Essential Work Samples for Evaluating AI Project ROI Skills

Artificial Intelligence projects represent significant investments for organizations, and the ability to accurately calculate and effectively communicate their return on investment is a critical skill in today's data-driven business environment. Whether hiring for a dedicated AI ROI analyst or evaluating this competency in AI project managers, data scientists, or business analysts, using practical work samples provides invaluable insights into a candidate's capabilities.

The challenge with evaluating AI ROI skills lies in their multifaceted nature. Candidates must demonstrate not only financial acumen and analytical rigor but also a deep understanding of AI technologies and their business applications. They need to balance technical precision with stakeholder-friendly communication, translating complex calculations into compelling business cases.

Traditional interviews often fail to reveal a candidate's true proficiency in these areas. Candidates may articulate theoretical knowledge of ROI frameworks without demonstrating the practical ability to apply them in real-world scenarios. They might claim communication skills without proving they can effectively present technical financial analyses to non-technical audiences.

The following work samples are designed to evaluate a candidate's comprehensive abilities in calculating and presenting AI project ROI. These exercises simulate authentic workplace challenges, requiring candidates to analyze data, make calculations, develop frameworks, and communicate findings effectively. By observing candidates tackle these realistic scenarios, hiring managers can make more informed decisions about which individuals truly possess the skills needed to drive value from AI investments.

Activity #1: AI Implementation ROI Case Study

This exercise evaluates a candidate's ability to analyze a specific AI implementation scenario, identify relevant costs and benefits, perform ROI calculations, and present their findings in a clear, compelling manner. It tests both analytical rigor and business acumen while revealing how candidates approach complex financial analyses of AI projects.

Directions for the Company:

• Prepare a detailed case study of a fictional or anonymized real AI implementation (e.g., a customer service chatbot, predictive maintenance system, or fraud detection algorithm).
• Include information about implementation costs, ongoing expenses, expected benefits, and relevant business context.
• Provide spreadsheets with some initial data but leave gaps the candidate must fill through analysis and reasonable assumptions.
• Allow 60-90 minutes for this exercise, which can be conducted remotely or in-person.
• Prepare evaluation criteria focusing on calculation accuracy, methodology soundness, assumption quality, and presentation clarity.

Directions for the Candidate:

• Review the provided AI implementation case and associated data.
• Identify all relevant costs (implementation, maintenance, training, etc.) and benefits (labor savings, revenue increases, risk reduction, etc.).
• Develop a comprehensive ROI analysis including payback period, NPV, IRR, and other relevant metrics.
• Create a 5-7 slide presentation summarizing your analysis, methodology, key assumptions, and recommendations.
• Be prepared to present your findings and answer questions about your approach and calculations.

Feedback Mechanism:

• After the presentation, provide specific feedback on one strength (e.g., "Your sensitivity analysis effectively highlighted the impact of adoption rates on overall ROI") and one area for improvement (e.g., "Your model didn't account for the declining cost of cloud computing over time").
• Give the candidate 15 minutes to revise one aspect of their analysis based on the improvement feedback.
• Observe how receptive they are to feedback and how effectively they incorporate it into their revised analysis.

Activity #2: Executive ROI Presentation Role Play

This role play assesses a candidate's ability to communicate complex ROI analyses to senior stakeholders, handle challenging questions, and adapt their presentation style to different audience needs. It reveals communication skills, stakeholder management abilities, and how well candidates can translate technical calculations into business value propositions.

Directions for the Company:

• Prepare a pre-calculated ROI analysis for an AI project with supporting data and slides.
• Assign 2-3 team members to play executive roles with different perspectives (e.g., a skeptical CFO, a technology-enthusiastic CTO, and a results-focused COO).
• Provide executives with specific concerns and questions to raise during the presentation.
• Schedule 30 minutes for the presentation and Q&A.
• Evaluate the candidate on communication clarity, ability to address concerns, adaptation to different stakeholder perspectives, and overall persuasiveness.

Directions for the Candidate:

• Review the provided ROI analysis and supporting materials (you'll have 30 minutes for preparation).
• Prepare to present the ROI findings to the executive team in a 10-minute presentation.
• Focus on clearly communicating the business value, key assumptions, risks, and recommendations.
• Be prepared to answer challenging questions about your methodology, assumptions, and conclusions.
• Adapt your communication style to address the specific concerns of different executives.

Feedback Mechanism:

• After the initial presentation and Q&A, one "executive" should provide specific feedback on a communication aspect that could be improved (e.g., "Your technical explanation of the model accuracy's impact on ROI was difficult for non-technical stakeholders to follow").
• Give the candidate 10 minutes to prepare and then 5 minutes to re-present that specific section incorporating the feedback.
• Assess how effectively they adapt their communication approach based on feedback.

Activity #3: AI ROI Framework Development

This exercise evaluates a candidate's ability to develop a strategic framework for calculating and tracking ROI across multiple AI initiatives. It tests strategic thinking, planning abilities, and understanding of how AI creates different types of business value across various applications and industries.

Directions for the Company:

• Provide information about a fictional company with multiple planned AI initiatives across different departments (e.g., HR, operations, marketing, customer service).
• Include brief descriptions of each initiative, their objectives, and the company's overall strategic goals.
• Supply templates or examples of ROI frameworks if desired, but allow room for candidate creativity.
• Allocate 60 minutes for this exercise.
• Evaluate the comprehensiveness of the framework, its adaptability to different AI use cases, and its alignment with business objectives.

Directions for the Candidate:

• Develop a comprehensive framework for evaluating and tracking ROI across the company's diverse AI initiatives.
• Your framework should include:
• Key cost categories to consider for different types of AI projects
• Benefit categories (both tangible and intangible) for various AI applications
• Recommended calculation methodologies for different project types
• Suggested metrics and KPIs for ongoing ROI tracking
• A timeline for when different benefits might be realized
• Create a visual representation of your framework and a 1-2 page explanation of how it would be implemented.
• Be prepared to explain how your framework accommodates the unique aspects of different AI applications.

Feedback Mechanism:

• Provide feedback on one strength of the framework (e.g., "Your inclusion of risk-adjusted ROI calculations adds valuable perspective") and one area for improvement (e.g., "The framework doesn't adequately address how to value intangible benefits like improved decision quality").
• Give the candidate 15 minutes to enhance their framework based on the improvement feedback.
• Assess their ability to incorporate feedback while maintaining the overall integrity of their approach.

Activity #4: AI ROI Calculation and Sensitivity Analysis

This tactical exercise tests a candidate's technical proficiency in performing detailed ROI calculations for AI projects and conducting sensitivity analyses to account for uncertainty. It reveals analytical rigor, attention to detail, and ability to work with complex financial models.

Directions for the Company:

• Prepare a dataset and scenario for an AI implementation with multiple cost and benefit components (e.g., a predictive maintenance system for manufacturing equipment).
• Include historical data on equipment failures, maintenance costs, and production impacts.
• Provide a partially completed Excel model or similar tool that the candidate will need to complete and enhance.
• Allow 45-60 minutes for this exercise.
• Evaluate calculation accuracy, model structure, sensitivity analysis approach, and insights derived from the analysis.

Directions for the Candidate:

• Complete the provided ROI calculation model for the AI implementation scenario.
• Identify and incorporate any missing cost or benefit components.
• Perform a comprehensive sensitivity analysis examining how ROI varies based on:
• Changes in implementation costs
• Variations in model accuracy/performance
• Different adoption rates or implementation timelines
• Changes in business conditions (e.g., production volumes, labor costs)
• Create at least two data visualizations that effectively communicate the ROI analysis and sensitivity findings.
• Prepare a one-page executive summary explaining your key findings and recommendations based on the analysis.

Feedback Mechanism:

• Provide specific feedback on one strength (e.g., "Your tornado chart effectively visualized the relative impact of different variables on ROI") and one area for improvement (e.g., "Your model doesn't account for the learning curve in AI implementation and how that affects benefit realization timing").
• Give the candidate 15 minutes to enhance their analysis based on the improvement feedback.
• Assess how effectively they incorporate the feedback while maintaining analytical rigor.

Frequently Asked Questions

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

Each exercise requires 45-90 minutes including preparation, execution, and feedback. We recommend selecting 1-2 exercises most relevant to your specific role rather than attempting all four. These can be conducted during an on-site interview day or as take-home assignments with a follow-up presentation.

Should we use real company data for these exercises?

While using anonymized real data can make exercises more relevant, it often raises confidentiality concerns. We recommend creating realistic fictional scenarios based on your industry and specific AI applications. This ensures consistency across candidates while protecting sensitive information.

How should we evaluate candidates who have experience with different ROI methodologies than what we use?

Focus on the soundness of their approach rather than exact alignment with your methodology. Strong candidates should be able to explain their chosen methodology and its advantages. The ability to adapt to new frameworks is often more valuable than familiarity with a specific approach.

What if a candidate challenges the premise of traditional ROI for AI projects?

This can actually be a positive sign of critical thinking. Some AI benefits are genuinely difficult to quantify in traditional ROI frameworks. Evaluate whether their challenge is based on thoughtful analysis of AI's unique characteristics and whether they propose reasonable alternatives for measuring value.

How can we make these exercises fair for candidates with different industry backgrounds?

Provide sufficient context about your industry and specific use cases. The core skills of ROI analysis transfer across industries, but candidates may need background information to make reasonable assumptions. Consider allowing candidates to ask clarifying questions before beginning the exercise.

Should we share our evaluation criteria with candidates beforehand?

Yes, transparency about how you'll evaluate their performance helps candidates showcase relevant skills and reduces anxiety. You don't need to share detailed scoring rubrics, but communicating the key competencies you're assessing improves the candidate experience and yields more representative results.

Effectively evaluating a candidate's ability to calculate and present AI project ROI requires going beyond traditional interviews to observe these skills in action. The work samples outlined above provide a comprehensive assessment framework that reveals both technical proficiency and communication abilities. By implementing these exercises, organizations can identify candidates who will not only accurately calculate AI project returns but also effectively communicate that value to drive organizational adoption and investment.

For organizations looking to build comprehensive hiring processes for AI-related roles, Yardstick offers additional resources to support your recruitment efforts. Explore our tools for creating AI-optimized job descriptions, generating effective interview questions, and developing complete interview guides.

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