I just tuned into the latest conversation on the upcoming Product at Heart 2026, and it hit on the exact challenges product leaders are navigating right now: curating meaningful content in a world where AI moves faster than our agendas, designing formats that create real connection, and ensuring every minute earns its place. Listening to Petra Wille and Teresa Torres map out the speaker lineup, workshops, and structural shifts, I found myself nodding along—this is the kind of thoughtful curation we need if we want product teams and product leaders to walk away with practical value, not just inspiration.
Listen to this episode on: Spotify | Apple Podcasts
What stood out immediately is the bold move to a single-track conference for 2026. In an era of gen ai hype and endless breakouts, this choice signals clear intent: tighter curation, a shared experience, and less FOMO. The team isn’t carving out a separate AI track—and I love that decision. Their stance is simple and sensible: No AI track—AI will show up everywhere, but not as a siloed topic. The team sees it as part of the everyday toolkit. That mirrors how high-performing, empowered product teams actually work today—AI Strategy and AI workflows are part of the operating system, not a side show.
The keynote lineup is already compelling. Christian Idiodi (SVPG) brings storytelling that turns product principles into habits you can actually use on Monday. Elaine Kasket, cyber-psychologist, exploring digital afterlife and AI replicas, will push us to think more deeply about the human side of our systems. And Teresa Torres will be sharing what she’s learning about AI—exactly the kind of continuous discovery mindset we need as we integrate LLMs into product discovery and delivery.
I’m also thrilled to see roundtables become what they’re calling an “alternative track.” That’s a smart way to deepen learning without fragmenting attention. The best conference ROI I’ve had often comes from targeted small-group conversations—where product trios compare approaches, swap metrics frameworks, or challenge each other’s product strategy assumptions. It’s a design choice that rewards curiosity and builds communities of practice.
We also get a behind-the-scenes look at Teresa’s Maker Studio workshop, where participants will build personal AI workflows. That’s exactly the hands-on, practitioner-first approach teams need right now—less demo theater, more systems that stick. If your roadmap includes integrating LLMs into continuous discovery or augmenting your team’s decision velocity, this kind of guided practice is gold.
The broader workshop slate looks deep and balanced. Expect returning favorites and practical frameworks: Rich Mironov on the realities of product leadership in complex orgs; Büşra’s metrics workshop translating outcomes into action; and an overview of additional workshops from Rich Mironov, Büşra Coşkuner, Marcus Castenfors, and Özlem Yüce. From success metrics to toolkits for product managers, the content spans IC to product management leadership—ideal if you’re stepping into new roles or scaling empowered product teams.
One of the most exciting evolutions is the Product Leadership Event, now a 1.5-day retreat. The format blends talk sessions, mini-workshops, dinners, and small-group excursions (boat rides, improv, etc.), giving leaders time and space to exchange playbooks, stress-test decisions, and build real relationships. It’s capped at 60 attendees (all in product leadership roles) to keep it intimate and useful. As someone who believes in outcomes vs output OKRs and first principles decision making, I appreciate how this structure encourages depth over breadth—and real accountability among peers.
Here are the core takeaways I’m carrying into my own planning: single-track means tighter curation, so every talk has to earn its place. Roundtables are growing into an “alternative track,” offering more ways to engage beyond stage talks. Workshops go deep and meet you where you are—IC, manager, or executive. And the leadership retreat expands to maximize learning from peers, not just from the stage. If you care about product discovery, product strategy, and conference networking that leads to actual business impact, this program looks thoughtfully engineered.
If you’re planning your 2026 calendar—or just curious how conferences evolve alongside the craft—this is a thoughtful walkthrough of what to expect. Come say hi to Teresa and Petra—on stage, at a roundtable, or somewhere in the hallway conversations that make these events memorable.
For more context and resources mentioned, explore: Product at Heart, Arne Kittler, Mind the Product, Christian Idiodi of Silicon Valley Product Group, Elaine Kasket, House of Beautiful Business, The 7 Habits of Highly Effective People by Stephen Covey, Rich Mironov, Marty Cagan, Claude Code, Codex by OpenAI, Marcus Castenfors, Büşra Coşkuner and her Success Metrics: A Playbook for Product Managers, Özlem Yüce’s Essential Toolkit for Product Managers, Petra’s Product Leadership Wheel (PLwheel), and Netlight.
Follow Teresa Torres: https://ProductTalk.org
Follow Petra Wille: https://Petra-Wille.com
Full transcripts are only available for paid subscribers.
I’ve led product teams through countless discovery cycles, and nothing has accelerated our learning loops like AI. By weaving AI into our continuous discovery practice at HighLevel, I cut time-to-insight, reduce risk earlier, and keep our product strategy relentlessly focused on customer outcomes.
AI streamlines product discovery by accelerating research, prototyping, and validation, enabling teams to make faster, smarter, and user-driven decisions.
In the research phase, I use gen ai and LLMs for product managers to synthesize interviews, cluster themes, and surface unmet needs in minutes instead of days. Pairing those qualitative insights with behavioral signals in Amplitude analytics helps me spot high-intent cohorts and friction points at scale, so our problem framing is both human-centered and data-backed.
From there, I translate insights into crisp hypotheses and prioritize with the Kano Model and outcomes vs output OKRs. To keep experiments honest, I define a minimum detectable effect (MDE) up front and design A/B testing plans that reflect realistic traffic and seasonality, ensuring our decisions are statistically grounded rather than anecdotal.
Prototyping is where gen ai for product prototyping really shines. I spin up multiple UX flows, UI copy variants, and edge-case scenarios using prompt engineering, then iterate with rapid feedback from product trios. When needed, I mock in-app guides and product tours to validate onboarding concepts before we commit to code, preserving velocity without sacrificing quality.
For validation, I lean on a mix of lightweight experiments—fake-door tests, concierge pilots, and targeted A/B testing—augmented by in-product surveys via Pendo or Intercom. For AI-powered features, I apply eval-driven development to measure relevance, latency, and safety, so we can ship responsibly while maintaining the pace of learning.
This approach only works when the team is structured to move fast. Empowered product teams and product trios own discovery end-to-end, with clear guardrails around data governance, privacy-by-design, and AI risk management. That alignment lets us shift from opinions to evidence, and from output to outcomes, without friction.
If you’re getting started, pick one discovery loop to transform: automate research synthesis, prototype two to three variants with AI, and validate with a tightly scoped experiment. Instrument your analytics, track time-to-insight and time-to-prototype, and iterate your product roadmapping and sprint planning with what you learn. The payoff is immediate: faster cycles, stronger conviction, and a more user-driven path to product-led growth.
Anyone who has lived inside construction tendering knows the grind. "When a construction company receives a bid request, someone has to open that email, parse the attached PDF (sometimes 1,800 pages describing an entire building), figure out which products are relevant, look up pricing, and draft a quote—all before the deadline. It's tedious, error-prone, and surprisingly manual." That painful reality is exactly why this conversation about Tendos AI caught my attention—and why it matters for product leaders building agentic AI in complex, document-heavy workflows.
I listened as Daniel Kappler and Matthias Hilscher from Tendos AI walked through how they’re automating the tendering workflow for manufacturers in the construction industry. What began as a narrow prototype—matching radiator requests to product catalogs—has matured into a full agentic system that does the heavy lifting from email categorization to offer generation. The end result: a scalable AI workflow that tackles messy inputs, orchestrates specialized agents, and produces quotes that are ready for human review—or even straight-through processing.
What impressed me most was the rigor. They validated the opportunity with a design partner, spent a week on-site observing real workflows, and then engineered a multi-agent architecture where specialized agents collaborate, including a "review agent" that checks work before anything reaches a human. They evaluate each agent independently (not just the whole chain), built custom observability when off-the-shelf tooling fell short, and use human-in-the-loop feedback to push toward a self-learning system.
From a product management perspective, this is agentic AI done right. It blends continuous discovery with eval-driven development, thoughtful UX decisions, and pragmatic guardrails. Evaluating agents individually makes debugging tractable and change detection transparent; a dedicated "review agent" mirrors code review to reduce error propagation; and custom tracing plus Agent Analytics provide the observability needed to operate AI workflows reliably at scale.
My key takeaway: "Start narrow to prove value: Tendos AI began with just radiators for one design partner before expanding to all building products"—a classic wedge strategy that accelerates learning while building credibility.
Another takeaway I’ll adopt in future roadmaps: "Own the interface: building a web application (vs. integrating into legacy systems) gave them control over UX and the ability to iterate toward full automation." Controlling the surface area let them move faster than a purely backend integration ever could.
On measurement and reliability, I loved this: "Evaluate each agent, not just the chain: per-agent evals make debugging tractable and show exactly where performance changed." That’s true eval-driven development—aligning metrics to decision points rather than only outcomes.
Quality gates matter in automation, and they nailed it: "Use review agents: a separate agent that checks work (like code review) catches errors before they reach humans." It’s a simple pattern with outsized ROI.
Finally, the product-market signal is unmistakable: "Let customers pull you: customers asked Tendos to replace their CPQ software—strong signals of product-market fit." When buyers invite you to displace existing systems, you’re past validation and into expansion.
If you’re exploring agentic AI for enterprise workflows, the themes here are gold: the tendering chain in construction is ripe for automation; domain expertise accelerates opportunity discovery; robust entity extraction across PDFs ranging from 1 to 1,800+ pages is non-negotiable; planning patterns for creating and updating task plans matter; agents must reason about product fit against customer requirements; custom tracing and observability unlock debugging for complex agent chains; and human feedback loops pave the path to self-learning systems.
Want to dive deeper? Listen to this episode on: Spotify | Apple Podcasts.
Explore the team and product: Tendos AI.
For builders of agentic AI, here’s my playbook distilled from this story: start narrow to earn trust and accuracy; own the interface to speed iteration; use per-agent evaluations to localize issues; add a "review agent" as a quality gate; invest early in tracing, observability, and Agent Analytics; keep humans in the loop until your metrics justify autonomy; and let strong pull signals guide your roadmap. That’s how you turn complex emails and massive PDFs into precise, production-grade quotes—consistently.
Happy New Year! I’m kicking off 2026 with a behind-the-scenes look at what’s changing in my product practice, the experiments I’m running with my teams at HighLevel, and the trends I’m most energized by—especially around continuous discovery, AI workflows, and building stronger coaching cultures.
If you want to listen to the conversation that sparked many of these reflections, you can find it here: Spotify | Apple Podcasts.
Why Teresa sunset the live deep-dive cohorts—and how on-demand and the new Discovery Habits Toolbox better support real behavior change. This pivot resonated with my own experience: some skills, especially discovery habits, only stick when they’re reinforced in the flow of real product work, not just in a time-boxed cohort. In my org, we’re leaning into on-demand learning paired with manager coaching to drive durable behavior change.
What leaders actually need to coach interviewing, assumption testing, and core discovery habits inside their orgs. I’ve found that empowered product teams thrive when leaders have lightweight coaching tools, practical prompts, and clear expectations for product trios. This is less about one-off training and more about building communities of practice where deliberate practice and feedback loops become routine.
Why training is shifting toward ongoing, leader-supported learning (and how AI will accelerate the shift). AI Strategy isn’t just about tools—it’s about learning systems. For LLMs for product managers to create leverage, we need eval-driven development, privacy-by-design, and clear guardrails. I’m building AI workflows that enable managers to review interviews, spot anti-patterns, and nudge teams toward better decisions—without replacing critical thinking.
Teresa’s move into paid subscriptions and why AI content doesn’t fit the classic “design once, run for years” course model. I see the same reality in my content roadmap: the half-life of AI guidance is short. That pushes us toward subscription models, tighter feedback loops, and a more adaptive go-to-market strategy for education products.
A sneak peek into the AI tools Teresa is building for discovery work—from interview coaching to near-ready interview snapshot generation. I’m particularly excited by tooling that scaffolds better interviews, sharpens assumption testing, and speeds up synthesis without skipping the human judgment step. These capabilities map directly to where I want my teams investing time: spending less energy on admin and more on learning from customers.
Petra’s plans for the year: community building with Product at Heart, a new product leadership email course, her Product Leadership Wheel, and workshops launching in Cairo. As someone who believes in conferences as high-quality “energy wells,” I’m inspired by how these programs create momentum for leaders who are upgrading their coaching muscles.
The role of conferences and retreats in staying grounded, inspired, and connected. I treat these gatherings as strategic resets—spaces to test ideas, confront blind spots, and deepen my network for future collaboration. The best outcomes often come from serendipitous hallway conversations and hands-on sessions where you can pressure test frameworks with peers.
How Teresa is staying on top of academic research (and why “synthetic users” aren’t ready for prime time). I agree: while synthetic data can be useful for scaffolding, it’s not a substitute for direct customer contact. Combine academic rigor with real-world interviewing and strong data governance—especially when operating under General Data Protection Regulation (GDPR).
The shared challenge of evaluating vendors and conference speakers making questionable AI claims. My heuristic: ask for clear problem statements, reproducible evaluations, grounded benchmarks, and a path to safe deployment. If a pitch can’t show measurable uplift or ignores compliance, it’s not ready for empowered product teams.
Key takeaways I’m carrying into 2026: delivery models matter; leaders need coaching tools, not just training; AI is reshaping how we teach and learn; experimentation is the theme of 2026; and community still energizes. That’s the blueprint I’m using to strengthen continuous discovery, refine our AI workflows, and sustain high standards in product management leadership.
What about you? How are you integrating AI workflows into your discovery practice, and what coaching tools are helping your managers reinforce the right habits? Share your approach—I’d love to learn what’s working in your context.
Resources & Links:
Follow Teresa Torres: https://ProductTalk.org
Follow Petra Wille: https://Petra-Wille.com
Teresa’s website: Product Talk
General Data Protection Regulation (GDPR)
Product Talk Academy
Deliberate Practice – ATP episode where Teresa talked about the ending live cohorts for Deep Dive classes
I’ve spent the last few years turning AI from an intriguing demo into an operational advantage, and the clearest wins come when we treat agents as productized workflows—not toys. In practice, that means aligning agentic AI to a sharp product strategy, instrumenting everything, and scaling what works across the organization.
Learn how companies like Replit are consolidating workflows, creating one-person departments, and building systems for scale with Amplitude
When I talk about agentic AI, I’m focused on outcomes: fewer handoffs, faster cycle times, and measurable uplift in activation, retention, and NPS. The most successful rollouts start with a specific job-to-be-done, translate it into clear AI workflows, and then iterate with a tight feedback loop between data, design, and engineering.
My implementation playbook is simple and disciplined. First, choose a high-friction workflow and define success upfront. Second, make the build vs buy call on the foundation model, orchestration layer, and connectors. Third, establish AI risk management and safeguards early—before scale amplifies errors. Finally, run small, eval-driven releases and promote what performs.
Instrumentation is where the leverage compounds. With Amplitude analytics as a unified analytics platform, I design purposeful events (agent intent, tool calls, resolution state, human handoff), map funnels from user input to agent outcome, and cohort users by context to pinpoint lift. This gives me an honest read on where agents help, where they hinder, and what to tune next.
The “one-person departments” concept isn’t about doing more with less at all costs; it’s about assembling a tight loop of product management leadership, data, and automation so one operator can own a business outcome end-to-end. An agent handles the repeatable work, while the human focuses on judgment, edge cases, and continuous improvement that compounds.
As we scale, I look for platform scalability patterns: shared tools and policies, reusable prompt libraries, standardized evaluation suites, and consistent governance. That structure keeps agent performance predictable while preserving speed, and it aligns beautifully with product-led growth when agents are embedded directly in the product experience.
If you’re starting now, begin with a single, valuable workflow. Instrument it thoroughly with Amplitude analytics, make decisions from the data you see—not the demos you remember—and expand only after you’ve proven uplift. Iteration beats ambition here: agentic AI rewards teams who measure relentlessly and scale only what truly works.
Inspired by this post on Amplitude – Perspectives.
How do you help disadvantaged students take action on opportunities they don't even know exist? That question has been top of mind for me as I’ve explored how AI can augment—not replace—human mentorship. Recently, I dug into the work behind Zero Gravity, a UK-based platform using mentoring, community, and learning pathways to unlock elite career opportunities for state school students. Their approach reframed a core problem I care deeply about: the "knowing-doing gap."
I sat down with Elliot Little (Product Manager) and Dan St. Paul (Software Engineer) from Zero Gravity to unpack how they’re tackling this gap with an AI career co‑pilot. They’ve intentionally positioned the system as an orchestrator, not an automation tool—bridging the space between knowing what to do and actually doing it. As a product leader, I see this as a powerful pattern for Generative AI: use AI to coordinate steps, personalize guidance, and empower action in moments where confidence and clarity are fragile.
What resonated most was the humility of their build journey. They started with grand visions of AI mentors and synthetic avatars, then scaled back to something simpler and more effective. The first prototype—a job suitability summary—didn’t deliver the "wow moment" they expected. And they discovered that hiding the "LLM magic" backfired—students needed to feel the personalization. That insight aligns with my own experience: users must perceive the value for trust and motivation to compound.
From a UX standpoint, the team chose text chat over voice input and leaned into guided prompts rather than empty text boxes. That decision lowered cognitive load and increased completion rates—classic product management tradeoffs that privilege momentum over novelty. In my view, this is what good AI product strategy looks like: invite action with structure, then expand autonomy as confidence grows.
The technical backbone is equally thoughtful. Multi‑month journeys require rigorous context window management to avoid exploding token counts and degrading quality. I appreciated their pragmatic toolkit: context management techniques like removing stale tool calls, summarizing history, exposing tools conditionally. They also used application logic rather than complex RAG architectures to manage tool availability and context freshness. This is the kind of disciplined engineering that keeps systems reliable at scale without overcomplicating the stack.
Model selection was fit‑for‑purpose, not one‑size‑fits‑all. They’re using different models for different tasks, including "GPT-5 Nano for structured outputs, lighter models for quick replies." That modularity enables speed and cost control while preserving high‑fidelity moments where structure matters most.
Safeguarding was treated as a first‑class concern—non‑negotiable when you’re building AI for 16‑year‑olds. Their safeguarding architecture pairs moderation endpoints with external verification via Unitary. They also invested in building a failure taxonomy through internal red team/green team exercises. This is AI risk management done right: define failure modes early, test ruthlessly, and wire safety into the product surface area—not just the model layer.
Evaluation was grounded in outcomes, not demos. The team focused on whether students progressed from insight to action: applying, interviewing, and engaging with mentors. That aligns with how I run eval‑driven development—ship narrowly, measure real behavior, and iterate toward a repeatable "wow moment" that students can actually feel.
Looking ahead, I’m excited by what’s next: long‑term memory management for multi‑year student journeys. It’s a hard problem—balancing privacy, provenance, and portability—but it’s precisely where an AI career co‑pilot can compound value over time. The vision is compelling: a resilient companion that remembers goals, adapts to context, and orchestrates the right next step.
If you want to dive deeper, you can listen to the full conversation on Spotify and Apple Podcasts:
Listen to this episode on: Spotify | Apple Podcasts
Blue Dot Impact AI Safety Course – free AI safety course Elliot recommended: https://bluedot.org/
My key takeaways: build AI that augments human relationships, not replaces them; don’t hide the personalization—let learners feel it; privilege application logic over unnecessary architectural complexity; and treat safety, context, and evaluation as product features, not afterthoughts. That’s how we bridge the "knowing-doing gap" with integrity and scale.
Every planning cycle, I’m asked the same high-stakes question: should we build or buy? In 2026, with generative AI reshaping the software landscape and budgets under scrutiny, the classic calculus needs an upgrade. The right call can accelerate time to value, protect precious engineering capacity, and sharpen competitive differentiation—while the wrong one can quietly inflate total cost of ownership for years.
“Navigate the build vs buy software dilemma, learn how AI is changing the game, and what you should leverage (and when).” That’s been my north star for product strategy this year, and it’s how I guide teams when the pressure is on.
My first principle is simple: build where we differentiate, buy where we need parity. If the capability is central to our value proposition or our defensibility, I’m inclined to build—often with a phased approach that de-risks scope. If it’s a non-differentiating layer (think billing, analytics plumbing, basic CRM integration), I’ll buy to accelerate, then revisit once scale and specialization justify a deeper internal investment.
AI changes the equation on both sides. On the “buy” side, modern platforms now ship agentic AI, fine-tuning options, and robust APIs that let us compose advanced capabilities fast. On the “build” side, AI workflows and toolchains (from code copilots to eval-driven development) compress cycle time, making bespoke solutions more attainable. The trade-off has shifted from pure functionality to questions of AI risk management, model governance, data privacy, and the portability of prompts, embeddings, and training data.
I evaluate decisions across two economic horizons: time to value versus total cost of ownership. Buying often wins the first round—faster deployment, proven reliability, and lower initial lift. But TCO can creep: integration work, per-seat or consumption SaaS pricing, training, vendor-driven roadmap gaps, and the “shadow ops” of maintaining connectors in our CI/CD. Building flips that profile: slower early velocity, higher upfront complexity, but potentially lower long-run costs and tighter fit with our platform scalability goals.
Operational risk matters just as much as features. I look at incident management posture, SRE maturity, SLAs, and DORA metrics to gauge resilience. If a vendor can’t meet our uptime and recovery expectations—or if their roadmap pace mismatches our deployment frequency—we’re effectively renting risk we can’t control. Conversely, if our team can’t realistically support the operational burden, buying is the safer choice.
Security, regulatory compliance, and data governance are non-negotiables. I assess privacy-by-design, data residency, audit logs, role-based access, SOC2/ISO coverage, and threat detection and response. For AI-heavy systems, I add model lineage, red-teaming practices, PII handling, and retention policies. If we can’t verifiably meet our obligations in a build scenario within the launch window, we buy and require clear data exit and portability clauses.
To keep decisions objective, I use a lightweight scorecard across five dimensions: differentiation, urgency/time to value, regulatory/security risk, integration complexity, and AI leverage/portability. We weight criteria with product trios (PM, design, engineering), run discovery spikes, and validate assumptions with stakeholder management up front. A disciplined scorecard curbs recency bias and helps us communicate trade-offs to leadership.
In practice, I favor staged commitments. When uncertainty is high, we buy to learn—ship value quickly, instrument usage, and collect evidence. If adoption proves sticky and integration pain remains moderate, we double down with deeper vendor integration. If we uncover unique needs or cost inflection points, we pivot to a build plan that reuses learnings, data models, and UX patterns from the bought solution to reduce risk.
AI-specific choices deserve their own pass. For example, if we need retrieval-augmented generation, I’ll often buy for the orchestration and observability layer while building our domain-specific retrieval-first pipeline and prompt engineering guardrails. That split gives us speed plus control: we retain our IP and data gravity while tapping best-in-class tooling that evolves with the ecosystem.
Vendor strategy matters as much as technology. I negotiate clear data export, transparent API quotas, sandbox environments for continuous discovery, and price protections for growth. I pressure-test roadmaps, ask for integration references, and align on outcome-based milestones rather than feature checklists. Strong partners welcome this rigor; weak ones stall—another useful signal.
On the build side, I right-size ambition. We target minimum lovable scope, isolate risk in early sprints, and leverage open source where it’s mature and secure. We design for modularity so we can swap components without rewriting the world, and we budget time for in-app guides and product tours to smooth adoption, because user activation is the real finish line.
Here’s the playbook I return to: buy to validate and compress time to value; build to differentiate and reduce long-run TCO; continuously re-evaluate as the AI toolchain and our scale evolve. With a transparent scorecard, a bias for learning, and a clear view of risk, the build vs buy decision becomes less of a leap of faith and more of a repeatable product management capability.
2026 will reward teams that move fast without mortgaging the future. Make the call deliberately, instrument the outcomes, and stay humble—because the best strategy is the one you can adapt as new evidence arrives.
I wanted to cut through the hype and see what’s actually changing inside customer service teams as AI agents like Fin move from pilots to production. So I analyzed 166 interviews with support leaders, managers, and frontline specialists to understand how roles, workflows, and team structures evolve once AI becomes part of everyday work.
The anecdotes were already loud: AI tools are transforming customer support. But the scale, shape, and consistency of that transformation? Less clear. I went to the source—the practitioners living it—to quantify what’s real and what’s next for customer support AI strategy.
Here’s what I gleaned from the data.
TL;DR — What’s changing
AI is reorganizing core CS operations: Nearly every team (≈95%) reported meaningful workflow changes. Triage, routing, translation, and categorization are increasingly automated. Hybrid human+AI systems are taking their place.
Frontline work is changing to AI oversight: Humans now QA, monitor, and test AI outputs. When it comes to handling queries, they step in for nuance, rather than repetition.
Structural change is widespread but uneven across companies: 83% reported new responsibilities or roles. Some built AI pods, while others retained traditional setups.
Tier 1 headcount demand is falling: 28% saw hiring freezes, slowdowns, or natural attrition at Tier 1 level as AI Agents manage more requests and improve operational efficiency.
Skill gaps are widening inside teams: Data literacy, QA, and cross-functional communication are all rising in value. For many companies, long-term role strategy is lagging behind.
Research methodology
The goal of this research is to understand how many customer service teams have changed their roles, responsibilities and ways of working due to adopting AI agents, as well as understanding how these changes manifest within their organizations.
For this study, the data chosen consists of interviews conducted by the research team, either with Intercom customers or prospects. This data was chosen because the focus of the interviews revolved around the individual experience of the participant, which gives a higher chance of information related to role changes to be present.
The data was collected using Snowflake by pulling all interviews stored in gong conducted by a member of the research team from 01-01-2025 to 14-10-2025.
After the data was pulled, a python script was used to clean the conversation corpus for each conversation retrieved. Common English stopwords (e.g. “and”, “very”, “with”, etc.) were removed, as well as all the text associated with a speaker in the conversation that was not the interview participant(s). This was done to reduce the computational power required for the conversation coding, avoid API timeouts and reduce costs.
After the corpus was cleaned, the OpenAI API was employed, alongside a prompt, to code each conversation using closed codes defined in a closed codebook.
The codes used were:
No role change mentioned: No explicit changes to roles, teams, or reporting lines are attributed to AI/Fin.
Role responsibilities changed due to AI/Fin: Duties/ownership moved between humans and AI/Fin, or scope of a role changed because AI/Fin handles tasks.
Team structure/reporting changed due to AI/Fin: Org/team boundaries, team charters, or reporting lines changed due to adopting AI/Fin.
Headcount/hiring impacted due to AI/Fin: Hiring plans, headcount, staffing coverage, or shifts/rotations changed due to AI/Fin.
Workflow/process changed due to AI/Fin: Steps, triage/escalations, routing, or playbooks changed because AI/Fin alters the process.
Other organizational changes due to AI/Fin: Other changes inside the organization due to AI/Fin that don’t involve a change in responsibilities, team structure/reporting lines, headcount or workflow/processes changes.
Data analysis
166 conversations were retrieved. More than 90% of all conversations report some sort of change either in their role, team, or processes due to implementing Fin, or a similar AI product, with only 13 participants reporting no changes.
Across these conversations, each one could have multiple types of change associated with it (M = 2.35, Med = 2, Min = 1, Max = 4, N = 166).
More specifically, after implementing Fin or a similar AI product:
94.58% participants reported having their processes and workflows disrupted
82.53% participants reported seeing their role and responsibilities change
27.71% participants reported changes in company headcount or hiring
6.02% participants reported their team structure or reporting lines changing as a result
Additionally, 16.27% participants reported a change for a different reason from the ones highlighted above (“Other organizational changes due to AI/Fin”).
Sample representativeness
The sample is representative with a confidence level of 90% and a margin of error of ±6.4% (accounting for an overall unknown population size). The individual confidence intervals for each type of change are as follows.
Workflow/process changed due to AI/Fin: 157 (94.6%), 90% CI: 91.7% – 97.5%
Role responsibilities changed due to AI/Fin: 137 (82.5%), 90% CI: 77.7% – 87.4%
Headcount/hiring impacted due to AI/Fin: 46 (27.7%), 90% CI: 22.0% – 33.4%
Other organizational changes due to AI/Fin: 27 (16.3%), 90% CI: 11.6% – 21.0%
No role change mentioned: 13 (7.8%), 90% CI: 4.4% – 11.3%
Team structure/reporting changed due to AI/Fin: 10 (6.0%), 90% CI: 3.0% – 9.1%
Thematic analysis
1) Automation and AI integration replacing manual steps (94.58%). I see AI workflows embedding into every stage of support. Manual triage, routing, translations, and repetitive responses shift to Fin or similar systems, while agents focus on human-in-the-loop oversight.
Agents’ day-to-day work now revolves around monitoring or fine-tuning AI outputs, not replying to the same questions. In many teams, conversations enter Fin first; humans only step in when nuance or exception handling is required. Testing, QA, and rollout practices have matured too—teams track Fin’s accuracy and iterate intentionally.
2) Humans shift to oversight, AI handles execution (82.53%). The role resets are unmistakable. Support agents and managers move from high-volume execution to optimization, configuration, and measurement. New roles emerge—AI specialists, automation managers, Fin owners—while responsibilities migrate toward strategic analysis and quality assurance.
Duties are redistributed: Fin takes on refunds, triage, simple messaging, even parts of the sales process. I’ve watched some careers pivot toward product/ops or AI systems strategy as managers coordinate testing and monitor adoption metrics.
3) Reductions or slower growth due to efficiency gains (27.71%). Efficiency is real. Many teams reduce Tier 1 headcount needs or slow hiring because AI absorbs simpler requests. Others reallocate people to complex work or AI management. A few still expand—adding automation engineers, implementation specialists, or technical AI leads—but not at past growth rates.
The upshot: organizations handle more volume while stabilizing or reducing staffing, especially at the frontline tier.
4) New AI teams, flatter orgs, fewer escalation layers (6.02%). I’m seeing organizational design catch up to the tech. Some companies form dedicated LLM or automation teams. Others flatten hierarchies, design around workflow complexity instead of region, or merge roles. Dedicated escalation layers shrink as Fin routes or resolves more autonomously.
Team design is getting more modular and data-driven, with clearer ownership for configuration, governance, and Agent Analytics.
5) Broader digital transformation and operational modernization (16.27%). Beyond support, companies are modernizing their operating model: automation-first, digital self-service, better data foundations, and new vendor ecosystems. Collaboration patterns between data, ops, CX, and product/engineering are tightening, with a culture of experimentation and continuous improvement taking hold.
How have customer service roles and responsibilities changed due to Fin/AI agent implementation?
Implementing Fin or a similar AI agent profoundly changes how an organization operates, with around 95% of participants reporting some level of change in their processes after implementation. These systems have significantly reshaped the workflows that customer service teams are used to. Tasks once performed manually, such as ticket triage, routing, repetitive responses, and translations are now handled by AI agents.
“This marks a clear transformation in how customer service agents work: moving away from directly resolving customer queries to focusing on more analytical and procedural work”
As a result, customer service agents’ responsibilities have shifted from performing manual tasks to monitoring and fine-tuning the AI agent whenever its output is inaccurate or incomplete. This marks a clear transformation in how customer service agents work: moving away from directly resolving customer queries to focusing on more analytical and procedural work, such as testing, QA, and performance analysis of AI outputs.
Human agents who still handle conversations tend to do so either because the AI agent cannot yet respond adequately, or because of an organizational choice to retain human involvement for sensitive or high-value interactions. Nevertheless, the need for such roles is diminishing. Around 28% of participants reported a reduction in Tier 1 staff or a hiring slowdown or a full hiring freeze, as AI agents increasingly manage simple requests and organizational attention shifts towards improving automation efficiency.
“In some cases, this has led to the creation of specialized AI teams, reorganizations around workflow complexity, or the merging and redefinition of existing roles”
However, this transformation is not uniform across companies. While some roles have disappeared (particularly escalation layers), others have emerged. Many organizations are reallocating existing staff to AI management or hiring new technical profiles such as automation engineers, implementation specialists, and AI leads. In some cases, this has led to the creation of specialized AI teams, reorganizations around workflow complexity, or the merging and redefinition of existing roles.
Around 83% of participants reported changes to their roles or responsibilities following the introduction of Fin or similar AI agents. Specifically, customer service agents who no longer handle basic queries now focus on managing AI performance, reviewing Fin tasks and improving automation outputs. Managers oversee AI evaluation and implementation, coordinate testing, and monitor AI metrics such as resolution and involvement rates. In some organizations, new dedicated roles have emerged—AI specialists, automation managers, or Fin owners—reflecting a strategic shift toward automation-first, digital self-service models.
These structural shifts are also cultural. I’m seeing teams embrace experimentation, versioning, and eval-driven development while deepening collaboration with data, operations, and product/engineering. The move from outcomes vs output OKRs is palpable: leaders are measuring containment, deflection, CSAT, and time-to-resolution with new rigor.
Overall, a widespread transformation is underway. Roles are broadening, responsibilities are diversifying, and cross-functional collaboration is becoming the norm. Given the pace of gen ai improvement and the rise of agentic AI patterns, I expect these shifts to intensify.
This evolution raises two important questions
Firstly, do customer service agents possess the skills required to succeed in these new roles? While they are experts in customer interaction and company policy, their work now demands new competencies in data analysis (e.g. reporting AI agent performance and how it changes over time), quality assurance/debugging (e.g. Fin output testing and versioning), and cross-functional communication (e.g. if help from another team is required, drafting a business case to justify the resources required could be needed).
Secondly, what long-term strategies are companies adopting to support these evolving roles? Some are reorganizing entirely around automation, while others retain traditional structures. For those undergoing transformation, it remains unclear whether these changes are part of a deliberate strategic plan aimed at achieving specific performance outcomes, or the result of experimentation without defined goals.
Ultimately, Fin’s success— and of AI in customer service more broadly— depends not only on the technology itself but on the people and strategies that shape its use. In my experience, the winners invest early in data literacy, robust QA, clear ownership, and governance; they align product, ops, and CX around a shared AI roadmap; and they measure what matters with disciplined Agent Analytics. That’s how you turn AI workflows into durable customer and business outcomes.
I’ve lost count of how many times I’ve been asked for a “quick AI agent” that can autonomously fix customer problems, write code, or run sales ops. The promise is intoxicating—and I get why. But in practice, sustainable impact comes from disciplined product thinking, not wishful automation. Drawing on my experience leading product for complex, agentic AI initiatives, I want to debunk four misconceptions I see repeatedly and share what actually works.
Misconception 1: AI agents are plug-and-play. The reality is that effective agentic AI behaves more like a new product line than a feature toggle. It needs clear job stories, domain grounding, tool access, and guardrails. I start by narrowing scope to one painful job to be done, then design AI workflows that reflect real constraints (SLAs, compliance, edge cases). From day one, I instrument with Agent Analytics and set up eval-driven development so we can see failure modes early and iterate with intent.
What consistently moves the needle is treating the agent like a teammate you onboard: define responsibilities, provide the right tools, and measure outcomes. I pair scripted validations with live evals, track containment rates and handoff quality, and balance precision/recall depending on the risk profile. This is slow to fast, not fast to broken.
Misconception 2: Bigger models make better agents. In my experience, architecture outperforms horsepower. A retrieval-first pipeline, tight context window management, and practical prompt engineering often beat an oversized model that hallucinates. Tool use matters more than model size: give the agent reliable APIs, clear schemas, and deterministic fallbacks. For LLMs for product managers, the play is to right-size the foundation model and invest in data quality, prompts, and evaluators that reflect your true acceptance criteria.
When I see erratic behavior, I don’t immediately swap models; I improve retrieval, prune irrelevant context, and clarify the agent’s planning loop. Most performance gains come from better state management and grounding rather than a pricier token budget.
Misconception 3: Agents replace teams. High-performing organizations design human-in-the-loop systems. I implement human review on high-risk actions, explicit escalation paths, and simple override mechanisms. That’s not just safety theater—it’s good product design. AI risk management and data governance are part of the product backlog, not an afterthought. In customer support ai strategy, for example, the agent drafts, a specialist approves, and the system learns from deltas to tighten future responses.
The social system matters as much as the technical one: clear role boundaries, audit trails, and feedback loops turn the agent into a force multiplier. Teams gain leverage without surrendering accountability.
Misconception 4: Shipping the agent equals success. Adoption is earned, not announced. I treat agent launches like any product-led growth motion: define activation events, remove friction with in-app guides and product tours, and A/B test prompts, tool choices, and UI affordances. We track time-to-value, task completion rate, and user trust signals (edits, undo patterns, and escalation requests). When we get those leading indicators right, retention follows.
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.
My playbook is simple and repeatable: frame the problem narrowly, ground the agent with the right tools and data, measure with eval-driven development and Agent Analytics, then grow adoption with a disciplined go-to-market inside the product. The agents that win don’t feel like magic—they feel dependable. That’s what customers trust, and that’s what scales.
Every week, I watch the cybersecurity landscape shift under our feet. As a VP of Product Management, I’m responsible for building secure, resilient products—and that means understanding how artificial intelligence is transforming the way IT teams defend, respond, and even anticipate attacks.
Learn the ways in which AI is transforming both cybersecurity offense and defense for IT teams.
First, AI supercharges threat detection and prevention. Pattern-recognition models now sift through endpoint telemetry, identity signals, and network flows to surface anomalies in near real time. In practice, that means fewer false positives, faster prioritization, and earlier containment. We’re pairing behavioral analytics with enrichment from our SIEM/EDR stack so analysts get a ranked, explainable view of risk instead of a noisy alert queue—directly improving mean time to detect and laying the groundwork for scalable threat detection and response.
Second, AI accelerates incident response. We’ve embedded LLM-powered copilots into our SOC workflows to summarize alerts, propose next-best actions, and auto-generate draft remediation steps from playbooks. Orchestration then executes routine tasks—isolating endpoints, rotating credentials, updating tickets—while keeping a human-in-the-loop for approvals. To keep this safe, we use privacy-by-design principles, a retrieval-first pipeline for authoritative playbook content, and eval-driven development to measure precision/recall on suggested actions. The result is meaningful reduction in mean time to recover and more consistent incident management.
Third, the offense is getting smarter—and we need to be honest about it. Adversaries use gen AI to craft targeted spear-phishing, deepfake executive voice notes, and polymorphic malware that evades signature-based tools. We counter by red-teaming with AI, deploying deception tech to waste attacker cycles, and hardening identity as the new perimeter (MFA, conditional access, continuous risk scoring). Education matters, too: when employees see how convincing AI-generated lures have become, phishing reports spike and successful compromise rates drop.
None of this works without strong governance. We treat AI like any high-impact capability: rigorous data governance, model access controls, and AI risk management across the lifecycle. We log model prompts and outputs, restrict sensitive data via contextual policies, and continuously test for drift and bias. This is as much an IT leadership challenge as it is a technical one—clear ownership, well-defined runbooks, and regular tabletop exercises make the difference between resilience and chaos.
If you’re getting started, I recommend a focused 90-day plan: identify one high-signal detection use case, one response playbook ripe for automation, and one employee risk area (usually phishing) for immediate uplift. Instrument everything—latency, precision/recall, MTTR—and iterate with a cross-functional group spanning security engineering, SRE, and product management leadership. With disciplined AI strategy and guardrails in place, you can move faster, reduce noise, and stay ahead of adversaries without compromising data or trust.
Every quarter, I revisit the same three questions: Are we accelerating adoption, lowering cost-to-serve, and managing risk without slowing the roadmap? Tools that help me answer all three with clarity earn a place in my stack. That’s why the concept behind Pendo’s Agent Analytics resonates so strongly—it gives product leaders a way to see, in one view, how users engage with AI-powered assistants, in-app guides, and core workflows, and how those behaviors translate into product-led growth.
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, Agent Analytics functions as a unified analytics platform for the modern product team. I can observe how users interact with agents and nudges inside the product, connect those interactions to user activation and retention analysis, and prioritize improvements that deliver measurable outcomes. The result is fewer blind spots across the journey and a tighter feedback loop between discovery and delivery.
The real value shows up when I pair analytics with targeted interventions. For example, I’ll instrument critical paths, baseline activation, then use in-app guides to remove friction at the exact moment users need help. I incorporate A/B testing and continuous discovery to validate which prompts, pathways, or workflows actually move the needle. With a clean view of adoption, engagement, and time-to-value, my team can double down on what works and retire what doesn’t—faster.
Risk reduction is equally important. With clear behavioral signals, I can spot confusing prompts, unhelpful agent responses, or unexpected drop-offs before they scale into churn or support volume. That visibility informs our product strategy, aligns stakeholders on trade-offs, and keeps our governance tight without stifling innovation—especially critical as AI Strategy becomes part of everyday product decisions.
If you’re weighing whether Agent Analytics deserves a place in your toolkit, consider this: better instrumentation yields better decisions. When you unify guide interactions, agent engagement, and core product usage, you can attribute uplift more precisely, forecast impact with greater confidence, and operationalize product-led growth. That’s how we increase adoption, cut unnecessary cost, and de-risk the roadmap—while building experiences customers actually love.
In my role leading product, I’ve learned that the fastest path to higher-quality deliverables from large language models (LLMs) is not a clever prompt—it’s rigorous context. I call the practice AI context pulling: a repeatable way to assemble, compress, and structure the most relevant knowledge before the model ever starts generating. Done well, it turns generative AI into a dependable partner for discovery, prioritization, and execution.
AI context pulling means I proactively gather the right artifacts (customer insights, analytics, strategy, constraints), manage context windows intentionally, and shape the model’s task with clear objectives and guardrails. This reduces hallucinations, improves alignment, and creates traceability back to sources—critical for product management leadership and stakeholder trust.
Learn a new way in which product professionals can collaborate with AI to get even better results on their projects.
Here’s the simple flow I use: first, I define the intent (e.g., “synthesize discovery interviews for a positioning brief”). Next, I inventory relevant context: top customer pains from product discovery, usage patterns from Amplitude analytics, recent support trends from Intercom, and any constraints from our product strategy. Then I run a retrieval-first pipeline to select only the most pertinent slices—favoring recency, representativeness, and canonical sources.
Because context window management matters, I compress long documents into short, source-cited summaries and keep raw excerpts handy when nuance is important. My prompts follow a consistent structure: role and objective, constraints and audience, curated context, the explicit ask, preferred output format, and a brief self-check (e.g., “cite sources and flag uncertainty”). This is prompt engineering for reliability, not theatrics.
A quick example: when drafting a one-page feature brief, I attach three items—the product strategy paragraph that sets the frame, a usage cohort analysis that highlights who’s affected, and five verbatim customer quotes. I ask the LLM to propose a problem statement, success criteria, and a shortlist of solution hypotheses, each tied to a cited piece of evidence. The result is a grounded, decision-ready artifact I can share with product trios and stakeholders.
Tooling-wise, I keep it pragmatic. A lightweight retrieval-first pipeline (embeddings, metadata filters, and recency rules) ensures the LLM pulls what matters. I version prompts and contexts together so I can run quick A/B testing on output quality. And I log decisions and sources to support eval-driven development and continuous discovery.
Common pitfalls are avoidable. Too little context yields generic answers; too much overwhelms the model. Stale docs can mislead; curate aggressively. Vague asks invite fluffy prose; specify outcomes, audiences, and formats. If the task is high risk, I bias toward smaller, well-cited outputs and expand iteratively with human review in the loop.
To measure impact, I track rework rate, review time, and stakeholder alignment on first pass. Over time, teams adopting AI context pulling report clearer artifacts, faster synthesis cycles, and more confident decisions—because every recommendation traces back to evidence. That’s how humans and LLMs truly collaborate better: we provide the right context, and the model amplifies our judgment.
If you’re ready to operationalize this, start by templatizing your most common product workflows—discovery synthesis, roadmap rationale, and release notes—and attach small, high-signal context packs. With a retrieval-first mindset and disciplined prompting, AI becomes an extension of your product craft, not a gamble.
