Of course. This is a classic value proposition testing problem. To provide your client with a clear, actionable path forward, we need a structured research plan. Here is a business research proposal designed to do just that.

Business Research Proposal: AI Interviewer Value Proposition Testing

This proposal outlines a two-phase research plan to determine the most trustworthy value proposition for a new AI interviewer tool targeting non-technical business roles.

1. Information Collection

This phase focuses on gathering the necessary raw data from the market and target users. We will use a combination of web searches to understand the landscape and in-depth user interviews to uncover core motivations and test our hypotheses.

A. Web Search

Purpose: To gather contextual data that will inform our understanding of the core problems (or "Jobs") our target users are trying to solve. This will help us frame our interview questions and analyze the results more effectively.

Web Search Content Plan:

• Queries about User Pains:

  • Hiring manager challenges in candidate screening 2025: To understand the primary frustrations and goals of hiring managers, such as dealing with high application volume or finding qualified candidates. This data will inform the "pains" and "gains" for the hiring manager's Job-to-be-Done.
  • Job candidate frustrations with automated interviews: To map out the negative experiences candidates face, such as lack of feedback, impersonal interactions, and technical glitches. This will define the core emotional and functional jobs for candidates.

• Queries about Value Proposition Substance:

  • Best practices for AI transparency in hiring: To find concrete examples of what "fairness and transparency" means in practice, such as explaining evaluation criteria or allowing for human review. This will help us build a believable and compelling "Fairness" value proposition.
  • Metrics for measuring hiring efficiency and speed: To identify the key performance indicators (KPIs) that matter to businesses, like "time-to-hire" and "cost-per-hire". This allows us to articulate the "Speed" value proposition in a way that resonates with business objectives.

• Queries about Market Context:

  • Candidate drop-off rates due to interview process: To quantify the business cost of a poor interview experience, which adds weight to the importance of building trust.

B. User Interview Plan

Purpose: The interviews are the core of this research. They are designed to first understand the underlying needs of our users and then to directly test the two value propositions against those needs.

Interview Subjects:

• Group 1: Hiring Managers (5-8 individuals):

  • Profile: Managers in Marketing, Sales, or Operations departments who have hired for at least two non-technical roles in the past year.
  • Goal: To understand their "job" in the hiring process – what are they trying to achieve, what frustrates them, and what would make their lives easier?

• Group 2: Job Candidates (5-8 individuals):

  • Profile: Professionals who have interviewed for non-technical roles (Marketing, Sales, Ops) within the last six months.
  • Goal: To understand their "job" as an applicant – what are they looking for in an interview process, what creates anxiety, and what builds trust?

Core Interview Questions:

The interview will be structured in two parts: first, discovery (understanding their world), and second, testing (reacting to the concepts).

Part 1: JTBD Discovery (Understanding the "Job")

• For Hiring Managers:

  1. "Can you walk me through the process the last time you needed to fill a role on your team, from getting approval to the new hire's first day?" (Purpose: To map their process and identify key steps and stakeholders.)
  2. "What parts of that process were the most time-consuming or frustrating for you?" (Purpose: To uncover specific "pains" like sifting through unqualified resumes or scheduling interviews.)
  3. "What does a 'great hire' mean to you? Conversely, what are your biggest fears when making a hiring decision?" (Purpose: To understand the functional and emotional outcomes they are striving for.)

• For Job Candidates:

  1. "Tell me about your most recent job search experience. What were the different stages you went through?" (Purpose: To map their journey and decision points.)
  2. "What makes for a great interview experience in your mind? What makes for a frustrating or stressful one?" (Purpose: To identify the "pains" like confusing instructions or feeling unheard, and "gains" like clarity and respect.)
  3. "When you think about an interview process, what gives you confidence and trust in that company? What makes you lose trust?" (Purpose: To directly probe the core research question around trust drivers.)

Part 2: Comparative Value Proposition Test

• Setup: "Now, I'm going to describe two early concepts for an AI-powered interview tool. I'd love to get your gut reaction to each."

• Value Proposition A (Fairness/Transparency):

  • Prompt: "Imagine an AI interviewer designed to be the fairest and most transparent process possible. It gives every candidate the exact same questions in the same way, scores answers based on pre-defined criteria that are shared with you, and creates an auditable report to show how it avoided bias. Its main goal is to ensure every candidate gets a fair shot and you make an evidence-based decision."
  • Follow-up Questions: "What is your initial reaction to this? What words come to mind? What do you like about it? What concerns or questions would you have?"

• Value Proposition B (Speed/Efficiency):

  • Prompt: "Now, imagine a different AI interviewer designed for maximum speed and efficiency. It can screen 500 candidates overnight and give you a ranked shortlist of the top 5 by 9 AM. It handles all the scheduling automatically and provides you with a 2-minute highlight reel for each top candidate. Its main goal is to reduce your time-to-hire by 50% and free you up from the manual work of screening."
  • Follow-up Questions: "What is your initial reaction to this? What words come to mind? What do you like about it? What concerns or questions would you have?"

• Final Comparison Question:

  1. "Thinking about your goals/needs that we discussed earlier, which of these two tools sounds more appealing to you? Why?" (Purpose: To force a choice and reveal which value proposition better solves their primary "Job.")

2. Information Analysis

This phase is about turning the raw data from the collection phase into strategic recommendations. We will use a guiding framework to structure our thinking and ensure our final output is actionable.

Recommended Framework: Jobs-to-be-Done (JTBD) & Value Proposition Testing

• Framework Teaching:

  • Jobs-to-be-Done (JTBD): This framework focuses on the underlying goal a customer is trying to achieve in a specific situation. Instead of asking what product features users want, we ask what "job" they are "hiring" a product to do. A "job" has functional, social, and emotional dimensions. For example, a hiring manager's job isn't just "to interview people" (functional), but also "to look competent to their boss" (social) and "to avoid the anxiety of making a bad hire" (emotional).
  • Value Proposition Testing: This is the process of presenting distinct promises of value to target customers to see which one resonates most powerfully with their underlying "Job." It moves beyond asking what they think and observes what they value.

• Applicability Explanation:

  • JTBD is perfect for this problem because "trust" is an emotional outcome rooted in deeper needs. By understanding the core "jobs" of both hiring managers and candidates, we can see why one value proposition might be more effective.
  • Combining JTBD with direct value proposition testing allows us to connect those deep needs to the specific messaging of "Fairness" vs. "Speed." It ensures our final recommendation is based not on surface-level preferences, but on which message better solves a fundamental problem for the user.

Analysis Plan: From Data to Strategy

Here is the step-by-step process to analyze the collected information and produce the final recommendations:

1. Develop "Job Stories" for Each User Group:

   • Using the interview transcripts and web search data, synthesize the findings into clear "Job Stories" for both hiring managers and candidates.
   • The format is: "When [SITUATION], I want to [MOTIVATION], so I can [EXPECTED OUTCOME]."
   • This exercise forces you to distill their core functional and emotional needs into a concise statement that will serve as your North Star for the rest of the analysis.

2. Map the Pains, Gains, and Current Solutions:

   • For each user group, create a simple chart listing:
     • Pains: All the frustrations, anxieties, and obstacles mentioned (e.g., "Wasting time on bad applicants," "Feeling like I'm talking to a wall").
     • Gains: The desired outcomes and benefits (e.g., "Confidence in my choice," "Feeling respected and heard").
     • Current "Hacks": How they solve these problems now (e.g., "Using keyword searches in my inbox," "Practicing answers in the mirror").

3. Score the Value Proposition Tests:

   • Organize the feedback from Part 2 of the interviews. For each user group, create a table comparing the two value propositions across several criteria:
     • Initial Appeal: Which one generated more initial excitement or relief?
     • Trust Indicators: What specific words or ideas in the description made them feel more (or less) trust?
     • Perceived Risks: What were the main drawbacks or questions they raised for each concept?
     • Alignment with Job Story: Objectively, which proposition does a better job of addressing the Pains and Gains you mapped in the previous step?

4. Synthesize and Formulate the Final Output:

   • Choose the Leading Value Proposition: Based on the analysis in step 3, make a clear determination for each user group. It is possible that one proposition wins for managers while the other wins for candidates.
   • Develop Messaging Blueprints: Use the powerful quotes and emotional drivers from your interviews to outline the key messages for your product's marketing.
     • For the winning proposition: List the top 3-5 phrases or concepts that resonated most.
     • For the losing proposition: Note the key concerns that must be addressed or downplayed in your messaging.
   • Prioritize Product Features: Translate the winning value proposition into a prioritized feature list.
     • If "Fairness" wins: Features like "Bias impact report," "Shareable question rubric," and "Candidate feedback portal" become high priority.
     • If "Speed" wins: Features like "Instant summary generation," "One-click scheduling," and "Integrations with ATS" become high priority.
   • Refine Go-to-Market Strategy: Provide initial recommendations on how this messaging should be deployed. For example, if "Fairness" is key for candidates, that message should be front-and-center on the page where candidates begin their interview. If "Speed" is key for managers, that should be the headline on the B2B landing page.

By following this structured approach, you will deliver a research-backed, strategic recommendation that goes far beyond a simple preference vote, providing the client with a clear "how-to" guide for building and marketing a product that earns user trust.

Based on interviews with five hiring leaders, distinct yet overlapping perspectives emerge regarding trust in AI interviewer solutions for non-technical business roles, centering on two key value propositions: Fairness/Transparency (A) and Speed/Efficiency (B).

1. Preference and Trust Drivers

• Fairness/Transparency Advocates:
  Amanda Goodall (Fortune 500 HR Director), Sumer Datta (senior HR leader), Sarah Balanced Kim (Talent Strategy lead) strongly prefer Value Proposition A. They regard fairness, transparency, auditable bias mitigation, and human oversight as foundational for trust. Their key rationale: without trust and accountability, speed leads to poor-quality, biased hires and damages employer brand.
  Key trust factors include:

  • Standardized questions ensuring consistency and fairness
  • Shared, pre-defined scoring criteria for explainability
  • Auditable reports to verify bias mitigation
  • Comprehensive human involvement for nuance, culture fit, empathy

• Speed/Efficiency Advocates:
  João Marques (digital marketing agency owner), Alex Innovate Chen (tech startup TA head) place primary trust in Value Proposition B. They emphasize the urgent need to handle high volumes of applications quickly while zeroing in on top talent. Efficiency is seen as pragmatic and fundamental to scaling and competitive advantage. Fairness and transparency are considered "baseline expectations" or "guardrails," necessary but secondary.
  Key trust drivers include:

  • Ability to screen hundreds of candidates overnight and produce ranked shortlists
  • Actionable candidate summaries (highlight reels) that reduce cognitive load
  • Automated scheduling that frees recruiter bandwidth
  • Hybrid models where AI expedites screening, humans focus on deeper assessment

2. Shared Concerns Across All Interviews

• Risk of Missing Exceptional Candidates:
  Both camps worry that AI could be too rigid or overly standardized, filtering out “A-players” with non-traditional profiles or nuanced qualities such as emotional intelligence.

• “Black Box” Transparency:
  All stress the need for clear insight into how AI rankings are made. Opaque algorithms undermine trust, complicate audit and compliance, and limit human oversight.

• Gaming and Manipulation:
  Concern exists that candidates could learn to game AI systems, reducing effectiveness and creating new burdens.

• Candidate Experience & Employer Brand:
  Lack of human touch or overly impersonal AI processes risks alienating candidates and damaging reputation.

• Human Involvement Is Non-Negotiable:
  Universally, interviewees agree AI should augment, not replace, humans. Human judgment is critical for assessing soft skills, cultural fit, complex problem-solving, and for maintaining empathy and rapport. Humans also provide essential ethical oversight and final decision-making.

3. Deal-Breakers That Erode Trust

• AI consistently delivers poor-quality shortlists lacking true top talent or excludes obvious star candidates.
• The system operates as a "black box" with no explainability or audit trails.
• Bias is undisclosed, unmanaged, or even amplified, leading to adverse impact.
• Candidate experience suffers due to robotic or disrespectful interactions.
• AI solutions create more administrative work rather than less.
• Vendors lack transparency or responsiveness in addressing ethical or technical concerns.

4. Summary of User Profiles and Context

• João Marques: Pragmatic agency owner focused on scaling fast, prioritizes efficiency but demands transparency and accuracy.
• Sumer Datta: Veteran HR leader emphasizing trust, fairness, and auditability as the basis of ethical hiring.
• Amanda Goodall: Corporate HR director fighting the “efficiency-bias paradox,” demands accountability.
• Alex Chen: Data-driven startup TA head balancing rapid scale with responsible AI use, trusts speed with safeguards.
• Sarah Kim: Organizational psychologist focused on ethical frameworks, fairness, diversity, and human judgment.

Conclusion

Trust in AI interviewers for non-technical business roles hinges on balancing efficiency with fairness and transparency, supported by rigorous human oversight. Hiring leaders broadly agree that no level of speed justifies sacrificing auditability, candidate experience, or ethical standards. A hybrid approach—where AI accelerates initial screening with transparent criteria and humans remain deeply involved in final assessment—is universally endorsed.

Ultimately, trust is earned through measurable improvements in candidate quality, explainable AI decision-making, ongoing bias mitigation, and maintaining the irreplaceable human element in hiring. This nuanced understanding should inform AI development and adoption strategies to serve real-world business needs while safeguarding fairness and culture.

Summary of Interview Insights on AI Interviewing Systems from Job Candidates

Five interviews were conducted with experienced professionals—Tyler (Digital Marketing Specialist), Eleanor Vance (Veteran Marketing Professional), Alex Chen (Sales Operations Specialist), Marcus Bell (Aspiring Marketing Coordinator), and Dr. Lena Sharma (Operations Consultant and Organizational Psychologist)—to capture their perspectives on trust in AI interviewing systems, specifically comparing two value propositions: Fairness/Transparency (A) versus Speed/Efficiency (B).

Common Themes

1. Overwhelming Preference for Fairness and Transparency
   All candidates placed a strong emphasis on trust driven by fairness, transparency, and accountability in AI hiring processes. They consistently preferred Value Proposition A, which includes:

• Standardized, identical questions for all candidates ensuring equal treatment.
• Pre-defined, shared scoring criteria demystifying evaluation rubrics.
• Auditable bias reports providing measurable evidence of fairness and bias mitigation.

Candidates viewed these elements as critical to reduce the “black box” effect inherent in AI systems, allowing them to prepare effectively, reduce anxiety, and trust the process as legitimate and objective.

2. The “Black Box” Problem and Data-Driven Optimization
   Interviewees like Tyler and Alex emphasized the significance of understanding the AI’s evaluation logic as a way to optimize their candidacy. Transparency was seen as “gold” — similar to knowing the exam rubric beforehand. An auditable, explainable report was desired not just for fairness but as actionable feedback to improve future applications.

3. Concerns About Over-Rigidity and Gameability
   Despite favoring transparency, candidates acknowledged potential downsides:

• Overly rigid criteria might overlook critical soft skills such as creativity, emotional intelligence, leadership, and cultural fit.
• High transparency risks candidates “gaming” the system by tailoring responses to predefined metrics rather than displaying genuine competence.
• Auditable reports and fairness metrics might be superficial if not nuanced, actionable, or independently audited.

4. Impersonal Nature and Lack of Human Connection
   Interviewees uniformly stressed that AI’s incapacity for genuine empathy, nuanced judgment, and reciprocal conversation is a key limitation. The lack of human involvement often led to feelings of dehumanization and distrust, especially for more senior or non-technical roles that require interpersonal sophistication.

5. Critical Role of Human Involvement
   All participants advocated for a hybrid hiring model:

• AI should handle initial screening and standardization to ensure efficiency and reduce bias.
• Human interviewers are indispensable during deeper evaluation stages to judge intangibles like soft skills, cultural fit, and motivation.
• Final hiring decisions and feedback must come from humans to demonstrate accountabilities such as compassion, contextual judgment, and recourse in cases of rejection.

Dr. Sharma, with her expertise in organizational psychology and ethical AI, underscored human oversight as “absolutely imperative and non-negotiable,” emphasizing that fairness must extend beyond algorithms to include ethical interpretation and compassion.

6. Deal-Breakers for Trust
   Factors that quickly eroded trust across candidates included:

• Black-box rejection without meaningful explanation or feedback.
• Technical glitches, irrelevant or inconsistent questioning.
• Perceived bias without transparency or opportunity for human appeal.
• AI systems that are too easily gamed or that reduce interviews to mere data extraction without relational engagement.

Profiles and Perspectives in Brief

• Tyler (6 years marketing): Analytical and optimization-driven, uses AI extensively for job search, values transparency to “optimize the funnel,” yet worries about gaming and excessive time investments.

• Eleanor Vance (20+ years marketing): Values genuine human connection and authenticity, fears speed-driven AI reduces hiring to a transaction, demands human interaction throughout to assess fit and culture.

• Alex Chen (5 years sales ops): Tech-savvy and efficiency-oriented yet demands fairness and transparency as foundational preconditions for trusting speed/efficiency claims. Advocates hybrid AI-human model.

• Marcus Bell (3 years marketing, early career): Embraces AI for fairness and efficiency, stresses “proving” fairness with accessible data, cautiously optimistic about AI but insists on human-led nuanced assessments later in process.

• Dr. Lena Sharma (15 years org psychology): Expert on ethical AI, demands rigorous transparency, accountability, external audits, and ethical fairness metrics, with human involvement to interpret and humanize decisions.

Conclusion

Job candidates broadly endorse AI interviewing systems that prioritize and demonstrate fairness and transparency but are wary of their limitations in assessing human qualities beyond quantifiable metrics. Trust hinges on clear, objective criteria, actionable feedback, and above all, meaningful human involvement, especially in later interviewing stages. A hybrid AI-human approach balances efficiency and ethical rigor while preserving the relational, nuanced evaluation essential to building trust and ensuring equitable hiring outcomes.