ATSRadar Blog — #knowledge workers

AI proficiency is no longer a niche signal in hiring, especially for knowledge workers.

Using ATS Radar job data (all geographies), we analyzed how often postings mention AI-tool proficiency and whether employers frame it as required, preferred, or general context.

This report focuses on knowledge-worker roles first, then compares that with the broader workforce.

Charts and Data Breakdown

Snapshot refreshed: Mar 11, 2026 02:54 UTC.

Chart 1: Share of job postings mentioning AI tools (30/60/90 days)

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Window	Total jobs	AI mention jobs	AI mention %	Required+Preferred %
30d	110705	27963	25.26%	5.95%
60d	136590	32906	24.09%	5.81%
90d	144462	34169	23.65%	5.71%

Window	Total jobs	AI mention jobs	AI mention %	Required+Preferred %
30d	110705	27963	25.26%	5.95%
60d	136590	32906	24.09%	5.81%
90d	144462	34169	23.65%	5.71%

Chart 2: Top role families by AI-mention rate

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Role family	Total jobs	AI mention %	Required+Preferred %
Data	4902	45.90%	18.32%
Product	2417	43.03%	15.64%
Engineering	27478	39.29%	9.56%
Customer Success	1324	38.75%	10.73%
Security	6280	36.27%	6.02%
Design	4595	31.19%	10.82%
Finance	5259	22.78%	7.00%
Marketing	8453	21.21%	4.02%
Operations/RevOps	11753	19.65%	3.03%
Sales	15363	17.26%	2.43%

Role family	Total jobs	AI mention %	Required+Preferred %
Data	4902	45.90%	18.32%
Product	2417	43.03%	15.64%
Engineering	27478	39.29%	9.56%
Customer Success	1324	38.75%	10.73%
Security	6280	36.27%	6.02%

Showing first 5 of 10 rows.

Chart 3: Top industries by AI-mention rate

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Industry	Total jobs	AI mention %	Required+Preferred %
Software/SaaS	44821	34.21%	8.04%
AI/ML	21947	29.58%	10.45%
Marketplace/Logistics	17686	24.25%	3.56%
Fintech	8817	21.15%	4.58%
Cybersecurity	8107	20.11%	4.05%
Education	3165	16.05%	0.95%
Energy/Climate	1711	15.55%	2.28%
Other/Unknown	20051	11.64%	2.34%
Healthcare	12169	9.59%	3.20%
Government/Nonprofit	1891	5.55%	1.22%

Industry	Total jobs	AI mention %	Required+Preferred %
Software/SaaS	44821	34.21%	8.04%
AI/ML	21947	29.58%	10.45%
Marketplace/Logistics	17686	24.25%	3.56%
Fintech	8817	21.15%	4.58%
Cybersecurity	8107	20.11%	4.05%

Showing first 5 of 10 rows.

Chart 4: Required vs preferred vs general AI mention split

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Class	Jobs	% of AI mentions	% of all jobs
Required / expected	7160	20.95%	4.96%
Preferred / bonus	1096	3.21%	0.76%
General mention	24320	71.18%	16.83%
Company / product context	1593	4.66%	1.10%

Class	Jobs	% of AI mentions	% of all jobs
Required / expected	7160	20.95%	4.96%
Preferred / bonus	1096	3.21%	0.76%
General mention	24320	71.18%	16.83%
Company / product context	1593	4.66%	1.10%

Chart 5: Entry-level vs mid-level vs leadership AI mention rates

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Seniority	Total jobs	AI mention %	Required+Preferred %
Mid-level	91985	23.47%	5.85%
Leadership	44105	24.82%	5.24%
Entry-level	8372	19.58%	6.75%

Seniority	Total jobs	AI mention %	Required+Preferred %
Mid-level	91985	23.47%	5.85%
Leadership	44105	24.82%	5.24%
Entry-level	8372	19.58%	6.75%

Chart 6: Most commonly named AI tools

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Tool	Mentioning jobs	% of AI-mention jobs
Claude	1286	3.76%
ChatGPT	956	2.80%
GitHub Copilot	798	2.34%
OpenAI	782	2.29%
Cursor	712	2.08%
Anthropic	708	2.07%
Gemini	707	2.07%
Midjourney	102	0.30%
Perplexity	57	0.17%
Llama	54	0.16%

Tool	Mentioning jobs	% of AI-mention jobs
Claude	1286	3.76%
ChatGPT	956	2.80%
GitHub Copilot	798	2.34%
OpenAI	782	2.29%
Cursor	712	2.08%

Showing first 5 of 10 rows.

Chart 7: Knowledge workers vs broader workforce

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Segment	Total jobs	AI mention %	Required+Preferred %
All jobs	144462	23.65%	5.71%
Knowledge workers	92154	29.25%	7.06%
Non-knowledge workforce	52308	13.80%	3.34%

Segment	Total jobs	AI mention %	Required+Preferred %
All jobs	144462	23.65%	5.71%
Knowledge workers	92154	29.25%	7.06%
Non-knowledge workforce	52308	13.80%	3.34%

Key findings (last 90 days)

• 23.65% of analyzable postings mention AI tools or AI proficiency in some form.
• In knowledge-worker roles, that rises to 29.25%.
• Explicitly required or preferred AI proficiency appears in 5.71% of all postings and 7.06% of knowledge-worker postings.
• AI mention rates have increased from 23.65% (90d) to 25.26% (30d).
• Remote roles show higher AI-mention rates (35.94%) than hybrid (26.94%) or onsite (18.02%).

30 / 60 / 90-day trend

Window	Total jobs	Jobs mentioning AI	AI mention rate	Required rate	Preferred rate
90 days	144,462	34,169	23.65%	4.96%	0.76%
60 days	136,590	32,906	24.09%	5.07%	0.74%
30 days	110,705	27,963	25.26%	5.26%	0.69%

Takeaway: AI mentions are rising in recent postings, but the biggest growth is still in general expectation language, not explicit preferred/bonus language.

Knowledge workers vs broader workforce

Segment	Total jobs	AI mention rate	Required + preferred
Knowledge-worker roles	92,154	29.25%	7.06%
Non-knowledge roles	52,308	13.80%	3.34%
All analyzable roles	144,462	23.65%	5.71%

This gap is material: knowledge-worker postings are about 2.1x as likely to mention AI.

Which knowledge-work role families mention AI the most?

Role family	Total jobs	AI mention rate	Required + preferred
Data	4,902	45.90%	18.32%
Product	2,417	43.03%	15.64%
Engineering	27,478	39.29%	9.56%
Customer Success	1,324	38.75%	10.73%
Security	6,280	36.27%	6.02%
Design	4,595	31.19%	10.82%
Finance	5,259	22.78%	7.00%
Marketing	8,453	21.21%	4.02%
Operations / RevOps	11,753	19.65%	3.03%
Sales	15,363	17.26%	2.43%

AI expectations are strongest in technical and analytical families, but non-technical corporate functions are clearly involved.

Which industries mention AI proficiency most?

Industry	Total jobs	AI mention rate	Required + preferred
Software/SaaS	44,821	34.21%	8.04%
AI/ML	21,947	29.58%	10.45%
Marketplace/Logistics	17,686	24.25%	3.56%
Fintech	8,817	21.15%	4.58%
Cybersecurity	8,107	20.11%	4.05%
Education	3,165	16.05%	0.95%
Energy/Climate	1,711	15.55%	2.28%
Healthcare	12,169	9.59%	3.20%

Interpretation: AI language is broadest in software-heavy sectors, but it is not limited to AI-native companies.

Required vs preferred vs general mention

Across all AI-mention postings (90d):

Class	Jobs	Share of AI mentions	Share of all jobs
Required / expected	7,160	20.95%	4.96%
Preferred / bonus	1,096	3.21%	0.76%
General mention	24,320	71.18%	16.83%
Company/product context only	1,593	4.66%	1.10%

The big signal right now: AI is often treated as baseline context in job language, while explicit “must-have” phrasing is growing but not yet dominant.

Entry-level vs leadership: who gets AI expectations?

Seniority group	Total jobs	AI mention rate	Required + preferred
Entry-level	8,372	19.58%	6.75%
Mid-level IC	91,985	23.47%	5.85%
Leadership (Manager+)	44,105	24.82%	5.24%

Leadership postings mention AI more often overall, but explicit required/preferred phrasing is not dramatically higher than IC roles.

Which AI tools are named directly?

Named-tool mentions are still a minority vs generic AI language.

Tool	Mentioning jobs	% of AI-mention jobs
Claude	1,286	3.76%
ChatGPT	956	2.80%
GitHub Copilot	798	2.34%
OpenAI	782	2.29%
Cursor	712	2.08%
Anthropic	708	2.07%
Gemini	707	2.07%

For job seekers: optimize for AI workflows and outcomes, not just one branded tool.

Practical read for job seekers

If you are applying into knowledge-work roles in 2026, assume AI literacy is now an expected part of your toolkit, even when postings are not explicit.

A practical approach:

1. Add concrete AI workflow examples to resume bullets (analysis, drafting, automation, QA).
2. Show business outcomes, not just tool names.
3. Prepare interview stories for responsible AI use and quality control.
4. For data/product/engineering roles, expect materially higher AI expectations.
5. For non-technical roles, focus on productivity, documentation, and process automation use cases.

Methodology (short)

• Analysis run date: March 10, 2026 (America/Los_Angeles).
• Windows: rolling last 30 / 60 / 90 days.
• Geography: all available geographies in ATS Radar source data.
• Population: active jobs with coalesce(posted_at, scanned_at) in window.
• Analyzable text filter: description_text length >= 150 and non-harvester parser rows.
• Analyzed 90-day base: 144,462 postings.
• AI detection: rule-based mention detection across AI terms and named tools, then classification into:
  • required/expected,
  • preferred/bonus,
  • general mention,
  • company/product context.
• Job family and seniority: ATS Radar’s existing rule-based role classification heuristics.
• Knowledge-worker definition: all ATS Radar job families except Other.

Limitations and confidence

• This is a text-signal analysis, not a verification of how strictly hiring teams enforce each requirement.
• Some ATS sources provide shallow descriptions; those were excluded from proficiency classification.
• Multi-language posting text and non-standard formatting can cause under-detection or misclassification.
• Classification is designed to be conservative for explicit required/preferred labels.

Confidence level: moderate-high for directional trend and segment comparisons, moderate for exact required/preferred split.

Chart-ready data files are available in content/blog/data/ai-tool-proficiency.

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Related ATS Radar reads:

• Remote Jobs Reality Check (Last 30 Days)
• Entry-Level vs Senior: Where the Market Actually Is
• Daily Hiring Trends by Geography & Industry