Essential Work Samples for Evaluating AI Product Metrics Skills

Defining success metrics for AI products requires a unique blend of technical understanding, business acumen, and strategic thinking. Unlike traditional software products, AI solutions often involve probabilistic outcomes, evolving performance, and complex user interactions that make measurement challenging. The ability to establish meaningful metrics that align technical performance with business objectives is a critical skill for anyone involved in AI product development or management.

Effective AI metrics specialists understand that success extends beyond simple accuracy measures. They recognize the importance of balancing technical performance indicators with business impact metrics and user experience measures. They can translate complex AI capabilities into quantifiable outcomes that stakeholders across the organization can understand and rally behind.

When evaluating candidates for roles requiring AI metrics expertise, traditional interviews often fall short. Candidates may articulate theoretical knowledge without demonstrating practical application skills. Work samples provide a window into how candidates approach real-world challenges in defining, measuring, and optimizing AI product performance.

The following work samples are designed to assess a candidate's ability to develop comprehensive metrics frameworks, align technical and business objectives, communicate effectively with diverse stakeholders, and implement practical measurement approaches. These exercises simulate the actual challenges faced when defining success for AI products and provide valuable insights into a candidate's problem-solving approach and strategic thinking.

Activity #1: AI Product Metrics Framework Development

This exercise evaluates a candidate's ability to develop a holistic metrics framework for an AI product. It tests their understanding of different metric categories (technical, business, user experience), their ability to align metrics with business objectives, and their skill in creating a balanced measurement approach that captures both short-term performance and long-term value.

Directions for the Company:

• Provide the candidate with a brief description of a fictional AI product (e.g., a recommendation system, a content moderation tool, a predictive maintenance solution).
• Include information about the product's purpose, target users, business objectives, and technical approach.
• Allow 45-60 minutes for the candidate to complete the exercise.
• Provide access to a whiteboard or digital collaboration tool for the candidate to create their framework.
• Have a product leader or AI specialist available to answer clarifying questions about the product.

Directions for the Candidate:

• Review the AI product description and identify the key stakeholders who would care about its performance.
• Develop a comprehensive metrics framework that includes:
• Technical performance metrics (accuracy, precision, recall, etc.)
• Business impact metrics (revenue, cost savings, efficiency gains)
• User experience metrics (adoption, satisfaction, engagement)
• Operational metrics (reliability, latency, resource utilization)
• For each metric, define:
• How it will be calculated
• What data sources are required
• How frequently it should be measured
• What thresholds indicate success vs. concern
• Create a visual representation of how these metrics relate to each other and to overall product success.
• Prepare to present your framework and explain your rationale in 10 minutes.

Feedback Mechanism:

• After the presentation, provide feedback on one strength of the framework (e.g., comprehensiveness, alignment with business goals) and one area for improvement (e.g., missing metrics, implementation challenges).
• Give the candidate 10 minutes to revise their framework based on the feedback.
• Ask them to explain how their revisions address the feedback and strengthen the overall approach.

Activity #2: AI Feature Performance KPI Definition

This exercise assesses a candidate's ability to translate high-level AI capabilities into specific, measurable KPIs. It tests their understanding of technical metrics, their ability to connect technical performance to user and business outcomes, and their skill in defining practical measurement approaches.

Directions for the Company:

• Select an existing or fictional AI feature (e.g., a chatbot's intent recognition, an image recognition system's object detection).
• Prepare a brief that includes:
• The feature's purpose and functionality
• Current performance challenges or questions
• Available data sources
• Key stakeholders and their concerns
• Provide sample data outputs if relevant (e.g., confusion matrices, user feedback).
• Allow 30-45 minutes for the exercise.

Directions for the Candidate:

• Review the AI feature brief and identify the core capabilities that need measurement.
• Define 5-7 specific KPIs that would effectively measure the feature's performance, including:
• At least 2 technical performance metrics
• At least 2 user impact metrics
• At least 1 business impact metric
• For each KPI, specify:
• The exact calculation method
• Required data sources
• Measurement frequency
• Visualization approach
• Target values or improvement goals
• Explain how these KPIs would help product teams identify issues, prioritize improvements, and track progress.
• Outline any additional data collection needed to support these metrics.

Feedback Mechanism:

• Provide feedback on the practicality of the proposed KPIs and the completeness of the measurement approach.
• Highlight one area where the metrics could be more specific or actionable.
• Ask the candidate to refine their approach to address this feedback, focusing on making the metrics more implementable or insightful.
• Evaluate their ability to adapt their thinking and improve the practical value of their metrics.

Activity #3: Stakeholder Alignment on AI Success Metrics

This exercise evaluates a candidate's ability to navigate competing perspectives on AI product success and develop metrics that satisfy diverse stakeholders. It tests their communication skills, strategic thinking, and ability to balance technical and business considerations.

Directions for the Company:

• Create a scenario involving an AI product with multiple stakeholders who have different priorities:
• Technical team focused on model performance
• Business leaders concerned with ROI and market impact
• UX team prioritizing user adoption and satisfaction
• Legal/compliance team worried about fairness and transparency
• Prepare role descriptions for 2-3 stakeholders with specific concerns and priorities.
• Assign company representatives to play these stakeholder roles.
• Allow 45-60 minutes for the exercise.

Directions for the Candidate:

• Review the AI product scenario and stakeholder descriptions.
• Prepare for and conduct a 30-minute meeting with the stakeholders to align on success metrics.
• Your goal is to:
• Understand each stakeholder's definition of success
• Identify areas of alignment and conflict
• Propose a balanced set of metrics that addresses key concerns
• Gain buy-in on a unified measurement approach
• During the meeting, demonstrate active listening, effective facilitation, and strategic thinking.
• After the stakeholder discussion, document the agreed-upon metrics framework, highlighting how it addresses each stakeholder's priorities.
• Identify any trade-offs made and explain your rationale.

Feedback Mechanism:

• The stakeholders should provide feedback on how well the candidate understood and addressed their concerns.
• Highlight one aspect of the facilitation or proposed framework that could be improved.
• Give the candidate 10 minutes to revise their metrics framework based on the feedback.
• Evaluate their ability to incorporate diverse perspectives while maintaining a coherent, implementable approach.

Activity #4: AI Metrics Implementation Planning

This exercise assesses a candidate's ability to move from metric definition to practical implementation. It tests their understanding of data infrastructure, measurement methodologies, and the operational aspects of tracking AI product performance.

Directions for the Company:

• Provide a scenario involving an AI product with an established set of success metrics that need to be implemented.
• Include details about:
• The product's architecture and data flows
• Available data sources and tools
• Resource constraints (time, engineering capacity)
• Reporting requirements and stakeholders
• Prepare a simple system diagram or data flow chart if helpful.
• Allow 45-60 minutes for the exercise.

Directions for the Candidate:

• Review the AI product and metrics information.
• Develop an implementation plan that outlines:
• Data collection requirements for each metric
• Necessary instrumentation or logging changes
• Data processing and storage approaches
• Calculation methodologies and frequency
• Visualization and reporting mechanisms
• Implementation priorities and timeline
• Identify potential implementation challenges and propose solutions.
• Consider both immediate measurement needs and long-term scalability.
• Create a phased approach that delivers value quickly while building toward comprehensive measurement.
• Prepare a brief presentation of your implementation plan, focusing on practicality and impact.

Feedback Mechanism:

• Provide feedback on the technical feasibility of the implementation plan and its alignment with business priorities.
• Identify one area where the plan could be more efficient or effective.
• Ask the candidate to revise that portion of their plan, addressing the specific challenge you've highlighted.
• Evaluate their ability to balance technical considerations with practical constraints and business needs.

Frequently Asked Questions

How much technical AI knowledge should candidates have for these exercises?

While deep technical expertise is beneficial, these exercises focus more on the ability to translate technical concepts into business-relevant metrics. Candidates should understand AI fundamentals and common performance measures, but don't need to be AI researchers or engineers unless the role specifically requires it.

Should we use our actual AI products in these exercises?

You can use simplified versions of your actual products if confidentiality allows, but fictional scenarios often work better. They level the playing field for candidates without prior knowledge of your specific products and focus the assessment on general skills rather than domain-specific knowledge.

How should we evaluate candidates who propose metrics different from our current approach?

Different doesn't mean wrong. Evaluate the reasoning behind their proposals, not just alignment with your current thinking. Strong candidates might challenge your existing approach with valid alternatives. Look for logical thinking, business alignment, and practical implementation considerations rather than specific metric choices.

Can these exercises be adapted for remote interviews?

Yes, all these exercises can be conducted remotely using video conferencing and collaborative tools like Miro, Figma, or Google Docs. For the stakeholder alignment exercise, ensure all participants have stable connections and consider recording the session (with permission) for later review.

How much time should we allocate for these exercises in our interview process?

Each exercise requires 45-60 minutes plus time for feedback and discussion. Consider using just one or two exercises as part of a broader interview process, selecting those most relevant to your specific needs. Alternatively, you could create a half-day assessment center approach using multiple exercises for senior roles.

Should candidates have access to resources or references during these exercises?

Yes, allowing access to online resources simulates real-world conditions and focuses assessment on problem-solving approach rather than memorized knowledge. However, be clear about expectations regarding original thinking versus researched answers.

Defining appropriate success metrics is often the difference between AI products that deliver measurable value and those that struggle to demonstrate impact. By incorporating these work samples into your hiring process, you can identify candidates who not only understand AI technology but can translate that understanding into meaningful measurement frameworks that drive product success.

The ability to define, implement, and evolve metrics as AI products mature is a critical skill that traditional interviews often fail to assess. These practical exercises provide deeper insights into how candidates approach the complex challenge of measuring AI product performance across technical, business, and user dimensions.

For more resources to improve your AI talent acquisition process, explore Yardstick's comprehensive tools for creating AI-optimized job descriptions, generating effective interview questions, and developing complete interview guides.

Build a complete interview guide for evaluating AI metrics skills by signing up for a free Yardstick account