The AI Workforce Impact Report (Preview)

So far, AI appears to be more of a job augmenter. AI skill requirements have spread beyond tech roles like AI Applied Engineer. We’re seeing job postings requiring AI skills for roles like Marketing Managers, Operations Managers, and Business Analysts. According to our survey, 61% of employers are rewriting job descriptions due to AI transformation, 40% are struggling to find talent with sufficient AI fluency, and 74% are investing in upskilling.

AI jobs are typically understood to be roles where AI is the primary function (software developers, data analysts), but according to Lightcast data, that's only 1-2% of positions. AI skills — the ability to work effectively with AI tools, evaluate outputs, and adapt as they change — are now starting to be embedded across roles in other occupations such as finance, marketing, HR, operations, and customer service. The same job title can command dramatically different compensation depending on the job description and AI requirements.

The wage strategy varies by organization. Right now, approaches are scattered, but 58% of organizations are rewarding AI skills with pay premiums either now or in the next 12 months. However, 23% have already moved from "premium" to "required baseline." Additionally, a fifth of organizations (19%) haven't changed compensation for AI skills at all.

Traditional surveys lag the market. You need frequently refreshed market data that isolates skills differentials at the time of hire. Almost half (48%) of organizations say their current benchmarking no longer reflects the skills required in AI-transformed roles. Access to demand data with skills differentials is your competitive advantage.

A majority (56%) of employees say that they should receive higher pay for developing AI skills. Your internal talent learning on the job may watch external hires command significant premiums and be tempted to interview to get a pay bump. Without a pay strategy to reward upskilling, you are at risk of attrition.

Technology roles still command the highest wages for AI skills and tech jobs are needed in industries outside of the tech sector, but AI skills are expanding beyond technology. Prioritize jobs where AI is transforming the day-to-day work and where you compete hardest to hire.

It depends on the specific job and skills. For example, for a Business Process Analyst II, Agentic AI and Machine Learning might be competitive skills worth 9% higher median pay whereas those skills may be standard for a Machine Learning Engineer resulting in 0% higher median pay.

Pricing AI roles accurately requires three data inputs working together, not sequentially: 1) current market data refreshed continuously throughout the year; 2) skills differentials that separate which AI capabilities still command a premium from which have become table stakes; and 3) hiring demand data that shows where competition for AI talent is accelerating fastest.

This is exactly what Payscale Ascent, powered by Peer data, aims to deliver. Ascent integrates your salary structures with daily-refreshed market data and isolates skills differentials so you can price AI jobs. Peer aggregates compensation data across thousands of organizations, straight from HR-reported employee salary data in HRIS, so your benchmarks reflect today's market, not last year's cycle.

If you don't make an investment in talent now, you risk spending more backfilling or upskilling later. Run a pay compression report to flag who's below market before they start interviewing. Waiting until they have an offer in hand is typically more expensive than being proactive.

Frame it as retention risk with a dollar figure. Over half (56%) of employees expect higher pay for new skills. Without a strategy, your top performers may walk out the door. Turnover costs 1.5-2x salary. The premium is probably cheaper than fully replacing talent you've trained to understand your organization and who have taken it upon themselves to build AI skills.

Traditional surveys refresh annually. Almost half (48%) of organizations admit their benchmarks don't reflect current AI skill requirements. You need data refreshed frequently, with competitive skills differentiations surfaced as you price jobs.

The more important shift is moving from event-based benchmarking to always-on monitoring. Payscale is innovating around this transition by providing software that can help organizations upgrade from annual cycles to continuous pay monitoring that catches pay compression, when employees fall below range, and market movement before they become people problems.

The transformation is happening in real time, with 61% of organizations rewriting job descriptions and almost half (49%) saying their salary structures are lagging. Your job architecture has to be reviewed too. If your job descriptions haven't been updated to reflect AI skills requirements, you can't price them accurately, set fair ranges, or defend pay decisions.

Payscale Intelligence Cloud goes beyond Ascent for benchmarking with skills. Layer on JobNav to manage job profile creation and job description compliance with Paycycle to enforce the structure continuously.