Essential Work Sample Exercises for Evaluating AI Support Ticket Routing Skills

AI-powered support ticket routing and prioritization has become a critical capability for modern customer service operations. As organizations face increasing ticket volumes and customer expectations for rapid resolution, the ability to intelligently classify, route, and prioritize support tickets can dramatically improve efficiency, customer satisfaction, and agent productivity. However, finding candidates with the right blend of AI technical knowledge and customer support operational understanding presents a significant challenge.

Evaluating candidates for roles involving AI in support ticket routing requires assessing both theoretical understanding and practical application skills. The ideal candidate must demonstrate proficiency in machine learning concepts, natural language processing, data analysis, and an understanding of support operations workflows. They should be able to design systems that accurately categorize tickets, assign appropriate priority levels, and route them to the most suitable agents or teams.

The work samples provided below are designed to evaluate a candidate's ability to plan, implement, troubleshoot, and communicate about AI-powered support systems. These exercises simulate real-world scenarios that professionals in this field encounter, allowing hiring managers to assess how candidates approach complex problems, apply technical knowledge, and balance competing priorities.

By incorporating these work samples into your interview process, you can move beyond resume claims and theoretical discussions to observe candidates demonstrating their skills in action. This approach provides deeper insights into their capabilities and helps identify those who can truly deliver value in implementing and managing AI for support ticket operations.

Activity #1: Support Ticket Classification System Design

This exercise evaluates a candidate's ability to design an AI-based ticket classification system from the ground up. It tests their understanding of machine learning approaches, feature engineering, and how to translate business requirements into technical specifications. This foundational skill is essential for anyone working with AI in support operations.

Directions for the Company:

• Provide the candidate with a document describing your company's support ticket categories (e.g., billing issues, technical problems, account access, feature requests) and sample tickets from each category (10-15 examples).
• Include information about your current routing process and pain points (e.g., misrouting, delays in escalation).
• Allow the candidate 45-60 minutes to complete this exercise.
• Prepare a whiteboard or collaborative document for the candidate to sketch their solution.

Directions for the Candidate:

• Review the provided support ticket categories and sample data.
• Design a machine learning approach to automatically classify incoming tickets into the appropriate categories.
• Identify what features you would extract from tickets to make classification decisions.
• Outline how you would handle edge cases, such as tickets that could belong to multiple categories.
• Create a simple diagram showing the system architecture and data flow.
• Explain how you would measure the success of your classification system.

Feedback Mechanism:

• After the candidate presents their solution, provide feedback on one aspect they handled well (e.g., feature selection, handling of edge cases) and one area for improvement (e.g., overlooking a particular challenge, complexity of implementation).
• Ask the candidate to revise their approach based on the improvement feedback, giving them 10 minutes to adjust their design and explain the changes.

Activity #2: Ticket Prioritization Algorithm Troubleshooting

This exercise tests a candidate's ability to identify and resolve issues in an existing AI prioritization system. It evaluates their analytical thinking, debugging skills, and understanding of how different factors should influence ticket priority. These troubleshooting skills are crucial for maintaining and improving AI systems in production environments.

Directions for the Company:

• Create a scenario description of an AI ticket prioritization system that is not performing correctly (e.g., critical issues being assigned low priority, certain customer segments consistently getting incorrect prioritization).
• Provide a simplified version of the algorithm's logic and the features it considers (customer tier, issue type, keywords, etc.).
• Include a dataset of 20-30 sample tickets with their current priority assignments and indicators of which ones are incorrectly prioritized.
• Prepare to discuss the business impact of these prioritization errors.

Directions for the Candidate:

• Review the prioritization algorithm and the sample dataset of tickets.
• Identify patterns in the incorrectly prioritized tickets.
• Diagnose potential causes for the prioritization errors.
• Recommend specific changes to the algorithm to address these issues.
• Explain how you would validate that your changes fixed the problem without creating new issues.
• Suggest additional data points or features that might improve prioritization accuracy.

Feedback Mechanism:

• Provide feedback on the candidate's analytical approach and the quality of their diagnosis.
• Highlight one aspect of their solution that was particularly insightful and one area where their approach could be enhanced.
• Ask the candidate to refine their solution based on the improvement feedback, giving them 10 minutes to address the specific concern you raised.

Activity #3: Stakeholder Communication Role Play

This exercise evaluates a candidate's ability to communicate complex AI concepts to non-technical stakeholders. It tests their communication skills, business acumen, and ability to translate technical details into business value. This skill is essential for gaining buy-in and ensuring alignment between technical implementation and business objectives.

Directions for the Company:

• Prepare a role play scenario where the candidate must explain a proposed AI ticket routing system to a mixed audience of support team managers, executives, and frontline agents.
• Create a one-page brief describing the AI system the candidate should explain (include key features, expected benefits, and implementation timeline).
• Assign roles to your interview team members (e.g., skeptical executive concerned about ROI, support manager worried about agent adoption, IT leader concerned about integration).
• Allow the candidate 15 minutes to prepare after receiving the brief.

Directions for the Candidate:

• Review the AI system brief and prepare a 10-minute presentation explaining the system to the stakeholder group.
• Focus on explaining how the AI routing system works in non-technical terms.
• Address the business benefits, implementation considerations, and potential challenges.
• Be prepared to answer questions from different stakeholder perspectives.
• Include how you would measure success and demonstrate ROI to the business.
• Consider how the system might affect the day-to-day work of support agents.

Feedback Mechanism:

• After the role play, provide feedback on one communication strength (e.g., clear explanations, addressing stakeholder concerns) and one area for improvement (e.g., technical jargon, missing a key stakeholder concern).
• Ask the candidate to re-address a specific part of their explanation incorporating the feedback, giving them 5 minutes to prepare and deliver a revised explanation.

Activity #4: Routing Efficiency Analysis and Optimization

This exercise tests a candidate's ability to analyze the performance of an existing routing system and identify opportunities for improvement. It evaluates their data analysis skills, understanding of support operations metrics, and ability to translate insights into actionable recommendations. This skill is crucial for continuously improving AI systems based on real-world performance.

Directions for the Company:

• Prepare a dataset showing 3-6 months of ticket routing performance (e.g., routing accuracy, reassignment rates, resolution times by team).
• Include information about the current routing rules or AI model being used.
• Provide context about team structures, agent specializations, and business SLAs.
• Create a simple dashboard or spreadsheet with the relevant metrics.
• Allow the candidate 45-60 minutes to analyze the data and prepare recommendations.

Directions for the Candidate:

• Analyze the provided ticket routing performance data.
• Identify patterns, bottlenecks, and opportunities for improvement in the current routing system.
• Calculate the potential impact of routing improvements on key metrics like first-contact resolution, average handle time, and SLA compliance.
• Recommend specific changes to the routing logic or AI model to improve performance.
• Outline how you would implement these changes and measure their impact.
• Prepare a brief presentation of your findings and recommendations.

Feedback Mechanism:

• After the candidate presents their analysis, provide feedback on one analytical strength (e.g., insightful pattern recognition, quantification of impact) and one area for improvement (e.g., overlooking a key metric, implementation feasibility).
• Ask the candidate to address the improvement area by revising part of their analysis or recommendations, giving them 10-15 minutes to make adjustments and explain their revised approach.

Frequently Asked Questions

How much technical AI knowledge should candidates demonstrate in these exercises?

Candidates should demonstrate a working understanding of machine learning concepts and approaches relevant to text classification and prioritization. However, the focus should be on practical application rather than theoretical depth. Look for candidates who can explain their technical choices in business terms and who understand the tradeoffs between different approaches.

Should we provide real company data for these exercises?

While using real data would make the exercises more relevant, it's usually sufficient (and often preferable) to create anonymized or synthetic data that resembles your actual support tickets. This protects customer privacy while still allowing candidates to demonstrate their skills in a realistic context.

How should we evaluate candidates who propose solutions different from our current approach?

Different approaches should be evaluated on their merits rather than how closely they match your existing systems. The best candidates might challenge your assumptions or introduce novel approaches. Evaluate based on whether their solution addresses the core business needs, demonstrates sound reasoning, and shows awareness of practical implementation considerations.

What if a candidate doesn't have experience with the specific AI tools we use?

Focus on evaluating the candidate's problem-solving approach and understanding of fundamental concepts rather than familiarity with specific tools. A strong candidate with experience in similar technologies can typically learn your specific tools quickly. Look for transferable skills and adaptability in their solutions.

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

Provide sufficient context and background information so candidates without specific domain knowledge can still demonstrate their skills. Be clear about what information you're providing versus what you expect candidates to know or figure out. Consider allowing candidates to ask clarifying questions before or during the exercise to level the playing field.

Should we expect candidates to write actual code during these exercises?

For most roles involving AI in support ticket routing, understanding the approach and system design is more important than coding skills. However, for more technical roles, you might include a simplified coding exercise focused on a specific aspect (e.g., feature extraction from ticket text). If coding is included, be realistic about what can be accomplished in the time allowed.

AI-powered support ticket routing and prioritization represents a significant opportunity to transform customer support operations. By using these work samples in your hiring process, you can identify candidates who not only understand the technical aspects of AI but can also apply that knowledge to create real business value. The right talent in this area can help your organization reduce response times, improve first-contact resolution rates, and enhance both customer and agent satisfaction.

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