🚨 The AI confidence split: 75% vs. 42%

In most companies, the executives who talk the most about AI transformation are also the furthest from doing the actual transforming.

The people setting the AI vision at the top and the people translating it into daily decisions are living in two different realities, and most succession plans still assume they're the same person.

COMING UP

• 👑 75% of C-suite leaders feel fully prepared for AI, versus just 42% of VPs, new executive search research finds

• 🤖 AI-skill requirements jumped from 15% to 73% of tech job postings in just over two years, per Dice

• 🏷️ AI job titles have spread far beyond tech, now touching truck drivers, physical therapists, and real estate agents in the US

• 🧮 AI aces the math on employee survey data but drops as low as 33% accuracy on turning it into a story, a new benchmark finds

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👑 75% of C-Suite Leaders Feel AI-Ready. Only 42% of VPs Agree.

New research on senior executives surfaced an uncomfortable pattern: confidence about AI is concentrated at the very top, and it thins out fast the further you get from the corner office.

That's a preview of what happens when one of those less-confident leaders gets asked to step into a bigger seat.

The AI Readiness Gap, In 4 Numbers:

• 🏢 68% of C-suite leaders call their organization very prepared for AI, compared with 40% of VPs. That's the same gap one level up, which means it isn't just a feelings problem, it's a resourcing one.

• 🎯 Nearly half of executives (46%) say succession planning at their company isn't proactively managed. Waiting for a seat to open before defining what "AI-ready" means for it guarantees a scramble.

• 🔁 29% of organizations still replace departing executives with a like-for-like profile, even though 94% say AI is already changing what the role requires. Old job descriptions are already out of step with the job itself.

• 🚪 81% of senior leaders say they're open to new opportunities if the right one comes along. The leaders best equipped to close the readiness gap are also the ones a competitor could poach tomorrow.

Read more: ON Partners: The Widening Gap, 2026 Leadership Report

🤖 AI Skills Went From 15% to 73% of Tech Job Postings in Two Years

For years, AI expertise was a specialty add-on for tech postings, something you listed if your team happened to be building it.

New hiring data suggests that era is over. Employers now write AI skill requirements into job postings across nearly every technical function, treating them more like a baseline qualification.

4 Stats That Show Why Tech Hiring Split In Two:

• 💰 AI/ML roles carry a median salary 22% higher than the broader tech market. Budgeting for "an engineer" without budgeting for AI fluency already underprices the role.

• 🏦 Tech postings grew faster in finance (+47%), manufacturing (+27%), and insurance and aerospace (+23% each) than in tech itself. Some of the toughest competition for tech talent is coming from industries that don't call themselves tech companies.

• 🎓 Entry-level postings overall sit 38% below 2019 levels, even as specific junior roles like Data Engineer (+89%) and Service Desk Technician (+115%) are booming. A blanket freeze on junior hiring is likely leaving real opportunities on the table.

• 📍 Tech postings grew 22% in Detroit and Baltimore while falling 26% in Richmond and 19% in Providence. Geography is now doing as much work as job title in determining who gets hired.

Read more: Dice: Tech Hiring Myths vs. Reality

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🚚 63% of AI Job Titles in the US Are Now Outside Tech

Job titles are usually the last thing to catch up with how work changes, which makes it notable that employers are now writing "AI" directly into roles for truck drivers, physical therapists, and real estate agents.

It's showing up in the titles of jobs that have existed for decades, just described in a new vocabulary.

Where AI Job Titles Are Spreading Next:

• 📈 The number of distinct AI-touched job titles in the US jumped from 159 in 2023 to 822 in the first quarter of 2026. What started as a niche label multiplied more than fivefold in three years.

• 🇩🇪 Germany leads Europe with 288 AI-touched job titles, ahead of the UK (160), France (138), the Netherlands (84), and Spain (81). The US isn't alone in this shift, Europe is just a few steps behind.

• 🧑‍🏫 A new cluster of "AI instruction" roles, coaches, tutors, and corporate trainers, is emerging specifically to teach colleagues and clients how to use AI tools. Teaching AI literacy is becoming its own job category, not a side task.

• 🇪🇸 Spain is the outlier: 64% of its AI-touched job titles are still concentrated in tech roles, unlike every other market studied. Not every region is spreading AI language at the same pace.

Read more: Indeed Hiring Lab: AI Is No Longer Just a Tech Occupation Story

🧮 Even The Best AI Model Drops From 82% to 66% on the Hard Part

A new benchmark built for HR employee-listening work put seven leading AI models through real analyst tasks: calculating survey stats, then turning those stats into an executive-ready narrative. 

The models are already reliable at the arithmetic but the moment the task shifts to judgment, what a result means and what to do about it, performance gets shakier fast.

Why Synthesis Is AI's Weakest Skill on People Data:

• 🧠 Across all seven models tested, "Synthesis," pulling scattered data points together into a recommendation, was the lowest-scoring capability measured, dropping as low as 14%. Raw computing power didn't fix this weakness, even the most reasoning-heavy models struggled with it.

• 📉 Models performed worst on topics like Engagement, Change & Innovation, and Future/Vision, and best on clear-cut topics like DEIB and Performance Enablement. The themes AI struggles with most are often the ones leaders most need clear thinking on.

• ⚠️ Every model tested had at least one "overclaim" failure, and one model fabricated a statistic outright. Even strong benchmark scores don't rule out AI inventing something that was never in the data.

• ⏱️ Human experts estimated the full task set would take about 20 hours to complete manually. That efficiency upside is real, but only for the tasks AI is actually reliable at.

Read more: PYX Labs: PYX-Voice Benchmark