Effective Work Samples to Evaluate AI for Sales Team Resource Allocation Skills

In today's competitive sales landscape, organizations are increasingly turning to artificial intelligence to optimize how they allocate their most precious resources: their sales professionals' time, attention, and efforts. The ability to leverage AI for sales team resource allocation has become a critical skill for sales operations leaders, revenue operations professionals, and sales strategists. When implemented effectively, AI-driven resource allocation can dramatically improve sales productivity, territory coverage, account prioritization, and ultimately, revenue generation.

Evaluating candidates' abilities in this specialized area requires more than just theoretical questions or resume reviews. Practical work samples provide a window into how candidates approach real-world challenges in applying AI to sales resource allocation problems. These exercises reveal a candidate's technical understanding of AI capabilities, their sales operations acumen, and their ability to translate complex data insights into actionable sales strategies.

The work samples outlined below are designed to assess candidates' proficiency in key areas: analyzing sales data for AI-ready insights, designing AI-powered allocation frameworks, implementing AI solutions within existing sales processes, and effectively communicating AI-driven strategies to stakeholders. Each exercise simulates challenges that professionals in this field regularly encounter.

By incorporating these work samples into your interview process, you'll gain deeper insights into candidates' practical abilities to leverage AI for optimizing sales resource allocation. This approach helps identify individuals who not only understand the theoretical applications of AI in sales but can also execute practical solutions that drive measurable business outcomes.

Activity #1: Sales Territory Optimization Analysis

This activity evaluates a candidate's ability to analyze sales territory data and recommend an AI-driven approach to optimize territory allocation. It tests their understanding of how AI can be applied to balance workloads, maximize coverage, and increase sales effectiveness through data-driven territory design.

Directions for the Company:

• Provide the candidate with a dataset containing information about 50-100 sales territories, including: geographic boundaries, number of accounts, revenue potential, current sales rep assignments, travel time between accounts, and historical performance metrics.
• Include some obvious imbalances in the current territory design (e.g., some territories with too many accounts, others with too few; uneven revenue potential distribution).
• Allow candidates 45-60 minutes to analyze the data and prepare their recommendations.
• Provide access to basic data analysis tools (Excel, Google Sheets, or similar).

Directions for the Candidate:

• Review the provided sales territory data and identify key imbalances or inefficiencies in the current allocation.
• Outline an AI-driven approach to optimize territory allocation, including:
• What specific AI/ML techniques would be appropriate for this problem
• What additional data points might improve the model
• How you would measure success of the AI implementation
• Key constraints the AI model should consider (e.g., maintaining customer relationships, minimizing disruption)
• Create a simple visualization or framework showing how the AI solution would work to balance territories more effectively.
• Prepare a brief explanation of how your approach would improve sales effectiveness and resource utilization.

Feedback Mechanism:

• After the candidate presents their approach, provide feedback on one strength (e.g., "Your consideration of travel time as a key constraint was excellent") and one area for improvement (e.g., "Your model didn't account for varying account complexity").
• Ask the candidate to refine one aspect of their approach based on the feedback, giving them 5-10 minutes to make adjustments and explain how this improves their solution.

Activity #2: AI-Powered Account Prioritization Framework

This exercise tests a candidate's ability to design an AI framework for prioritizing accounts across a sales team. It evaluates their understanding of predictive modeling for sales opportunities and how to translate those predictions into actionable resource allocation decisions.

Directions for the Company:

• Prepare a dataset with 200-300 accounts containing fields such as: company size, industry, previous purchase history, engagement metrics (email opens, website visits, etc.), sales cycle length, and win/loss outcomes.
• Include a mix of won and lost opportunities with various characteristics.
• Provide a brief on current challenges: "Sales reps are spending too much time on low-probability deals and missing high-potential opportunities."
• Allow 60 minutes for the exercise.
• Provide access to a whiteboard or digital drawing tool.

Directions for the Candidate:

• Analyze the provided account data to identify patterns that might predict deal success.
• Design an AI-powered framework that would:
• Score accounts based on likelihood to close
• Recommend optimal resource allocation across the sales team
• Adapt recommendations based on changing conditions
• Outline the key features your AI model would use to prioritize accounts.
• Create a simple mockup of how this would appear to sales reps and managers in a dashboard or tool.
• Explain how you would measure the effectiveness of your AI prioritization system.
• Be prepared to discuss how your framework handles edge cases (e.g., strategic accounts that may not score high but are important to the business).

Feedback Mechanism:

• Provide specific feedback on the candidate's approach, highlighting one strong element (e.g., "Your inclusion of engagement metrics as a predictive factor was well-reasoned") and one area for improvement (e.g., "The model doesn't account for seasonal variations in buying patterns").
• Ask the candidate to revise their framework to address the improvement area, giving them 10 minutes to adjust their approach and explain the changes.

Activity #3: Sales Capacity Planning Simulation

This activity assesses the candidate's ability to use AI to optimize sales headcount allocation across regions, products, or market segments. It tests their understanding of how AI can improve workforce planning and resource distribution to maximize revenue potential.

Directions for the Company:

• Create a scenario with 5-7 different sales regions or product lines with varying growth rates, sales cycles, and revenue potential.
• Provide historical data showing performance trends, quota attainment, and headcount allocation over the past 2-3 years.
• Include constraints such as budget limitations (can only hire X new reps) and minimum coverage requirements.
• Allow 45-60 minutes for the exercise.
• Provide a template spreadsheet or tool for calculations.

Directions for the Candidate:

• Review the historical performance data and current headcount allocation.
• Design an AI-driven approach to optimize sales capacity planning that:
• Predicts future revenue potential by region/product
• Recommends optimal headcount allocation to maximize revenue
• Accounts for ramp time of new hires and productivity variations
• Balances short-term results with long-term growth potential
• Create a model that shows recommended headcount changes and expected impact on revenue.
• Explain what data inputs would be most critical for your AI model and why.
• Outline how your approach would adapt to changing market conditions throughout the year.

Feedback Mechanism:

• After the candidate presents their capacity planning approach, provide feedback on one strength (e.g., "Your consideration of varying ramp times by region was insightful") and one area for improvement (e.g., "Your model doesn't account for the cost differences between regions").
• Ask the candidate to refine their model based on the feedback, giving them 10 minutes to incorporate the improvement and explain how it enhances their solution.

Activity #4: AI Implementation Roadmap for Sales Resource Allocation

This exercise evaluates a candidate's ability to plan the practical implementation of an AI solution for sales resource allocation. It tests their understanding of change management, technical implementation considerations, and how to drive adoption of AI tools within sales organizations.

Directions for the Company:

• Provide a scenario of a sales organization with 150 reps across 3 regions selling 5 product lines, currently using a CRM system and basic sales analytics.
• Include details about current challenges: inconsistent territory design, subjective account prioritization, and reactive resource allocation.
• Describe the technical environment (CRM, data warehouses, existing analytics tools).
• Allow 60 minutes for the exercise.
• Provide access to a whiteboard or digital planning tool.

Directions for the Candidate:

• Create a comprehensive implementation roadmap for introducing AI-powered resource allocation to this sales organization.
• Your roadmap should include:
• Assessment of current data readiness and gaps
• Phased approach to implementation (which AI capabilities to roll out first)
• Required integrations with existing systems
• Change management and training considerations
• Timeline with key milestones
• Success metrics and measurement approach
• Potential challenges and mitigation strategies
• Prepare a brief presentation explaining your implementation approach and why you've prioritized certain elements.
• Be ready to discuss how you would handle resistance from sales leadership or reps.

Feedback Mechanism:

• Provide feedback on one strength of the implementation plan (e.g., "Your phased approach starting with territory optimization before moving to account prioritization makes sense") and one area for improvement (e.g., "Your plan doesn't address how to handle the transition period when moving from the old system to the AI-driven approach").
• Ask the candidate to revise the specific portion of their roadmap that needs improvement, giving them 10 minutes to adjust their approach and explain how this enhances the implementation plan.

Frequently Asked Questions

How long should each of these work sample exercises take?

Each exercise is designed to take 45-60 minutes for the candidate to complete, plus additional time for presentation and feedback. You can adjust the complexity or scope of the data provided to fit your interview timeframe. For a comprehensive assessment, consider spreading these exercises across multiple interview stages rather than attempting all in one session.

Do candidates need to have coding or data science experience to complete these exercises?

No, these exercises are designed to test strategic thinking about AI applications rather than coding ability. Candidates should understand AI concepts and applications but don't need to write algorithms. If the role specifically requires technical AI implementation skills, you may want to add a coding component to one of the exercises.

What if we don't have sample data to provide for these exercises?

You can create simplified synthetic data that mimics your actual sales environment. Alternatively, there are publicly available sales datasets that can be adapted for these exercises. The key is ensuring the data includes enough variables and patterns to allow for meaningful analysis and AI application discussions.

How should we evaluate candidates who propose different AI approaches than what we're currently considering?

Different approaches should be evaluated on their merit rather than conformity to your current thinking. Look for sound reasoning, understanding of AI capabilities and limitations, and alignment with business objectives. Candidates who propose novel but well-reasoned approaches may bring valuable new perspectives to your organization.

Should we share information about our current AI initiatives with candidates before these exercises?

It's generally best to provide only basic information about current challenges and systems rather than detailed information about your AI roadmap. This allows you to assess candidates' independent thinking. However, you should provide enough context about your sales organization and processes for candidates to make informed recommendations.

How can we adapt these exercises for remote interviews?

These exercises work well in remote settings using video conferencing tools with screen sharing capabilities. Provide data and instructions in advance, use collaborative tools like Google Sheets or Miro for analysis and presentations, and ensure candidates have a way to present their findings visually during the video interview.

The ability to effectively leverage AI for sales team resource allocation represents a significant competitive advantage in today's data-driven sales environment. By incorporating these practical work samples into your interview process, you'll be able to identify candidates who not only understand AI concepts but can apply them to real-world sales challenges. The right talent in this area can help transform your sales organization from reactive to predictive, ensuring your valuable sales resources are deployed where they can generate the greatest return.

For more resources to optimize your hiring process, explore Yardstick's suite of AI-powered hiring tools, including our AI job descriptions generator, interview question generator, and comprehensive interview guide creator.

Build a complete interview guide for AI sales resource allocation skills by signing up for a free Yardstick account here