Effective Work Samples to Evaluate AI Skills for Support Team Optimization

Integrating artificial intelligence into support team operations represents a significant opportunity for organizations to enhance efficiency, improve customer satisfaction, and drive better business outcomes. However, finding candidates who truly understand how to leverage AI technologies within support environments requires more than just reviewing resumes or conducting standard interviews. The intersection of AI expertise and support operations knowledge is specialized, and traditional evaluation methods often fail to reveal a candidate's true capabilities in this domain.

Work samples and practical exercises provide a window into how candidates approach real-world challenges in AI-driven support optimization. These exercises reveal not only technical understanding but also critical thinking, problem-solving abilities, and the capacity to translate AI capabilities into tangible support team improvements. By observing candidates as they work through realistic scenarios, hiring managers can better assess their potential impact on support operations.

The most effective candidates in this space possess a unique blend of technical AI knowledge, data analysis skills, support operations expertise, and change management capabilities. They understand both the possibilities and limitations of AI technologies in support contexts and can develop implementation strategies that account for the human elements of support teams. Traditional interviews rarely surface these multifaceted skills effectively.

The following work samples are designed to evaluate candidates' abilities to analyze support data, identify opportunities for AI implementation, develop strategic plans, and execute tactical improvements. Each exercise simulates challenges that professionals working with AI in support environments regularly encounter, providing a comprehensive view of a candidate's readiness to drive performance optimization through AI technologies.

Activity #1: Support Data Analysis and AI Opportunity Identification

This exercise evaluates a candidate's ability to analyze support team performance data, identify patterns that indicate opportunities for AI intervention, and propose specific AI solutions that address the identified challenges. Success in AI-driven support optimization begins with the ability to recognize where and how AI can create the most significant impact, requiring both analytical skills and AI solution knowledge.

Directions for the Company:

• Prepare a sanitized dataset of support metrics that includes: ticket volume by category, average resolution time, customer satisfaction scores, agent utilization rates, and common customer inquiries.
• Include some obvious patterns that suggest opportunities for AI intervention (e.g., repetitive questions, inconsistent resolution times, or specific categories with low satisfaction).
• Provide access to this data in a spreadsheet format that the candidate can analyze during the exercise.
• Allow 45-60 minutes for the candidate to review the data and prepare their findings and recommendations.
• Have a support operations leader available to answer clarifying questions about the data.

Directions for the Candidate:

• Review the provided support team performance data to identify key challenges and opportunities for improvement.
• Analyze patterns in the data that suggest areas where AI technologies could have the greatest impact.
• Prepare a brief presentation (5-7 slides or equivalent) that outlines:

1. Three key performance challenges identified in the data
2. Specific AI solutions or technologies that could address each challenge
3. Expected impact of each proposed solution on key performance metrics
4. Implementation considerations for each solution

• Be prepared to present and discuss your findings with the interview team.

Feedback Mechanism:

• After the presentation, provide feedback on one strength in the candidate's analysis and one area where their approach could be improved or where they missed an important consideration.
• Ask the candidate to take 10 minutes to revise one section of their proposal based on the feedback, focusing specifically on how they would address the improvement area.
• Observe how receptive the candidate is to feedback and how effectively they incorporate it into their revised approach.

Activity #2: AI Chatbot Optimization Role Play

This role play assesses the candidate's ability to evaluate an existing AI chatbot implementation and recommend tactical improvements to enhance its performance. It tests practical knowledge of conversational AI technologies, understanding of support workflows, and the ability to balance automation with human intervention for optimal customer experience.

Directions for the Company:

• Create a scenario brief describing a support team that implemented an AI chatbot 6 months ago with mixed results: some efficiency gains but also customer frustration in certain scenarios.
• Provide sample chatbot conversation logs showing both successful interactions and problematic ones.
• Include basic performance metrics for the chatbot (containment rate, escalation rate, customer satisfaction).
• Assign someone to play the role of the Support Team Manager who is frustrated with aspects of the chatbot performance.
• Schedule 30 minutes for the role play followed by 15 minutes for feedback and revision.

Directions for the Candidate:

• Review the provided chatbot performance data and conversation logs.
• Prepare for a meeting with the Support Team Manager to discuss optimization strategies.
• During the role play:

1. Demonstrate active listening to understand the manager's concerns
2. Identify specific issues in the chatbot implementation based on the data
3. Recommend 3-5 specific tactical improvements to the chatbot
4. Explain how these changes would impact key performance metrics
5. Discuss how to measure success of the proposed changes

• Be prepared to handle pushback or questions about your recommendations.

Feedback Mechanism:

• After the role play, provide feedback on one aspect of the candidate's recommendations that was particularly insightful and one area where their approach could be more effective.
• Ask the candidate to take 10 minutes to revise their approach to the identified improvement area.
• Have them briefly explain their revised recommendation and implementation approach.
• Evaluate their ability to incorporate feedback constructively and adapt their approach.

Activity #3: AI Implementation Planning for Support Team

This exercise evaluates the candidate's ability to develop a comprehensive plan for implementing a new AI solution within a support environment. It tests strategic thinking, project planning skills, change management awareness, and understanding of the technical and human factors involved in successful AI adoption.

Directions for the Company:

• Create a scenario brief for implementing a specific AI solution in a support environment (e.g., an AI-powered quality assurance tool that analyzes support interactions).
• Include details about the current support team structure, technology stack, and key challenges.
• Provide information about team size, current processes, and any relevant constraints.
• Make available any templates or tools the candidate might need for creating a project plan.
• Allow 60-90 minutes for the candidate to develop their implementation plan.

Directions for the Candidate:

• Review the scenario information about the support team and the proposed AI solution.
• Develop a comprehensive implementation plan that includes:

1. Project phases and timeline (from initial assessment through full deployment)
2. Key stakeholders and their involvement at each stage
3. Required resources (technical, human, financial)
4. Change management and training approach
5. Success metrics and measurement plan
6. Risk assessment and mitigation strategies

• Create a visual representation of your implementation plan (Gantt chart, project roadmap, or similar format).
• Prepare to present and explain your plan to the interview team.

Feedback Mechanism:

• After the presentation, provide feedback on one strength of the implementation plan and one area that needs more development or consideration.
• Ask the candidate to take 15 minutes to revise the identified section of their plan.
• Have them explain their revisions and the reasoning behind them.
• Evaluate their ability to incorporate feedback and strengthen their planning approach.

Activity #4: Support Agent AI Tool Evaluation Exercise

This exercise assesses the candidate's ability to evaluate the effectiveness of AI tools from the perspective of support agents and make recommendations to improve agent experience and tool adoption. It tests understanding of the human factors in AI implementation, user experience considerations, and the ability to balance technological capabilities with practical usability.

Directions for the Company:

• Prepare a description of an AI tool currently used by support agents (e.g., an AI-powered knowledge suggestion tool or sentiment analysis feature).
• Create a set of agent feedback quotes expressing both positive aspects and frustrations with the tool.
• Include basic usage statistics showing adoption rates and performance impact.
• Provide screenshots or a demo video of the tool interface if possible.
• Allow 45-60 minutes for the candidate to review materials and prepare recommendations.

Directions for the Candidate:

• Review the provided information about the AI tool, including agent feedback and usage statistics.
• Analyze the apparent strengths and weaknesses of the tool from both a technical and user experience perspective.
• Prepare a brief report or presentation that includes:

1. Assessment of the tool's current effectiveness and adoption challenges
2. Analysis of specific friction points for agents using the tool
3. Recommendations for 3-5 specific improvements to increase adoption and effectiveness
4. Approach for measuring improvement after implementing your recommendations
5. Suggestions for ongoing agent feedback collection and tool optimization

• Be prepared to present and discuss your evaluation with the interview team.

Feedback Mechanism:

• After the presentation, provide feedback on one particularly insightful aspect of the candidate's evaluation and one area where their analysis could be deeper or more comprehensive.
• Ask the candidate to take 10-15 minutes to develop a more detailed approach to the improvement area identified.
• Have them explain their enhanced recommendation and implementation approach.
• Evaluate their receptiveness to feedback and ability to deepen their analysis when prompted.

Frequently Asked Questions

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

Each exercise requires approximately 1-2 hours total, including preparation, execution, feedback, and revision. For senior roles, consider using one in-depth exercise or two shorter ones. For mid-level roles, a single exercise with appropriate scope adjustment is typically sufficient. These can be conducted during an onsite interview day or as a take-home assignment with a follow-up discussion.

Should we provide these exercises to candidates in advance?

For Activities #1 and #3, providing the scenario information 24-48 hours in advance allows candidates to prepare thoughtfully, which better simulates real-world conditions. For Activities #2 and #4, providing basic context in advance while saving specific details for the interview session creates a balance between preparation and spontaneous problem-solving.

How should we adapt these exercises for candidates with different experience levels?

For more junior candidates, simplify the data sets, provide more structured templates, and focus evaluation more on technical understanding and less on strategic elements. For senior candidates, include more ambiguity, complex organizational factors, and expect more sophisticated change management and ROI considerations in their responses.

What if our company doesn't currently use AI in our support operations?

These exercises remain valuable even if you're just beginning your AI journey. Frame the exercises as planning for future implementation rather than optimizing existing systems. Focus more on the candidate's ability to identify opportunities, develop implementation strategies, and demonstrate understanding of AI capabilities in support contexts.

How do we evaluate candidates who have strong AI knowledge but limited support experience, or vice versa?

Look for candidates who demonstrate awareness of their knowledge gaps and show how they would address them. Strong candidates with asymmetric experience will ask good questions, make reasonable assumptions, and acknowledge areas where they would need to collaborate with subject matter experts. The feedback portion of each exercise is particularly valuable for assessing adaptability in these cases.

Can these exercises be conducted remotely?

Yes, all of these exercises can be adapted for remote interviews using video conferencing, collaborative documents, and screen sharing. For data analysis exercises, provide access to the data in advance. For role plays, ensure clear audio and video connections. The key is maintaining the interactive elements and feedback mechanisms that make these exercises valuable.

Implementing effective work samples for evaluating AI skills in support team optimization helps organizations identify candidates who can truly drive performance improvements through intelligent technology implementation. By assessing both technical understanding and practical application skills, these exercises provide a comprehensive view of a candidate's potential impact on your support operations.

At Yardstick, we understand the critical importance of finding the right talent to lead your AI initiatives in customer support. Our AI-powered tools can help you further refine your hiring process for these specialized roles. To explore more resources for optimizing your hiring process, visit our AI Job Descriptions, AI Interview Question Generator, and AI Interview Guide Generator.

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