Effective Work Samples for Evaluating AI Feasibility Analysis Skills

AI Feasibility Analysis has become a critical skill in today's technology landscape as organizations seek to leverage artificial intelligence solutions for business challenges. This specialized skill requires a unique blend of technical knowledge, business acumen, and strategic thinking to determine whether AI solutions are appropriate, practical, and valuable for specific use cases.

Evaluating candidates for AI Feasibility Analysis roles presents unique challenges. Traditional interviews often fail to reveal a candidate's true ability to assess complex scenarios, identify appropriate AI applications, and communicate technical constraints to stakeholders. Without proper evaluation methods, organizations risk hiring individuals who may recommend impractical AI implementations or miss valuable opportunities for AI adoption.

Work samples provide a window into how candidates approach real-world AI feasibility challenges. By observing candidates as they analyze business problems, evaluate data requirements, and make recommendations, hiring teams can gain valuable insights into their analytical processes, technical knowledge, and communication skills. These practical exercises reveal capabilities that might remain hidden in traditional question-and-answer formats.

The following work samples are designed to evaluate key competencies required for effective AI Feasibility Analysis. Each exercise simulates realistic scenarios that analysts encounter in their work, allowing candidates to demonstrate their ability to assess business needs, evaluate technical constraints, plan implementation approaches, and communicate recommendations to stakeholders. By incorporating these exercises into your hiring process, you'll be better equipped to identify candidates who can successfully guide your organization's AI adoption journey.

Activity #1: Business Problem Assessment and AI Solution Recommendation

This activity evaluates a candidate's ability to analyze a business problem, determine if AI is an appropriate solution, and recommend specific approaches. It tests their understanding of AI capabilities and limitations, business acumen, and ability to match technological solutions to business needs. This foundational skill is essential for anyone conducting AI feasibility analyses, as it prevents the pursuit of AI solutions for problems where traditional approaches might be more effective.

Directions for the Company:

• Prepare a detailed business case study describing a specific challenge your organization faces or a hypothetical scenario relevant to your industry.
• Include information about available data, current processes, business objectives, and constraints.
• Allow 45-60 minutes for the candidate to review the materials and prepare their analysis.
• Have a panel of 2-3 evaluators (ideally including both technical and business stakeholders) to assess the candidate's recommendations.
• Provide the candidate with the case study at least 24 hours before the interview to allow for thoughtful preparation.

Directions for the Candidate:

• Review the provided business case and analyze whether AI solutions could address the described challenges.
• Prepare a 15-minute presentation that includes:
• Your assessment of whether AI is appropriate for this problem
• If AI is appropriate, which specific AI approaches would be most suitable
• If AI is not appropriate, what alternative solutions you would recommend
• Key data requirements and potential challenges
• High-level implementation considerations and timeline
• Be prepared to justify your recommendations and answer questions from the panel.

Feedback Mechanism:

• After the presentation, provide the candidate with specific feedback on one aspect they handled well (e.g., "Your analysis of data requirements was thorough and practical").
• Offer one area for improvement (e.g., "Your timeline estimates didn't account for the data cleaning phase").
• Ask the candidate to revise their implementation timeline or approach based on this feedback, giving them 5-10 minutes to adjust their recommendation.

Activity #2: Data Readiness Assessment

This activity tests a candidate's ability to evaluate data quality, availability, and suitability for AI applications. A critical aspect of AI feasibility analysis is determining whether an organization has the necessary data to train and implement AI models effectively. This exercise reveals the candidate's technical understanding of data requirements for different AI approaches and their ability to identify potential data-related challenges.

Directions for the Company:

• Prepare a dataset sample and metadata description that represents real or realistic data from your organization (with sensitive information removed or anonymized).
• Include information about data sources, collection methods, update frequency, and known quality issues.
• Provide a brief description of a potential AI use case that would utilize this data.
• Allow 30-45 minutes for the candidate to review the materials and prepare their assessment.
• Consider including some intentional data quality issues or gaps that a skilled analyst should identify.

Directions for the Candidate:

• Review the provided dataset and metadata to assess its suitability for the proposed AI application.
• Prepare a written assessment (1-2 pages) that includes:
• Evaluation of data completeness, quality, and relevance
• Identification of potential data gaps or quality issues
• Recommendations for additional data collection or preprocessing steps
• Assessment of whether the available data is sufficient for the proposed AI application
• Potential risks or limitations based on the data characteristics
• Be prepared to discuss your assessment and answer questions about your recommendations.

Feedback Mechanism:

• Provide feedback on the thoroughness of the candidate's data quality assessment, highlighting one strength in their analysis.
• Identify one important data issue or consideration they may have missed or underemphasized.
• Ask the candidate to verbally explain how they would address this overlooked issue and how it might impact their overall feasibility assessment.

Activity #3: AI Implementation Planning and Resource Estimation

This activity evaluates a candidate's ability to plan the implementation of an AI solution, including resource requirements, timeline estimation, and risk management. Effective AI feasibility analysis requires not just determining if AI is appropriate, but also providing realistic expectations about implementation complexity, costs, and timeframes. This exercise reveals the candidate's project planning abilities and practical understanding of AI development processes.

Directions for the Company:

• Create a scenario where an AI solution has been deemed appropriate for a specific business problem.
• Provide details about the business context, available resources, technical environment, and project constraints.
• Include information about stakeholder expectations and business objectives.
• Allow 60 minutes for the candidate to develop their implementation plan.
• Consider including some challenging constraints (budget limitations, tight deadlines, legacy system integration) to test the candidate's problem-solving abilities.

Directions for the Candidate:

• Develop a comprehensive implementation plan for the proposed AI solution that includes:
• Breakdown of key implementation phases and tasks
• Resource requirements (personnel, technology, data, etc.)
• Timeline estimates with key milestones
• Budget considerations and ROI projections
• Risk assessment and mitigation strategies
• Success metrics and evaluation approach
• Create a visual representation of your implementation plan (Gantt chart, project roadmap, etc.)
• Be prepared to present and justify your plan to the interview panel.

Feedback Mechanism:

• Provide feedback on the comprehensiveness and practicality of the implementation plan, highlighting one particularly strong element.
• Identify one area where the plan could be more realistic or detailed.
• Ask the candidate to revise that specific portion of their plan based on your feedback, giving them 10-15 minutes to make adjustments.

Activity #4: Stakeholder Communication and Technical Translation

This activity assesses a candidate's ability to communicate complex AI concepts and feasibility considerations to non-technical stakeholders. A crucial aspect of AI feasibility analysis is effectively explaining technical constraints, requirements, and expectations to business leaders who may have limited technical knowledge. This exercise reveals the candidate's communication skills and ability to translate technical concepts into business terms.

Directions for the Company:

• Prepare a technical AI feasibility report that includes complex concepts, technical limitations, and implementation considerations.
• Create a scenario where the candidate must explain the key findings to a non-technical executive audience who will make the final decision on project approval.
• Assign 1-2 team members to role-play as executives with varying levels of AI knowledge.
• Prepare specific questions that executives might ask, focusing on business impact, costs, and risks.
• Allow the candidate 30 minutes to prepare their communication strategy.

Directions for the Candidate:

• Review the technical feasibility report and identify the key points that would be most relevant to business executives.
• Prepare a 10-minute non-technical explanation of:
• Whether the proposed AI solution is feasible and appropriate
• The expected business benefits and potential ROI
• Key implementation considerations and timeline
• Potential risks and limitations
• Resource requirements and next steps
• Avoid unnecessary technical jargon while still conveying important technical considerations.
• Be prepared to answer questions from the "executive team" and address concerns about the feasibility assessment.

Feedback Mechanism:

• Provide feedback on the clarity and effectiveness of the candidate's communication, highlighting one aspect they explained particularly well.
• Identify one concept that could have been explained more clearly or effectively to non-technical stakeholders.
• Ask the candidate to re-explain that specific concept using a different approach or analogy, giving them 5 minutes to prepare and deliver the improved explanation.

Frequently Asked Questions

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

Each activity requires approximately 1-1.5 hours, including preparation, execution, and feedback. We recommend selecting 1-2 activities most relevant to your specific needs rather than attempting all four in a single interview session. Consider spreading them across different interview stages or combining complementary activities.

Should we provide these exercises to candidates in advance?

For Activities #1 and #3, providing materials 24 hours in advance allows candidates to prepare thoughtful analyses, which better reflects real-world conditions. For Activities #2 and #4, providing materials 30-60 minutes before the interview is sufficient and helps assess how candidates handle time pressure.

How should we evaluate candidates who have different approaches to AI feasibility?

Focus on the quality of reasoning rather than specific conclusions. Strong candidates should justify their recommendations with clear logic, demonstrate awareness of multiple approaches, acknowledge limitations, and consider business context. The "right answer" may vary, but the analytical process should be sound.

What if our organization doesn't have suitable real-world data or scenarios to use?

You can create hypothetical scenarios based on publicly available information about your industry or similar businesses. Alternatively, use anonymized or simplified versions of real projects. The key is ensuring the scenario is realistic enough to test relevant skills while protecting sensitive information.

How can we ensure these exercises don't disadvantage candidates from different backgrounds?

Provide clear instructions and evaluation criteria to all candidates. Ensure scenarios don't require specialized industry knowledge unless it's truly essential for the role. Consider offering accommodations for candidates who might need them, and train your evaluation team to recognize and mitigate potential biases in assessment.

Should we expect candidates to produce perfect analyses in these time-constrained exercises?

No. These exercises assess approach and reasoning rather than perfect execution. Strong candidates will acknowledge limitations of their analysis given time constraints, prioritize key considerations, and demonstrate how they would expand their analysis with more time or information.

AI Feasibility Analysis is a complex skill that combines technical expertise, business acumen, and strategic thinking. By incorporating these practical work samples into your hiring process, you'll gain deeper insights into candidates' abilities to evaluate AI opportunities, plan implementations, and communicate effectively with stakeholders. This approach helps identify candidates who can truly drive successful AI initiatives rather than those who simply interview well.

For more resources to enhance your hiring process, explore Yardstick's suite of AI-powered tools, including our AI Job Descriptions generator, AI Interview Question Generator, and AI Interview Guide Generator.

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