Why You Didn’t Get the Job—And How to Spot (and Fix) Broken Hiring Systems

Ever felt ghosted, rejected, or misjudged during a job interview—and had no idea why?

 Recorded as part of World Quality Week, this episode challenges the way we define ‘quality’ in hiring—and what fairness really looks like for today’s job seeker. 

This episode pulls back the curtain on hiring decisions to reveal what really drives quality hires—and why so many processes are still unfair. If you’re job hunting, changing careers, or preparing to lead a team, understanding this could change everything.

HR strategist Olajumoke Fatoki shares how simple, no-cost frameworks—and smart use of AI—can make hiring more inclusive, consistent, and human. Whether you're navigating job applications or getting ready to hire, you'll walk away with tools to empower your next step.

🎧 Listen in to discover:

• Why your résumé or interview might have failed you—even if you were qualified (and how to avoid it next time)
• How to spot biased hiring practices before they cost you an opportunity—and what fair hiring actually looks like
• How future-focused leaders (like you) can build careers and teams with integrity, clarity, and confidence

👉 If you’ve ever wondered why you didn’t land the role—or want to make hiring more fair from the inside out—tap play now. Your career clarity starts here.

Transcript

Olajumoke Fatoki (01:13.55)
 Hello everyone, and happy World Quality Day. I’m Olajumoke Fatoki, but most of my friends call me Ola. I work at the intersection of people, processes, and outcomes. I’m an HR professional, a fund development manager, and a career strategist. I juggle many roles, but today I want to focus on one thing: quality in hiring. Not just as a buzzword, but as a measurable system.

I'll also be sharing how simple AI guardrails can improve fairness, reliability, and accountability in hiring—without requiring new software or a data science team. Let's get into it.

Olajumoke Fatoki (02:22.092)
 So again, how can simple AI guardrails improve fairness, reliability, and accountability in your hiring process? That’s what we’re about to unpack. Join me.

Olajumoke Fatoki (02:48.28)
 Let me first demystify why this subject matters. Every hiring decision is a quality decision. It affects performance, safety, culture—and most importantly, trust within your organization and community.

Olajumoke Fatoki (03:23.916)
 There’s a lot of hype around digital quality tools. But here’s the truth: quality comes from the process, not the tool. Contrary to popular belief, tools don’t create quality. Processes do.

Olajumoke Fatoki (03:49.888)
 As an HR professional, I can tell you that decades of research agree on two key truths in talent selection:

1. Structured methods matter. This means work samples and structured interviews using clear, consistent, predictive, and fair criteria. These outperform unstructured chats and produce repeatable, measurable results.
2. Algorithms scale process—good or bad. If your process is weak, AI will amplify the noise or the bias.

Olajumoke Fatoki (04:52.046)
 Without quality checks in place, AI will either simplify bias or amplify it. So we must be intentional.

Olajumoke Fatoki (05:42.158)
 So I’ve put together three practical steps you can apply in any organization—what I call the "Three-Part Roadmap."

Olajumoke Fatoki (06:39.608)
 Step 1: Build a Simple Quality Stack for Hiring
Start by defining what a quality hire means for each specific role.

• Set standards that are concrete and observable.
• Instrument the process.
• Measure the results.

For example, if you're hiring a frontline coordinator, here are four simple metrics:

1. 90-day reliability (attendance, schedule adherence)
2. Service quality (feedback scores or supervisor checklists)
3. Time to productivity
4. Value alignment (assessed via structured interviews)

If it's not on a scorecard, don’t measure it. If you can’t measure it, don’t base hiring decisions on it.

Olajumoke Fatoki (11:06.656)
 Step 2: Instrument the Process
Turn your scorecard into structured tools:

• A screening rubric with consistent criteria, weight, and pass thresholds for all candidates.
• A structured interview guide with 4–6 behavior-based questions and anchored rating scales.

Interviewers must be trained to capture evidence—not rely on "vibes."

Olajumoke Fatoki (13:06.572)
 Then, measure your results. Track 3 outcome metrics:

1. Supervisor ratings post-hire using the same scorecard.
2. Retention (30, 90, 180 days).
3. Equity signals (e.g., selection rates by legal categories using the 4/5ths rule).

Olajumoke Fatoki (15:16.28)
 This creates a system that’s clear, structured, and includes feedback loops. AI only helps after this foundation is set.

Olajumoke Fatoki (19:03.052)
 Step 3: Add Simple AI Guardrails
People often worry about AI in hiring. But guardrails make AI auditable, explainable, and fair. Here are five simple ways to implement them:

1. Standardize your prompts. Build a prompt library that reflects your scorecard criteria.

Example prompt: "Using the scorecard below, summarize the candidate's experience for each competency. List evidence verbatim. Rate on a 1-5 anchored scale. Flag missing evidence."

1. Evidence before rating. Require the model to extract evidence first, then assign ratings. This supports transparency.
2. Use calibration sets. Test your AI tools before launch to ensure consistent scoring.
3. Do fairness checks. Compare pass rates across groups. If women pass at 40% and men at 60%, pause and re-evaluate the cause.
4. Keep a paper trail. Document which prompt, model, and reviewer made each decision. Respect privacy. Don’t store unnecessary data.

Olajumoke Fatoki (27:45.505)
 These guardrails build trust and allow you to explain decisions clearly to candidates, leaders, or auditors.

Olajumoke Fatoki (34:53.164)
 Example 1: Retail Hiring
A store once hired based on charm. But when they measured actual 90-day performance, they found consistent follow-through—not charisma—drove success. Using AI to summarize interview notes helped, but decisions were still human-led using rubrics.

Olajumoke Fatoki (36:24.526)
 Example 2: Community Organizations
Shifting casual interviews to structured ones tied to values like dignity, boundaries, and service made hiring more consistent and less biased. Adding a quick fairness check prevented accidental exclusion.

Olajumoke Fatoki (37:25.89)
 Bias thrives in ambiguity. But structured, evidence-based systems reduce stereotypes. AI can help organize, but quality comes from clarity.

Olajumoke Fatoki (38:05.42)
 If you remember one thing: AI multiplies the quality of your process. So make the process good.

Olajumoke Fatoki (40:16.438)
 As I close, remember: quality doesn’t come from tools. It comes from process design. Write the standard. Instrument the process. Measure and guard it. Improve it over time.

Olajumoke Fatoki (40:54.656)
 If AI is a force multiplier, let it amplify good systems, not blind spots. Let’s build hiring systems that are measurable, fair, and human.

Olajumoke Fatoki (42:11.138)
 Thank you for listening. My name is Olajumoke Fatoki, and I’m cheering you on to build hiring systems people can trust. Happy World Quality Day.