Reshape how expertise is developed
The AI conversation has largely focused on office work. But we're missing a new and urgent reality for millions of skilled trade, technical, and service workers globally. AI is reshaping these jobs at the same time that expertise and practical know-how are becoming harder to access, preserve, and pass down.
So how do we make sure that education, training, and workplace learning can help early-career talent develop the skills they need to work safely, confidently, and effectively in an AI-infused world? Our new report sheds light on some pathways forward.
Five key findings
Together, these findings show that now is the time to prepare early-career workers for AI-enabled work in skilled trade, technical, and service roles. The defining question is how to help build expertise in an AI-infused world.
1. AI is rapidly reshaping occupations not typically associated with the AI conversation
AI is becoming part of how work gets done across skilled trade, technical and service occupations, changing tasks, workflows and expectations of competence.
2. Demand is high and rising for skilled trade, technical and service workers
Workforce demand is already high and rising at the same, critical moment those jobs are being reshaped by AI. Much of that demand will be driven by people retiring or leaving skilled jobs.
3. A new and distinctive triple capability gap is emerging
Workers need role-specific AI literacy, human capabilities, and the institutional knowledge and practical know-how that retiring experts are taking with them.
4. Closing this gap requires three things at once
Education, training, and workplace learning must evolve to build AI literacy, strengthen human capabilities, and expand opportunities to develop practical know-how.
5. The cost of inaction is significant
Without action, we risk leaving workers behind, creating dangerous gaps in safety-critical occupations and limiting employers’ ability to find the skilled workers they need.
For Every Future: Preparing Skilled Talent for an AI-Infused World
AI is reshaping skilled trade, technical, and service occupations, while demand for skilled talent continues to rise. Pearson's latest research identifies a growing triple capability gap: workers need AI literacy, human capabilities, and practical expertise.
Demand is rising as expertise walks out the door.
Across labor markets, workforce demand is rising at exactly the moment practical expertise, institutional knowledge and occupational know-how are becoming harder to preserve and pass down.
The challenge is not simply filling jobs. It is building expertise faster in an AI-infused world and transferring practical knowledge from one generation of workers to the next.
United States
98.5%
of annual openings in US skilled trade, technical and service occupations are driven by replacement demand.
(14.1M of 14.3M annual openings are replacement hires).
United Kingdom
99%
of skilled job openings in the UK stem from replacing workers who retire, change careers or leave the workforce.
(4M+ openings over the decade are replacement hires).
China
130
MILLION
additional skilled trade, technical and service jobs projected in China by 2035.
(from 347M to 477M).
The new triple capability gap
As AI reshapes skilled work, early-career workers must build three capabilities at once.
Closing this gap requires education, training, and workplace learning to evolve together, building AI literacy, strengthening human capabilities and expanding opportunities to develop practical know-how.
Role-specific AI literacy & critical judgment
Knowing when to use AI, when to question it and how to apply it safely and responsibly.
Human capabilities
Communication, collaboration, adaptability, emotional intelligence and learning agility.
Institutional knowledge & practical know-how
Knowledge developed through mentoring, observation, feedback, simulation and hands-on experience.
AI trust calibration is the ability to assess when to accept AI recommendations, when to supplement them with field verification, and when to override them with human judgment.
Expertise still depends on people
AI is not replacing skilled workers. It is reshaping how expertise is built and what workforce readiness looks like. Our research provides examples of the challenges and opportunities facing early career workers in two contrasting skilled trade and technical roles:
Pharmacy technicians
The vast majority of early career pharmacy technicians say AI was not explicitly covered in their training.
Most valued capabilities for this role:
Patient communication
Judgment
Pharmacy foundations
Industrial machinery mechanics
Fewer than half the industrial machinery mechanics surveyed felt confident their training was preparing them for AI-augmented work.
Most valued capabilities for this role:
Interpersonal skills
Curiosity and metacognitive agility
Technical and mechanical knowledge
Four priorities for action
The challenge now is practical: how do we help workers build AI literacy, human capabilities, and practical know-how at the same time? Our research identifies four priorities for action.
1. Establish trust & responsibility
Establish clear boundaries for when AI can be trusted, questioned, or overridden and who is accountable when it fails.
2. Build AI-ready education & training
Build pathways that can adapt as fast as work itself. Ensure role-specific AI literacy is prioritized alongside foundational AI skills.
3. Accelerate experience & knowledge transfer
Capture the expertise of retiring workers while helping early-career talent build judgment through mentoring, observation, simulation, and hands-on experience.
4. Strengthen the human advantage
Treat communication, collaboration, emotional intelligence, and adaptability as core capabilities to be taught deliberately, not left to chance.
AI may recommend a solution, but experienced professionals must determine whether that recommendation is appropriate, safe and practical.
The supervisor or mentor role will become more important, not less. AI may help guide tasks, answer questions and suggest troubleshooting steps, but someone still has to teach judgment, safety and when not to trust the tool.
What should leaders do next?
Human consciousness, ethical judgment, and moral responsibility will remain essential for the effective and responsible use of AI technologies.
At Pearson, we believe every positive scenario for the future of AI depends on human development. Human development depends on learning as the connective tissue between education, employment and lifelong capability.
Building expertise for every future
AI is changing skilled work. The imperative now is building expertise.
To close the triple capability gap, leaders must act now to build AI literacy, strengthen human capabilities and accelerate the transfer of practical know-how. This is how we prepare the next generation of skilled trade, technical and service workers for an AI-infused world.
