Updated: September 23, 2025- 18 min read
It’s no news that AI is no longer optional for product managers — it’s become mission-critical.
Enterprise leaders have poured $33.9 billion into generative AI in 2024 alone, yet only 1 in 100 companies have managed to scale it beyond pilot projects (1). Even worse, 74% of firms are struggling to unlock real value from their AI investments (2).
Meanwhile, 65% of PMs are already using AI. But 70% fear it could sideline them, and 21% worry they're missing the skills to truly harness its power (3).
Enter AI evals. It’s a systematic, data‑driven way for PMs to assess and improve their AI features. With AI now poised to boost in time‑to‑market and improve decision‑making, mastering evals is the single skill that will separate successful AI product managers from the rest.
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What Are AI Evals or AI Evaluations?
AI evaluations, or AI evals, are structured processes for testing and measuring the performance, accuracy, and reliability of AI systems. In simple terms, AI evals help you answer the question: “Is this AI doing what we expect it to do and is it doing it well enough for users to trust it?”
For product managers, AI evaluations are not just about accuracy metrics like precision or recall. They’re about understanding how an AI agents or AI product strategy performs under real-world conditions, how it affects product experience, and whether it delivers value aligned with product goals.
An AI evals example for product managers
Let’s say your team builds a feature that uses AI to automatically summarize customer support tickets. An AI product manager, or data product manager, would need to evaluate whether this AI is doing its job well. A strong AI eval wouldn’t stop at measuring accuracy against a labeled dataset. Instead, it would ask:
How often do human agents still need to edit these summaries?
Does this AI tool reduce average ticket resolution time?
Are customers getting faster or better support outcomes as a result?
Is the AI performing consistently across different types of tickets (length, topic, complexity)?
You’d also want to check for biases, hallucinations (fabricating information), and edge cases where the AI might fail in unexpected ways.
Why AI evals matter in product management
AI systems don’t behave like traditional software. They don’t always give you predictable outputs, and their performance can degrade when exposed to new data or user behavior.
Evaluation isn’t just some engineering vanity or a nice-to-have. It is the steering wheel of a product’s quality, the bedrock of user trust, and the critical input for building versus buy strategy. It’s your primary defense against escalating any legal, brand, and compliance risk.
— Kunal Mishra, Group Product Manager at Amazon, on The Product School Webinar
That’s why AI evals are critical. They help you establish benchmarks for performance, monitor how well AI features hold up over time, and identify areas for continuous improvement.
In practice, AI evals often include:
Offline testing with curated datasets
Live testing in a sandbox or controlled environments
Human-in-the-loop feedback for qualitative insights
Continuous monitoring post-launch to catch unexpected issues
Without robust evaluations, you risk launching AI features that confuse users, erode trust, or fail to deliver the promised value — outcomes no product manager wants tied to their name.
How To Conduct AI Evaluations Effectively as a Product Manager
AI evaluations can’t be treated like a standard feature QA checklist. They require a thoughtful, structured approach rooted in real-world AI use cases, product goals, and a North Star.
This guide, regardless if you’re using Open AI evals, Llama AI, or other generative AIs, breaks down how product managers can run AI evals that lead to better decisions, better products, and ultimately, better outcomes for users.
1. Start with clear objectives
Effective AI evals begin with clarity. Before you look at metrics or datasets, you need to align your evaluation with the bigger picture: What’s the purpose of this AI in your product, and what outcomes are you aiming to deliver?
This is where many AI product managers, data product managers, and even product analysts stumble. They default to measuring what’s easy — accuracy, F1 score, precision — instead of focusing on what actually matters to users and the business.
If you’re wondering what these metrics mean:
Accuracy tells you how often the AI got the answer right overall.
Precision measures how often the AI’s “yes” answers were actually correct.
F1 score balances precision and another measure called recall — which looks at how many of the right answers the AI found in total.
These metrics help data scientists understand if a model is technically performing well. A model with 90% accuracy might look impressive on paper but still fail to drive product adoption if it’s solving the wrong problem or introducing friction into the product experience.
To set strong objectives, ask questions like:
What user problem are we trying to solve with this AI?
How does this AI feature fit into the overall product strategy?
What behaviors or business outcomes will signal success?
For example, if you’re building an AI writing assistant for customer support, your objective might not be “high accuracy in summarizing tickets” — it might be “reduce average handling time by 20% while improving user retention by 2%.”
Clarity here ensures you’re measuring the things that truly move the needle.
2. Define evaluation criteria and metrics
Once your objectives are clear, the next step is deciding how you’ll measure success. This is where evaluation criteria and key metrics come in. Simply put, it’s worth spending time here to get this right.
With AI, there are typically three layers of metrics you’ll need to consider:
Model-level metrics
This is what your data scientists will care about. These metrics help you understand how well the AI is performing in a technical sense. Here’s a quick breakdown of the most common ones:
Accuracy: Out of all the predictions the AI made, how many were correct? It gives you a general sense of performance but can be misleading if your data is imbalanced (for example, if 90% of your tickets are about the same issue, the AI can look “accurate” just by guessing the most common answer).
Precision: Of the things the AI labeled as “correct,” how many were actually right? This helps avoid false positives. Imagine a fraud detection model — you don’t want it flagging every harmless transaction as fraud.
Recall: Of all the things it should have labeled correctly, how many did it catch? This helps avoid false negatives. In the fraud example, you don’t want real fraud slipping through.
F1 score: A balanced measure that combines precision and recall. It’s useful when you need a single metric that captures both sides of the equation — catching enough of the right things, while minimizing mistakes.
These are important for your AI team, but they only tell part of the story.
Product-level metrics
These reflect how your AI feature performs in the real world, in the hands of users. Think:
Are people using this feature regularly?
Is it saving them time?
Is it reducing support tickets?
Are users reporting better outcomes (higher satisfaction, fewer complaints, faster results)?
These metrics are usually tied to product adoption, engagement, and user retention. They help you see whether the AI is solving the right problem and improving user experience.
Business-level metrics
Efficiency gains (fewer manual processes, faster workflows)
Cost savings (reduced operational expenses)
Revenue impact (upsell opportunities, customer retention, new users)
Risk reduction (compliance, fewer errors)
Your job as a PM is to balance these three layers. Too many teams get fixated on model metrics and forget about user impact or business outcomes. Strong AI evals bridge the gap between the technical and the practical.
Before any single line of code is written, define your success across four buckets: Business success, user success, technical success, and risk mitigation. If it’s not on paper, it doesn’t exist.
— Kunal Mishra, Group Product Manager at Amazon, on The Product School Webinar
Therefore, make sure your metrics are:
Aligned with your original objectives → Business success
Your metrics should clearly tie back to what the business is trying to achieve. If you're aiming for faster support response times, higher conversion, or more retention, your evaluation should reflect that. No alignment = no impact.Outcome, not output-oriented → User success
Instead of just counting model responses (outputs), measure whether users are getting what they need. Are they finding answers faster? Feeling more confident in the product? Getting tangible value?Clear enough that non-technical stakeholders understand them → Risk mitigation
If stakeholders can’t understand your metrics, they can’t flag risks. Clear metrics help catch early signs of failure, bias, or drift before they hit production.Actionable, they should guide decisions, not just report numbers → Technical success
An actionable metric leads to engineering decisions. If performance dips or latency spikes, the team knows how to respond. This is how metrics serve development, not just documentation.
This foundation will shape how you test, measure, and ultimately improve your AI-powered feature.
3. Design a robust PM evaluation framework
With your objectives and metrics defined, the next step is to design how you’ll actually run the evaluation. This means building a structured framework that helps you answer two questions clearly:
Is this AI working as intended, both technically and from a user perspective?
Can we trust it enough to release it to real users at scale?
Here’s what a pragmatic AI evaluation framework looks like in practice for product managers:
1. Offline testing (pre-launch validation)
This is your first line of defense. You run the AI agent or AI feature on historical or synthetic datasets (not on live users yet) to check whether it performs well against known answers.
Think of it as your “lab environment” for catching obvious issues.
What to evaluate here:
Does the AI achieve acceptable levels of accuracy, precision, recall, and F1 score?
Does it perform consistently across different subsets of data (regions, languages, edge cases)?
Does it behave ethically (no offensive outputs, no bias against certain groups)?
Are there clear failure modes, and do we understand them?
Example: If you’re building an AI feature that recommends financial products, offline testing should reveal if it systematically favors certain demographics or ignores key user inputs.
2. Online testing (real-world simulation)
Once offline testing looks good, you move to iterative testing in controlled live environments. This might mean:
Shadow mode: The AI makes decisions in the background, but users don’t see or act on them. You compare AI decisions to what humans did.
Canary releases: Expose a small percentage of users to the AI feature and monitor results.
What to evaluate here:
Is the AI’s behavior consistent with offline tests?
How do users interact with the AI-driven feature? Are they confused, misled, or delighted?
Does it introduce any unexpected edge cases when exposed to real user behavior?
Does it impact downstream key metrics — support tickets, NPS, churn, conversion?
Example: A product manager rolling out AI-powered search on a marketplace might A/B test it on 5% of traffic, comparing conversion rates and customer feedback against the old search experience.
3. Human-in-the-loop (HITL) evaluation
Involve humans where AI judgment isn’t yet fully trustworthy. This is especially important for AI systems making high-stakes decisions (financial recommendations, healthcare suggestions, safety-critical outputs).
As Kunal Mishra, Group Product Manager at Amazon, points out on The Product School Webinar: “Always have humans and AI coexist when you’re doing your evaluation. The benchmarks can get you so far, but the brand tone and cultural context will be missed if you don’t have the humans in the loop.”
What to evaluate here:
How often does the AI output need human correction?
Where do humans disagree with the AI most frequently?
Are humans able to understand and intervene effectively?
Example: For a content moderation tool, you might have human moderators review AI-flagged posts to see if they agree with its decisions and why.
4. Bias, fairness, and edge case evaluation
AI can fail in weird and specific ways. Part of a robust eval is trying to “break it” before your users do. Test the AI deliberately with edge cases, diverse user profiles, and unusual inputs.
What to evaluate here:
Does the AI perform equally well across user demographics (age, gender, geography, language)?
Does it behave predictably with strange or adversarial inputs?
Are there any scenarios where it consistently fails or produces harmful results?
Example: Testing an AI chatbot’s ability to handle slang, sarcasm, or sensitive topics without escalating issues.
4. Collaborate with cross-functional teams
AI systems touch too many parts of the business, and evaluating them properly requires perspectives and expertise from multiple teams. As the PM, your role is to orchestrate this cross-functional collaboration so that evaluations aren’t just technically sound but aligned with real-world needs.
Here’s what this looks like in practice.
Who to collaborate with and why:
1. Data scientists / ML engineers
These are your core partners for model-level evaluation. They understand the technical nuances of how the AI works, how it’s trained, and what its known limitations are. They’ll own the experiments, datasets, and iterations needed to improve technical performance.
What you need from them:
Clear reports on accuracy, precision, recall, F1 score, and any model drift
Insights into where the model struggles (specific types of data, edge cases, etc.)
Agreement on thresholds for acceptable performance
2. UX researchers / product designers
AI can fundamentally change how users interact with your product — sometimes in confusing or frustrating ways. UX teams help you evaluate whether the AI feature is understandable, trustworthy, and actually improves the product experience.
What you need from them:
User testing to gather qualitative feedback on how people experience the AI
Research on how AI impacts trust, usability, and product adoption
Recommendations for improving explainability or user control
3. Product analytics / data teams
Once you move into live testing, you’ll need robust product analytics to track user behavior and impact. Your analytics partners help ensure you’re measuring the right things and interpreting the data correctly.
What you need from them:
Tracking plans for product-level and business-level metrics
Dashboards that monitor AI performance post-launch
Support in analyzing unexpected trends or anomalies
4. Legal, compliance, and ethics teams
AI opens up new risks, from privacy concerns to algorithmic bias. These teams help you ensure your evaluations account for regulatory and ethical considerations.
What you need from them:
Guidance on acceptable risk thresholds
Approval processes for launching AI features, especially in sensitive industries
Recommendations for documenting decisions and audit trails
What strong collaboration looks like:
Shared understanding of what “good” looks like across teams
Regular check-ins during evaluation phases to align on findings
Clear product documentation of decisions, risks, and mitigation plans
A unified story you can tell product leadership about the AI’s readiness
5. Run evaluations iteratively and continuously
Unlike traditional features, AI systems behave differently over time. Models degrade, user data shifts, new edge cases emerge. All of this can impact performance, user trust, and business results long after the initial release.
As an AI product manager, you need to think of AI evaluation as an ongoing discipline, not a one-time milestone. Continuous evaluation helps you:
Catch issues early (before users do)
Ensure the AI continues to meet your success criteria
Identify opportunities to improve, retrain, or refine the model
Here’s how to run evaluations in a way that accounts for real-world change:
1. Evaluate during development (iterative testing cycles)
You shouldn’t be waiting until you have a “final” model to evaluate. Build evaluation into every cycle:
After each major model update or retraining
As new datasets are introduced
When you make changes to prompts, APIs, or architecture
Ask: Is this iteration moving us closer to solving the user problem? Are we seeing any new risks or regressions?
2. Evaluate before release (pre-launch readiness)
This is your standard gatekeeping phase — the formal sign-off on whether the AI meets launch criteria.
Validate model metrics (accuracy, F1, etc.)
Confirm product-level impact through pilot groups or canary testing
Review legal, compliance, and ethics approvals
3. Evaluate after release (continuous monitoring)
This is where many teams drop the ball. After product launch, you need live monitoring systems in place to track AI behavior in production.
What to monitor:
Drift in model outputs or accuracy over time
Changes in user engagement or satisfaction
Emerging failure modes or harmful outputs
Shifts in business outcomes tied to the AI feature
4. Schedule regular audits
Quarterly or bi-annual audits help you step back and assess:
Is the AI still aligned with user needs and product goals?
Are we seeing new risks due to regulatory changes, market shifts, or new competitors?
Is the AI contributing to or undermining user trust?
Audits should combine metrics review, user research, technical evaluations, and risk assessments.
The Challenges of Developing AI Evals
AI evaluations are deceptively complex. On the surface, it sounds simple: measure if the AI is working. In practice, product managers face several challenges that don’t exist with traditional software. These challenges are why many AI-powered products fail to meet expectations, or worse, damage user trust.
Here’s what makes AI evals so tricky in the real world:
AI doesn’t have a clear definition of “correct”
AI systems work in probabilities, not absolutes. What’s “correct” is often subjective and changes depending on user expectations, context, or business needs.Success depends on context, not just metrics
Strong accuracy or F1 scores don’t guarantee business impact or user satisfaction. AI evaluations must account for user behavior, adoption, and business outcomes, not just model performance.Data quality is never perfect
AI performance depends on high-quality, representative data, which is often incomplete, biased, outdated, or difficult to gather. Poor data leads to misleading evaluations and poor product decisions.Continuous evaluation requires ongoing effort
AI models degrade over time as user behavior, data patterns, and external factors shift. Running evaluations continuously demands time, resources, and long-term commitment.Edge cases and biases are hard to uncover
AI systems can behave unpredictably with edge cases or amplify biases hidden in the data. Identifying and evaluating these risks requires deliberate effort and thoughtful test design.Cross-functional alignment is difficult
Effective AI evals require collaboration across data science, engineering, UX, legal, and business teams. These groups often have different priorities and definitions of success, which can slow progress and lead to misalignment.
Best Practices for Running AI Evals (Beyond the Obvious)
Run qualitative and quantitative data evals side-by-side:
Don’t rely solely on dashboards. Pair data with user feedback sessions, expert reviews, and hands-on testing to catch what metrics miss.Involve non-technical stakeholders early:
Bring in legal, marketing, support, and ops teams during evaluation — not just post-launch — to catch risks and misalignments before they escalate.Design for failure modes, not just success:
Create tests that deliberately stress the AI with edge cases, rare scenarios, and adversarial inputs. Failure data is as valuable as success metrics.Treat evals like product discovery, not QA:
Approach evaluations as learning opportunities about user behavior, trust, and expectations — not just technical validation exercises.Benchmark against human performance:
Where possible, measure AI outputs against how well trained humans would perform the same task to create realistic expectations for accuracy and impact.Run scenario-based evaluations, not just static datasets:
Evaluate how the AI performs across end-to-end workflows, not isolated predictions. This reveals gaps in user experience and downstream consequences.Use “red team” exercises to hunt for failure:
Assign team members to actively try to break the AI, uncover biases, or expose weaknesses. Structured adversarial testing is critical for real-world resilience.Document assumptions explicitly:
Capture and revisit assumptions about data, users, and model limitations regularly — this prevents surprises when reality shifts post-launch.Monitor impact on user trust, not just performance:
Track trust signals over time (e.g., do users abandon AI features, escalate to humans, complain more?). These are leading indicators of deeper problems.Adopt a “trust but verify” mindset for vendors:
If you use third-party AI models, don’t trust vendor metrics alone. Run your own evals to ensure alignment with your product and users.
AI Evaluation for Product Managers Is a Must-Have Skill
The difference between AI that drives real business value and AI that erodes user trust almost always comes down to evaluation. Without rigorous, ongoing AI evaluations, you’re flying blind.
For product managers, AI is about understanding how AI systems behave in the wild, how users interact with them, and how they impact the business. It’s a skillset that helps you build trust, reduce risk, and deliver AI-powered features that actually work.
The best product managers today — and tomorrow — will be the ones who can confidently evaluate AI through the lenses of performance, user value, and long-term impact. It’s a core part of the job.
If you don’t evaluate AI properly, someone else — your users, your competitors, or regulators — eventually will. And it better not come down to them first.
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(1) Wall Street Journal - Companies are struggling to drive a return on AI
(2) BCG - AI Adoption in 2024: 74% of Companies Struggle to Achieve and Scale Value
Updated: September 23, 2025
