Effective Work Samples to Evaluate AI for Internal Mobility Matching Skills

Internal mobility has become a critical strategy for organizations looking to retain top talent, fill skill gaps, and create more agile workforces. AI-powered matching systems have emerged as powerful tools to connect employees with internal opportunities based on skills, experience, potential, and career aspirations. However, finding candidates who truly understand both the technical aspects of AI implementation and the nuanced human elements of internal mobility presents a significant challenge.

Evaluating candidates for roles focused on AI for internal mobility matching requires assessing a unique combination of technical expertise, HR knowledge, and strategic thinking. Traditional interviews often fail to reveal a candidate's true capabilities in designing, implementing, and optimizing AI-driven internal mobility solutions. Work samples provide a window into how candidates approach real-world challenges in this specialized field.

The right candidate must demonstrate proficiency in AI/ML concepts, data analysis, and model development while also showing a deep understanding of talent management principles, career development frameworks, and the human factors that influence internal mobility decisions. They need to balance technical sophistication with practical implementation considerations and stakeholder management.

By incorporating the following work samples into your interview process, you can more effectively evaluate candidates' abilities to leverage AI for internal mobility matching. These exercises simulate authentic challenges they would face on the job, revealing their problem-solving approaches, technical capabilities, and understanding of the unique considerations in this domain.

Activity #1: AI Model Design for Skills-Based Matching

This activity assesses the candidate's ability to design an AI solution specifically for internal mobility matching based on skills. It evaluates their understanding of machine learning approaches, data requirements, and how to translate HR needs into technical specifications. This fundamental skill is essential for anyone working on AI-powered internal mobility systems.

Directions for the Company:

• Provide the candidate with a brief describing a fictional company's internal mobility challenges, including information about their current manual matching process, available data sources (HRIS, performance reviews, skills inventories, etc.), and key objectives for implementing an AI solution.
• Include a simplified dataset sample showing employee profiles and job requisition data.
• Allow 45-60 minutes for this exercise.
• Have a technical team member and an HR stakeholder present to evaluate both technical soundness and HR applicability.

Directions for the Candidate:

• Review the company brief and sample data provided.
• Design a conceptual AI model for matching employees to internal opportunities based on skills and potential.
• Create a one-page diagram or flowchart showing your proposed solution architecture.
• Prepare a brief explanation of:
• The type of AI/ML approach you would recommend and why
• Key data inputs and how they would be weighted/processed
• How your model would address common challenges like skills inference and potential assessment
• How you would measure the effectiveness of your solution

Feedback Mechanism:

• The technical evaluator should provide feedback on the AI approach selected, data utilization strategy, and technical feasibility.
• The HR evaluator should provide feedback on how well the solution addresses real internal mobility challenges and practical implementation considerations.
• After receiving feedback, give the candidate 10-15 minutes to revise one aspect of their proposal, focusing on incorporating the improvement suggestion.

Activity #2: Bias Identification and Mitigation Strategy

This activity tests the candidate's awareness of and strategies for addressing algorithmic bias in internal mobility AI systems. It evaluates their ethical approach to AI implementation and understanding of how bias can impact career opportunities and organizational diversity goals.

Directions for the Company:

• Prepare a case study describing an internal mobility AI system that has produced potentially biased outcomes (e.g., consistently matching certain demographic groups to lower-level positions or failing to recommend diverse candidates for leadership roles).
• Include sample output data showing the potential bias patterns.
• Provide information about the company's diversity goals and current workforce demographics.
• Allow 30-45 minutes for this exercise.

Directions for the Candidate:

• Review the case study and identify potential sources of bias in the AI system.
• Analyze the provided data to determine patterns that suggest algorithmic bias.
• Develop a comprehensive strategy to:
• Identify and measure bias in the existing system
• Modify the algorithm to reduce bias
• Implement ongoing monitoring and governance
• Balance bias mitigation with system effectiveness
• Prepare a brief presentation (5-7 minutes) outlining your findings and recommendations.

Feedback Mechanism:

• Provide feedback on the thoroughness of the bias identification and the practicality of the proposed mitigation strategies.
• Offer one specific suggestion for strengthening the monitoring approach or governance structure.
• Allow the candidate 5-10 minutes to expand on how they would implement the suggested improvement.

Activity #3: Data Integration and Preprocessing Plan

This activity evaluates the candidate's ability to plan for the critical data foundation needed for an effective AI internal mobility system. It tests their understanding of diverse HR data sources, data quality issues, and preprocessing requirements specific to talent matching applications.

Directions for the Company:

• Create a scenario describing a company with multiple disconnected HR systems containing relevant data for internal mobility matching (e.g., HRIS, learning management system, performance management system, project management tools, etc.).
• Provide sample data snippets from each system showing different formats, fields, and quality issues.
• Include a list of key business requirements for the internal mobility matching system.
• Allow 45-60 minutes for this exercise.

Directions for the Candidate:

• Review the scenario and sample data provided.
• Create a comprehensive data integration and preprocessing plan that includes:
• Identification of all relevant data sources and specific fields needed
• Data quality assessment and cleaning approach for each source
• Strategy for standardizing skills terminology across systems
• Methods for inferring missing skills or attributes
• Approach for creating a unified employee profile
• Considerations for data privacy, security, and compliance
• Prepare a written plan (1-2 pages) or a structured presentation of your approach.

Feedback Mechanism:

• Provide feedback on the completeness of the data integration strategy and the candidate's understanding of HR data complexities.
• Highlight one area where the preprocessing approach could be strengthened or made more efficient.
• Ask the candidate to spend 10 minutes revising their approach to address the identified improvement area.

Activity #4: Stakeholder Communication Role Play

This activity assesses the candidate's ability to communicate complex AI concepts to non-technical HR stakeholders and address concerns about implementing AI for internal mobility. It evaluates their communication skills, stakeholder management abilities, and understanding of change management in HR technology implementations.

Directions for the Company:

• Prepare a scenario where the candidate must present a proposed AI internal mobility solution to a skeptical HR leadership team.
• Create a brief for the candidate that includes details about the proposed solution and known stakeholder concerns (e.g., fear of replacing human judgment, concerns about bias, questions about ROI, etc.).
• Assign company interviewers to play the roles of different stakeholders with specific concerns:
• CHRO concerned about maintaining the human element in career development
• Diversity & Inclusion leader worried about algorithmic bias
• IT Security Director with data privacy concerns
• Finance leader questioning the ROI
• Allow 15 minutes for preparation and 20 minutes for the role play.

Directions for the Candidate:

• Review the scenario and prepare a brief (5-minute) presentation explaining the AI internal mobility solution in non-technical terms.
• Focus on addressing the benefits, implementation approach, and how you'll address known concerns.
• Be prepared to respond to questions and objections from the stakeholder panel.
• Your goal is to build stakeholder confidence in the solution while demonstrating understanding of their legitimate concerns.

Feedback Mechanism:

• Provide feedback on communication clarity, empathy in addressing concerns, and technical accuracy.
• Identify one stakeholder whose concerns were not fully addressed or could have been handled more effectively.
• Give the candidate 5-10 minutes to rethink and re-present their approach to that specific stakeholder.

Frequently Asked Questions

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

Each activity requires 30-60 minutes to complete, plus time for feedback and improvement. Consider spreading them across different interview stages or selecting the 1-2 most relevant to your specific needs. For senior roles, you might conduct a more comprehensive assessment using multiple activities over a half-day interview.

Should candidates complete these exercises before or during the interview?

Activities #1 and #3 can work well as take-home assignments with a time limit, followed by an in-person discussion of their approach. Activities #2 and #4 are more effective when conducted live during the interview process to assess real-time thinking and communication skills.

How can we adapt these for candidates with different experience levels?

For more junior candidates, provide additional structure and guidance in the prompts. For senior candidates, add complexity by introducing constraints like limited data availability or competing stakeholder priorities. Adjust your evaluation criteria based on the expected expertise level for the role.

What if our organization doesn't have sophisticated HR data systems yet?

These exercises can still be valuable. Frame the scenario as building a new solution rather than integrating with existing systems. Focus more on the candidate's approach to determining what data would be needed and how they would design a system from the ground up.

How should we weigh technical AI expertise versus HR domain knowledge?

The right balance depends on your team composition and specific needs. If you already have strong HR expertise on the team, you might prioritize technical capabilities. Conversely, if you have AI engineers but need someone who can translate HR requirements, emphasize domain knowledge and communication skills in your evaluation.

Can these exercises be adapted for vendor selection rather than hiring?

Yes, these activities can be modified to evaluate vendors offering AI-powered internal mobility solutions. Instead of individual candidates, have the vendor's team complete relevant exercises to demonstrate their approach and expertise before making a purchasing decision.

AI for internal mobility matching represents a powerful intersection of technology and human resource management. By using these work samples, you can identify candidates who not only understand the technical aspects of AI implementation but also appreciate the nuanced human elements of career development and organizational talent management. The right hire will help your organization build internal mobility systems that are technically sound, ethically implemented, and aligned with your broader talent strategy.

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