Month: October 2025

6 Hard Questions Your AI Agents Must Answer to Win: Performance, Risk, and Real ROI

“Do you know how your AI agents are performing?” I ask this question in every review because it exposes whether we’re managing by outcomes or by anecdotes. Too often, teams point to latency, token counts, or completion rates and call it a day—useful signals, but not the story.

In my role, shipping agentic AI into production means I need decision-quality evidence, not vibes. That starts with Agent Analytics built on a unified analytics platform and instrumentation that lets me trace behavior, quantify value, and manage risk. Below are the six questions I use to separate novelty from durable impact.

1) What outcome are we optimizing for—and how do we measure it? If we can’t map the agent’s work to outcomes vs output OKRs, we’re optimizing noise. I anchor on task success rate, time-to-resolution, containment rate (no human handoff), cost per successful outcome, and downstream business impact (retention, conversion, NPS/CSAT) to keep us honest.

2) Are the right guardrails in place for AI risk management and data governance? I expect documented policies for prompt injection defenses, PII redaction, access control, and auditability. Every tool call should be permissioned, every data boundary explicit, and every failure mode observable. If we can’t demonstrate compliance by design, we’re scaling risk instead of value.

3) Can I explain every decision the agent made? Agentic AI needs traceability: prompts, intermediate reasoning, tool calls, retrieved context, and final outputs. I route key events into Amplitude analytics so product, engineering, and risk can slice behavior end to end. If we can’t reconstruct the path to an answer, we can’t debug, improve, or trust it.

4) What is the true cost per successful outcome? Raw token spend is misleading. I model total cost of ownership across retries, tool usage, escalations, and human review time—then benchmark against a consumption SaaS pricing lens. If cost per resolution trends up as volume grows, we haven’t built a scalable system; we’ve built a demo.

5) How does the agent learn without breaking what already works? My bar is a disciplined experimentation loop: offline evals, online A/B testing with clear guardrails, and a rollback plan. We predefine a minimum threshold for improvement before rollout and track regressions by persona, task type, and channel so we can localize fixes quickly.

6) Where is this agent creating durable differentiation? I look for capabilities competitors can’t easily copy: unique data advantages, superior tool orchestration, or workflows that compound learning. If the edge is just a base model prompt, the moat will evaporate; if it’s embedded in product workflows and proprietary signals, we’re building advantage.

Answering these six questions turns agentic AI from a novelty into a managed system. With Agent Analytics feeding a unified analytics platform, we can tie behavior to business outcomes, enforce governance, and make portfolio trade-offs grounded in evidence. The result is a product management leadership motion that prioritizes real ROI over vanity metrics—and scales with confidence.

If you’re not satisfied with the answers today, start by instrumenting the journey end to end, aligning metrics to OKRs, and setting clear risk thresholds. The compounding effects show up quickly when every iteration is measurable, explainable, and accountable.

Inspired by this post on Pendo – Best Practices.

October 24, 2025
How I Scale Revenue with Pendo Predict: Cut Costs, Reduce Risk, and Drive Product Adoption

When my team and I set out to accelerate growth without ballooning costs, we leaned into Pendo Predict as a keystone of our product-led growth strategy. Predict gives us a practical, data-driven way to focus on the right users at the right moments, align teams around measurable outcomes, and turn product usage signals into revenue impact.

“Increase revenue, cut costs, and reduce risk with Pendo’s Software Experience Management platform. Optimize the entire software experience to drive adoption and improve engagement.” That statement maps exactly to how we operate: we use the platform to understand user behavior, guide users through high-value actions, and instrument the experience so we can learn, iterate, and scale with confidence.

To scale revenue, we identify high-intent segments based on product behaviors and run targeted in-app guides and product tours that shorten time-to-value and boost conversion. Predict helps us surface which features correlate with expansion and retention, so our onboarding flows nudge users into those paths. This approach compounds: better activation drives stronger engagement, which fuels a healthier pipeline for cross-sell and upsell.

On the cost side, we reduce support load with contextual guidance—tooltips, checklists, and just-in-time education—so customers self-serve through common friction points. We consolidate insights in a unified analytics platform, enabling product, success, and go-to-market teams to work from the same source of truth. The result is fewer reactive escalations, tighter prioritization, and more engineering time invested in features that move retention and revenue.

Risk reduction comes from visibility and control. With predictive signals and retention analysis, we spot churn risk early, intervene with timely in-app messaging, and de-risk launches by rolling out features to targeted cohorts while monitoring adoption and engagement. We pair this with disciplined experimentation and A/B testing to validate changes before scaling broadly.

If you’re considering a similar motion, a simple playbook works: define your adoption and engagement metrics, instrument key workflows, create predictive segments, ship focused in-app guides, and measure impact against outcomes—not just outputs. Over time, this turns your product into a durable growth engine that consistently improves user experience and business performance.

Inspired by this post on Pendo – Best Practices.

October 24, 2025
How AI Is Supercharging Product Design: Faster Prototyping, Smarter Testing, and Better UX Outcomes

AI has fundamentally changed how I lead design and testing, not by replacing craft, but by compounding it. When my teams pair generative models with time‑tested product management practices, we move faster, learn sooner, and ship with more confidence—without compromising privacy-by-design or quality. The result is a tighter loop from product discovery to product-market fit lessons. Learn how Pendo’s product design team is using genAI and traditional tools to speed up design and development. That single line captures my own operating model: blend genAI with established toolchains to accelerate, not shortcut. In practice, I treat AI as a force multiplier for product trios—PM, design, and engineering—so empowered product teams can explore broader solution spaces while staying anchored to outcomes vs output OKRs. In discovery, genAI helps me synthesize qualitative inputs at scale—interviews, support threads, and in-app behaviors—into testable opportunity statements. I triangulate those insights with a unified analytics platform and Amplitude analytics to spot friction, then use in-app guides and product tours to target learning, recruit the right cohorts, and validate problems before we overbuild. For prototyping, gen ai for product prototyping is a game-changer. I generate multiple UX writing variants, microcopy, and flows in minutes, then narrow the set using heuristics and stakeholder feedback. Before any A/B testing, we precompute the minimum detectable effect (MDE) and sample size, making sure our experiments are powered to detect meaningful differences, not noise. In testing, I combine classic A/B testing with AI-assisted analysis to surface patterns faster. GenAI drafts experiment summaries, flags anomalous segments, and proposes follow-up tests, while my team makes the final calls. We deploy targeted in-app guides to onboard users into trials, monitor adoption via event telemetry, and iterate quickly until the value proposition is unmistakable. Execution depends on rigor and guardrails. We codify AI risk management and data governance policies, keep humans-in-the-loop for critical judgments, and log model prompts and outputs for auditability. This lets us move with speed and integrity, aligning stakeholder management, product roadmapping and sprint planning, and go-to-market strategy around measurable outcomes. The payoff is material: shorter cycle times, clearer value narratives, and stronger product-led growth curves. By fusing genAI with traditional practices, we preserve the craft of design while scaling our capacity to learn. That’s how we differentiate—through faster insight generation, smarter testing, and experiences that feel unmistakably intuitive.

Inspired by this post on Pendo – Best Practices.

October 24, 2025
SaaS + AI Is Here: How Our Summer 2025 Release Builds an Intelligent Foundation to Win

Leading product at HighLevel, I’m watching the convergence of SaaS + AI reshape how we build, price, and scale software. The winners will combine a sharp AI Strategy with disciplined product management leadership to ship real outcomes, not just demos. That’s why my team and I have been focused on giving you pragmatic ways to move fast without breaking trust. Give your company an intelligent foundation for the SaaS + AI era with our Summer 2025 Release. When I set priorities for this release, I optimized for three things: speed with quality, responsible AI, and measurable business impact. Practically, that means enabling agentic AI and gen ai workflows where they actually create leverage, unifying analytics so teams can make decisions from a single source of truth, and hardwiring data governance and privacy-by-design into every layer. If you’re wondering how to keep up, here’s what’s working for us and our customers: tighten product roadmapping and sprint planning around clear outcomes, not outputs; align teams with simple, observable OKRs; and empower product trios to run lean product discovery loops. These practices reduce cycle time while raising confidence, especially when introducing AI into core experiences. On the go-to-market side, I’m doubling down on product-led growth—shipping value into the product with in-app guides, thoughtful product tours, and frictionless onboarding. Pair that with rigorous retention analysis and A/B testing, and you’ll see which AI-powered moments actually move activation, adoption, and expansion. Don’t overlook the fundamentals either: smart SaaS pricing (including consumption models where it fits) can unlock the economics that sustain AI investments. My goal is to give you a foundation that is both ambitious and accountable—a platform you can trust to scale responsibly while your teams iterate quickly. If you’re planning your 2H roadmap, this release is built to help you ship faster, de-risk AI, and create outsized customer value in the moments that matter most.

Inspired by this post on Pendo – Perspectives.

October 24, 2025
Prioritize, Build, and Measure AI with Confidence: Lessons I Apply from PendomoniumX NYC

AI is moving faster than any product wave I’ve seen in my career, and that urgency demands rigor. At HighLevel, I anchor our AI Strategy around measurable outcomes, responsible delivery, and pragmatic execution—principles that a recent PendomoniumX NYC customer discussion reinforced for me. “Three product leaders sat down with Pendo to discuss how they’re balancing AI investments, building their AI roadmap, and measuring success.” When I decide what to fund, I start with outcomes vs output OKRs. If an initiative cannot tie to a defensible customer outcome—time-to-value reduction, revenue expansion, retention lift, or cost-to-serve efficiency—it doesn’t make the cut. From there, I pressure-test feasibility and risk through data governance and AI risk management lenses: model choice, training data readiness, privacy-by-design, security posture, and responsible use guardrails. Building the roadmap is where discipline meets speed. I use empowered product teams—product trios across PM, design, and engineering—to run tight discovery sprints. We validate desirability and viability with gen ai for product prototyping, then graduate concepts into delivery using product roadmapping and sprint planning habits that prioritize smallest shippable value. I’ve found the try do consider framework helpful to stage bets from low-risk utilities to higher-impact, agentic AI workflows. Measuring impact is nonnegotiable. I define success up front with a minimum detectable effect (MDE), then instrument adoption and behavioral change via Pendo and Amplitude analytics. A/B testing gives me causal confidence, while retention analysis tells me if AI features are durable value, not novelty. If we can’t attribute improvement to a metric that matters, we iterate or retire. Governance is a product requirement, not an afterthought. We maintain data governance standards, threat detection and response controls, and clear model evaluation criteria before anything reaches customers. That operating model helps us move quickly without compromising trust—a cornerstone in any product-led growth motion. For go-to-market and adoption, I rely on in-app guides, product tours, and contextual tooltips to shorten the learning curve. We measure feature discovery, task completion, and ongoing engagement to ensure the experience is intuitive. The goal is to make AI feel like a natural extension of the workflow, not a science project bolted onto the product. My simple playbook: prioritize by customer outcomes and risk posture, build with validated learning and smallest shippable value, and measure with rigorous analytics and OKRs. Repeat that loop, and AI stops being a buzzword—it becomes a compounding advantage.

Inspired by this post on Pendo – Perspectives.

October 24, 2025
3 Powerful Ways AI Is Reshaping Cybersecurity—from Ruthless Attacks to Rapid Defense

Every week, I watch the cybersecurity landscape bend under the pressure of AI. The pace isn’t linear—it’s compounding. What worked for IT teams last quarter often needs a rethink today, and the difference between merely coping and truly competing lies in how quickly we adapt our strategy, tooling, and operating rhythms.

Learn the ways in which AI is transforming both cybersecurity offense and defense for IT teams.

From my vantage point leading product strategy, I see three shifts that matter most right now: AI is supercharging attackers, accelerating defenders, and reshaping governance. Together, they redefine how we prioritize investments, measure risk, and align product and security roadmaps.

First, AI has leveled up the offense. Large language models can industrialize social engineering—hyper-personalized spear-phishing at scale, deepfake voice notes that spoof executives, and highly convincing support chats that trick users into bypassing controls. Code-generation tools lower the barrier to crafting polymorphic malware and automating reconnaissance. The net effect is ruthless efficiency: more credible lures, faster campaigns, and broader reach with fewer human operators. I now assume adversaries have an AI co-pilot—and plan defenses accordingly.

Second, AI is accelerating the defense. Modern detection and response stacks are moving beyond rules to behavioral analytics—correlating identity signals, endpoint telemetry, and network events to spot subtle anomalies that signature-based tools miss. Copilot-style assistants are augmenting SecOps by summarizing incidents, explaining probable root cause, and proposing next steps. The aim isn’t blind automation; it’s decision acceleration—shrinking mean time to detect and respond while reducing analyst toil. On the build side, AI-assisted code scanning and dependency analysis help teams shift security left, catching vulnerabilities earlier and turning secure defaults into muscle memory.

Third, governance is being rewritten in real time. As AI models ingest sensitive data and generate code and content, data governance and privacy-by-design move from compliance checklists to active risk management. We’re formalizing AI risk management alongside traditional AppSec: model inventories, usage policies, red-teaming prompts, and guardrails against prompt injection and data leakage. Identity remains the control plane—zero trust principles, least privilege, and continuous verification become nonnegotiable. I’ve found that aligning security, product, and IT leadership on a single policy-as-code backbone prevents drift and keeps audits predictable.

Practically, I guide teams to start with a crown-jewel inventory: What data and systems would materially impact customers, revenue, or brand if compromised? Map data flows, instrument comprehensive telemetry, and prioritize detection coverage where it matters most. Choose AI to augment before you automate—prove the loop with humans in the middle, then graduate to higher autonomy levels with clear rollback paths and audit logs.

Culturally, this is a product problem as much as a security one. We bring empowered product teams and SecOps into the same room, set measurable objectives (signal-to-noise ratio, mean time to contain, escaped defect rate), and iterate with the same cadence we use for product features. When security outcomes are treated as customer outcomes, adoption soars and friction recedes.

The takeaway: AI has tilted the field, but not inevitably against defenders. With a clear AI strategy, disciplined data governance, and pragmatic automation, IT leaders can turn reactive security into a proactive advantage—meeting attackers’ speed with speed, and outlasting them with better judgment.

Inspired by this post on Pendo – Perspectives.

October 24, 2025
The Real Reason Pendo Built Agent Analytics—and How It Drives Adoption, Revenue, and Trust

I’ve learned the hard way that the toughest part of launching in-app agents and guided experiences isn’t the build—it’s proving, quickly and credibly, that they move the business. If I can’t quantify adoption, engagement, deflection, and time-to-value, stakeholder confidence erodes and iteration slows. That’s exactly why an Agent Analytics capability matters: it turns opaque interactions into measurable outcomes that product, customer success, and engineering can all act on.

When I evaluate a capability like Agent Analytics, I anchor on a few questions. Which segments adopt the agent, and where does engagement drop? What fraction of issues are successfully deflected versus escalated? Which prompts, product tours, and in-app guides drive conversion and retention—and which add friction? How does agent usage correlate with onboarding completion, core feature activation, and long-term retention analysis? If I can answer those with a unified analytics platform, I can prioritize confidently.

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

In practice, I map an outcomes-first measurement plan: define a north-star (e.g., activated accounts), articulate contributing metrics (guide completion rate, agent task success, session depth), then run targeted A/B testing on copy, timing, and placements. With the right analytics, I can compare cohorts exposed to in-app guides and product tours against a control, validate impact, and double down on the patterns that consistently improve adoption and stickiness.

Cost and risk are just as important as growth. An effective Agent Analytics view helps me model support deflection, time-to-resolution, and escalation rates so I can quantify cost savings without sacrificing quality. On the risk side, I look for early-warning signals—low-confidence responses, repeated handoffs, or anomalous usage—so I can intervene before they turn into churn or brand concerns. The point isn’t vanity metrics; it’s operational clarity that enables responsible, scalable product-led growth.

This also changes team dynamics. Product trios get a shared source of truth for decisions, engineering gains sharper specs informed by real behavior, and customer-facing teams can see which experiences reliably unlock value for each segment. Instead of debating opinions, we iterate on evidence—tightening the loop between product roadmapping and sprint planning, UX writing, and go-to-market strategy.

My 90-day playbook looks like this: establish a baseline for adoption and engagement; instrument agent interactions end to end; ship two or three small, high-leverage experiments in onboarding and help experiences; and review results in weekly rituals. By day 90, I expect to see a clear line from agent engagement to activation and retention, along with a repeatable testing cadence that compounds learning.

I’ve seen the same pattern across products and markets: once teams illuminate the black box of in-app assistance with rigorous, actionable analytics, customer confidence rises, onboarding accelerates, and roadmaps get sharper. If you’re evaluating Pendo or already running it, put Agent Analytics at the center of your measurement strategy—and let your data, not assumptions, guide the next iteration.

Inspired by this post on Pendo – Perspectives.

October 24, 2025
5 powerful reasons I can’t wait for INDUSTRY 2025: The Product Conference to supercharge strategy

I’m gearing up for INDUSTRY 2025: The Product Conference in Cleveland, Ohio, and I can already feel the energy that comes when the brightest product minds gather. As someone who lives at the intersection of product management leadership, execution discipline, and customer-centric innovation, this event is where I refine my craft and pressure-test my roadmap against the best.

Join Pendo at INDUSTRY in Cleveland, Ohio.

Reason 1: Elevate strategy from outputs to outcomes. I’m looking forward to sharpening how we align outcomes vs output OKRs with product roadmapping and sprint planning. INDUSTRY consistently surfaces practical frameworks to translate vision into measurable value—exactly what empowered product teams need to prioritize with confidence and communicate trade-offs to stakeholders.

Reason 2: Deepen discovery with data that actually drives decisions. I plan to compare notes on product discovery techniques that blend qual and quant—pairing interviews with a unified analytics platform, retention analysis, and a clear minimum detectable effect (MDE) to validate signal. The bar keeps rising on evidence-based decisions, and I’m eager to bring back new ways to reduce bias while accelerating learning.

Reason 3: Double down on product-led growth. From onboarding to activation, I’m focused on refining in-app guides and product tours that meet users at the moment of need. INDUSTRY is a great place to trade patterns for scalable, context-aware experiences that convert, retain, and expand without adding friction—fueling a durable product-led growth motion.

Reason 4: Build a responsible, practical AI Strategy. The conversations around gen ai for product prototyping, agentic AI, data governance, and privacy-by-design are evolving fast. I’m excited to learn how teams are balancing speed with AI risk management—turning experimentation into real features while protecting customers and preserving trust.

Reason 5: Level up leadership and influence. Product management leadership is as much about people as it is about prioritization. I’m excited to trade tactics on stakeholder management, strengthening product trios, and growing ICs through the IC to manager transition. These are the muscles that turn strategy into momentum.

Between keynotes, hallway conversations, and hands-on sessions, I plan to leave Cleveland with fresh approaches to discovery, clearer OKR alignment, and new ideas to operationalize PLG at scale. If you’re passionate about building products that customers love—and businesses rely on—let’s connect and compare notes on what’s working now.

I’ll share my takeaways after the conference, including actionable frameworks, templates, and experiments to run with your teams the very next sprint. If you see me in a session on analytics, onboarding, or AI, say hello—I’m always up for a quick debrief and a few what-would-it-take questions.

Inspired by this post on Pendo – Perspectives.

October 24, 2025
4 Hidden AI Risks Every CIO Must Tackle Now—and a Proven Playbook to Mitigate Them

Across enterprises, I’m watching AI sprint from lab experiments to business-critical workflows. That velocity is exciting—and it’s also where risk compounds. In my role partnering with CIOs and IT leadership, I’ve learned that winning with AI is as much about disciplined risk management as it is about breakthrough use cases.

Learn about the risks that AI poses to IT teams, and how they can mitigate them.

I frame the challenge as “4 AI risks for CIOs (and a guide to solve them)”: data governance and compliance, model reliability and bias, security and supply chain exposure, and operational cost/ROI drift. Below, I outline the risks I see most often and the concrete actions I take to de-risk them without slowing innovation.

Risk 1: Data governance and compliance. The fastest way to stall an AI Strategy is to overlook consent, lineage, and access controls. I establish privacy-by-design from day one: data minimization, clear retention policies, role-based access control, and auditable logs for training, inference, and feedback loops. I also insist on defensible vendor reviews (DPA, SOC2/ISO, regional data residency), PII classification, and internal model cards that document sources, sensitivities, and acceptable-use constraints. This makes IT leadership comfortable scaling from prototype to production.

Risk 2: Model reliability, hallucinations, and bias. AI that fabricates or skews output erodes trust and creates downstream risk. I operationalize quality with evaluation harnesses, golden datasets, human-in-the-loop review for high-impact actions, and red-teaming for safety. Retrieval-augmented generation with citations, content filters, and grounded prompts reduce error rates. To quantify progress, I define precision/recall targets and a minimum detectable effect (MDE) for experiments so we know when a change is truly better—not just different.

Risk 3: Security and AI supply chain. New surface area invites prompt injection, data exfiltration, and compromised dependencies. I apply zero-trust principles: strict allow/deny lists for tools and connectors, secrets isolation, egress controls, sandboxed environments for agents, and output validation before execution. Every model and plugin goes through threat modeling, dependency scanning, and vendor security reviews. For agentic AI patterns, I gate high-risk actions behind explicit approvals and granular scopes.

Risk 4: Operational cost and ROI drift. AI workloads can balloon with hidden inference costs, shadow IT, and duplicated platforms. I put governance around spend using consumption SaaS pricing guardrails, usage caps by environment, tagging by app/team, and a unified analytics platform to monitor latency, quality, and cost per transaction. This lets me reallocate budget toward the highest-impact use cases while sunsetting low-yield experiments.

Your 90-day playbook. Days 0–30: Inventory AI use cases, classify data sensitivity, choose one or two critical business workflows, and stand up core guardrails (access, audit, red-teaming). Days 31–60: Pilot with a cross-functional product trio (PM, design, engineering), define OKRs, instrument evaluations, and enable human-in-the-loop. Days 61–90: Productionize the winning flow, set usage and spend policies, enable observability dashboards, and roll out training for frontline teams with clear escalation paths.

The organizational layer matters as much as the technical one. I align stakeholders early, empower product trios to iterate quickly within boundaries, and deploy forward deployed engineers to embed with the business. This keeps trust high, reduces handoffs, and ensures that governance accelerates value rather than blocking it.

Done well, these practices turn AI risk into a competitive moat. By pairing disciplined governance with pragmatic experimentation, we capture the upside of gen ai while protecting customers, teams, and the business. That’s how I’ve helped enterprises move from scattered pilots to measurable, scalable impact—safely.

Inspired by this post on Pendo – Perspectives.

October 24, 2025
Implementing Agentforce the Smart Way: My Proven Playbook for Salesforce Agentic Success

Implementing Agentforce isn’t a feature rollout—it’s a strategic shift. In my role building AI-driven products, I treat Agentforce as its own product with clear outcomes, rigorous governance, and disciplined iteration. The objective is to create durable operational leverage inside Salesforce without compromising trust, data integrity, or customer experience.

Learn the ways in which Pendo helps companies design and iterate on their agentic strategy for Salesforce.

I start with product discovery. That means selecting the right use cases, defining the target user, and aligning on measurable outcomes rather than outputs. In practice, I prioritize use cases across sales, service, and marketing using an impact–effort–risk lens, then set crisp success metrics—response time, deflection rate, case resolution, win rate lift, and user adoption. This keeps everyone focused on value creation, not just model novelty.

Next, I design the agentic system with guardrails. I specify agent roles, tools, and policies; define when to escalate to humans; and embed privacy-by-design and data governance from day one. I also build an evaluation harness with offline tests and live A/B testing, ensuring we have a minimum detectable effect that’s meaningful for the business. The goal is to measure outcomes reliably and course-correct quickly.

When building the first slice, I scope narrow and ship fast. For example, start with a constrained service workflow—classify the case, propose a response, and take a safe action—with clear affordances in Salesforce so users understand what the agent did and why. I instrument the experience end-to-end and use Pendo for in-app guides, surveys, and behavioral analytics to reduce onboarding friction and capture real-time feedback at scale.

Iteration is where value compounds. I run weekly reviews of conversations, error taxonomies, and edge cases; adjust prompts and tool access; and maintain a steady experiment cadence. We track outcomes vs output to avoid vanity metrics, and we document learnings to de-risk the next use case. This steady drumbeat builds credibility with stakeholders and confidence with frontline users.

Change management is non-negotiable. I align leaders early, set expectations on what the agent can and cannot do, and define SLAs for humans-in-the-loop. I use product tours to teach new behavior, highlight quick wins, and establish transparent feedback channels. This combination of enablement and accountability accelerates adoption and creates a culture that embraces agentic AI responsibly.

Finally, I scale thoughtfully. Once the first use case demonstrates value, I standardize patterns, unify analytics, and evolve governance as usage grows. I review risk regularly, align OKRs with the roadmap, and keep a tight feedback loop between product, ops, and go-to-market teams. Treating Agentforce as an evolving product—not a one-off project—maximizes impact while protecting the customer experience.

Inspired by this post on Pendo – Perspectives.

October 24, 2025
Our Pendo-Powered Playbook: Orchestrating a High-Impact Summer Release with Product-Led Growth

We set out to promote the Pendo Summer Release using the most authentic approach possible: we used Pendo to market Pendo. That decision anchored our strategy in product-led growth, letting us reach users in context, guide them through new capabilities, and measure impact in real time without adding friction or cost.

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

Our objectives were clear: drive adoption of new features, accelerate onboarding for existing customers, and improve engagement across key workflows. We framed the work with outcomes vs output OKRs, clarified the value proposition for each persona, and aligned our product positioning to highlight points of parity and genuine differentiation.

Execution centered on in-app guides, product tours, and purposeful tooltip design. We segmented by role, lifecycle stage, and behavior to keep messages timely and relevant, then layered in A/B testing with a defined minimum detectable effect (MDE) so we could learn fast without overexposing users. Product trios partnered closely with design and forward-deployed engineers to iterate quickly on copy, UX writing, and guide placement.

On the measurement side, we instrumented clear goals and tracked conversions through the funnel, pairing event analytics with retention analysis to understand depth of usage, not just clicks. We captured qualitative signal through micro-surveys and in-context feedback, feeding insights back into product roadmapping and sprint planning to sharpen our next set of in-app experiments.

Governance mattered as much as growth. We applied privacy-by-design principles, ensured strong data governance, and kept stakeholder management tight so each guide had a clear owner, sunset plan, and success criteria. That discipline helped us sustain momentum without cluttering the experience.

The biggest lesson: when done thoughtfully, in-app education scales like a dedicated success team—at a fraction of the cost—while teaching you exactly where users find value. This Pendo-powered launch playbook now underpins our onboarding, cross-sell motions, and QBRs alike, giving us a repeatable way to promote releases, validate hypotheses, and deepen engagement with every iteration.

Inspired by this post on Pendo – Perspectives.

October 24, 2025
Inside Pendo’s Decision: Replacing the Website Chatbot With an AI Agent to Boost ROI

Traditional website chatbots promised instant answers but rarely delivered the depth, context, and actionability modern buyers expect. After seeing patterns of high drop-off and shallow engagement, I stepped back and reframed the problem: We did not need another scripted bot—we needed an AI Agent capable of understanding intent, personalizing responses, and taking meaningful actions in the flow of discovery.

That is why Pendo replaced the website chatbot with an AI Agent. From a product management lens, the decision hinged on three criteria: accelerate time-to-value for visitors, reduce operational overhead through automation, and improve the quality of demand captured at the top of the funnel. An agentic AI approach met all three.

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

This statement crystallizes the business case. An AI Agent can translate product intent into measurable outcomes by connecting to knowledge sources, analytics, and workflows. Instead of handing off a prospect to a form or a static knowledge article, the agent can surface relevant guidance, qualify interest, book meetings, and even trigger product tours—closing the loop between marketing, product, and customer success.

We anchored the implementation in data governance and privacy-by-design. That meant carefully curating training corpora, instituting role-based access controls, applying guardrails for sensitive topics, and designing graceful human-in-the-loop fallbacks. The result was not just a smarter front door, but a safer one—critical for regulated buyers and enterprise stakeholders.

To validate impact, we ran disciplined A/B testing with a clearly defined minimum detectable effect across conversion, engagement depth, and time-to-response. We also monitored secondary signals such as escalation rate to human support, session quality, and downstream product adoption. Early signals showed more qualified conversations, fewer dead ends, and faster paths to value—exactly the outcomes a product-led growth motion requires.

The experience uplift did not stop at the website. By aligning the agent with in-app guides and product tours, we created continuity from pre-signup exploration to onboarding and activation. Visitors received consistent, contextual help before and after they became users, which strengthened our product positioning and reduced friction across the journey.

Operationally, the shift lowered the marginal cost of each high-quality interaction while improving reliability. Agent handoffs to sales or support became intentional rather than reactive, and insights from conversations fed directly into product discovery. That closed feedback loop informed roadmap decisions and sharpened our go-to-market strategy.

If you are considering a similar move, start with a clear AI Strategy tied to measurable outcomes, a robust governance model, and a pragmatic rollout plan. Focus the agent on high-intent moments first, surround it with analytics and experimentation, and let the data guide expansion. The goal is not to replace humans—it is to elevate them by letting the AI Agent handle the repetitive, high-volume work so your teams can focus on complex, high-value interactions.

Inspired by this post on Pendo – Perspectives.

October 24, 2025