Essential Work Samples to Evaluate AI Skills for Proposal Generation and RFP Responses

In today's competitive business landscape, organizations are increasingly turning to artificial intelligence to streamline and enhance their proposal generation and RFP response processes. The ability to effectively leverage AI tools for these critical business development functions can dramatically improve win rates, reduce response time, and free up valuable resources. However, finding professionals who truly understand how to implement and optimize AI for proposal generation requires careful evaluation beyond traditional interviews.

The intersection of AI expertise and proposal development demands a unique skill set. Candidates must not only understand AI capabilities and limitations but also possess deep knowledge of proposal best practices and the ability to translate between these domains. They need to balance automation with the human touch that winning proposals require, knowing when to rely on AI and when human expertise should take precedence.

Work samples and practical exercises provide the most reliable method for evaluating a candidate's proficiency in applying AI to proposal processes. Through carefully designed scenarios, you can observe how candidates approach AI tool selection, implementation planning, content enhancement, and process optimization—all critical components of successful AI-powered proposal systems.

The following four activities are designed to evaluate candidates' abilities across the spectrum of skills needed for AI-powered proposal generation and RFP responses. These exercises reveal not just technical knowledge but also strategic thinking, practical implementation skills, and the ability to measure and improve AI-assisted processes. By incorporating these work samples into your interview process, you'll gain valuable insights that traditional questioning simply cannot provide.

Activity #1: AI Tool Selection and Implementation Planning

This activity evaluates a candidate's strategic thinking about AI implementation for proposal processes. It tests their understanding of available AI technologies, ability to align tools with business objectives, and skill in planning a practical implementation that addresses common challenges. This exercise reveals how candidates approach the critical first step of any AI initiative: selecting the right tools and creating a viable implementation roadmap.

Directions for the Company:

• Provide the candidate with a brief description of a fictional company that needs to improve its proposal process using AI. Include details about current challenges (e.g., slow turnaround, inconsistent quality, knowledge management issues).
• Supply information about the company's proposal volume, typical complexity, team structure, and current technology stack.
• Ask the candidate to prepare a 2-page implementation plan and a 10-minute presentation.
• Allow the candidate 24-48 hours to prepare their response.
• Have 1-2 evaluators play the role of company stakeholders during the presentation.

Directions for the Candidate:

• Review the company information provided and develop a strategic plan for implementing AI tools to improve the proposal generation process.
• Your plan should include:

1. Recommended AI tools/platforms and justification for selection
2. Implementation timeline and key milestones
3. Required resources and potential ROI
4. Anticipated challenges and mitigation strategies
5. Success metrics and evaluation approach

• Prepare a 10-minute presentation of your plan, followed by 5-10 minutes of Q&A.
• Focus on practical implementation rather than theoretical possibilities.

Feedback Mechanism:

• After the presentation, provide feedback on one aspect the candidate handled well (e.g., "Your approach to measuring ROI was particularly strong").
• Offer one area for improvement (e.g., "Your timeline might benefit from more attention to change management").
• Ask the candidate to spend 5 minutes revising one section of their plan based on the feedback, explaining their adjustments.

Activity #2: AI-Assisted RFP Response Simulation

This exercise tests a candidate's tactical ability to use AI tools in responding to specific RFP requirements. It evaluates their skill in prompt engineering, output evaluation, and content refinement—all essential capabilities for effectively applying AI to proposal development. The activity reveals how candidates balance AI assistance with human expertise in a time-constrained situation.

Directions for the Company:

• Select a section from a past RFP (anonymized if necessary) that includes 3-5 complex requirements. Choose a section that would typically take 1-2 hours to respond to manually.
• Provide access to an AI tool like ChatGPT, Claude, or your company's preferred AI platform.
• Also provide relevant company information that would normally be used to respond to this RFP section (capabilities documents, past proposals, etc.).
• Allow 45-60 minutes for the exercise.
• Observe how the candidate interacts with the AI tool, particularly their prompt engineering approach.

Directions for the Candidate:

• You will be responding to a section of an RFP using AI assistance.
• Review the RFP requirements and supporting company information provided.
• Use the AI tool to help generate initial content, but ensure the final response:

1. Fully addresses all requirements
2. Incorporates company-specific information accurately
3. Follows proposal best practices (clear value propositions, evidence, etc.)
4. Maintains a consistent voice and appropriate tone

• Document your prompt strategy and any refinements you made to the AI-generated content.
• Complete the response within the allotted time.

Feedback Mechanism:

• Provide feedback on the candidate's effective use of AI (e.g., "Your prompts effectively extracted relevant information").
• Offer one suggestion for improvement (e.g., "The AI-generated content could use more customization to our company voice").
• Allow the candidate 10 minutes to revise one paragraph based on feedback, explaining their approach to improvement.

Activity #3: Proposal Content Enhancement with AI

This activity evaluates a candidate's ability to use AI to improve existing proposal content. It tests their skill in identifying content weaknesses, selecting appropriate AI enhancement strategies, and maintaining proposal integrity while leveraging AI capabilities. This exercise reveals how candidates can use AI not just for content generation but for substantive quality improvements.

Directions for the Company:

• Provide a sample proposal section (2-3 pages) with intentional weaknesses such as:
• Vague value propositions
• Lack of supporting evidence
• Inconsistent messaging
• Passive voice or other stylistic issues
• Give access to an AI tool and any relevant company materials (style guides, messaging documents, etc.).
• Allow 45-60 minutes for the exercise.
• Prepare specific questions about the candidate's enhancement approach.

Directions for the Candidate:

• Review the provided proposal section and identify areas for improvement.
• Use the AI tool to help enhance the content in the following ways:

1. Strengthen value propositions and messaging
2. Add appropriate evidence and specificity
3. Improve readability and engagement
4. Ensure consistency with company style and voice

• Document your approach, including:
• What issues you identified
• How you used AI to address each issue
• What manual refinements you made to the AI suggestions
• Be prepared to explain your enhancement decisions.

Feedback Mechanism:

• Highlight one effective enhancement the candidate made (e.g., "Your strengthening of the value proposition significantly improved the section").
• Suggest one area where their approach could be improved (e.g., "The evidence added could be more quantitative").
• Ask the candidate to spend 10 minutes applying this feedback to another paragraph, explaining their revised approach.

Activity #4: AI Process Evaluation and Optimization

This exercise tests a candidate's analytical abilities in measuring and improving AI-assisted proposal processes. It evaluates their understanding of relevant metrics, ability to identify process inefficiencies, and skill in developing practical optimization strategies. This activity reveals how candidates approach continuous improvement in AI implementation—a critical factor for long-term success.

Directions for the Company:

• Create a scenario describing an organization that implemented AI for proposal generation 6 months ago but is experiencing mixed results.
• Provide a data set showing relevant metrics before and after AI implementation, such as:
• Proposal completion times
• Win rates
• Resource utilization
• Quality scores
• User adoption rates
• Include qualitative feedback from proposal team members about their experiences.
• Allow 60 minutes for analysis and recommendation development.

Directions for the Candidate:

• Review the scenario and data provided about the organization's AI implementation for proposals.
• Analyze the metrics to identify what's working well and what needs improvement.
• Develop a comprehensive optimization plan that includes:

1. Key findings from your analysis
2. Recommended process changes
3. AI tool adjustments or enhancements
4. Training or change management initiatives
5. Updated metrics and monitoring approach

• Create a one-page executive summary and a more detailed 2-3 page recommendation document.
• Be prepared to present and defend your recommendations.

Feedback Mechanism:

• Provide feedback on the strength of the candidate's analysis (e.g., "Your identification of the correlation between training and adoption rates was insightful").
• Suggest one area where their recommendations could be enhanced (e.g., "Consider how you might address the cultural resistance more directly").
• Ask the candidate to spend 10 minutes refining one recommendation based on this feedback, explaining their adjustments.

Frequently Asked Questions

How much AI expertise should candidates have versus proposal experience?

The ideal balance depends on your specific needs, but generally, look for candidates who demonstrate both technical AI understanding and practical proposal knowledge. Someone with deep proposal experience can learn AI tools, while an AI expert can learn proposal best practices, but the best candidates show competency in both areas.

Should we provide access to our actual AI tools during these exercises?

If possible, yes. Using your actual tools gives the most relevant assessment. However, if that's not feasible, publicly available AI tools like ChatGPT, Claude, or your company's preferred AI platform can serve as reasonable substitutes. Just be clear about any limitations compared to your production tools.

How should we evaluate candidates who use different AI approaches than we currently employ?

Assess their reasoning rather than just their specific choices. A candidate who selects different tools but provides sound justification may bring valuable new perspectives. The key is whether their approach would effectively solve your business challenges, not whether it matches your current methods.

What if a candidate has limited hands-on experience with AI but shows strong potential?

Focus on their learning agility and problem-solving approach. In the rapidly evolving AI landscape, the ability to adapt and learn new tools quickly can be more valuable than experience with specific current technologies. Look for candidates who demonstrate strong analytical thinking and a structured approach to implementation.

How can we ensure these exercises don't take too much of the candidate's time?

Be transparent about time expectations upfront. Consider compensating candidates for extensive exercises, especially for senior roles. You can also scale the complexity based on the position level—simpler exercises for junior roles and more comprehensive ones for leadership positions.

Should we customize these exercises for different types of proposals (government, commercial, etc.)?

Yes, tailoring exercises to your specific proposal types will provide the most relevant assessment. Government proposals require different approaches than commercial ones, and AI implementation strategies should reflect these differences. Use actual examples from your proposal environment whenever possible.

The effective implementation of AI for proposal generation and RFP responses represents a significant competitive advantage in today's business environment. By incorporating these practical work samples into your interview process, you'll be able to identify candidates who can truly drive value through AI-enhanced proposal processes. Remember that the most successful candidates will demonstrate not just technical AI knowledge but also a deep understanding of proposal strategy and the ability to bridge these domains effectively.

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