RADAR108

Why AI-Heavy Job Descriptions Are Attracting the Wrong MAAD Candidates

<h2 data-start="1521" data-end="1543" style="margin-top: 20px; margin-bottom: 10px; transition-property: all; line-height: 1.1; color: rgb(0, 0, 0); font-size: 30px; white-space-collapse: preserve-breaks;">AI-heavy job descriptions are usually written with good intentions. Teams want to appear modern, efficient, and future-ready. They list tools, models, platforms, and workflows to show they are serious about innovation.But job descriptions do not just describe work.They act as signals.And in MAAD hiring, signals shape behavior long before any recruiter sees a resume.In economics, signaling theory explains how people infer value when direct information is unavailable. In hiring, candidates cannot see the real work environment, decision culture, or leadership quality. So they rely on signals embedded in job descriptions to decide whether a role is worth pursuing.AI-heavy job descriptions send a very specific signal. Often unintentionally.They suggest that execution speed is the primary source of value. They imply that productivity gains matter more than judgment. They frame the role as tool-driven rather than decision-driven.This does not attract “better” candidates.It attracts a different kind of candidate.Sociologically, job descriptions also function as identity filters. They communicate who belongs and who does not. When a role is dominated by AI tools and technical jargon, it signals that success comes from mastery of systems rather than understanding of context.As a result, candidates who are strong at framing problems, navigating ambiguity, or influencing decisions often self-select out. They assume the role will reward execution more than thinking.Meanwhile, candidates who enjoy operating within tightly defined systems and optimising workflows are more likely to apply. Not because they are less capable, but because the signal matches their strengths.Over time, this creates a talent skew that teams often misinterpret as a “market reality.”There is also a cognitive bias at play known as instrumental rationality. Organizations tend to priorities means over ends, especially when new tools emerge. The presence of AI makes workflows more visible, measurable, and impressive. Judgment, by contrast, remains invisible.So job descriptions increasingly describe how work gets done instead of why decisions are made. The role becomes a catalogue of tools rather than a position in a decision system.This shift feels rational. It is also misleading.Paradoxically, the more AI-heavy a role becomes, the more important human judgment actually is. AI expands the range of possible actions. Someone still has to decide which actions matter.But job descriptions rarely reflect this. They talk about automation, not interpretation. They describe scale, not choice. They list skills, not responsibilities for deciding trade-offs.This creates what organizational theorists call a role expectation gap. Candidates who are drawn in expect to be evaluated on execution. Teams later realise they need someone who can think beyond the tools.The mismatch is baked in from the start.There is also a reputational cost that teams underestimate.Strong MAAD professionals read job descriptions as cultural artifacts. They look for signs of how decisions are made, how much autonomy exists, and whether thinking is valued. When a description feels overly mechanistic, it signals low trust and high control, even if that was never the intention.These candidates do not apply and they do not announce why.The team then concludes that such talent is scarce, when in reality it was quietly repelled.</h2><h3 data-start="265" data-end="303" style="margin-top: 20px; margin-bottom: 10px; transition-property: all; line-height: 1.1; color: rgb(0, 0, 0); font-size: 24px; white-space-collapse: preserve-breaks;">What Employers Can Actually Change</h3><h2 data-start="1521" data-end="1543" style="margin-top: 20px; margin-bottom: 10px; transition-property: all; line-height: 1.1; color: rgb(0, 0, 0); font-size: 30px; white-space-collapse: preserve-breaks;">If you are an employer and your hiring isn’t working the way you expect, the first place to look is not the talent pool. It’s the way the role is described.Most job descriptions today read like shopping lists. Tools, platforms, workflows, speed. That teaches candidates exactly what you care about. For example, if a performance marketing role lists five ad platforms and talks about “moving fast” and “scaling aggressively,” people who are good at execution will apply. People who spend time questioning attribution, pressure-testing assumptions, or slowing spend when things feel off will quietly skip it.If you want different outcomes, you have to change the signal.Instead of listing tools, describe a real decision the person will own. Say something like: “This role decides when a campaign should not be scaled, even if early numbers look good.” Or “This person is expected to push back when a brief is unclear, not just execute it.” That single sentence does more than any list of platforms.The goal is not to sound smarter. The goal is to show how thinking actually happens on your team. When candidates can see where judgment is required, the right people lean in and the wrong people self-filter out. That saves time, improves fit, and reduces the constant frustration of hiring people who look good on paper but struggle when things get messy.Hiring improves when roles stop describing activity and start describing responsibility.What MAAD Professionals Should Do Differently</h2><h2 data-start="1521" data-end="1543" style="margin-top: 20px; margin-bottom: 10px; transition-property: all; line-height: 1.1; color: rgb(0, 0, 0); font-size: 30px; white-space-collapse: preserve-breaks;">If you’re a MAAD professional and you feel like everyone around you suddenly sounds the same, you’re not imagining it. When everyone uses the same tools, outputs start to look identical. Adding another platform or workflow no longer helps you stand out.What does help is being precise about your thinking.Instead of saying, “I ran this campaign and improved performance,” explain a moment where you chose not to act. For example: “The numbers looked strong early, but I paused scaling because attribution was unclear and the audience overlap was higher than expected.” That tells an employer far more than a dashboard ever could.Another example: instead of saying you automated a process, explain why you didn’t automate part of it. “We kept this step manual because the context changed too often and automation was causing errors.” That shows judgment, not just efficiency.These details matter because most teams don’t fail when things are obvious. They fail when signals are mixed and decisions are unclear. People who can explain how they think in those moments become trusted faster, even if they are not the loudest or most technical in the room.</h2>

Posted on 2/25/26, 12:00:00 AM.000

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