple Podcasts. I took notes with my team in mind and translated the discussion into a simple, values-driven framework that any product organization can adopt.
Why does annual reflection matter for product people? Because our work lives at the intersection of ambiguity, trade-offs, and time. If we only measure ourselves by shipped output or quarterly OKRs, we overlook the compounding value of learning, relationships, and judgement. I treat this ritual as a strategic reset: a chance to surface patterns, adjust expectations, and recommit to outcomes over output.
My own reflection habit started scrappy—paper notebooks, messy timelines, and even artful visualizations inspired by Dear Data by Giorgia Lupi & Stefanie Posavec. Like Petra, I’ve found that tactile, analog artifacts unlock insights I miss in a spreadsheet. Over time, I’ve kept the spirit and simplified the mechanics: a “what went well” review, a short list of hard lessons, and a handful of decisions that paid off—or didn’t.
The biggest evolution for me has been moving from rigid annual goals to values and themes. I still run OKRs, but I use them to track progress, not identity. The lens of process vs. outcome goals—reinforced by ideas from Atomic Habits—helped me set fewer, better commitments. For example, instead of “launch X by Y,” I’ll emphasize the cadence of customer discovery, the health of the product trio, and the quality of decisions made along the way.
One exercise that changed my practice is the “100 wishes” list. It’s powerful—and surprisingly difficult. Pushing past 30 or 40 wishes forces me to name latent interests and long-range intentions I rarely say out loud. Combined with decade-level themes, the list helps me balance ambition with patience. I don’t try to do it all next year; I use it to spotlight direction, not deadlines.
I also review patterns across years: Where did over-scheduling create hidden costs? When did I protect focus time and what did that unlock? Paul Graham’s Maker’s Schedule, Manager’s Schedule remains a useful calibration tool here. And when I feel the pull toward constant throughput, I revisit Stefan Sagmeister’s The Power of Time Off (TED Talk) to remind myself why strategically creating space often yields the most valuable ideas.
Of course, not every year follows plan—and that’s normal. Reflection helps me spot unrealistic expectations early and let them go. When setbacks hit, I’ll rewatch Dealing with Setbacks and re-ground in continuous discovery. The question isn’t “Did we do everything?” but “Did we learn fast, protect customer value, and make trade-offs aligned with our values?” That’s how empowered product teams compound impact.
My sharing philosophy has become more nuanced over time. Some reflections are public to invite dialogue and accountability; others stay private so I can process honestly. I’ve found it helpful to publish what I’m saying no to, capture a theme for the year ahead, and keep the rest for myself and my team. This balance preserves motivation while still contributing to the broader product management leadership community.
If you’re designing your own ritual, consider this lightweight flow: review wins and tough calls, write your “100 wishes,” extract a few values-based themes, then translate those into process goals for Q1. Revisit monthly, not just annually. If you like structured prompts, Chris Guillebeau’s How to Conduct Your Own Annual Review from The Art of Nonconformity offers a practical template you can adapt to your context.
For deeper dives and complementary ideas, I bookmarked these as part of my year-end reset: What I’m Saying No to This Year—And Why, Ask Teresa: My Leaders Still Want Roadmaps with Timelines—What Should I Do?, Scaling Impact: A Look at the Year Ahead (2022), Let’s Connect in 2025: A Look at the Year Ahead, The Interview Coach, and Petra’s own year-ahead reflections (here and her 2026 version). I also recommend revisiting the prior conversation on leadership and change: Role of Leadership in Transformations.
I’d love to hear how you approach your end-of-year reflection. What questions bring you the most clarity? Which practices help you set an intentional, values-driven path for the next year? Share your process—I’m always looking to learn from other product creators and leaders.
Inspired by this post on Product Talk.
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Inside the Engine Room: How I Drive Scalable Analytics APIs, Reliability, and Performance
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.
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Playing the 25-Year Game: Rethinking Networking, Ditching OKRs, and Owning the Full Stack
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.
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Train Leaders First: How Product Leadership Unlocks Real Transformation and Discovery
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.
Inspired by this post on Product Talk.
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Make Every Answer the Last: Building a Self-Improving AI Support Engine for 2026
Once I’ve defined the right roles on my team, the next move is to design an operating model that makes progress a habit. My goal is simple: every interaction should strengthen the system so the AI Agent keeps improving over time.
I anchor the team on a mantra that has never failed me: “The first time you answer a question should be the last.” That single statement reframes support as a compounding system rather than a one-off activity.
The ambition is to ensure every resolution makes the next one faster and more accurate, so fewer issues repeat, quality compounds, and support scales naturally. That doesn’t happen by accident—it requires intentional design.
In practice, this comes down to four essentials: clear ownership of performance, guardrails that make iteration fast and safe, feedback loops that turn learning into routine upgrades, and a culture that celebrates the work of improvement—not just the outcomes. Here’s how I put that into play.
First, I start with clear ownership. Ambiguity is one of the most common reasons AI performance plateaus. When no one truly owns how the AI Agent performs, feedback gets lost, issues linger, and improvements stall.
On high-performing teams, I assign a single owner—often an AI ops lead—responsible for making the AI Agent better. They review resolution trends to spot underperformance, make targeted updates to content, configuration, and behavior, coordinate with product and engineering on systemic blockers, and set improvement priorities, targets, and timelines. The title matters less than the mandate; what matters is clear authority to drive change across teams.
Real-world example: At Dotdigital, AI performance plateaued after a strong start—resolving around 2,800 conversations per month for three consecutive months. To drive resolution rates up, the team created a dedicated support operations specialist role, filled by an experienced agent with deep product knowledge. This person will focus on refining snippets, improving content, and enhancing the AI’s resolution capabilities.
Second, I make iteration fast and safe. As the AI Agent takes on more volume and complexity, change can start to feel risky—so teams hesitate, and performance stalls. Lightweight governance fixes that by making the path from insight to action predictable.
I keep the rules simple and explicit: which changes need review (and which don’t), who the decision-makers are, how we test updates before they go live, where feedback flows so it’s seen and acted on, and when progress gets reviewed on a steady cadence. Governance isn’t bureaucracy—it’s what keeps improvement routine and safe.
Real-world example: Anthropic ran a focused “Fin hackathon” sprint to improve their AI Agent’s resolution rate. The team audited unresolved queries, identified underperforming topics, and created or updated content to close gaps. They converted frequently used macros into AI-usable snippets, monitored Fin’s performance during live support, and continuously refined content based on real interactions. This structured approach enabled rapid improvement while maintaining quality standards.
Third, I build a system that learns by default. AI performance isn’t static, but many organizations treat it like a one-time implementation. The most successful teams operationalize learning: they analyze where the AI Agent struggles and feed those insights directly into structured improvements.
The signals are straightforward: review common handoffs to humans, track unresolved queries by topic or intent, measure resolution rate trends over time, and use those inputs to prioritize fixes and content upgrades. Whether you follow a formal loop like the Fin Flywheel framework or something lighter, the goal is the same—make improvement inevitable.
Fourth, I treat content as competitive infrastructure. Your AI Agent is only as good as what it knows. As George Dilthey, Head of Support at Clay, put it: “That’s when we realized: AI doesn’t just come up with information out of nowhere, you have to feed it. We were spending all our time evaluating tools when we should’ve been focused on content.”
I operationalize knowledge like infrastructure: every topic has a clear owner, content is structured, versioned, and ingestion-ready, new products ship with source-of-truth content by default, and changes ship on a schedule—not when someone finds time. This is the backbone that differentiates teams who scale confidently from those who stall out.
In my organization, we’ve evolved our New Product Introduction (NPI) process by aligning early with R&D on a single, canonical source of truth that becomes the foundation for all downstream content—including what the AI Agent uses to resolve queries. By embedding content creation into launch readiness, not as an afterthought, we’ve consistently hit 50%+ resolution rates on new features from day one.
Finally, I make belief visible. Even the best system will stagnate if people stop believing in it. Belief can fade quietly unless you reinforce it on purpose. I keep it strong by sharing specific wins regularly, highlighting improvements with metrics, and recognizing the people behind the gains—then giving them space to lead. This isn’t just about morale; it keeps everyone aligned on the bigger play.
When you put it all together—clear ownership, safe iteration, a learning system by default, and content as infrastructure—AI performance compounds. As the AI Agent gets better, the entire support model becomes faster, more reliable, and truly scalable. That’s the foundation of a modern, AI-first support organization.
Next, I’ll take this a level deeper and share how capacity planning changes when AI handles the majority of inbound volume and your team shifts into higher-value roles. If scaling with confidence is the goal, this is where the operating model pays off.
Inspired by this post on The Intercom Blog.
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AIUC-1 Certified: How Intercom Raises the Bar for Trustworthy, Enterprise-Ready AI Agents
I build products on the belief that trust is earned in every design decision and every deployment. Trust has always been a first principle at Intercom, from our early investments in security and privacy to the globally recognized certifications that shape our approach today.
As AI becomes more deeply embedded in customer-facing work, it’s essential that businesses can rely on systems that are safe, reliable, and governed to the highest standards. That’s why we’re proud to share that Intercom is now AIUC-1 certified, becoming one of the first companies to meet the world’s first standard designed specifically for AI Agents. For leaders navigating AI Strategy and AI risk management, this is more than a badge—it’s a measurable leap forward in governance and operational rigor.
AIUC-1 is the first certification tailored to the unique risks and challenges of AI Agents. It complements broader AI governance frameworks like ISO 42001 by focusing on enterprise-specific concerns like security, customer safety, system reliability, data and privacy, society, and accountability. In practice, this alignment helps us translate policy into deployable safeguards across cybersecurity, data governance, and regulatory compliance.
To achieve certification, organizations undergo independent third-party audits and quarterly adversarial testing across more than a thousand enterprise risk scenarios. This continuous technical evaluation ensures that AI systems remain robust against fast-evolving threats and that safeguards keep pace with rapid progress in the field. As a product leader, I welcome this level of scrutiny—it’s how we operationalize threat detection and response and make agentic AI dependable at scale.
AIUC-1 itself evolves every quarter, incorporating new research, threat patterns, and global best practices. The standard is shaped by the AIUC-1 Consortium, launched in November with more than 50 founding members who collectively handle tens of trillions of dollars in payments and serve over a billion people daily. Intercom is proud not only to be certified, but to be recognized as a founding technical contributor helping shape the development of the standard. That continuous, community-driven iteration mirrors how we build—measure, learn, and harden—so our customers benefit from real-world, enterprise-ready AI.
Intercom has decades of combined experience in security, compliance, and trust, and we’ve consistently demonstrated that robust governance and fast innovation can coexist. Achieving AIUC-1 certification reinforces that the same rigor we apply across our platform also extends to Fin, our AI Agent. I’ve seen first-hand how risk and procurement teams evaluate generative AI: they expect clarity, evidence, and controls. This certification delivers independent proof that our approach meets those expectations.
For our customers, this certification provides independent validation that Intercom’s AI systems are safe, resilient, and enterprise-ready. It confirms that our AI is tested regularly, built with strong safeguards, and aligned with the expectations of modern security and risk teams. It also signals our continued leadership in shaping responsible AI practices globally, ensuring our customers benefit from standards built for real-world use. In short, you can move faster with confidence—without compromising on governance.
Intercom has always approached trust as an ongoing commitment. AIUC-1 strengthens the foundation we’ve built across other frameworks and certifications, including SOC 2, ISO 27001, ISO 27701, ISO 27018, HIPAA, HDS, and ISO 42001. Together, these certifications create a comprehensive control fabric across privacy, security, and reliability—critical pillars for any enterprise deploying gen AI into production workflows.
As AI technology accelerates, we will continue to evolve our safeguards, deepen our governance practices, and contribute to the standards that shape responsible AI. Our promise is simple: to build AI that is not only powerful and efficient, but safe, transparent, and deserving of the trust our customers place in us. That’s how we turn innovation into durable value.
You can learn more about our certifications and access our security and compliance documentation through the Intercom Trust Center.
Get started with Fin and see how an AIUC-1 certified, enterprise-ready AI Agent can elevate your customer experience with confidence.
Inspired by this post on The Intercom Blog.
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A Product Strategist & Evangelist’s Playbook at Amplitude: Turning Analytics into Growth
I’ve long believed that the Product Strategist & Evangelist role is where analytics meets impact. When I work with teams using Amplitude, my focus is simple: turn product data into decisions that compound, and tell the story in a way that mobilizes people—customers, stakeholders, and empowered product teams alike.
At its core, this role aligns product strategy with business outcomes. I anchor planning to outcomes vs output OKRs, partner closely with product trios, and run continuous discovery to ensure every roadmap item is tied to a measurable customer problem and value proposition. That discipline keeps us honest about what moves the needle.
Analytics is the engine. I start with a clean event taxonomy, dependable instrumentation, and a self-serve insight layer in Amplitude analytics. From activation to retention analysis, I define a few sharp metrics that predict sustainable product-led growth—then I build dashboards the whole organization can trust and use.
Experimentation is where insight becomes action. I operationalize A/B testing with clear hypotheses, guardrails for minimum detectable effect, and crisp success criteria. The goal is speed with rigor: learn fast, ship what works, and retire what doesn’t. Over time, this creates a culture where teams default to evidence rather than opinions.
Evangelism turns analytics into momentum. I practice developer evangelism to meet practitioners where they are, and I translate complex findings into accessible narratives for executives and customer-facing teams. That means live walkthroughs, in-app guides, product tours, and field enablement that shows not just the what, but the why and the how.
Under the hood, a unified analytics platform is essential. I pair it with pragmatic data governance and privacy-by-design so we can scale insights confidently. The result is a flywheel: reliable data, repeatable workflows, and reusable patterns that accelerate every subsequent initiative.
On the go-to-market front, I connect product strategy to positioning, packaging, and enablement. The stories we tell in the market should mirror the value we measure in the product. That alignment makes launches sharper, sales motions clearer, and adoption smoother.
In practice, my playbook is straightforward: clarify the North Star and adjacent metrics, stand up trustworthy pipelines and dashboards, institutionalize experimentation, and continuously translate insights for decision-makers. Done well, analytics stops being a report and becomes a system for growth.
If you’re building or evolving this function, start small and intentional: instrument the few events that matter, ship one meaningful A/B test, and circulate a concise narrative on what you learned. Consistency beats complexity, and momentum compounds quickly when teams see their decisions move the metrics that matter.
Inspired by this post on Amplitude – Perspectives.
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Slash Time to Value to Skyrocket Retention: A Proven Playbook for Faster Impact
I’m relentlessly focused on time to value because it’s the fastest, most reliable lever I have to drive user retention and product-led growth. When new users experience an unmistakable win quickly, they stick around, explore deeper features, and become advocates. When they don’t, the best onboarding or marketing can’t save the experience.
Accelerate retention by reducing time to value. Learn how faster product impact drives growth, reduces costs, and keeps users engaged in the long term.
Here’s how I define it in practice: time to value (TTV) is the elapsed time between a user’s first meaningful interaction and the first moment they feel the product’s core value. That “aha” moment is not a vanity milestone; it’s a measurable behavior that correlates with long-term retention in your retention analysis and cohort curves.
In my role leading product teams at HighLevel, I treat TTV as a leading indicator for retention and expansion. It shapes our product discovery, influences our value proposition, and anchors our outcomes vs output OKRs. If a roadmap item doesn’t shorten TTV or deepen recurring value, it rarely makes the cut.
My playbook for reducing TTV starts by identifying the activation metric—what’s the smallest observable action that best predicts retention? For a messaging product it might be sending the first message to three contacts; for a workflow tool, publishing the first automated flow. Once this activation is clear, the job becomes simple: engineer the shortest, most delightful path to that outcome.
Next, I eliminate onboarding friction. I default to progressive profiling instead of long forms, ship sensible defaults, preload sample data, and offer ready-to-use templates. I complement this with lightweight in-app guides, product tours, and well-timed tooltip design—just enough guidance to build momentum without overwhelming the user.
To validate changes, I rely on rigorous experimentation. A/B testing with a defined minimum detectable effect ensures we’re not overfitting noise. I track activation rate, time to first value, feature adoption, and day 7/30 retention. If an experiment improves activation but hurts short-term retention, I dig into the “why” with session replays, targeted surveys, and follow-up interviews.
This approach also reduces costs. Faster activation lowers support volume, decreases onboarding hand-holding, and shortens payback periods. On the GTM side, TTV-aligned messaging clarifies our value proposition, improving conversion quality and reducing churn from poorly qualified signups.
Cross-functional alignment is essential. Product, design, engineering, and customer success must agree on the definition of value, the activation metric, and the telemetry required to measure progress. I use product trios to maintain discovery momentum and ensure decisions connect cleanly to measurable outcomes.
A practical 30/60/90 plan helps teams move fast. In the first 30 days, define activation, instrument analytics, and map the current journey. By day 60, ship friction-killing improvements, launch in-app guides, and run your first A/B tests. By day 90, refine templates, tighten empty states, and codify wins into the onboarding system so improvements compound.
The biggest pitfall I see is chasing more features instead of more value, faster. When we focus on shortening the path to a single compelling outcome—and proving it with data—retention follows. Users don’t need more; they need the right result sooner.
If you’re serious about retention, make time to value your team’s most visible operating metric. Shine a bright light on it in weekly reviews, tie it to goals, and celebrate every step that helps users succeed faster. Do this consistently, and you’ll see growth accelerate, support costs drop, and engagement deepen in ways that are both measurable and enduring.
Inspired by this post on Amplitude – Perspectives.
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Enterprise GTM Mastery: How I Partner with Product Marketers to Drive Adoption at Scale
I spend a lot of time turning strong product capabilities into enterprise wins, and that almost always starts with a tight partnership between product management and product marketing. The most effective go-to-market strategy is built where customer insight, product value, and revenue goals intersect—and product marketers are the connective tissue that makes this real.
“Michele Morales is a product marketing manager at Amplitude, focusing on go-to-market solutions for enterprise customers”
In my experience, partnering with product marketing leaders on enterprise go-to-market means aligning early on the ICP, the value proposition, and the differentiated messaging that sales can activate. We map buyer committees, refine product positioning against points of parity and competitive differentiation, and ensure our narrative translates cleanly from website to demo to proof-of-concept.
For data-driven execution, I lean on Amplitude analytics and a unified analytics platform approach to validate our hypotheses. We set clear activation and adoption milestones, monitor user activation cohorts, and close the loop with retention analysis to understand which messages and features actually move enterprise accounts from trial to expansion. This is where product-led growth complements sales-led motions, giving us empirical signal across the funnel.
On the launch front, we pressure-test enablement and in-product experiences together: crisp messaging frameworks, in-app guides, and product tours that shorten time-to-value for complex enterprise use cases. The result is a go-to-market strategy that’s both technically accurate and emotionally resonant—clear enough for executives and actionable for end users.
What consistently works: start with real customer pain, express value succinctly, and make the path to first success obvious. Then instrument everything. When product, marketing, and sales can all see the same truth in the data, empowered product teams iterate faster, positioning sharpens, and adoption compounds.
This approach respects the craft of product marketing while grounding decisions in measurable outcomes. It’s how we turn a promising roadmap into repeatable enterprise impact—and why close PM–PMM collaboration remains one of my most reliable growth levers.
Inspired by this post on Amplitude – Best Practices.
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From Stone Soup to Insights: Eval-Driven Development That Supercharges AI Analytics
I’ve learned that the most powerful AI features rarely emerge from lone-wolf brilliance—they’re born when a community rallies around a shared objective. “Building Amplitude’s AI for insight automation felt a lot like the fable of travelers making stone soup with their community.” That spirit captures how I approach shipping AI for analytics: bring focused ingredients, invite contributions, and let rigorous evaluation transform the result into something extraordinary.
At the core is Eval-Driven Development. Rather than debating preferences, we define explicit evaluation sets, success thresholds, and guardrails, then wire them into CI/CD so every change improves reliability, quality, and relevance. For AI-driven analytics, our evals combine offline judgment tests (precision, recall, hallucination rates), user-centric measures (time-to-insight, actionability), and production health signals (failure modes, latency). When the bar rises, the product improves—continuously and measurably.
We made “stone soup” by inviting contributions from every function. Data science established gold-standard datasets and baselines. Engineering implemented retrieval, orchestration, and safe deployment paths. Product and design framed high-value use cases, in-app guides, and UX writing that clarified intent. Customer success and support piped real-world edge cases into our evals so the system improved where it mattered. Product trios kept us outcome-focused and empowered product teams moved quickly without sacrificing governance.
Why this matters for analytics: AI insight automation reduces the heavy lift of exploring funnels, cohorts, anomalies, and retention patterns—accelerating activation and product-led growth. With a unified analytics platform and strong data governance, we can surface relevant patterns proactively, explain the “why” behind movements, and recommend next best actions without drowning users in noise. The result is faster decisions, cleaner handoffs between teams, and a tighter loop from observation to intervention.
Our practical playbook is simple but strict: define a clear north-star outcome; curate representative eval sets that mirror real user questions; simulate A/B testing offline before live traffic; instrument time-to-insight and adoption; and integrate evals into CI/CD so regressions never ship. We monitor DORA metrics to maintain delivery velocity while holding quality lines, and we use human-in-the-loop review to continuously refine prompts, patterns, and explanations.
We also learned what doesn’t work. General-purpose prompts seldom transfer cleanly to analytics without domain grounding and context window management. A retrieval-first pipeline improves factuality, but only if metadata and event taxonomies are consistent. And while generative UX can delight in demos, it must earn trust in production through transparent reasoning, privacy-by-design, and predictable behavior under load.
In the end, the stone soup metaphor isn’t about cute storytelling—it’s about disciplined collaboration. When a cross-functional community contributes the right ingredients and Eval-Driven Development keeps us honest, AI for insight automation becomes both credible and compounding. That’s how we turn analytics into action—and how we ship AI products that users rely on every day.
Inspired by this post on Amplitude – Best Practices.
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See What AI Really Says About Your Brand with Amplitude AI Visibility: Score, Rank, Win
Every week, I ask a simple question with massive implications for our AI Strategy: what do large language models actually say about our brand? As a VP of Product Management at HighLevel, I’ve learned that competitive differentiation now lives as much in AI-generated responses as it does in traditional search or social. That’s why a reliable, unified analytics platform for AI visibility is quickly becoming table stakes for product management leadership.
Discover how Amplitude AI Visibility helps you track your visibility score, uncover competitor rankings, and prove business impact—all in one platform.
Here’s why that matters. A visibility score gives me a measurable baseline—our AI share of voice—so I can see whether our product-led growth and go-to-market strategy are landing in the places where buyers increasingly look for answers. Competitor rankings reveal points of parity and opportunities to differentiate, which directly inform product positioning and our value proposition. And the ability to prove business impact closes the loop between AI exposure and outcomes that executives care about.
Operationally, I would start by benchmarking our visibility score against key competitors, then segment by core use cases to identify where our story underperforms. Those insights feed product discovery, content strategy, and enablement—tightening the narrative to better align with buyer intent. I’d translate the findings into prioritized bets for the roadmap and partner closely with marketing to amplify wins and address gaps.
For teams exploring LLMs for product managers and GenAI-driven growth, this approach creates a disciplined feedback loop: measure what AI says, experiment to improve it, and verify the impact across the funnel. It’s a pragmatic way to connect messaging, discovery, and differentiation—without guessing what the models are surfacing about your brand.
I’ve followed Amplitude analytics for years, and Amplitude AI Visibility slots naturally into a modern operating model: one platform to monitor the signals that matter, align stakeholders, and make faster, evidence-based decisions. If your mandate includes scaling product-led growth and sharpening competitive differentiation, this is a timely, actionable way to see—and shape—how AI represents you.
Inspired by this post on Amplitude – Best Practices.