Effective Work Samples to Evaluate AI Feature Prioritization Skills

In today's product development landscape, the ability to effectively leverage AI for feature prioritization has become a critical skill. As organizations face increasing complexity in product decisions, professionals who can harness AI to support these decisions bring tremendous value. The intersection of product management expertise and AI capabilities creates a powerful combination that can transform how companies allocate resources and build their roadmaps.

Evaluating candidates for roles requiring AI feature prioritization skills presents unique challenges. Traditional interviews often fail to reveal a candidate's practical abilities in this specialized domain. While a candidate might articulate theoretical knowledge about AI or prioritization frameworks, their actual capability to implement these concepts in real-world scenarios remains untested in conventional interview formats.

Work samples provide a window into how candidates approach complex prioritization problems using AI tools and methodologies. They reveal not just technical proficiency with AI concepts, but also critical thinking, business acumen, and the ability to translate data insights into actionable product decisions. These exercises demonstrate whether a candidate can bridge the gap between AI capabilities and business objectives—a rare and valuable skill set.

The following work samples are designed to evaluate a candidate's ability to leverage AI for feature prioritization across different dimensions: framework development, technical evaluation, stakeholder communication, and impact analysis. By observing candidates complete these exercises, hiring teams can gain deeper insights into their potential contribution to AI-driven decision-making processes.

Activity #1: AI-Powered Feature Prioritization Framework

This exercise evaluates a candidate's ability to design a systematic approach to feature prioritization that leverages AI capabilities. It tests their understanding of both product management principles and how AI can enhance decision-making processes. The candidate must demonstrate strategic thinking about how AI can transform traditional prioritization methods while addressing practical implementation considerations.

Directions for the Company:

• Provide the candidate with a list of 10-15 potential features for a product in your domain (use a real product or a realistic fictional one).
• Include basic information about each feature: estimated development effort, potential user impact, alignment with company strategy, and any available user data or feedback.
• Allow the candidate 45-60 minutes to complete this exercise.
• Provide access to a whiteboard or digital collaboration tool for the candidate to sketch their framework.
• Have a product manager or technical leader present to evaluate the response and provide feedback.

Directions for the Candidate:

• Design a framework for using AI to prioritize the provided feature list.
• Your framework should:
• Identify what data inputs would be needed
• Explain what AI techniques would be appropriate and why
• Outline how the AI system would process and weigh different factors
• Describe how human judgment would complement the AI analysis
• Address potential limitations or biases in the approach
• Create a visual representation of your framework
• Explain how this framework would be implemented in practice, including necessary tools or resources
• Demonstrate how you would apply this framework to prioritize the top 3 features from the provided list

Feedback Mechanism:

• The interviewer should provide feedback on one strength of the framework (e.g., "I appreciated how you incorporated customer feedback data into the AI model") and one area for improvement (e.g., "The framework could better address how to handle features with limited historical data").
• Give the candidate 10 minutes to refine their approach based on the improvement feedback, focusing specifically on that aspect of the framework.
• Observe how receptive the candidate is to feedback and how effectively they incorporate it into their thinking.

Activity #2: AI Model Evaluation for Feature Decision

This technical exercise assesses the candidate's ability to critically evaluate AI models for their appropriateness in feature prioritization decisions. It tests their technical understanding of AI capabilities and limitations, as well as their ability to match the right technology to specific business problems. This skill is crucial for ensuring that AI-driven prioritization is based on sound technical foundations.

Directions for the Company:

• Prepare a scenario where your team needs to decide between two competing features based on predicted user engagement.
• Create a document describing two different AI approaches that could be used:

1. A machine learning model based on historical user behavior data
2. A natural language processing model analyzing user feedback and support tickets

• Include sample outputs from both models with their predictions and confidence levels.
• Provide relevant context about your product, user base, and business objectives.
• Allow the candidate 30-40 minutes to complete this exercise.

Directions for the Candidate:

• Review the two AI approaches and their outputs.
• Evaluate the strengths and weaknesses of each approach for this specific feature prioritization decision.
• Consider factors such as:
• Data quality and quantity requirements
• Model accuracy and reliability
• Potential biases or limitations
• Implementation complexity and maintenance needs
• Alignment with business objectives
• Recommend which approach would be more appropriate for this decision and explain your reasoning.
• Suggest any modifications or additional data that could improve the selected approach.
• Outline how you would validate the model's recommendations before making the final feature decision.

Feedback Mechanism:

• The interviewer should highlight one insightful aspect of the candidate's analysis and one technical consideration they may have overlooked.
• Ask the candidate to spend 5-10 minutes addressing the overlooked consideration and explaining how it would affect their recommendation.
• Evaluate the candidate's technical depth, critical thinking, and ability to connect technical details to business outcomes.

Activity #3: Stakeholder Communication Role Play

This role play evaluates the candidate's ability to effectively communicate AI-driven feature prioritization decisions to stakeholders with varying levels of technical knowledge. Success in this domain requires not just technical and product expertise, but also the ability to build trust and alignment around AI-supported decisions. This exercise reveals how candidates handle the human side of AI decision support.

Directions for the Company:

• Prepare a scenario where an AI system has recommended prioritizing Feature A over Feature B, contrary to what some key stakeholders expected.
• Create a brief document explaining the AI's recommendation, including the data it considered and its reasoning.
• Assign company representatives to play the roles of different stakeholders:
• A non-technical executive concerned about business impact
• An engineer skeptical about the AI's technical approach
• A sales representative worried about customer promises
• Brief these role players on their specific concerns and questions.
• Allow the candidate 10 minutes to review the materials before the 20-minute role play.

Directions for the Candidate:

• Review the AI recommendation and supporting information.
• Prepare to lead a 20-minute meeting with the stakeholders to explain the AI-driven prioritization decision.
• Your goals are to:
• Clearly explain how the AI arrived at its recommendation in accessible terms
• Address the specific concerns of each stakeholder
• Acknowledge the limitations of the AI approach
• Build confidence in the decision while remaining open to input
• Achieve alignment on next steps
• Be prepared to answer technical questions about the AI approach as well as business questions about the impact of the decision.
• Consider how to balance AI insights with human judgment in your communication.

Feedback Mechanism:

• After the role play, one of the stakeholders should provide feedback on an aspect of the communication that was particularly effective and one area where the explanation could have been more convincing or clear.
• Give the candidate 5 minutes to reflect and then re-address the specific concern that was highlighted for improvement.
• Evaluate the candidate's ability to translate complex AI concepts into business value and their skill in building stakeholder confidence.

Activity #4: Data-Driven Feature Impact Analysis

This exercise assesses the candidate's ability to analyze data and use AI tools to predict and measure the impact of feature implementations. It tests their analytical skills, understanding of metrics, and ability to design measurement frameworks that can validate AI-driven prioritization decisions. This skill is essential for creating a feedback loop that improves future prioritization accuracy.

Directions for the Company:

• Prepare a dataset representing user behavior before and after the implementation of a specific feature.
• Include multiple metrics such as engagement rates, conversion, retention, and user satisfaction scores.
• Add some complexity to the data, such as different user segments showing different patterns or some conflicting signals.
• Provide information about the business objectives the feature was intended to support.
• Allow the candidate 45-60 minutes to complete this analysis.
• Make available basic data analysis tools (spreadsheet, notebook, or BI tool).

Directions for the Candidate:

• Analyze the provided dataset to determine the impact of the implemented feature.
• Use appropriate analytical techniques to:
• Identify significant changes in key metrics
• Segment the impact across different user groups
• Control for potential confounding factors
• Quantify the business value created
• Design an AI-powered monitoring system that could:
• Continuously track the feature's performance
• Detect unexpected patterns or issues
• Provide insights for potential optimizations
• Feed back into the prioritization system for future decisions
• Prepare a brief report summarizing your findings and recommendations.
• Explain how this analysis would influence your approach to prioritizing similar features in the future.

Feedback Mechanism:

• The interviewer should provide feedback on one strength of the analysis (e.g., "Your segmentation approach revealed important insights") and one area that could be enhanced (e.g., "The causal analysis could be strengthened").
• Ask the candidate to spend 10 minutes addressing the area for improvement, either by refining their analysis or explaining how they would approach it with additional time or resources.
• Evaluate the candidate's analytical rigor, ability to connect data to business outcomes, and understanding of how to create measurement systems that support AI-driven decisions.

Frequently Asked Questions

How long should we allocate for these work samples in our interview process?

Each of these exercises requires 30-60 minutes for completion, plus time for setup, feedback, and discussion. Consider spreading them across different interview stages or selecting the 1-2 most relevant to your specific needs. For senior roles, you might dedicate a half-day assessment that includes multiple exercises.

Should candidates be allowed to use AI tools like ChatGPT during these exercises?

This depends on your company's approach to AI tools. Since the role involves AI for decision support, allowing candidates to demonstrate how they leverage these tools can provide valuable insights into their workflow. If you permit this, make it clear to all candidates and evaluate not just the output but how effectively they prompt and direct the AI tools.

How can we adapt these exercises for remote interviews?

All these exercises can be conducted remotely using video conferencing and collaborative tools. For the framework design, use digital whiteboarding tools. For data analysis, consider screen sharing or providing access to cloud-based analysis environments. For role plays, ensure all participants have stable connections and consider recording sessions (with permission) for later review.

What if our company doesn't have extensive AI capabilities yet?

These exercises can still be valuable even if you're early in your AI journey. Frame them as forward-looking exercises that will help build your AI capabilities. You can simplify some technical aspects while maintaining focus on the candidate's ability to think strategically about how AI could enhance prioritization decisions in the future.

How should we weigh these practical exercises against traditional interview questions?

Work samples typically provide stronger signals of on-the-job performance than traditional interviews. Consider giving these exercises significant weight (40-60%) in your overall evaluation, complemented by behavioral interviews that assess collaboration, leadership, and other soft skills critical for the role.

Should we provide these exercises to candidates in advance?

For complex exercises like the framework design or impact analysis, providing the scenario 24-48 hours in advance can yield more thoughtful responses. However, the role play and model evaluation exercises often benefit from being conducted live to assess the candidate's ability to think on their feet. Consider your specific needs and the seniority of the role when deciding.

The ability to effectively leverage AI for feature prioritization represents a competitive advantage in today's product development landscape. By incorporating these work samples into your hiring process, you can identify candidates who not only understand AI concepts but can apply them practically to make better product decisions.

These exercises go beyond theoretical knowledge to reveal how candidates approach complex prioritization problems, communicate with stakeholders, evaluate technical approaches, and measure outcomes. The combination of these skills is increasingly valuable as organizations seek to make more data-driven, AI-supported product decisions.

For more resources to enhance your hiring process, explore Yardstick's suite of AI-powered hiring tools, including our AI job descriptions generator, interview question generator, and comprehensive interview guide creator.

Build a complete interview guide for AI feature prioritization skills by signing up for a free Yardstick account