Beyond the Buzzword: What Recruiters Must Learn from the HiringLab AI Context Deficit Study

Gem Siocon
Latest posts by Gem Siocon (see all)

Artificial intelligence (AI) is everywhere these days. Every job board seems to mention “AI,” “GenAI,” or “AI-powered” in its job descriptions. 

But as employers rush to ride the AI wave, a new realization is emerging: many of these job posts offer no clarity on what “AI” really means. In fact, Indeed Hiring Lab revealed a concerning pattern: although AI mentions in job ads have surged, about 25% of those postings provide little to no explanation of how AI is used in the role. They also found: 

  • Over the past year, job postings referencing AI, GenAI, or related terms grew significantly.
  • Among those AI-related postings, 52% mentioned building or directly using AI models, which suggests those roles involve real AI work. 
  • But about 1 in 4 AI-mentioning postings provided almost no context on how the employer intended to use AI. 
  • In many cases, job descriptions defaulted to broad, generic phrases (“AI,” “GenAI”) rather than referencing specific tools, skill sets, or use cases.

What do these findings tell us?

It seems many employers may be adding “AI” to job descriptions simply as a signal: to appear modern, attract attention, or project a specific brand image, without thinking through what the role actually does with AI.

ai in job ads

In this article, we unpack the Hiring Lab findings, explain why vague AI wording is risky, and share practical steps that TA teams can use to write clearer, compliant job descriptions.

The Stakes: Why Recruiters Should Care

For recruiters, senior HR leaders, and hiring teams at large enterprises, this isn’t just sloppy writing. It could signal a compliance, efficiency, and candidate-experience problem:

1. Poor Candidate Experience:
Candidates drawn by AI buzz, especially those curious about growth and upskilling, may apply only to realize the job has nothing to do with real AI work. That leads to frustration, poor employer brand, or high drop-off rates.

2. Wasted Hiring Time and Budget:
 Hiring teams sift through unqualified applicants who opportunistically chase “AI”, leading to wasted time and effort screening and interviewing candidates. 

3. Compliance and Risk Exposure:
Vaguely worded AI requirements, especially in roles involving ethics, compliance, and decision-making, can introduce bias or legal vulnerability. If a job doesn’t specify oversight or human judgment, it could potentially lead to AI misuse. 

In short: AI buzz without context isn’t just ineffective, it’s actually counterproductive.

Decoding the AI Context Deficit

To fix the problem, we need to understand what companies are actually missing when they drop “AI” into job descriptions without additional context. 

The Hiring Lab report shows a clear pattern: employers overwhelmingly use general AI terms (“AI,” “GenAI”) far more than specific, technical language or tool names. For example, nearly three-quarters of AI-mentioning postings used generic terms like “AI,” while only a small fraction referenced more concrete keywords like ChatGPT, “LLM,” “NLP,” etc.

That’s a red flag. When a candidate sees “AI” in a JD, they may reasonably expect to do tasks like building models, designing algorithms, or integrating systems. But the employer may only intend someone to use an AI-enabled tool occasionally. Or worse, just brand the role as “AI-ready.” This mismatch between candidate expectations and the realities of the job is what the “deficit” captures.

What Recruiters are Actually Missing 

From the report, there are three critical dimensions often neglected:

  1. Skills Context: Is the employee building the AI (core developer), or just using it (end user)? Many JDs only say “experience with AI,” with no differentiation.
  2. Tool Context: Which specific tools? (e.g., “Use ChatGPT to draft marketing copy” vs. “Integrate our proprietary AI models”).
  3. Ethical Context: Most JDs omit any mention of ethics, human review, or compliance,  leaving a governance blind spot. For AI-enabled decision-making roles, human judgment, oversight, and bias mitigation must be explicit.

How to Master AI Context

For TA leaders and hiring managers, here’s a checklist you can use to explain AI context in your job posting clearly and effectively:

Step 1: Move from Buzzwords to Behaviors — Use “Do” Statements

For every AI-related requirement, use “Do” Statements. In these statements, you must define the actual task, the expected output or volume, and the purpose (or why it matters). These details help candidates self-select while enabling recruiters to pre-screen more effectively.

Saying “Experience with Artificial Intelligence is a plus” does nothing to inform the candidate or screen.

Instead,  state “You will use large language models (LLMs) to generate and edit 50% of monthly content outputs, ensuring accuracy and brand voice alignment.” This kind of statement communicates what the job actually involves, not some vague buzzword to excite applicants. 

Step 2: Explain the Why and the Risk (Responsible AI)

For roles that use AI to make decisions (e.g., finance, legal, HR/Recruiting), the job description clarifies the requirement for human oversight.

In your job post, add a mandatory clause such as: “Must apply human judgment and ethical guidelines to all AI-generated outputs before final decision-making.”

Step 3: Standardize the Section (The Governance Layer)

AI skills and responsibilities must live in a dedicated, consistent section of the JD template, not scattered throughout.

For example, you can create a mandatory, templated section in the job description called “Digital Fluency & AI Integration”. 

The Strategic Advantage: JD Software as the Solution

You could follow the steps above and track them in a shared doc or spreadsheet. 

However, it would be tricky if you’re scaling up.  At large enterprises, you might have hundreds of recruiters across geographies, business units, and functions. Without a structured template and workflow, some JDs will omit AI context, others will misuse buzzwords, and a few will include oversight language.

With a  job description platform, you’ll ensure: 

  • Consistency: Every JD gets the same “Digital Fluency & AI Integration” section when relevant. No exceptions.
  • Compliance: The software can flag vague AI terms (e.g., “AI experience a plus”) and prompt the recruiter to include a “do statement” or oversight clause.
  • Scalability: As business needs change (e.g., legal/regulatory updates, evolving AI capabilities), leadership can update the template once and push the shift globally across all JDs in minutes.
  • Governance: For auditing, reporting, and compliance, you have a clear, centralized view of which roles use AI, how they use it, and under what oversight.

Job description software is not just a writing tool. It’s a governance system for clarity, compliance, and scale.

Why This Matters — Even for Non–“Core AI” Roles

Some may argue: “We’re not building AI. This is just a marketing or admin job.” Fair enough. 

But the context deficit also affects non-technical roles, especially as AI becomes a tool across functions. According to Hiring Lab’s report, AI use differs by occupation:

  • Tech, management, and creative roles often drive core AI development or direct application. 
  • Meanwhile, service, healthcare, HR, and administrative roles are increasingly adopting AI-based hiring, operations, or content workflows,  even if they aren’t “AI jobs.” 

This means that even in non-core AI functions, companies need clarity. As organizations scale AI use, ignoring the “context deficit” across the board amplifies risks from misaligned candidate expectations to compliance gaps.

How to Rewrite Job Descriptions with ‘AI’ in them

  1. Don’t: “Familiarity with AI tools preferred.”

Do:  “You will use our proprietary LLM-based content assistant to draft up to 30 marketing articles per quarter — then review, edit, and finalize to ensure brand consistency, accuracy, and human-quality output.

  1. Don’t: “AI/GenAI knowledge a plus.

Do:  “You will leverage large language models (LLMs) to summarize customer data, generate user insights, and produce first-draft reports; but you must apply human judgment and fact-check all AI-generated outputs before publishing.”

  1. Don’t: “Experience with AI is preferred.”

Do:  “In this role, you will collaborate with our data science team to integrate third-party AI analytics tools. You’ll also help monitor tool performance and flag any biases or ethical concerns for compliance review.”

Notice how each ‘do’ example makes clear statements: what, why, how often, and with what oversight.

The Bigger Picture: Governance Over Hype

We live in an age of AI hype where “AI-powered,” “GenAI-enabled,” and “automated with AI” are nearly mandatory marketing phrases. But the Hiring Lab report makes it clear: mentioning AI doesn’t mean anything.

For talent acquisition leaders who care about efficiency, candidate experience, compliance, and long-term scalability, the difference between buzzword-laden and context-rich job descriptions matters.

Misused AI language undermines trust; vague or inconsistent JDs slow hiring; lack of oversight or clarity invites compliance and ethical risks.

What separates forward-thinking enterprises from reactive ones is not just AI adoption. It’s AI governance.

And governance doesn’t happen by accident. It requires deliberate structure, consistent process, centralized oversight, and the right tools.

Bottom Line: Clarity, Not Complexity 

AI is here to stay. Indeed Hiring Lab’s data shows AI mentions in job postings are growing.

But so is the risk: misaligned expectations, poor candidate experience, wasted recruiting budget, and governance blind spots.

Your advantage isn’t simply calling for “AI skills.” It’s in how clearly you articulate what “AI” means for this role: what tasks the person will perform, what tools they’ll use, what ethical guardrails apply.

For large enterprises scaling across teams and geographies, only one solution delivers reliably: structured JD governance via software.

Ongig helps companies enforce that clarity, across every role, globally. If clarity, consistency, and compliance matter in your hiring process, schedule a demo with Ongig. We’ll show you how the world’s largest enterprises standardize AI language and governance across every job description.

by in AI Recruitment