At Pioneer 2025, we shared our most ambitious goal since we first set out to build Fin. I’ve spent my career building products that remove friction, and this is the boldest, most consequential shift I’ve seen for customer experience in years.
Fin will not be just the world’s best Customer Service Agent. It will be the world’s best Customer Agent, capable of handling the entire customer experience.
We’ll continue to obsess about Fin’s ability to support your customers, but now we’re broadening our focus. Fin will be able to contact your customers for the very first time; hold their hands through consideration and purchase; be there with them at every step; and know everything about their life with your business. That’s the level of continuity, empathy, and performance I expect from a truly unified AI Agent.
It’s a big shift, and it reflects the changing future of customer service and experience. As someone who lives at the intersection of product strategy and operations, I see an incredible opportunity for teams to elevate their impact.
Customer service leaders have been at the forefront of the AI transformation for the past two plus years. As this next evolution plays out, you’ll be uniquely positioned to lead how AI powers the entire customer experience. Your playbooks, data discipline, and operational rigor are the blueprint for what comes next.
You can watch the full Customer Agent keynote from Pioneer 2025 here.
Why we believe in the Customer Agent future comes down to two clear ideas that I’ve witnessed in practice across multiple organizations.
1. Multiple AI Agents will destroy the customer experience
We know that AI isn’t just changing customer service. Other teams like sales, success, and marketing are seeing the potential and starting to adopt it too. But if every function deploys its own Agent, you’ll end up with competing priorities, fragmented context, and inconsistent brand voice—exactly the kind of friction customers notice instantly.
But if all of these teams use their own AI Agent, you’ll end up with a mess of competing Agents that will destroy the customer experience. Each one will have its own priorities and configuration parameters; they won’t talk to each other or share customer information by default, and will likely engage with customers in different ways. This is a trap we need to avoid.
2. A truly exceptional customer experience is finally possible
If we can avoid that trap, we’ll finally be able to provide the type of seamless experience that customers have long expected, and long deserved. The AI Agents of today, like Fin, are capable of handling many different use cases across the entire customer journey: lead qualification, onboarding, support, success, and upsell. That opens the door for the first time to previously unimaginable customer experience; one that’s truly seamless, personal, and concierge-level.
We’ve reached another turning point in AI’s trajectory, and for customer service leaders, the opportunity around the corner is huge. In my own teams, the leaders who lean in now will shape standards for governance, measurement, and ROI across the business.
Customer service has been the proving ground for AI transformation. The systems, strategies, and learnings leaders in this space have accumulated over the last two years can define how AI is adopted by other functions. The keynote made this clear: you have the opportunity to lead how AI is rolled out across your organization, not just in customer service.
You already manage the most complex, high-volume customer interactions; you have rich data on customer needs and behavior; and you know how AI Agents perform in the real world. Those insights will be invaluable as AI scales across your business. The Customer Agent future will elevate the role of the customer service leader and give you the opportunity to lead AI implementation across the entire customer journey.
To achieve this vision of Fin becoming a unified Customer Agent, it will need to evolve from being a task-based system into a true agentic system that uses AI to make decisions and pursue high-level objectives. That shift—from task execution to outcome ownership—is the inflection point I’ve been anticipating.
Roles: Fin will have a range of roles (customer service being one) that it can fluidly move between and blend together. Each role will be deeply trained to be a world-class expert at what it does.
Goals: Fin will also have goals to pursue. You’ll be able to tell it your objectives and priorities (for your customers, company, and revenue) and Fin will pursue them, making appropriate trade-offs between goals as needed.
Memory: Fin will develop memory that persists and grows over the customer lifecycle, building deep context of who the customer is and what they’re trying to achieve. The customer priorities it learns on day one will be considered in year 10.
Knowledge: Fin will accumulate deep knowledge of your business – every product detail, policy, process, your history, and plans – to act on a complete view of your customer.
Interoperability: Fin will interoperate with different tools, systems, and channels.
This system will be able to do much more than answer questions or complete tasks. It will adapt on the fly, learn to get better, and use all the context it has to efficiently guide each customer to great outcomes. That’s how we turn AI from a helpful assistant into a dependable operator.
The Customer Agent vision isn’t a far-off idea. Many of our most pioneering customers have started to put Fin to work beyond customer service. They’re using it across the customer journey and want to push it further by applying it to other use cases to create a single, seamless customer experience. I’ve seen this expansion accelerate once leaders prove value in one high-stakes workflow.
Here’s an example: fitness wearables company WHOOP, facing one of their biggest product launches ever, needed a way to handle a very large influx of sales conversations. They used Fin to help manage this surge, and it’s now resolving 84% of their sales conversations.
These early examples show how Fin is already capable of handling multiple use cases across the customer journey. The signal is clear: a unified Customer Agent can drive measurable outcomes in both revenue and retention.
The Customer Agent future will be built from the inside out, starting with the customer service leaders who have been pioneering AI transformation since the very beginning. Your frameworks for quality, escalation, and measurement will set the bar for every other team that follows.
You know how to balance powerful AI with human empathy, and how to translate that into great customer experiences. Other teams will look to you, and you have the ability to lead them through this transformation. In my experience, this is the moment to define standards, instrument the journey, and scale wins deliberately.
The very best brands compete on customer experience. The Customer Agent opens that playing field for the brands that jump first. Those who move now will own the new benchmarks for responsiveness, personalization, and ROI.
We’ll be starting to roll out this new functionality to Fin – roles, goals, memory, knowledge, interoperability, and more – over the coming months. Stay tuned.
Your team has a polished GenAI prototype. The review goes well. Then the hard questions arrive: Which customer behavior should change? How often does the system fail? What context does it need? Who owns prompt and model changes? Can you release it without creating a permanent escalation queue?
The gap between an impressive demonstration and a dependable product is usually an operating-model problem. A useful GenAI discovery model connects customer evidence, system evaluations, field behavior, and production ownership. It lets you preserve the speed of AI-assisted prototyping while making each experiment answer a real product decision.
Key takeaways for GenAI product leaders
Prove value and capability separately. Customer demand does not prove that a model can perform reliably, and a strong evaluation score does not prove that anyone will change their behavior.
Begin with a workflow, an outcome, and a bounded role for AI. A generic AI use case is too loose to guide discovery or define acceptable performance.
Maintain an evidence chain. Connect each customer story to an opportunity, each opportunity to an assumption, each assumption to an evaluation scenario, and each released behavior to a product outcome.
Run customer learning and system learning in the same weekly loop. They require different methods, but they must meet at one decision: advance, narrow, change, or stop the bet.
Centralize reusable infrastructure and guardrails, not product judgment. Product teams should own the workflow and outcome while shared capabilities provide model access, evaluation tooling, observability, and policy controls.
Start with the workflow and outcome, not the model
GenAI discovery often starts in the wrong direction. A team gets access to a capable model, generates a list of possible features, and builds whichever idea looks most compelling in a demo. The team may learn that the model can produce something plausible, but it still does not know whether the capability removes meaningful friction from a real workflow.
Reverse the sequence. Start with a person trying to achieve an outcome in a specific context. Understand what that person does now, where the workflow breaks, what consequence follows, and which constraints shape a usable solution. Only then should you decide whether generation, retrieval, prediction, conversation, or agentic action is an appropriate intervention.
Write a bet brief that can be disproved
A GenAI bet should fit into a short brief before anyone commits significant engineering capacity. Use this framing:
For a specific actor in a specific workflow moment, the current behavior makes it harder to achieve a desired outcome. A bounded AI capability may improve an observable behavior, provided it stays within defined quality, control, privacy, and safety conditions.
Make every part concrete enough to test:
Actor and moment: Name who encounters the problem and where it occurs in the workflow. A role such as support agent is not enough; identify the decision or task the person is performing.
Current behavior: Describe what people actually do, including workarounds, handoffs, rework, and information they assemble manually. Ground this in past behavior rather than hypothetical interest.
Desired outcome: Define the customer or business result that should improve. Completion, adoption, rework, escalation, abandonment, and repeat use are product signals. A model-quality score is not the outcome.
AI contribution: State whether the system retrieves information, creates a draft, recommends an action, or performs an action. These are materially different product and risk propositions.
Operating boundary: Specify the data context, permissions, supported cases, human checkpoints, and fallback path. A capability without a boundary cannot be evaluated honestly.
Decision: Write what evidence would cause the team to proceed, narrow the scope, change the approach, or stop.
Suppose the opportunity is helping a support agent respond to a complex case. Producing a fluent answer is not the unit of value. The product may need to retrieve the right account context, create a grounded draft, expose its basis, let the agent correct it, and contribute to resolving the issue without avoidable rework. That framing changes what you prototype, what you evaluate, what you instrument, and what you ask users during discovery.
Choose the smallest coherent release
The right initial scope is not the smallest visible feature. It is the smallest end-to-end experience that can produce credible evidence. A narrow workflow with real context, a clear human checkpoint, outcome instrumentation, and a recovery path is more useful than a broad assistant that performs many disconnected tasks. This is how rapid prototyping becomes a method for reducing uncertainty instead of a factory for disposable demonstrations.
Scope the first release along four boundaries:
A defined user and workflow moment.
A bounded set of data and tools the system may use.
A clear level of authority over the resulting action.
A measurable product outcome and a separate quality floor.
Authority matters because the same model behavior can create different consequences. Showing relevant information is different from drafting a message. Drafting is different from recommending that it be sent. Recommending is different from sending it automatically. As authority rises and reversibility falls, require stronger evaluations, clearer permission boundaries, better auditability, and more deliberate human approval. If an action is costly, sensitive, or difficult to reverse, keep a human checkpoint and a safe fallback in the initial operating envelope.
Keep product success, system quality, and operating viability distinct. Product success asks whether behavior and outcomes improve. System quality asks whether outputs meet defined conditions. Operating viability asks whether the product can deliver that behavior with acceptable latency, cost, support burden, and failure recovery. A bet needs evidence in all three categories before a polished prototype should influence a roadmap commitment.
Build a traceable evidence chain
There are two uses of AI in this work, and leaders should not conflate them. AI can assist the discovery process by transcribing interviews, organizing material, and generating alternative interpretations. AI can also be the product capability under investigation. In the first role, its output is an analytical input. In the second, its behavior is an object of evaluation. Neither role turns model output into customer evidence.
Preserve the customer story before looking for patterns
Good discovery begins with a specific story about past behavior. Capture the person’s goal, the context, key moments in the experience, decisions, workarounds, and the needs or pain points that emerged. Synthesize that interview on its own before combining it with other interviews. This prevents a recurring operational mistake: compressing many transcripts into generic themes that are easy to present but impossible to design against.
A practical single-interview record includes participant context, a transcript-linked quote, the sequence of important moments, and opportunities expressed within that person’s situation. Cross-interview synthesis can then organize related opportunities without severing them from their origins. This separation between individual and cross-interview synthesis keeps insights contextual, actionable, and traceable.
Use AI as a notetaker or an additional analytical perspective, but establish simple provenance rules:
Every direct quote must link to the transcript location where it appears. If the wording cannot be verified, treat it as a paraphrase or discard it.
Every opportunity must identify the participant, workflow moment, and evidence from which it was derived.
Generated summaries must be checked against the original material before entering an opportunity map or decision record.
Tone, hesitation, visible confusion, and body language must come from a human observation note when they affect interpretation; a text transcript cannot preserve them reliably.
AI-generated themes may prompt a second look at the evidence, but they do not become evidence through repetition.
If the interview itself is shallow, automation will only process shallow material faster. A model cannot recover a missing motivation, workflow constraint, or decision context that nobody elicited. When synthesis produces vague insights, inspect the interview quality before rewriting the prompt.
Connect every artifact to a decision
The evidence chain should survive the entire path from discovery to production:
A customer story supports an opportunity.
The opportunity supports a product bet.
The bet contains assumptions about value, usability, feasibility, data, trust, and operations.
High-risk assumptions become experiments and evaluation scenarios.
Evaluation scenarios become regression cases when the product changes.
Field behavior connects the released capability to product and business outcomes.
Production failures feed new scenarios, product constraints, and discovery questions.
This chain prevents evidence laundering. A customer describing a difficult workflow does not prove that a proposed solution is desirable. A user liking a prototype does not prove that behavior will change. Passing an offline evaluation does not prove that the interaction fits the workflow. A successful pilot does not prove that the system is repeatable across customers. Each step answers a different question.
Decision
Minimum artifact
Evidence that advances the bet
What to do when it is missing
Is the problem worth solving?
Interview snapshots and an opportunity map
Specific past behavior, context, consequences, and constraints
Return to interviewing or workflow observation
Can GenAI contribute meaningfully?
Assumption ledger and a thin prototype
The capability performs the bounded task on representative examples
Narrow the task, change the system approach, or stop
Can users understand and control it?
Interaction prototype and failure scenarios
Users can interpret the result, correct it, and recover from failure
Change the interaction, authority level, or human checkpoint
Does it change real behavior?
Instrumented field pilot
Observed usage changes a leading product signal without unacceptable quality loss
Investigate workflow fit, adoption friction, or the original value hypothesis
Can the product be operated?
Evaluation harness, monitoring, ownership, and rollback path
Quality remains observable after release and a named owner can respond to degradation
Keep the release bounded until the operating controls exist
Use a living bet packet instead of a presentation deck
Keep the evidence chain in one lightweight packet. It should contain the current problem frame, links to customer evidence, the assumption ledger, prototype configuration, evaluation results, field observations, outcome measures, and the latest decision with its rationale. It is not a status report. It is the memory of the bet.
A new team member should be able to see why the work exists, which uncertainty is active, what failed previously, and what would change the decision. If the packet grows without changing a decision, reduce it. The purpose is traceability, not documentation volume.
Run a weekly dual-track discovery loop
GenAI discovery has two learning tracks. The customer track investigates the workflow, value, behavior, interaction, and trust. The system track investigates model behavior, context quality, tool use, failure modes, and operational constraints. Running only the first creates attractive concepts with unknown feasibility. Running only the second produces technically impressive capabilities looking for a problem.
A weekly loop is a useful default because it keeps the customer and system evidence synchronized. It also forces the team to choose a decision small enough to advance with the evidence available. The cadence is not a sequence of meetings. It is a sequence of testable changes.
Customer and workflow learning
This track uses customer interviews, workflow observation, usability tests, pilot behavior, support signals, and outcome instrumentation. Each activity should begin with the decision it is meant to inform. Do not schedule an interview merely to gather feedback. Decide whether you need to understand the current workflow, test the meaning of an opportunity, observe a recovery interaction, or determine why pilot users are abandoning the capability.
Invite engineering into customer learning when technical context changes the solution space. An engineer hearing how permissions, data fragmentation, or exceptional cases shape the workflow can identify constraints earlier than a written handoff would. The product manager should still own the connection between those constraints and the desired outcome.
System and evaluation learning
This track needs a scenario set, not a folder of memorable outputs. Build it from ordinary customer cases, meaningful edge cases, prohibited behavior, and failures already observed. Synthetic examples can widen coverage, but they should extend a foundation of real workflow evidence rather than replace it.
Each scenario should record:
The user task and its provenance in customer or field evidence.
The input, relevant context, permissions, and available tools.
Acceptable behavior, unacceptable behavior, and the scoring method.
The model, prompt, retrieval, tool, and data configuration used.
The actual result, the failure category when applicable, and any human correction.
The product consequence of the failure, not merely its technical label.
Define acceptance criteria before reviewing a preferred prototype. Otherwise, a persuasive output can cause the team to move the standard after the fact. Automate checks when correctness is directly observable, and retain structured human review where meaning, usefulness, or trust depends on context.
Treat the scenario set as part of the product, not disposable test material. Prompt versions, retrieval configuration, model changes, data quality, and tool behavior all belong to the release surface. Evaluation harnesses, prompt versioning, red-teaming, and production monitoring therefore need to begin in discovery and continue through CI/CD. A scenario that exposes a real failure during a pilot should become a regression case before the next release.
A decision-oriented weekly sequence
Name the decision. Choose the highest-risk assumption blocking progress rather than the most convenient activity.
Inspect the evidence. Review the relevant customer stories, field behavior, scenario results, and known failures.
Design the cheapest credible test. This may be another interview, a workflow prototype, a prompt or retrieval change, a human-assisted simulation, or a bounded field experiment.
Run offline scenarios first. Find obvious quality, safety, grounding, and permission failures before asking a customer to spend time on the prototype.
Observe the capability in the workflow. Watch what users accept, edit, ignore, misunderstand, override, or abandon.
Update both records. Add customer learning to the opportunity and assumption records; add system failures to the scenario set and failure taxonomy.
Make the decision explicit. Advance, narrow, change, pause, or stop. Record the evidence that caused the decision and identify the next uncertainty.
Parallel experimentation is useful only when each experiment resolves a distinct uncertainty. Generating many prototypes against the same vague question increases activity without increasing decision quality. The advantage of GenAI is a lower cost of learning; spend that advantage on better coverage of assumptions, not a larger pile of concepts.
Promote a prototype only when the evidence changes
A prototype should not graduate because it performs well in a stakeholder demonstration. Move it toward a broader pilot only when the team can show:
A valuable workflow and outcome grounded in customer behavior.
A bounded capability with explicit supported and unsupported cases.
An interaction that helps users understand uncertainty, correct results, and recover.
Instrumentation for product outcomes, system quality, overrides, and failures.
Ownership for monitoring, escalation, and rollback after release.
Keep the value gate and the quality gate separate. A system can meet its quality threshold and still fail because it adds a step, appears at the wrong moment, or solves a low-value problem. It can also attract strong usage while producing unacceptable errors. The first result sends you back to product discovery; the second requires narrowing authority, strengthening the system, or stopping the release. Blending the gates makes both diagnoses harder.
Design the organization around decision rights and learning
A fast learning loop will stall if every product choice waits for a central AI committee. It will also become fragile if each squad invents its own model access, evaluation approach, privacy controls, and incident response. The operating model must distinguish decisions that require local customer context from capabilities that should be reusable across the company.
Keep the product trio accountable for the outcome
The titles can vary, but the responsibilities cannot remain implicit:
Product leadership owns the desired outcome, opportunity framing, assumption sequence, value evidence, and decision record.
Design and research own workflow understanding, interaction behavior, user control, comprehension, correction, and recovery.
Engineering and data own system architecture, context and data quality, repeatable evaluations, observability, reliability, and rollback.
Domain, privacy, security, and risk partners define non-negotiable acceptance conditions and escalation paths where the use case requires them.
Go-to-market and customer-facing partners help identify adoption constraints and connect pilots to real workflows without substituting enthusiasm for evidence.
No single function can answer every release question. Product cannot declare model quality by itself. Engineering cannot infer customer value from technical performance. Governance cannot decide workflow desirability. Clarify who owns each decision, which evidence is required, and who can block a release when a non-negotiable condition fails.
Use forward deployed engineers where context is the constraint
Complex enterprise workflows often hide critical information in customer configurations, permissions, data conventions, and exception handling. A forward deployed engineer can work with product and design in the customer’s environment, shorten the path from observation to prototype, and turn real failures into reusable product knowledge. That field-facing role is especially useful when edge cases cannot be reproduced from a conference-room description.
The role needs a productization boundary. Otherwise, the team may prove that a skilled engineer can manually make each account successful rather than proving that the product is repeatable. Require every customer-specific exception to become one of four things: a reusable product requirement, an explicit configuration option, an evaluation scenario, or a documented unsupported condition. Field learning, end-to-end instrumentation, and responsible guardrails should strengthen the product system rather than disappear into account-specific work.
Centralize the paved road, not product judgment
A shared AI or platform capability should make the safe path easier. Depending on the organization, that can include approved model access, data controls, common logging, an evaluation framework, prompt and model versioning, reusable interaction patterns, cost and latency visibility, incident procedures, and privacy or safety templates.
The embedded product team should still own the opportunity, supported workflow, acceptance criteria, scenario relevance, product experience, pilot design, and outcome decision. A central group cannot judge those elements without the local customer context. It should provide leverage and enforce genuine non-negotiables, not become an approval queue for every prompt experiment.
Governance works best as a quality system with explicit rules:
Which use cases require review before customer exposure.
Which data classes, actions, and model behaviors are prohibited.
Which evidence must accompany a request to expand authority or reach.
Who approves exceptions and who owns the resulting risk.
What monitoring, audit, escalation, and rollback controls must exist after release.
This structure gives teams room to experiment inside known boundaries. It also prevents a common late-stage failure in which privacy, safety, or operational requirements appear only after the team has committed to an architecture and promised a launch.
Replace demo reviews with evidence reviews
A portfolio review should inspect what the team has learned, not how polished the prototype looks. Ask:
Which decision changed since the previous review, and what evidence changed it?
Which customer story and workflow moment support this opportunity?
What is the highest-consequence failure the current prototype still exhibits?
What product outcome must improve, and which quality condition must not regress?
What is the narrowest safe and coherent release that can produce field evidence?
Who owns the capability when its model, prompt, data, or tool behavior changes?
The answers reveal the right next move. No rich customer evidence means the team needs better discovery, not a better prompt. No scenario set means model comparisons are premature. Strong offline performance with weak field adoption points to workflow or value friction. A pilot that depends on hidden manual intervention is not yet a repeatable product. A capability without monitoring and rollback is not ready for production authority.
Take one active GenAI bet and replace its feature pitch with a falsifiable bet brief, a traceable evidence packet, and an explicit quality-and-value gate. Run the next portfolio conversation against those artifacts. Anything important that is missing is not paperwork to add later; it is the discovery work that should happen before the organization makes a larger commitment.
As a VP of Product Management at HighLevel, Inc., I wrestle with the build-versus-buy question nearly every week. It’s a timeless dilemma, now intensified by generative AI. As one summary puts it, “One topic that has been around since the beginning of the tech industry, is whether we should build or buy in order to solve some problem? This question applies to traditional IT, as well as to every product team. There are often one or more buy alternatives, but each comes with an associated cost, and…”
My take: build vs buy is not a procurement question—it’s a product strategy decision. The right answer depends on whether the capability creates durable differentiation, how quickly we need to learn, total cost of ownership, and the risks around data, compliance, and vendor lock-in. In practice, I anchor the debate in product discovery: what problem are we solving, for whom, and how will we know we’ve succeeded?
When I choose to build, it’s because the capability is core to our product’s competitive advantage, relies on proprietary data or unique workflows, or demands tight integration across the end-to-end customer journey. In these cases, my team and I accept the higher upfront investment because it compounds into long-term strategic control and faster iteration.
When I choose to buy, it’s because the capability is commoditized, speed-to-market matters more than novelty, or the vendor brings specialized compliance, uptime, or scale that would be expensive to replicate. Buying can be the fastest path to validated learning—especially when we need to unblock a roadmap dependency or de-risk a complex integration.
The AI era changes the calculus but not the fundamentals. With gen ai, we can prototype quickly using off-the-shelf models, then decide if we should converge on a managed service, an open-source stack, or a hybrid. The hidden work is real: evaluation harnesses, prompt governance, data pipelines, monitoring for model drift, and cost controls for inference. These become part of the true total cost of ownership—not just license fees versus engineering hours.
In my teams, I often deploy forward deployed engineers alongside product discovery to co-create solutions with customers. We use gen ai for product prototyping to validate value early, test prompts and retrieval patterns, and stress-test edge cases. If the prototype proves the value, we assess whether to keep the vendor in place or transition to a build for differentiation, control, and margin.
Here’s the practical playbook I use. First, define the outcome and non-negotiables: data privacy, latency, SLAs, and compliance. Second, run rapid experiments to quantify value—speed beats speculation. Third, model TCO across 12–24 months, including staffing, MLOps, eval frameworks, and expected usage growth. Fourth, pressure-test vendor lock-in: portability of prompts, embeddings, and fine-tunes; data ownership; exit paths. Fifth, stage-gate the decision: buy to learn fast, then build (or stay bought) based on evidence.
One recent example: we launched a gen ai capability using a vendor to achieve immediate time-to-value and validate demand. In parallel, we scoped a build option gated by adoption and unit economics. The vendor path gave us customer outcomes within weeks; the build path unlocked deeper integration and margin once the signal was strong. That dual-path strategy reduced risk without slowing us down.
Ultimately, the smartest build-versus-buy choices align with product management leadership principles: focus on customer outcomes, quantify opportunity cost, design for learning, and avoid irreversible commitments when uncertainty is high. In the age of AI, those principles still apply—only faster.
Your PM presents a bold strategy, but every difficult decision still comes back to you. Or the team ships reliably, yet the work rarely changes an important customer or business outcome.
These are different leadership problems. The first is an agency gap. The second is an ambition gap. Treating both as a generic performance issue leads to vague coaching, more oversight, and little improvement. You need to identify which capability is missing, change the conditions around it, and ask for observable evidence of progress.
Separate ambition from agency before you coach
Ambition is the drive to pursue greater impact, wider scope, or meaningful growth. Agency is the willingness and ability to own a problem, make decisions, and create momentum without repeatedly waiting for permission. Strong product managers need both capabilities, but one does not guarantee the other.
A confident presenter may have ambition without agency. A dependable delivery manager may have agency without ambition. If you praise the first person for vision and the second for output, you can reinforce the exact limitation you need each person to overcome.
Pattern
What you are likely to notice
Your leadership response
High ambition, high agency
The PM pursues consequential outcomes, reduces uncertainty, makes sound decisions, and creates momentum.
Protect autonomy, widen the problem space, and keep the outcome bar high.
High ambition, low agency
The PM describes a compelling future but stalls when evidence is incomplete, trade-offs appear, or stakeholders disagree.
Clarify decision rights, narrow the next reversible decision, and require a recommendation rather than another escalation.
High agency, low ambition
The PM delivers steadily but optimizes small requests or predetermined scope without questioning the size of the opportunity.
Reconnect the work to customer and business impact, then ask for a more consequential hypothesis.
Low ambition, low agency
The PM waits for tasks, avoids ownership, and cannot explain the outcome the work should produce.
Check the environment and expectations first. If clarity, access, and coaching do not change the pattern, examine role fit.
Do not assign someone to a quadrant from reputation or personality. Inspect recent work. Ask four questions:
You can approve an AI strategy, fund several prototypes, and still get almost no durable product change. The warning sign is familiar: demos multiply, customer impact remains hard to prove, and every release waits on roadmap, budget, handoff, and governance machinery built for more predictable software.
If that is your situation, the missing layer is an AI-era product operating model: the decisions, team boundaries, evidence, and guardrails that turn an uncertain capability into repeatable customer and business value. You do not need a parallel AI organization. You need a product system that learns quickly without giving up production quality or trust.
Redesign the unit of work around learning, not AI features
An AI assistant, agent, or workflow is not a useful unit of strategy. Those labels describe possible solutions. They do not identify whose behavior should change, which business result should move, or how the team will know the product is safe enough to expand. That distinction matters because a platform shift changes product strategy, architecture, discovery, and go-to-market decisions; it cannot be absorbed by adding AI features to an otherwise unchanged roadmap.
Make an outcome the unit of funding and accountability. A useful outcome statement has this shape: For a specific user in a specific workflow, improve a named measure from its current baseline, without crossing defined quality, trust, or business guardrails. The AI capability is one hypothesis for producing that result, not the result itself.
Require every AI bet to enter the portfolio with a one-page charter containing:
User and workflow: Who experiences the problem, what are they trying to complete, and where does the current workflow break down?
Outcome and baseline: Which customer or business measure should change, and what is its current state? If the eventual outcome will not move during discovery, name the leading indicator and explain the expected connection.
Why AI: What can an AI approach do that a rule, search experience, workflow redesign, or conventional automation cannot do adequately?
Riskiest assumptions: What must be true about value, usability, feasibility, and viability for the bet to work?
Trust boundary: What data may be used, what failure would be unacceptable, who could be affected, and what non-AI or human path remains available?
Next evidence: What is the smallest test that could materially change a decision?
Decision rule: What evidence would justify scaling, another iteration, or stopping?
The charter separates two types of uncertainty that often get mixed together. Model uncertainty asks whether the technology can perform a task under relevant conditions. Product uncertainty asks whether people will use it in a real workflow and whether that use will improve an outcome. A fluent demonstration can reduce the first uncertainty while saying almost nothing about the second.
If a team cannot name a baseline or observe the workflow, the bet may still deserve discovery funding. It does not yet deserve a production commitment. That distinction lets leaders support exploration without allowing every promising prototype to become an implied roadmap promise.
Move each bet through evidence states
Roadmap statuses such as planned, in progress, and complete describe activity. AI portfolios also need states that describe what has been learned:
Explore: The problem is credible, but the team is still testing the workflow, value proposition, technical approach, or failure boundary. Work should be small and reversible.
Prove: A solution has produced useful signals with target users. The team is testing a constrained production experience, instrumenting behavior, and validating that quality and trust controls hold outside a demo.
Scale: Customer behavior and the chosen outcome support broader investment, while known risks remain inside agreed limits. The team can now improve reliability, reach, economics, and operational readiness.
Capacity should increase as evidence improves. An executive sponsor’s confidence is not a substitute for customer behavior, and a model’s technical sophistication is not a substitute for outcome movement. Portfolio reviews should therefore ask what uncertainty was removed and what decision changed, not merely whether delivery is on schedule.
Give each outcome a durable product trio and elastic expertise
AI work can create additional dependencies on data, infrastructure, security, privacy, legal, and domain expertise. If each dependency becomes a handoff, the organization gets slower precisely when fast learning matters most. Keep a durable product trio accountable from discovery through production, then bring specialists into the decisions where their expertise changes the work.
Problem framing, outcome, viability assumptions, and evidence synthesis
Recommend whether to continue, change, scale, or stop the bet based on the charter
Product designer
End-to-end workflow, user comprehension, usability, and trust in the interaction
Choose how concepts are exposed to users and what usability evidence is required
Engineering lead
Technical feasibility, architecture, instrumentation, production quality, and operational trade-offs
Choose the technical path and release shape inside agreed constraints
Forward deployed engineer
Time-boxed customer immersion, rapid prototypes, and translation of workflow details into testable hypotheses
Choose the fastest responsible prototype for the current learning objective
Executive sponsor
Outcome priority, resource boundaries, organizational air cover, and cross-team escalation
Set the problem and constraints; avoid prescribing the solution
Security, privacy, legal, data, and domain specialists should have explicit consultation or approval points based on the consequence of the use case. They should not inherit ownership of the customer outcome. The product team remains accountable for integrating those constraints into a coherent experience.
Run an evidence cadence, not a status cadence
Give every discovery cycle one named learning question. Examples include whether users will delegate the task, whether they understand what the system did, whether the available data can support the workflow, or whether a failure can be detected before it causes harm. A prototype without a learning question is usually a demo; an experiment without a decision attached is usually activity.
For a pilot, a two-week evidence review is concrete enough to create accountability without turning every test into an approval meeting. Review the live charter, instrumented behavior, customer signals, and decision log. Ask five questions:
What did the team believe at the start of the cycle?
What did customers do, not merely say?
Which assumption became less uncertain?
Did the primary outcome or any guardrail move?
What decision changed, and what is the next critical question?
Keep the review focused on evidence. A long slide deck can hide the fact that no decision changed. A short decision log exposes that immediately.
Measure learning velocity as the time between asking a consequential question and obtaining credible evidence that changes a decision. That does not mean rewarding the raw number of experiments. Ten low-value tests can create less progress than one well-designed customer session or constrained release. Pair learning velocity with business outcomes so teams cannot optimize for experimentation while avoiding accountability for value.
Forward deployed assignments should also be time-boxed and documented. Record the workflow discovered, assumptions tested, prototype behavior, technical shortcuts, evidence collected, and production work still required. Rotate engineers through these assignments when practical. That spreads customer context and product judgment instead of concentrating both in a permanent hero team.
Govern AI bets by consequence, not by ceremony
AI governance fails when every experiment needs the same committee approval. It also fails when teams silently decide what data, errors, and customer consequences are acceptable. The useful middle ground is proportional governance: the higher the consequence and the harder the reversal, the stronger the evidence and independent review required.
Define consequence tiers in language your product, engineering, security, privacy, legal, and trust leaders accept:
Low consequence: The work is internal or tightly contained, uses approved non-sensitive data, cannot take consequential action, and is easy to reverse. The product team can usually proceed inside established policies.
Moderate consequence: The system influences a customer workflow, but its output is reviewable, the action is reversible, and a clear fallback exists. Require named product and technical owners plus the relevant privacy, security, or domain review.
High consequence: The system can move money, change access, affect eligibility, influence safety or legal rights, expose sensitive data, or take an action that is difficult to undo. Require qualified legal, security, privacy, safety, or domain review before customer exposure, along with human control and staged rollout where appropriate.
Do not treat these examples as universal legal classifications. Your specialists need to define the boundaries for the jurisdictions, customers, data, and decisions in scope. The operating-model requirement is that every team can determine the tier before building a release plan, not after the code is complete.
Use four gates from problem to scale
Problem gate: Name the user, workflow, baseline, desired outcome, and non-AI alternative. Explain why an AI approach is warranted. This prevents technology enthusiasm from becoming the problem statement.
Evidence gate: Test the system on tasks drawn from the intended workflow. Define useful behavior, known failure modes, unacceptable failure, and the evidence needed for value, usability, feasibility, and viability.
Exposure gate: Confirm data permissions, customer communication, logging, human review or fallback, support readiness, release owner, and rollback path. A successful prototype does not automatically satisfy this gate.
Scale gate: Require both outcome evidence and acceptable guardrail performance. Assign owners to unresolved failure modes before expanding reach or autonomy.
The gates should make autonomy safer, not eliminate it. Leaders set portfolio priorities and risk appetite. Specialists set non-negotiable data, compliance, security, and safety constraints. The product trio chooses the solution, experiment sequence, technical approach, and rollout details within those boundaries. If those decision rights remain ambiguous, governance meetings will repeatedly reopen product choices or teams will bypass the process to maintain speed.
Give every production AI bet a compact metric stack:
Business outcome: A measure such as activation, retention, expansion, conversion, or cost-to-serve that connects the work to enterprise value.
User behavior: Evidence that the target workflow changed, such as task completion, adoption, repeat use, escalation, or abandonment.
Quality and trust: The failure measures relevant to the use case, including human corrections, overrides, complaints, or occurrences of the unacceptable behavior defined in the charter.
Learning: Time to answer the current critical question, assumptions closed, and the decision produced by the evidence.
This is a menu, not a requirement to track every example. Choose one primary outcome and only the supporting measures needed to interpret it. If the primary outcome will take longer than the pilot to move, predeclare a leading indicator and its rationale. Do not replace a disappointing metric after the results arrive.
Clear baselines, measurable outcomes, and explicit ethical and trust guardrails let the team move faster because the boundaries are known. Vague risk language has the opposite effect: every reviewer imagines a different failure, so each decision is renegotiated from scratch.
Prove the operating model with a bounded 90-day pilot
Do not begin by announcing a company-wide AI transformation. Choose one or two problems that are important enough for leadership to care about, bounded enough for a team to affect, and observable enough to produce evidence. A pilot should test the operating model as well as the product bet.
A strong pilot candidate has:
A visible customer workflow with a specific friction point
A baseline or an attainable plan for establishing one
Access to target users throughout discovery
A path to shipping constrained increments rather than waiting for a complete platform
A meaningful connection to activation, retention, expansion, conversion, cost-to-serve, or another agreed business outcome
Dependencies that an executive sponsor can realistically unblock
A consequence level the organization can govern responsibly during the time box
Avoid picking a harmless showcase merely because it is easy to demo. It will not test difficult decision rights, customer discovery, production instrumentation, or governance. Also avoid starting with the most consequential and dependency-heavy workflow in the company. A pilot needs enough organizational reality to be credible without becoming a referendum on every unsolved platform issue.
Run the pilot in this sequence:
Publish the charter: State the problem, baseline, outcome, assumptions, consequence tier, team, decision rights, and scale-or-stop criteria on one page.
Staff a credible cross-functional team: Assign the product trio, add a forward deployed engineer where customer-side prototyping will reduce uncertainty, name the executive sponsor, and schedule specialist involvement before it becomes a blocker.
Establish evidence access: Arrange customer contact, instrument the current workflow, and create a shared place for test results and decisions.
Discover and deliver together: Explore multiple approaches, test the riskiest assumptions, and ship small increments when the evidence and consequence tier permit.
Review evidence every two weeks: Inspect customer signals, shipped behavior, outcome movement, guardrails, and decisions. Do not convert this into a project-status meeting.
Make the precommitted decision: At the 6-12-week decision window, choose to scale, iterate, or stop. Use the remainder of a roughly 90-day time box to verify repeatability, transfer the practices, or close the bet cleanly.
Define scale, iterate, and stop before results arrive
Scale: The workflow produces credible customer value, the business or predeclared leading measure is moving in the intended direction, guardrails hold, and the production path is viable.
Iterate: The problem remains important and evidence identifies a specific failed assumption or constrained next test. Iteration is not permission to continue indefinitely without a sharper question.
Stop: The value signal is weak, the workflow does not earn adoption, the economics are untenable, a critical risk cannot be controlled, or the non-AI alternative is better. Stopping is a valid return on discovery when it prevents a larger commitment.
The politics of a pilot can undermine otherwise sound work. Publish the criteria used to select the problem and team. Time-box special assignments. Do not hoard every high performer in a permanent AI lab. Show failed assumptions and changed decisions alongside successful demos. These practices make the pilot a path other teams can follow rather than evidence that only a protected group can succeed.
Scale the mechanics, not the heroics
After the pilot, codify the parts that made learning and delivery repeatable:
The one-page bet charter and evidence-state definitions
Team topology, specialist access, and forward deployed rotation rules
Decision rights for executives, product teams, and risk owners
The two-week evidence review and decision-log format
Consequence tiers, release gates, and escalation paths
Instrumentation for outcomes, behavior, quality, trust, and learning
The scale, iterate, and stop criteria
Do not standardize every discovery technique or technical implementation. Different workflows will need different tests and controls. Standardize the minimum system that makes evidence visible, decisions timely, and responsibility clear.
The real repeatability test is whether a second team can use the same mechanisms without relying on the original pilot’s personalities or executive attention. If it cannot, the organization has produced a hero story, not an operating model.
Key takeaways
Fund AI bets against customer and business outcomes, not solution labels such as assistant, agent, or copilot.
Require a one-page charter with a baseline, riskiest assumptions, trust boundary, next evidence, and precommitted decision rule.
Keep a durable product trio accountable end to end; use forward deployed engineers as time-boxed discovery accelerators.
Review evidence and changed decisions every two weeks during a pilot, rather than reviewing activity alone.
Apply stronger review as consequences and irreversibility increase, while preserving team autonomy inside explicit guardrails.
Use a roughly 90-day pilot to test repeatability, then scale the decision rights, cadence, instrumentation, and governance that another team can adopt.
Your next move is not to rewrite the entire product process. Pick one material, bounded workflow. Publish its one-page charter, staff the trio, set its consequence tier and baseline, schedule the evidence reviews, and precommit to a scale, iterate, or stop decision. The behavior leadership protects during that pilot, not the polish of its demo, is the operating model the rest of the organization will copy.
