AI Native is more than a feature set—it’s an operating system for the entire business. In my role leading product, I’ve seen that companies win when they treat AI as a first-class citizen across strategy, architecture, workflows, and go-to-market. In this narrative, I unpack what “AI Native: What It Means and How to Get There” looks like in practice, sharing the frameworks I use to align vision, technology, and teams around measurable customer outcomes.
When I say AI Native, I mean a company where core value creation, customer experience, and internal operations are powered by AI end-to-end. It’s not just bolting on a chatbot. It’s rethinking product strategy, data foundations, and execution so we can deliver differentiated experiences faster, at lower cost, and with higher reliability. This shift demands clarity on where AI truly creates leverage—and the courage to say no where it doesn’t.
The starting point is strategy. I ground teams in outcomes vs output OKRs and a crisp value proposition: Which customer jobs-to-be-done benefit most from generative AI? Where can we unlock 10x improvements in speed, accuracy, or personalization? We prioritize a small number of high-signal use cases, size impact, and design Minimum Viable Experiments (MVEs) to de-risk assumptions before scaling. This is where build vs buy decisions matter—use foundation models and platforms for commodity needs, and invest your scarce engineering time where differentiation lives.
Next comes architecture and data. AI Native products thrive on a retrieval-first pipeline, strong context window management, and model-agnostic abstraction so we can swap providers as needs evolve. I emphasize privacy-by-design, robust data governance, and observability across prompts, embeddings, latency, and cost. These guardrails let us move quickly without compromising trust, especially in regulated or enterprise settings.
Execution shifts as well. I organize empowered product teams and product trios around the highest-value workflows, not components. Continuous discovery pairs with CI/CD, feature flags, and telemetry so we can test safely in production. Eval-driven development is non-negotiable: we design offline and online evaluations that mirror real user success criteria—accuracy, helpfulness, safety, and business outcomes—then wire those evals into the build pipeline to prevent regressions.
On the intelligence layer, we increasingly rely on AI workflows and agentic AI to orchestrate multi-step tasks—retrieval, reasoning, tool use, and verification—with human-in-the-loop where appropriate. Clear system prompts, tool definitions, and fallbacks keep behavior predictable. This is where product craft meets prompt engineering and LLMs for product managers: the best teams codify patterns, share prompts in a living library, and standardize on a lightweight AI product toolbox.
Risk and reliability are part of the product, not an afterthought. I run AI risk management as a continuous program spanning red teaming, content filters, PII handling, audit trails, and incident response. We tie policies to concrete controls and create simple dashboards leaders can trust. The goal is to ship boldly with safety, maintainability, and scale in mind.
Becoming AI Native also changes how we grow. We lean into product-led growth with clear in-app guides, product tours, and activation paths that teach users where AI shines. CRM integration ensures sales and success teams have context to coach customers. Pricing experiments—often usage- or value-based—align revenue with the impact customers feel, while retention analysis helps us double down on the use cases that drive compounding value.
To make this real, I use a 90-day plan. Days 0–30: align on strategy, top use cases, and risk posture; stand up data pipelines and a basic retrieval-first stack; define evaluation metrics. Days 31–60: ship MVEs behind feature flags, run head-to-head evals, and instrument observability; start a cross-functional community of practice. Days 61–90: scale the winning use cases, formalize governance, and publish a roadmap tied to outcomes—not just features—with clear SLAs and success metrics.
The destination is a durable advantage: faster iteration cycles, smarter experiences, and a product strategy that compounds with every interaction. If you’re ready to make the leap, start small, measure obsessively, and build the muscle to ship, learn, and adapt. That’s the heart of becoming AI Native—and it’s well within reach.
I continually study how high-velocity teams turn AI ambition into shipped product, and Amplitude’s approach stands out. "Leo Jiang is the Head of Engineering, AI Products at Amplitude, focused on building new AI and marketing products. He has helped build Ask Amplitude, Agents, and AI Visibility." From a product management leadership lens, that portfolio signals a clear AI strategy: enable insight (Ask Amplitude), drive action (Agents), and ensure trust and observability (AI Visibility).
What I appreciate most is the sequencing: start with user-facing value, build agentic AI capabilities where tasks repeat and outcomes can be evaluated, and layer AI workflows with robust governance. For PMs and LLMs for product managers, the implication is to define success via eval-driven development—quantitative rubrics, offline test sets, and real-time feedback loops—before scaling automation. This also hints at an emerging discipline of Agent Analytics: instrument prompts, tool calls, and outcome quality so we can tune performance like we tune a funnel.
Ask Amplitude gives a relatable example: natural-language questions lower the activation barrier for product and growth teams inside an Amplitude analytics environment. When agents turn answers into next-best actions, product-led growth becomes measurable—from hypothesis to change to impact—inside a unified decision loop. That tight loop is where product strategy, design, and reliability meet to create compounding value.
Operationally, I organize a product trio around each capability and pair it with forward deployed engineers to accelerate discovery with customers. I also invest in privacy-by-design and data governance early, ensuring marketing use cases respect compliance while keeping iteration speed high. The goal is a repeatable path from prototype to scale that preserves momentum without compromising safety.
My takeaway for peers: pick one high-frequency workflow, define clear agent boundaries, ship a narrow slice, and measure relentlessly. Use retrieval-first pipeline patterns for grounding, add human-in-the-loop checkpoints, and close the loop with qualitative insights from in-app guides. When that works, expand capabilities—not just features—and let outcomes vs output OKRs steer prioritization.
Inspired by this post on Amplitude – Best Practices.
This is the year to build your personal operating system. For me, that line isn’t a slogan; it’s a commitment to eliminate context switching, compress decision cycles, and turn fragmented information into a reliable source of truth. As a product leader, I needed a system that blends judgment, data, and automation—so I built mine around Claude Code.
When I say “personal operating system,” I mean an integrated set of AI workflows, rituals, and tools that capture knowledge, structure decisions, and automate execution. It’s where product discovery meets delivery: a place to synthesize signals, prioritize with clarity, and move from insight to action without friction. The outcome is fewer ad hoc decisions, more deliberate strategy, and a calmer, more focused day.
Claude Code sits at the center because it helps me translate intent into working software and repeatable processes. I use it to scaffold small utilities, write adapters for APIs, and evolve prompts into robust patterns. It accelerates everything from research synthesis and PRD drafting to backlog grooming and stakeholder updates—while keeping me in the loop for final judgment.
Under the hood, I run a retrieval-first pipeline that connects notes, docs, tickets, research transcripts, and roadmaps into a searchable, living memory. With careful context window management, I feed only the most relevant snippets into Claude Code, preserving accuracy and speed. The result: richer answers, fewer hallucinations, and an assistant that “remembers” what matters without drowning in noise.
My daily loop is simple: capture, synthesize, decide, and act. I capture customer signals and meeting notes into a personal knowledge management vault; synthesize patterns with prompt engineering that emphasizes evidence; decide using outcomes vs output OKRs; and act by generating drafts, creating tasks, and updating artifacts. Claude Code helps me wire this end-to-end, so the system works even on my busiest days.
If you’re implementing this from scratch, start small. Pick one high-friction workflow—say, product feedback triage—and build a narrow agentic AI flow to classify, summarize, and route items. Use eval-driven development to test prompts against known edge cases. Add guardrails and privacy-by-design practices from day one, then expand to neighboring workflows once the first loop is reliable.
Governance matters. I treat AI risk management, data governance, and security as first-class citizens: limited data scopes, clear audit trails, human-in-the-loop approvals, and rollback plans. Feature flags control changes; observability tracks drift and quality; and a simple playbook documents how we deploy, monitor, and improve the system.
Measure what this personal operating system earns you. Track decision latency, cycle time from signal to action, meeting-to-output ratios, and the signal-to-noise ratio of inputs. When the system is working, you’ll feel it: fewer meetings, more momentum, and sharper product strategy supported by trustworthy AI workflows.
The goal isn’t to automate judgment—it’s to protect it. By letting Claude Code handle the glue work and information wrangling, I preserve energy for high-leverage thinking: positioning, sequencing, and trade-offs. Build your personal operating system now, and make this the year your product practice runs with clarity and composure.
I’ve watched AI adoption accelerate dramatically over the last year, and the momentum is undeniable. Teams everywhere are experimenting, piloting, and operationalizing AI—but the ways they’re doing it, and the outcomes they’re seeing, vary widely.
Our latest research shows that 82% of senior leaders invested in AI for customer service in 2025, and 87% plan to in 2026. That’s the new baseline. The differentiator now is depth—how far AI is embedded into core workflows, accountability, and measurement.
Teams with mature AI are almost twice as likely to achieve higher, more consistent support quality. Our survey shows 43% of advanced adopters citing this benefit compared with 24% of early deployments.
But while most teams are using AI, our 2026 “Customer Service Transformation Report” shows that this usage is not equal. A gap is opening up between teams that have deployed AI at a surface level and those that have integrated it deeply. I see this firsthand: shallow deployments answer FAQs; deep deployments redesign processes, policies, and teams.
Survey results highlight the AI deployment gap: nearly nine in ten organizations with mature AI see improved customer service metrics (87%), compared with 62% across all respondents, visualized with bold circles.
For this year’s report, we surveyed over 2,400 global customer service professionals across a range of industries to see how they’re using AI today, where it’s paying off, and what they’re betting on as they plan for 2026. The findings mirror my experience leading AI Strategy and AI workflows at scale.
As AI programs advance, measurement confidence surges. This chart shows how ROI tracking rises from 35% in exploring to 70% in mature deployments—evidence of a widening execution gap in customer service.
We found that for many teams, AI is still doing narrow work like answering simple questions or handling small parts of workflows. These teams are seeing benefits, but only a fraction of what’s possible. Meanwhile, a smaller group is pulling away. They’ve put AI at the core of their service operation, integrating it into critical workflows, giving it more responsibility, and continuously improving it over time. That’s the hallmark of mature deployment.
Customer service priorities are shifting fast. By 2026, improving CX tops the list at 58%, cost and efficiency climb, and quality moves to third as teams prepare to scale operations and evolve skills.
The difference in results and overall support experience – for both teams and customers – is significant. Here’s how I interpret the data and what I recommend to close the gap.
Survey insights from the 2026 customer service transformation report reveal how AI reshapes support roles: 45% of teams updated job descriptions and 40% ramped up AI training, while human agents focus more on complex escalations.
AI adoption is the norm, depth makes the difference. According to senior leaders, 82% of organizations invested in AI in 2025, with 87% planning to invest in the year ahead. Despite this widespread investment, only 10% of teams report having reached a mature level of deployment, where AI is fully integrated into operations and working at scale. In my playbook, maturity means end-to-end ownership of well-defined workflows, robust guardrails, and clear success criteria.
Early AI wins are fueling expansion beyond support. Survey results show 57% cite proven success, 49% aim for a unified customer experience, 33% need to scale without adding headcount, and 31% see demand from other teams.
Reaching this level of maturity is where AI’s real value lies. We found that 43% of teams with mature deployment report higher quality and consistency across support – nearly double the rate of those still in the exploration or initial deployment stages. That aligns with what I see when we move from point solutions to platform thinking and agentic AI patterns.
Leaders are racing ahead with real AI in support. Explore the 2026 Customer Service Transformation Report to see where deployment is stalling, benchmark your team, and get practical steps to scale automation that delights.
ROI becomes clearer with deeper integration. The economic benefits of AI tend to show up first in speed and throughput, and they show up fast. Across all respondents, 62% say their customer service metrics have improved since implementing AI. Most often, teams report their initial gains in efficiency and scale—faster responses, shorter handling times, and the ability to resolve more conversations with the same team—all driving lower cost per interaction.
But the deeper teams go with deployment, the more the results start to show in the metrics. We found that among teams that describe their AI deployment as mature, the cohort of respondents reporting improved metrics as a result of AI rises from 62% to 87%. What’s more, teams with more mature deployments are significantly more likely to say they can measure the return on their AI investment. My advice: instrument everything upfront, baseline rigorously, and use eval-driven development to iterate with confidence.
The bar has moved from ‘does it work?’ to ‘is it actually good?’ More than ever, teams are focused on improving customer experience and satisfaction, with 58% saying it’s the top priority for 2026. That number has more than doubled since last year, when just over a quarter (28%) of respondents cited it as a top priority. As AI assumes repetitive work, your people can shift from reactive triage to proactive journey design. Now is the time to invest in quality frameworks, prompt engineering standards, and LLMs for product managers to close the loop between product, ops, and CX.
Important support work now extends beyond the inbox. AI is reorganizing core customer service operations as it starts to take on a higher volume of work and more complex tasks. Even at the initial deployment stage, 16% of teams report spending less time handling support volume since implementing AI – and among teams who’ve reached maturity, that figure rises to 28%. I’ve seen new roles emerge—AI operations managers, conversation designers, and model evaluators—alongside upskilling for agents into higher-order troubleshooting and relationship building.
Support is creating the blueprint for AI deployment across the business. Support was the proving ground for AI, and our research suggests that businesses are now planning to expand its use to other areas based on the results it’s yielded so far. Fifty-two percent of respondents said that their organizations are actively planning to scale AI to departments like customer success, marketing, and sales in 2026. The two most cited driving forces behind this decision are the success support has seen with AI to date and a desire to create a unified customer experience. Treat your support stack as a reusable platform: shared services, governance, and reusable components accelerate adoption in adjacent functions.
Seize the opportunity to close the gap. Having or not having AI isn’t a question anymore. What you should be asking now is how close you are to mature deployment, where AI is capable of tackling nuanced, high-stakes work. Those who have reached this stage show that going deep is what unlocks real value. That’s the opportunity. Push AI to do more, bring it to more channels, use it to resolve the most complex queries, and close the gap before it becomes too wide to close.
This might seem daunting. But trying new things always is. What we’re experiencing now is a defining moment for customer service, and the teams that are leaning in are actively building the future. As this report shows, what works in customer service now will become the blueprint for how organizations transform the full customer journey with AI. If you want the benchmarks and the playbook to accelerate from pilots to production-grade outcomes, I recommend reviewing the full “2026 Customer Service Transformation Report.”
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.
2026 is closer than it feels, and the signals are already clear. I’ve been synthesizing what I’m seeing across empowered product teams, boards, and cross-functional partners into a practical view of what matters next. A sharp look at product management trends for 2026. Not guesses, but signals from top product leaders shaping how PMs will actually work next.
In this analysis, I distill eleven shifts that are changing the craft—from outcomes vs output OKRs and continuous discovery to stronger product strategy and tighter product roadmapping and sprint planning. The throughline is simple: prioritize customer value, ship with focus, and measure what moves the business. These aren’t headline trends; they’re working patterns I’m seeing across high-performing organizations.
AI is no longer a side project—it’s part of the product manager’s core toolkit. Agentic AI, LLMs for product managers, and trustworthy AI workflows are accelerating discovery, sharpening problem framing, and enabling faster iteration. The best teams pair this with disciplined evaluation and experimentation, so insight compounds without sacrificing safety, privacy, or product quality.
Execution is getting crisper through product trios and stronger stakeholder management. When design, product, and engineering co-own discovery and delivery, teams reduce handoffs and increase clarity. That alignment translates into better prioritization, fewer context-switches, and a roadmap that reflects real trade-offs—not wish lists.
On growth, product-led growth remains a durable engine when it’s anchored in a compelling value proposition and instrumented end-to-end. Clear activation moments, in-app guides, and thoughtful product tours outperform brute-force acquisition. When we connect these motions back to product strategy and the roadmap, we create a repeatable loop that compounds adoption and retention.
Governance and trust are now table stakes. Privacy-by-design, data governance, and a pragmatic approach to regulatory compliance protect both users and velocity. Teams that build these practices into their operating model move faster because they avoid late-stage rework and maintain stakeholder confidence.
If you’re leading a product org—or aspiring to—this is your field guide to 2026. I’ll unpack where these shifts are strongest, how to apply them in your context, and the pitfalls to avoid. The aim is to give you clear language, concrete practices, and a sharper edge as you shape what your team builds next.
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.
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.
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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.
Digital transformation set the foundation, but it’s no longer sufficient. In my work leading product teams, I’ve learned that real competitive advantage now comes from building systems that perceive, learn, and adapt—end to end, across the product lifecycle and the business operating model.
AI transformation goes beyond automation to create adaptive, intelligent organizations. Discover why it’s the next imperative and how to measure success.
Why is this the next imperative? Customers expect intelligent experiences, not just digitized workflows. Markets are shifting faster than roadmaps, and teams need systems that learn in production. For me, AI Strategy starts with a clear value thesis: where can intelligence amplify customer outcomes and compound business impact—whether in onboarding, customer support, or core product differentiation.
Practically, I frame AI transformation as a capability stack: data governance and privacy-by-design at the foundation; a retrieval-first pipeline to ground models in trusted context; agentic AI and AI workflows to orchestrate actions; and eval-driven development to continuously measure quality, safety, and relevance. Layered on top are operating rhythms—outcomes vs output OKRs, rapid experimentation, and incident management—that keep shipping disciplined and responsible.
I start with product discovery. Together with product trios, we target moments where intelligence removes friction or unlocks new value. We translate those opportunities into crisp outcomes (activation, time-to-first-value, resolution rate) and instrument them from day one. In customer support, for example, a customer support ai strategy might blend LLMs for product managers with retrieval-first grounding to deliver accurate, brand-safe answers and escalate seamlessly when needed.
On architecture, I prioritize context window management and robust integrations. CRM integration and event streams from tools like Intercom, HubSpot, Pendo, and a unified analytics platform provide the signals AI needs to adapt in real time. Prompt engineering patterns, guardrails, and privacy-by-design controls ensure responses remain trustworthy and compliant. When applicable, I explore agentic AI to orchestrate multi-step tasks with clear constraints and auditability.
Delivery is where transformation becomes measurable. I combine CI/CD practices with DORA metrics (deployment frequency, lead time, change failure rate, MTTR) to keep iteration fast and safe. On the product side, A/B testing with a minimum detectable effect (MDE) protects rigor, while eval-driven development tracks model accuracy, hallucination rates, and policy adherence before and after release. I tie these to business metrics like user activation, retention analysis, and support resolution time to ensure we’re shipping outcomes, not just output.
Governance is non-negotiable. AI risk management, regulatory compliance, and data governance anchor every phase—from dataset curation to prompt libraries and model routing. Threat detection and response and incident management processes are integrated so we can respond quickly when behavior drifts or new risks emerge.
Transformation also means evolving how teams work. I invest in empowered product teams, continuous discovery, and developer evangelism to spread best practices across domains. We share playbooks, reusable CustomGPT workflows, and an AI product toolbox to scale patterns like retrieval-first pipelines and safe prompt engineering across the portfolio.
The outcome is not just smarter features; it’s a more adaptive business. With clear OKRs, reliable telemetry, and responsible guardrails, AI becomes a force multiplier for product strategy and execution. If you’re moving beyond digital toward intelligence, start small, measure relentlessly, and let outcomes guide the journey.
I’ve spent the last year pushing our AI Strategy from slideware to shipped value, and one pattern keeps winning in real-world product teams: connecting agentic AI directly to trustworthy product analytics. That connection is where Model Context Protocol shines—safely bridging LLMs with the tools and data product managers rely on every day.
Model Context Protocol (MCP) gives AI agents access to your business data. Learn how MCP works, how product managers are using it, and how to connect Pendo’s MCP server to Claude, ChatGPT, or Cursor for instant product insights.
In practice, I treat MCP as a clean, auditable interface between LLMs and enterprise systems—decoupling the model choice from the data plane and enabling a retrieval-first pipeline with strong data governance. Because MCP standardizes the way agents discover resources and tools, it simplifies context window management, enforces least-privilege access, and makes it easier to evolve our stack without rewriting prompts or fragile glue code.
For product leaders, the immediate payoff is speed to insight. Instead of hopping across dashboards, I ask the agent questions in natural language—“Which onboarding step drives the biggest drop-off by segment?”—and get synthesized answers backed by traceable queries. That shift turns AI workflows into a daily habit, improving continuous discovery and accelerating product-led growth while maintaining privacy-by-design controls.
Under the hood, I think about MCP in four layers: resources (read-only data surfaces such as feature usage or retention cohorts), tools (safe operations like creating a note, exporting a segment, or proposing an in-app guide), prompts (task-scoped instructions tuned for LLMs for product managers), and observability (logs and evaluations). This structure keeps eval-driven development front and center and reduces operational risk.
Here’s how I connect Pendo analytics through MCP to my preferred assistants without compromising security or accuracy:
1) Prepare access: confirm your Pendo MCP server endpoint, authentication method, and scopes; apply least-privilege and redact any PII not required for analysis.
2) Register the server: in Claude, ChatGPT, or Cursor, add the MCP server with the provided URL and API key or token, then enable only the resources and tools your use case demands.
3) Validate the contract: prompt the agent to list available resources and describe tools; run harmless dry runs (e.g., “summarize top feature adoption trends last 30 days”) to confirm the interface behaves as expected.
4) Operationalize: standardize prompts for recurring analyses (QBRs vs OKRs, activation funnels, retention analysis), set guardrails, and log every interaction for audit. This is where prompt engineering meets governance.
5) Iterate with metrics: track answer quality, latency, and usage; expand scopes gradually and gate new tools behind human-in-the-loop until you reach reliable performance.
Once configured, I use the agent to surface weekly activation insights, identify outlier cohorts, and auto-draft product discovery notes with links back to Pendo reports. The result isn’t magic; it’s a disciplined AI product toolbox that brings the right context to the right question, fast.
If you’re starting from zero, pilot with one high-value question, one team, and one assistant. Keep the footprint small, measure outcomes, and then scale—with security, compliance, and stakeholder management baked in from day one. That’s how you turn MCP from an interesting protocol into a durable competitive advantage.
